Biblio

The current evaluation of API recommendation systems mainly focuses on correctness, which is calculated through matching results with ground-truth APIs. However, this measurement may be affected if there exist more than one APIs in a result. In practice, some APIs are used to implement basic functionalities (e.g., print and log generation). These APIs can be invoked everywhere, and they may contribute less than functionally related APIs to the given requirements in recommendation. To study the impacts of correct-but-useless APIs, we use utility to measure them. Our study is conducted on more than 5,000 matched results generated by two specification-based API recommendation techniques. The results show that the matched APIs are heavily overlapped, 10% APIs compose more than 80% matched results. The selected 10% APIs are all correct, but few of them are used to implement the required functionality. We further propose a heuristic approach to measure the utility and conduct an online evaluation with 15 developers. Their reports confirm that the matched results with higher utility score usually have more efforts on programming than the lower ones.

Traditional encryption algorithms are not suitable for modern mass speech situations, while some low-dimensional chaotic encryption algorithms are simple and easy to implement, but their key space often small, leading to poor security, so there is still a lot of room for improvement. Aiming at these problems, this paper proposes a new type of four-dimensional chaotic system and applies it to speech encryption. Simulation results show that the encryption scheme in this paper has higher key space and security, which can achieve the speech encryption goal.

To improve dynamic updating of privacy protected data release caused by multidimensional sensitivity attribute privacy differences in relational data, we propose a dynamic updating method for privacy protection data release based on the multidimensional privacy differences. By adopting the multi-sensitive bucketization technology (MSB), this method performs quantitative classification of the multidimensional sensitive privacy difference and the recorded value, provides the basic updating operation unit, and thereby realizes dynamic updating of privacy protection data release based on the privacy difference among relational data. The experiment confirms that the method can secure the data updating efficiency while ensuring the quality of data release.

Industrial robots are playing an important role in now a day industrial productions. However, due to the increasing in robot hardware modules and the rapid expansion of software modules, the reliability of operating systems for industrial robots is facing severe challenges, especially for the light-weight edge computing platforms. Based on current technologies on resource security isolation protection and access control, a novel resource management model for real-time edge system of multiple robot arms is proposed on light-weight edge devices. This novel resource management model can achieve the following functions: mission-critical resource classification, resource security access control, and multi-level security data isolation transmission. We also propose a fault location and isolation model on each lightweight edge device, which ensures the reliability of the entire system. Experimental results show that the robot operating system can meet the requirements of hierarchical management and resource access control. Compared with the existing methods, the fault location and isolation model can effectively locate and deal with the faults generated by the system.

The unequal area facility layout problem (UA-FLP) is to place some objects in a specified space according to certain requirements, which is a NP-hard problem in mathematics because of the complexity of its solution, the combination explosion and the complexity of engineering system. Particle swarm optimization (PSO) algorithm is a kind of swarm intelligence algorithm by simulating the predatory behavior of birds. Aiming at the minimization of material handling cost and the maximization of workshop area utilization, the optimization mathematical model of UA-FLPP is established, and it is solved by the particle swarm optimization (PSO) algorithm which simulates the design of birds' predation behavior. The improved PSO algorithm is constructed by using nonlinear inertia weight, dynamic inertia weight and other methods to solve static unequal area facility layout problem. The effectiveness of the proposed method is verified by simulation experiments.

In this paper, we study trust-related human factors in supervisory control of swarm robots with varied levels of autonomy (LOA) in a target foraging task. We compare three LOAs: manual, mixed-initiative (MI), and fully autonomous LOA. In the manual LOA, the human operator chooses headings for a flocking swarm, issuing new headings as needed. In the fully autonomous LOA, the swarm is redirected automatically by changing headings using a search algorithm. In the mixed-initiative LOA, if performance declines, control is switched from human to swarm or swarm to human. The result of this work extends the current knowledge on human factors in swarm supervisory control. Specifically, the finding that the relationship between trust and performance improved for passively monitoring operators (i.e., improved situation awareness in higher LOAs) is particularly novel in its contradiction of earlier work. We also discover that operators switch the degree of autonomy when their trust in the swarm system is low. Last, our analysis shows that operator's preference for a lower LOA is confirmed for a new domain of swarm control.

Hexagonal CdS and Fe-doped CdS nanorods were synthesised by a facile hydrothermal method and characterised by X-ray diffraction, energy dispersive X-ray spectroscopy, UV-vis absorption, photoluminescence, and X-ray photoelectron spectroscopy. The magnetic properties of undoped and Fe-doped CdS nanorods were investigated at room temperature. The experimental results demonstrate that the ferromagnetism of the Fe-doped CdS nanorods differs from that of the undoped CdS nanorods. The remanence magnetisation (Mr) and the coercive field (Hc) of the Fe-doped CdS nanorods were 4.9 × 10-3 emu/g and 270.6 Oe, respectively, while photoluminescence properties were not influenced by doping. First-principle calculations show that the ferromagnetism in Fe-doped CdS nanocrystal arose not only from the Fe dopants but also from the Cd vacancies, although the main contribution was due to the Fe dopants.

Macroscopic hysteresis loops and microscopic magnetic moment distributions have been determined by 3-D model for Co nanowire with various easy axis deviations from applied field. It is found that both the coercivity and the remanence decrease monotonously with the increase of easy axis deviation as well as the maximum magnetic product, indicating the large impact of the easy axis orientation on the magnetic performance. Moreover, the calculated angular distributions and the evolution of magnetic moments have been shown to explain the magnetic reversal process. It is demonstrated that the large demagnetization field in the two ends of the nanowire makes the occurrence of reversal domain nucleation easier, hence the magnetic reversal. In addition, the magnetic reversal was illustrated in terms of the analysis of the energy evolution.

