Jilin Province Science and Technology Department

This paper proposes a new application of particle swarm optimization for traveling salesman problem. We have developed some special methods for solving TSP using PSO. We have also proposed the concept of swap operator and swap sequence, and redefined some operators on the basis of them, in this way the paper has designed a special PSO. The experiments show that it can achieve good results.

The cloud computing is the development of distributed computing, parallel computing and grid computing, or defined as the commercial implementation of these computer science concepts. One of the fundamental issues in this environment is related to task scheduling. Cloud task scheduling is an NP-hard optimization problem, and many meta-heuristic algorithms have been proposed to solve it. A good task scheduler should adapt its scheduling strategy to the changing environment and the types of tasks. This paper proposes a cloud task scheduling policy based on Load Balancing Ant Colony Optimization (LBACO) algorithm. The main contribution of our work is to balance the entire system load while trying to minimizing the make span of a given tasks set. The new scheduling strategy was simulated using the CloudSim toolkit package. Experiments results showed the proposed LBACO algorithm outperformed FCFS (First Come First Serve) and the basic ACO (Ant Colony Optimization).

Abstract Conductive hydrogels as flexible electronic devices, not only have unique attractions but also meet the basic need of mechanical flexibility and intelligent sensing. How to endow anisotropy and a wide application temperature range for traditional homogeneous conductive hydrogels and flexible sensors is still a challenge. Herein, a directional freezing method is used to prepare anisotropic MXene conductive hydrogels that are inspired by ordered structures of muscles. Due to the anisotropy of MXene conductive hydrogels, the mechanical properties and electrical conductivity are enhanced in specific directions. The hydrogels have a wide temperature resistance range of −36 to 25 °C through solvent substitution. Thus, the muscle‐inspired MXene conductive hydrogels with anisotropy and low‐temperature resistance can be used as wearable flexible sensors. The sensing signals are further displayed on the mobile phone as images through wireless technology, and images will change with the collected signals to achieve motion detection. Multiple flexible sensors are also assembled into a 3D sensor array for detecting the magnitude and spatial distribution of forces or strains. The MXene conductive hydrogels with ordered orientation and anisotropy are promising for flexible sensors, which have broad application prospects in human–machine interface compatibility and medical monitoring.

CircRNAs are a class of endogenous RNA that regulates gene expression at the post-transcriptional or transcriptionallevel through interacting with other molecules or microRNAs. Increasing studies have demonstrated that circRNAs play a crucial role in biology processes. CircRNAs are proved as potentialbiomarkers in many diseases including cancers. However, the role of Cdr1as in Hepatocellular carcinoma (HCC) remains to be elucidated. We demonstrated that Cdr1as expression was upregulated in HCC tissues compared with the adjacent non-tumor tissues. In addtion, miR-7 expression was downregulated in HCC tissues compared with the adjacent non-tumor tissues. Moreover, the expression level of miR-7 was inversely correlated with that in HCC tissues. Knockdown of Cdr1as suppressed the HCC cell proliferation and invasion. Overexpression of miR-7 inhibited the HCC cell proliferation and invasion. Overexpression of miR-7 could suppress the direct target gene CCNE1 and PIK3CD expression. Knockdown of Cdr1as suppressed the expression of miR-7 and also inhibited the CCNE1 and PIK3CD expression. Furthermore, knockdown of Cdr1as suppressed the HCC cell proliferation and invasion through targeting miR-7. These data suggested that Cdr1as acted as an oncogene partly through targeting miR-7 in HCC.

We present COVERAGE - a novel database containing copy-move forged images and their originals with similar but genuine objects. COVERAGE is designed to highlight and address tamper detection ambiguity of popular methods, caused by self-similarity within natural images. In COVERAGE, forged-original pairs are annotated with (i) the duplicated and forged region masks, and (ii) the tampering factor/similarity metric. For benchmarking, forgery quality is evaluated using (i) computer vision-based methods, and (ii) human detection performance. We also propose a novel sparsity-based metric for efficiently estimating forgery quality. Experimental results show that (a) popular forgery detection methods perform poorly over COVERAGE, and (b) the proposed sparsity based metric best correlates with human detection performance. We release the COVERAGE database to the research community.

