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AbstractSchizothorax gulinensis sp. nov. is a new species of the genus Schizothorax from Sichuan, China (Cypriniformes: Cyprinidae). In this study, we have first reported the complete mitochondrial genome of S. gulinensis with Illumina sequencing. There were 16,587 nucleotide pairs in the mitochondrial genome (mitogenome) of S. gulinensis, including 13 protein-coding genes (PCGs), two ribosomal RNAs (rRNAs), and 22 transfer RNAs (tRNAs), as well as one non-coding control region (CR). The proportion of nucleotides in mitochondrial genome was 29.67% (A), 25.45% (T), 17.84% (G), 27.05% (C), and A + T content was 55.12%. All PCGs have the same start codon of the standard ATG, excepting for that of NADH dehydrogenase subunit 1 (nad1) which was the ATC, NADH dehydrogenase subunit 5 (nad5) which was the ATT and cytochrome c oxidase 1 (cox1) which was the ATC. Phylogenetic analysis results supported that S. gulinensis was closely related to Schizothorax grahami. The complete mitochondrial sequence of S. gulinensis will contribute to mitochondrial genome database and provide useful resources for population genetics and evolution analyses.

This study investigates the impact of different diets on fish growth and bacterial community structure present in the intestine of goldfish (Carassius auratus) and their aquaculture water under recirculating water conditions. We assumed that different types of diet would form different intestinal microbiota that may affect host growth. Using Illumina MiSeq high-throughput sequencing, we analyzed bacterial communities in goldfish fed with formulated pellet feed, Tubifex worms (Limnodrilus hoffmeisteri), and an alternating diet of both. Over a 14-day feeding trial, no significant differences in juvenile goldfish growth were observed between groups. After 7 days, diet changes significantly influenced the abundance and diversity of intestinal bacteria, with the alternating diet notably enhancing bacterial diversity in both the intestines and water. However, these differences in bacterial diversity decreased by day 14. The results indicate that diet type affects microbial community diversity in the intestines and water of goldfish, and that goldfish intestines maintain a stable core bacterial community structure. This highlights the potential for optimizing diet types to enhance microbial health and stability in aquaculture systems and, in addition, provides an important scientific basis for alternative diets in goldfish aquaculture in the industry.

The Tarantula Nebula in the Large Magellanic Cloud is known for its high star formation activity. At its center lies the young massive star cluster R136, providing a significant amount of the energy that makes the nebula shine so brightly at many wavelengths. Recently, young massive star clusters have been suggested to also efficiently produce very high-energy cosmic rays, potentially beyond PeV energies. Here, we report the detection of very-high-energy γ -ray emission from the direction of R136 with the High Energy Stereoscopic System, achieved through a multicomponent, likelihood-based modeling of the data. This supports the hypothesis that R136 is indeed a very powerful cosmic-ray accelerator. Moreover, from the same analysis, we provide an updated measurement of the γ -ray emission from 30 Dor C, the only superbubble detected at TeV energies presently. The γ -ray luminosity above 0.5 TeV of both sources is (2–3) × 10 ^35 erg s ^−1 . This exceeds by more than a factor of 2 the luminosity of HESS J1646−458, which is associated with the most massive young star cluster in the Milky Way, Westerlund 1. Furthermore, the γ -ray emission from each source is extended with a significance of >3 σ and a Gaussian width of about 30 pc. For 30 Dor C, a connection between the γ -ray emission and the nonthermal X-ray emission appears likely. Different interpretations of the γ -ray signal from R136 are discussed.