The notion of integrated circuit split manufacturing which delegates the front-end-of-line (FEOL) and back-end-of-line (BEOL) parts to different foundries, is to prevent overproduction, piracy of the intellectual property (IP), or targeted insertion of hardware Trojans by adversaries in the FEOL facility. In this work, we challenge the security promise of split manufacturing by formulating various layout-level placement and routing hints as vector- and image-based features. We construct a sophisticated deep neural network which can infer the missing BEOL connections with high accuracy. Compared with the publicly available network-flow attack [1], for the same set of ISCAS-85benchmarks, we achieve 1.21× accuracy when splitting on M1 and 1.12× accuracy when splitting on M3 with less than 1% running time.

We report a an experimental study of device-independent quantum random number generation based on an detection-loophole free Bell test with entangled photons. After considering statistical fluctuations and applying an 80 Gb × 45.6 Mb Toeplitz matrix hashing, we achieve a final random bit rate of 114 bits/s, with a failure probability less than 10-5.

With the rapid development of smart grid, smart meters are deployed at energy consumers' premises to collect real-time usage data. Although such a communication model can help the control center of the energy producer to improve the efficiency and reliability of electricity delivery, it also leads to some security issues. For example, this real-time data involves the customers' privacy. Attackers may violate the privacy for house breaking, or they may tamper with the transmitted data for their own benefits. For this purpose, many data aggregation schemes are proposed for privacy preservation. However, rare of them cares about both the data aggregation and fine-grained access control to improve the data utility. In this paper, we proposes a data aggregation scheme based on attribute decision tree. Security analysis illustrates that our scheme can achieve the data integrity, data privacy preservation and fine- grained data access control. Experiment results show that our scheme are more efficient than existing schemes.

In the 21st century, integrated transport, service and mobility concepts for real-life situations enabled by automation system and smarter connectivity. These services and ideas can be blessed from cloud computing, and big data management techniques for the transport system. These methods could also include automation, security, and integration with other modes. Integrated transport system can offer new means of communication among vehicles. This paper presents how hybrid could computing influence to make urban transportation smarter besides considering issues like security and privacy. However, a simple structured framework based on a hybrid cloud computing system might prevent common existing issues.

This paper proposes a novel scheme for RFID anti-counterfeiting by applying bisectional multivariate quadratic equations (BMQE) system into an RF tag data encryption. In the key generation process, arbitrarily choose two matrix sets (denoted as A and B) and a base Rab such that [AB] = λRABT, and generate 2n BMQ polynomials (denoted as p) over finite field Fq. Therefore, (Fq, p) is taken as a public key and (A, B, λ) as a private key. In the encryption process, the EPC code is hashed into a message digest dm. Then dm is padded to d'm which is a non-zero 2n×2n matrix over Fq. With (A, B, λ) and d'm, Sm is formed as an n-vector over F2. Unlike the existing anti-counterfeit scheme, the one we proposed is based on quantum cryptography, thus it is robust enough to resist the existing attacks and has high security.

Video surveillance has been widely adopted to ensure home security in recent years. Most video encoding standards such as H.264 and MPEG-4 compress the temporal redundancy in a video stream using difference coding, which only encodes the residual image between a frame and its reference frame. Difference coding can efficiently compress a video stream, but it causes side-channel information leakage even though the video stream is encrypted, as reported in this paper. Particularly, we observe that the traffic patterns of an encrypted video stream are different when a user conducts different basic activities of daily living, which must be kept private from third parties as obliged by HIPAA regulations. We also observe that by exploiting this side-channel information leakage, attackers can readily infer a user's basic activities of daily living based on only the traffic size data of an encrypted video stream. We validate such an attack using two off-the-shelf cameras, and the results indicate that the user's basic activities of daily living can be recognized with a high accuracy.

In this paper, we focus on energy management of distributed generators (DGs) and energy storage system (ESS) in microgrids (MG) considering uncertainties in renewable energy and load demand. The MG energy management problem is formulated as a two-stage stochastic programming model based on optimization principle. Then, the optimization model is decomposed into a mixed integer quadratic programming problem by using discrete stochastic scenarios to approximate the continuous random variables. A Scenarios generation approach based on time-homogeneous Markov chain model is proposed to generate simulated time-series of renewable energy generation and load demand. Finally, the proposed stochastic programming model is tested in a typical LV network and solved by Matlab optimization toolbox. The simulation results show that the proposed stochastic programming model has a better performance to obtain robust scheduling solutions and lower the operating cost compared to the deterministic optimization modeling methods.

Cloud computing paradigm provides an alternative and economical service for resource-constrained clients to perform large-scale data computation. Since large matrix determinant computation (DC) is ubiquitous in the fields of science and engineering, a first step is taken in this paper to design a protocol that enables clients to securely, verifiably, and efficiently outsource DC to a malicious cloud. The main idea to protect the privacy is employing some transformations on the original matrix to get an encrypted matrix which is sent to the cloud; and then transforming the result returned from the cloud to get the correct determinant of the original matrix. Afterwards, a randomized Monte Carlo verification algorithm with one-sided error is introduced, whose superiority in designing inexpensive result verification algorithm for secure outsourcing is well demonstrated. In addition, it is analytically shown that the proposed protocol simultaneously fulfills the goals of correctness, security, robust cheating resistance, and high-efficiency. Extensive theoretical analysis and experimental evaluation also show its high-efficiency and immediate practicability. It is hoped that the proposed protocol can shed light in designing other novel secure outsourcing protocols, and inspire powerful companies and working groups to finish the programming of the demanded all-inclusive scientific computations outsourcing software system. It is believed that such software system can be profitable by means of providing large-scale scientific computation services for so many potential clients.