We present a new de novo transcriptome assembler, Bridger, which takes advantage of techniques employed in Cufflinks to overcome limitations of the existing de novo assemblers. When tested on dog, human, and mouse RNA-seq data, Bridger assembled more full-length reference transcripts while reporting considerably fewer candidate transcripts, hence greatly reducing false positive transcripts in comparison with the state-of-the-art assemblers. It runs substantially faster and requires much less memory space than most assemblers. More interestingly, Bridger reaches a comparable level of sensitivity and accuracy with Cufflinks. Bridger is available at https://sourceforge.net/projects/rnaseqassembly/files/?source=navbar.

By using a controllable pillared-layer method, a novel visible-light responsive metal–organic framework (MOF) photocatalyst NNU-36 has been rationally constructed. The synthesized NNU-36 is of broad-range visible light absorption and good chemical stability which are beneficial to its application of photocatalysis. Photocatalytic experiments reveal that NNU-36 is highly efficient for Cr(VI) reduction and dye degradation in aqueous solution under visible light irradiation. Control experiments show that the pH value is vital for Cr(VI) reaction, and meanwhile, the use of hole scavenger of methanol promotes the photocatalytic reduction significantly. It has been also demonstrated that NNU-36 is efficient for dye degradation, in which the introduction of hydrogen peroxide (H 2 O 2 ) significantly enhances the photocatalytic efficiency of dye degradation. This study illustrates that the introduction of hole scavengers or oxidants in the MOF-mediated photocatalytic reaction is a feasible approach to enhance the catalytic efficiency by suppressing the recombination of photoexcited electron–hole pairs in MOFs photocatalysts.

对近年来聚类算法的研究现状与新进展进行归纳总结.一方面对近年来提出的较有代表性的聚类算法,从算法思想、关键技术和优缺点等方面进行分析概括;另一方面选择一些典型的聚类算法和一些知名的数据集,主要从正确率和运行效率两个方面进行模拟实验,并分别就同一种聚类算法、不同的数据集以及同一个数据集、不同的聚类算法的聚类情况进行对比分析.最后通过综合上述两方面信息给出聚类分析的研究热点、难点、不足和有待解决的一些问题.上述工作将为聚类分析和数据挖掘等研究提供有益的参考.

Black phosphorus (black P), which is a promising candidate as an anode material for lithium-ion batteries, was synthesized by a high-pressure and high-temperature (HPHT) method from white and red phosphorus. The study revealed the electrochemical activity of pure black P under different pressures and temperatures systematically. The sample shows higher crystallinity and purity by the HPHT method. Lithium-ion batteries containing black phosphorus as anode materials exhibited a high specific capacity and excellent cycling performance. Black phosphorus obtained from white phosphorus exhibited the highest first discharge and charge capacities of 2505 and 1354 mAh·g –1 at 4 GPa and 400 °C and that obtained from red phosphorus exhibited the highest first discharge and charge capacities of 2649 and 1425 mAh·g –1 at 4.5 GPa and 800 °C. Black P was characterized by X-ray diffraction, Raman microscopy, scanning electron microscopy, and high-resolution transmission electron microscopy.

