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This paper introduces a new one-dimensional chaotic system and a new image encryption algorithm. Firstly, the new chaotic system is analyzed. The bifurcation diagram and Lyapunov exponent show that the system has strong chaotic characteristics and is suitable for the field of image encryption. The chaotic sequence generated by the system is used in image encryption, scrambled according to its sequence value, and several sequences required for DNA coding operation are generated. The encrypted image is obtained by encoding, decoding, and diffusion operation according to the sequence. The initial values and parameters of system are generated by Hash algorithm based on plain image, so it has strong sensitivity and can effectively resist attacks such as selective plaintext attack. Experimental results show that the algorithm has high security and robustness, and can resist common attacks. [ABSTRACT FROM AUTHOR]

Industrial clusters, geographical concentrations of interconnected companies, aim to achieve technological innovation by acquiring common technology, which is the technology shared by all companies in an industrial cluster. Obtaining patents from universities is a primary way to gain common technology. However, existing patent recommendation methods have primarily focused on meeting the technological needs of individual companies, thus falling short in addressing the common technological requirements of all companies within an industrial cluster. To address the problem, we propose a deep learning (DL) method that recommends patents to industrial clusters based on common technological needs mining (DL_CTNM). The proposed method mines the common needs from patents owned by the companies and domain knowledge about potential technologies common to industries. Specifically, we mine the technological needs of the companies from their patents using long short-term memory networks and obtain their patent-based common needs by designing a candidate patent-aware attention mechanism. Then, we extract implicit technology directions from the domain knowledge using a capsule network and obtain domain knowledge-based common needs by designing an industrial cluster-aware attention mechanism. We evaluate the proposed method through offline and online experiments, comparing it to various benchmark methods. The experimental results demonstrate that our method outperforms these benchmarks in terms of recall and normalized discounted cumulative gain. [ABSTRACT FROM AUTHOR]

With the development of Location-Based Social Networks, successive Point Of Interest (POI) recommendation systems have become a hot spot in the field of recommendation systems. Successive POI recommendation systems suggest to users new and interesting places to visit. However, in real-life POI recommendation, there are often a small number of users facing a huge number of POIs. Traditional successive POI recommendation methods are not capable to deal with the large sparse datasets, as they only consider the simple relationship between users and POIs. They do not use the context information of users and POIs, which can enable better recommendation results. To utilize the context information, this paper proposes a novel successive POI recommendation method, SQPMF, which integrates user personal preferences, user social relationships and POI transition relationships into the system for accurate recommendation of the next POI. Our experimental evaluation using three real-life datasets, Gowalla, Foursquare and Brightkite, shows that our method SQPMF consistently outperforms all state-of-the-art methods in recommendation of successive POIs. Compared with other methods, SQPMF improves Precision, Recall and F1-score by an average of at least 6.1 % , 5.8 % and 5.7 % respectively on three publicly available datasets. [ABSTRACT FROM AUTHOR]

Communication industry has developed relying on technological advancement in microelectronics and large-scale integrated circuits. Modified polyimide (MPI) materials with low dielectric loss, relatively simple fabrication process, and cost-effectiveness have significant social and economic benefits. In this work, 6FAPB and 6FDA were used to polymerize fluorinated PI films by thermal imidization, and SiO2 was used to modify PI films for further obtaining low dielectric constant (ε)/loss (tanδ) and enhance mechanical properties. The values of dielectric constant (ε) and tan δ were 2.75 and 0.00491, respectively, under 10 GHz. Meanwhile, the elastic modulus of MPI films was enhanced by the SiO2 adding process, with a maximum tensile modulus of 2.42 GPa. The results indicate that low polarity Si–O–Si bonds lead to a decrease in overall polarity of PI films and lead to tan decrease. This work provides a simple method to increase mechanical properties and reduce the dielectric constant/loss (at 10 GHz high frequency) of PI film by introducing trifluoromethyl and SiO2. [ABSTRACT FROM AUTHOR]

In the area of Polymer dielectric materials, those with low dielectric constant and low dielectric loss have potential to be applied in high-frequency signal transmissions, such as antennae of mobile phones and millimeter-wave radars. This study is focused on producing fluorinated polyimide (PI) film with modified organosilicon (APDS). In this work, APDS was used to PI film. The spatial hindrance of Trifluoromethyl can reduce the high frequency dielectric loss of modified PI film. The length of molecular chain was decreased as increasing the proportion of APDS, which reduced the quantity of polar imide rings in unit volume. Meanwhile, the Si-O-Si bond combination in backbone decreased the integer polarity of PI film, which resulted in the decrease of tan 훿. The values of ε and tan 훿 were 2.48 and 0.00335 respectively under 10 GHz. The values of ε and tan훿 were reduced to 6.6% and 36.5% respectively compared with base membrane. Thus, this work provides a simple method to reduce the dielectric constant and dielectric loss of PI film. Meanwhile, the relationship between dielectric properties and molecular structure was proved. PI molecules were separated by low dielectric groups, which resulted in a decrease in dielectric constant and dielectric loss. [ABSTRACT FROM AUTHOR]