Mobile crowdsensing is a new paradigm in which a group of mobile users exploit their smart devices to cooperatively perform a large-scale sensing job. One of the users' main concerns is the cost of data uploading, which affects their willingness to participate in a crowdsensing task. In this paper, we propose an efficient Prediction-based User Recruitment for mobile crowdsEnsing (PURE), which separates the users into two groups corresponding to different price plans: Pay as you go (PAYG) and Pay monthly (PAYM). By regarding the PAYM users as destinations, the minimizing cost problem goes to recruiting the users that have the largest contact probability with a destination. We first propose a semi-Markov model to determine the probability distribution of user arrival time at points of interest (PoIs) and then get the inter-user contact probability. Next, an efficient prediction-based user-recruitment strategy for mobile crowdsensing is proposed to minimize the data uploading cost. We then propose PURE-DF by extending PURE to a case in which we address the tradeoff between the delivery ratio of sensing data and the recruiter number according to Delegation Forwarding. We conduct extensive simulations based on three widely-used real-world traces: roma/taxi, epfl, and geolife. The results show that, compared with other recruitment strategies, PURE achieves a lower recruitment payment and PURE-DF achieves the highest delivery efficiency.

This paper is a survey for smart home research, from definition to current research status. First we give a definition to smart home, and then describe the smart home elements, typical research projects, smart home networks research status, smart home appliances and challenges at last.

Ultrawideband (UWB) radar technology has emerged as one of the preferred choices for through-wall detection due to its high range resolution and good penetration. The resolution is a result of high bandwidth of UWB radar and helpful for better separation of multiple targets in complex environment. Detection of human targets through a wall is interesting in many applications. One significant characteristic of human is the periodic motion, such as breathing and limb movement. In this letter, we apply the UWB radar system in through-wall human detection and present the methods based on fast Fourier transform and S transform to detect and identify the human's life characteristic. In particular, we can extract the center frequencies of life signals and locate the position of human targets from experimental data with high accuracy. Compared with other research studies in through-wall detection, this letter is concentrated in the processing and identifying of the life signal under strong clutter. It has a high signal-to-noise ratio and simpler to implement in complex environment detection. We can use the method to search and locate the survivor trapped under the building debris during earthquake, explosion, or fire.

Federated learning (FL) is a promising new technology in the field of IoT intelligence. However, exchanging model-related data in FL may leak the sensitive information of participants. To address this problem, we propose a novel privacy-preserving FL framework based on an innovative chained secure multiparty computing technique, named chain-PPFL. Our scheme mainly leverages two mechanisms: 1) single-masking mechanism that protects information exchanged between participants and 2) chained-communication mechanism that enables masked information to be transferred between participants with a serial chain frame. We conduct extensive simulation-based experiments using two public data sets (MNIST and CIFAR-100) by comparing both training accuracy and leak defence with other state-of-the-art schemes. We set two data sample distributions (IID and NonIID) and three training models (CNN, MLP, and L-BFGS) in our experiments. The experimental results demonstrate that the chain-PPFL scheme can achieve practical privacy preservation (equivalent to differential privacy with ∈ approaching zero) for FL with some cost of communication and without impairing the accuracy and convergence speed of the training model.

To resist the increasingly serious radiation pollution, there is a great need for the fabrication of high-performance electromagnetic interference (EMI) shielding materials. However, it is a great challenge to prepare EMI shielding materials with high efficiency, lightweight, and flexibility for practical applications. Here, we demonstrate an efficient and facile approach to prepare freestanding, lightweight, and flexible crosslinking polyacrylonitrile (CPAN) nanofiber (NF)/metal nanoparticle (MNP) hybrid membranes with a high efficiency and reasonable strength via electrospinning followed by an electroless deposition process. In contrast to a Cu- and Ni-decorated CPAN NF membrane, the resultant CPAN NF/Ag nanoparticle (NP) hybrid membrane exhibited much better electrical conductivity. Furthermore, a superior EMI shielding effectiveness of ≈90 dB is achieved for the lightweight CPAN NF/Ag NP hybrid membrane (53 µm), which is superior to pure metal and most of the synthesized EMI shielding materials. The excellent EMI shielding efficiency is attributed to the high conductivity of MNPs and favorable porous structure in the hybrid NF membrane. In addition, the resultant CPAN NF/MNP hybrid membrane shows a reasonable mechanical strength and excellent flexibility. The prepared polymer NF/MNP hybrid membrane shows promising applications in smart portable and wearable electronics. A porous, lightweight membrane containing nanostructured polymers and metals can block more than 99.999999% of stray radio frequency radiation. Smartphones and other communication devices normally contain an internal wrapping layer of rigid metal designed to reflect and absorb unwanted electromagnetic signals. Xiaofeng Lu and Ce Wang from Jilin University in Changchun City, China, have now developed a shielding material that can be bent and folded thousands of times for applications including wearable electronics. The team spun nanoscale-thin fibers of polyacrylonitrile into a membrane, and then used electrical deposition techniques to coat the polymer with silver, copper, or nickel nanoparticles. This approach produced flexible and highly conductive materials containing multiple pore spaces that reflect electromagnetic noise. The membrane’s shielding efficiency surpassed that of pure silver or aluminum foils, even after multiple mechanical deformations. Freestanding, lightweight, and flexible crosslinking polyacrylonitrile (CPAN) nanofiber (NF)/metal nanoparticles (MNPs) hybrid membrane is used as high-performance electromagnetic interference (EMI) shielding material. A superior EMI shielding effectiveness with a small thickness of several tens of micrometers is achieved.

Ad hoc networks are a new wireless networking paradigm for mobile hosts. In this paper, we designed an intelligent transport system. The ITS (intelligent transport system) includes two big function modules: Information processing application system and Road condition information transferring system. The main task of the road condition information transferring module is in charge of the information exchange of the car inside, car to car and car to road. The module works in ad hoc network, we call the network VANET (vehicular ad-hoc network) . Vehicular networks are likely to become the most relevant form of mobile ad hoc networks. For the sake of insuring the system can run normally, the information can be transferring correctly and fleetly, the security of VANET (vehicular ad-hoc network) of the road condition information transferring system is crucial. So integrate the characteristics of ad hoc network itself, in the ITS of this paper, we concern the security issues of VANETs from some aspects and provide the appropriate solving measures. To make sure the ITS can be used under the security pattern.

Time synchronization and localization are basic services in a sensor network system. Although they often depend on each other, they are usually tackled independently. In this work, we investigate the time synchronization and localization problems in underwater sensor networks, where more challenges are introduced because of the unique characteristics of the water environment. These challenges include long propagation delay and transmission delay, low bandwidth, energy constraint, mobility, etc. We propose a joint solution for localization and time synchronization, in which the stratification effect of underwater medium is considered, so that the bias in the range estimates caused by assuming sound waves travel in straight lines in water environments is compensated. By combining time synchronization and localization, the accuracy of both are improved jointly. Additionally, an advanced tracking algorithm interactive multiple model (IMM) is adopted to improve the accuracy of localization in the mobile case. Furthermore, by combining both services, the number of required exchanged messages is significantly reduced, which saves on energy consumption. Simulation results show that both services are improved and benefit from this scheme.

Macrophage polarization toward M1 phenotype (pro-inflammation) is closely associated with the destructive phase of periodontal inflammation. Nanoceria is verified to inhibit M1 polarization of macrophages by the favorable ability of reactive oxygen species (ROS) scavenging. However, the function of nanoceria on macrophage polarization toward M2 phenotype (anti-inflammation) in reparative phase of periodontal inflammation is quite limited. In this work, by introducing an antioxidant drug quercetin onto nano-octahedral ceria, synergistic and intense regulation of host immunity against periodontal disease is realized. Such nanocomposite can control the phenotypic switch of macrophages by not only inhibition of M1 polarization for suppressing the damage in the destructive phase but also promotion of M2 polarization for regenerating the surrounding tissues in reparative phase of periodontal disease. As-prepared nanocomposite can effectively increase the M2/M1 ratio of macrophage polarization in inflammatory cellular models by lipopolysaccharide stimulation. More importantly, the nanocomposite also exerts an improved therapeutic potential against local inflammation by significant downregulation of pro-inflammatory cytokines and upregulation of anti-inflammatory cytokines in an animal model with periodontal inflammation. Therefore, this newly developed nanomedicine is efficient in ROS scavenging and driving pro-inflammatory macrophages to the anti-inflammatory phenotype to eliminate inflammation, thereby providing a promising candidate for treating periodontal inflammation.