Group behavior pattern mining in traffic scenarios is a challenging problem due to group variability and behavioral regionality. Most methods are either based on trajectory data stored in static databases regardless of the variability of group members or do not consider the influence of scene structures on behaviors. However, in traffic scenarios, information about group members may change over time, and objects' motions show regional characteristics owing to scene structures. To address these issues, we present a general framework of a moving cluster with scene constraints (MCSC) discovery consisting of semantic region segmentation, mapping, and an MCSC decision. In the first phase, a hidden Markov chain is adopted to model the evolution of behaviors along a video clip sequence, and a Markov topic model is proposed for semantic region analysis. During the mapping procedure, to generate snapshot clusters, moving objects are mapped into the corresponding sets of moving objects according to the semantic regions where they are located at each timestamp. In the MCSC decision phase, a candidate MCSC recognition algorithm and screening algorithm are designed to incrementally identify and output MCSCs. The effectiveness of the proposed approach is verified by experiments carried out using public road traffic data. [ABSTRACT FROM AUTHOR]

Cadmium (Cd) is a highly toxic metal that damages human health and normal plant metabolism, and often results in reduced crop growth and yield. To explore the Cd migration characteristic in paddy soil and ditch sediment in a small watershed, present study was conducted in two different Cd polluted paddy areas in Hunan Province, China. The Cd contents of ditch sediment and paddy soil in the typical polluted watershed (Beishan Town, Changsha County, China) and non-polluted watershed (Jinjing Town, Changsha County, China) were determinate. The results showed that the total Cd content and available Cd content in the ditch sediment and paddy soil in Beishan ranged from 0.50 to 6.29 mg kg−1 and 0.26–1.72 mg kg−1, respectively. The total Cd content and available Cd content in the ditch sediment and paddy soil in Jinjing ranged from 0.30 to 2.13 mg kg−1 and 0.05–0.44 mg kg−1, respectively. The total Cd content and available Cd content in the ditch sediment and paddy soil in Beishan presented logarithm decreasing trend with the runoff direction. However, the total Cd content and available Cd content in the ditch sediment and paddy soil in Jinjing presented linear increase trends with the runoff direction. Article Highlights: The total Cd contents and available Cd contents in the ditch sediment and paddy soil in polluted paddy area presented logarithmic decreased trend with the runoff direction, whilst Cd effective rates in downstream ditch sediment and paddy soil were higher than upstream. The total Cd contents and available Cd contents in the ditch sediment and paddy soil in non-polluted paddy area presented logarithmic increased trend with the runoff direction, whilst Cd effective rates in downstream ditch sediment and paddy soil were lower than upstream. [ABSTRACT FROM AUTHOR]

We provide an expression for the measure of coherence of quantum pure states, which is obtained by quantifying the deviation or distance between the measured state and the maximally coherent states in the same Hilbert space. We classify quantum pure states in a Hilbert space according to their coherence and prove that the variance measure of coherence is proper for all quantum pure states. We demonstrate that the variance measure of coherence is simpler and easier to obtain than the l1-norm of coherence and the relative entropy of coherence. [ABSTRACT FROM AUTHOR]

With the rapid growth of link speed, obtaining detailed traffic statistics becomes much more difficult. In order to reduce the resource consumption of measurement systems, more and more passive traffic measurement employs sampling at the packet level. Packet sampling has become an attractive and scalable method to measure flow data on high-speed links. However, knowing the length distributions of traffic flows passing through a network link is very useful for network operation and management. In this paper, we consider the problem of estimating flow length distributions based on sampled flow data. This paper introduces two algorithms for estimating flow length distributions, fitting estimation and factoring estimation. The fitting estimation uses piecewise Pareto distribution to fit the original traffic for a small sampling period. The factoring estimation is used for a large sampling period. It first factorizes the large sampling period into a product of some smaller integer factors, then iteratively invokes fitting estimation or other algorithms such as $${\textit{EM}}$$ in the arranged order of the factors. Evaluations of the proposed algorithms on large Internet traces obtained from several sources demonstrate that they have high measurement accuracy with low computation overhead. The algorithms allow us to recover the complete flow length distributions. [ABSTRACT FROM AUTHOR]

Internet attacks such as distributed denial-of-service (DDoS) attacks and worm attacks are increasing in severity. Identifying realtime attack detection and mitigation of Internet traffic is an important and challenging problem. For example, a compromised host doing fast scanning for worm propagation often makes an unusually high number of connections to distinct destinations within a short time. We call such a host a superpoint, which are sources that connect to a large number of distinct destinations. Detecting superpoints is very important in developing effective and efficient traffic engineering schemes. We propose two novel schemes for detecting superpoints and prove guarantees on their accuracy and memory requirements. These schemes are implemented by introducing a reversible counting Bloom filter (RCBF), a special counting Bloom filter. The RCBF consists of 4 hash functions which projectively select some consecutive bits from original strings as function values. We obtain the information of superpoints using the overlapping of hash bit strings of the RCBF. The theoretical analysis and experiment results show that our schemes can precisely and efficiently detect superpoints. [ABSTRACT FROM AUTHOR]