Semiconductor nanomaterials with photocatalytic activity have potential for many applications. An effective way of promoting photocatalytic activity is depositing noble metal nanoparticles (NPs) on a semiconductor, since the noble metal NPs act as excellent electron acceptors which inhibit the quick recombination of the photoexcited electron-hole pairs and thereby enhance the generation of reactive oxygen species (ROS). Herein, a highly effective platform, graphitic carbon nitride (g-C3N4) nanosheets with embedded Ag nanoparticles (Ag/g-C3N4), was synthesized by a facile route. Under visible light irradiation, the ROS production of Ag/g-C3N4 nanohybrids was greatly improved compared with pristine g-C3N4 nanosheets, and moreover, the nanohybrids showed enhanced antibacterial efficacy and ability to disperse bacterial biofilms. We demonstrate for the first time that the Ag/g-C3N4 nanohybrids are efficient bactericidal agents under visible light irradiation, and can also provide a new way for biofilm elimination. The enhanced antibacterial properties and biofilm-disrupting ability of Ag/g-C3N4 nanohybrids may offer many biomedical applications.

User intent detection plays a critical role in question-answering and dialog systems. Most previous works treat intent detection as a classification problem where utterances are labeled with predefined intents. However, it is labor-intensive and time-consuming to label users' utterances as intents are diversely expressed and novel intents will continually be involved. Instead, we study the zero-shot intent detection problem, which aims to detect emerging user intents where no labeled utterances are currently available. We propose two capsule-based architectures: INTENT-CAPSNET that extracts semantic features from utterances and aggregates them to discriminate existing intents, and INTENTCAPSNET-ZSL which gives INTENTCAPSNET the zero-shot learning ability to discriminate emerging intents via knowledge transfer from existing intents. Experiments on two real-world datasets show that our model not only can better discriminate diversely expressed existing intents, but is also able to discriminate emerging intents when no labeled utterances are available.

Human-curated knowledge graphs provide critical supportive information to various natural language processing tasks, but these graphs are usually incomplete, urging auto-completion of them (a.k.a. knowledge graph completion). Prevalent graph embedding approaches, e.g., TransE, learn structured knowledge via representing graph elements (i.e., entities/relations) into dense embeddings and capturing their triple-level relationship with spatial distance. However, they are hardly generalizable to the elements never visited in training and are intrinsically vulnerable to graph incompleteness. In contrast, textual encoding approaches, e.g., KG-BERT, resort to graph triple’s text and triple-level contextualized representations. They are generalizable enough and robust to the incompleteness, especially when coupled with pre-trained encoders. But two major drawbacks limit the performance: (1) high overheads due to the costly scoring of all possible triples in inference, and (2) a lack of structured knowledge in the textual encoder. In this paper, we follow the textual encoding paradigm and aim to alleviate its drawbacks by augmenting it with graph embedding techniques – a complementary hybrid of both paradigms. Specifically, we partition each triple into two asymmetric parts as in translation-based graph embedding approach, and encode both parts into contextualized representations by a Siamese-style textual encoder. Built upon the representations, our model employs both deterministic classifier and spatial measurement for representation and structure learning respectively. It thus reduces the overheads by reusing graph elements’ embeddings to avoid combinatorial explosion, and enhances structured knowledge by exploring the spatial characteristics. Moreover, we develop a self-adaptive ensemble scheme to further improve the performance by incorporating triple scores from an existing graph embedding model. In experiments, we achieve state-of-the-art performance on three benchmarks and a zero-shot dataset for link prediction, with highlights of inference costs reduced by 1-2 orders of magnitude compared to a sophisticated textual encoding method.