The effect of Mo addition on microstructure, impact property, and wear resistance of high boron Fe-4.5 wt% B surfacing alloy, which was developed by the three-arc double-wire welding method was investigated in this work. The phase structure and microstructure of the surfacing alloy were studied by X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), and Electron Backscattered Diffraction (EBSD). Besides, a two-body abrasive wear test and impact fracture test was carried out to study the wear behavior and fracture mechanism of the surfacing alloy. The results show, hypereutectic Fe-4.5 wt% B surfacing alloys with different Mo additions are mainly composed of primary (Fe, Mo)2B boride, eutectic matrix, and FeMo2B2. With the increase of Mo content in Fe-4.5 wt% B surfacing alloy, the continuous growth of the eutectic structure is blocked, which can inhibit the formation and expansion of cracks in the surfacing layer. With the increase of Mo, the wear resistance of Fe-4.5 wt% B alloy increases from 16.70 g-1 to 22.72 g-1. In the impact fracture experiment, Fe-4.5 wt% B surfacing alloy has high brittleness, the primary Fe2B is a transgranular fracture, and the interface between primary Fe2B boride and eutectic structure is an intergranular fracture. With the addition of Mo, the fracture mechanism of the surfacing alloy changes from transgranular fracture to transgranular fracture + ductile fracture, which reduces the fragmentation and spalling during the wear process, thereby improving its wear resistance.

Chlorantraniliprole (CAP) is a presentative diamide pesticide utilized in agricultural area and as well as rice-fish co-culture system for pest control. However, the understanding of toxic effects of CAP on fish species is still incomplete. In the present study, we performed an integrated study of the acute toxicity and bioaccumulation of CAP on the crucian carp, Carassius carassius, a fish species widely distributed in freshwater area in China and commonly farmed in the rice-fish co-culture systems. Besides, biochemical changes, transcriptional responses and gut microbiota of fish were investigated upon sub-chronic CAP exposure. The results showed that CAP is low toxic to crucian carp with a 96 h LC50 of 74.824 mg/L, but has considerable accumulation in the fish muscles when exposed to 3 mg/L of CAP for 14 d and still detectable after 18 d recovery in fresh water. For sub-chronic test, fish were exposed to CAP at 0, 0.3, 3 and 30 mg/L respectively for 14 d. CAP induced oxidative stress and detoxification inhibition in the liver of fish by decreasing antioxidative and detoxicated enzymes activities and downregulating relevant genes expression. In addition, disrupted gut flora composition was found in all experimental groups by the 16 S rRNA sequencing data, indicating the gut microbiota dysbiosis in crucian carp and potential adverse host effect. All the results suggest that CAP at sublethal concentrations has prominent toxic effect on crucian carp and more attentions should be paid especially using directly in an integrated aquaculture system.

Toxic effects of abamectin on non-target aquatic organisms have been well documented due to its extensive use in both agricultural and aquacultural areas. However, knowledge of the abamectin induced cytotoxicity in crustacean hepatopancreas is still incomplete. In this study, we investigated the cytotoxic effects of abamectin on hepatopancreas cells of Chinese mitten crab, Eriocheir sinensis by an in vitro assay. The results showed that abamectin inhibited cell viability with elevated reactive oxygen species (ROS) and malondialdehyde (MDA) levels in a dose-dependent manner. Increased olive tail moment (OTM) values and 8-hydroxy-2′-deoxyguanosine (8-OHdG) contents indicate the DNA damage under abamectin exposure. The up-regulation of the typical apoptosis-related protein BCL2-associated X protein (Bax) and the down-regulation of B cell leukemia/lymphoma 2 (Bcl-2) demonstrate apoptosis in hepatopancreas cells. Meanwhile, the activities of both caspase-3 and caspase-9 were increased, indicating caspase-mediated apoptosis. In addition, qRT-PCR results showed the up-regulation of antioxidant genes superoxide dismutase (SOD) and catalase (CAT). The mRNA expression of Cap ‘n′ Collar isoform-C (CncC) and c-Jun NH2-terminal kinases (JNK) was also significantly increased, implying the involvement of the Nrf2/MAPK pathway in the antioxidative response. The alteration of innate immune-associated genes Toll-like receptor (TLR) and myeloid differentiation primary response gene 88 (Myd88) also indicates the influence of abamectin on immune status. In summary, the present study reveals the cytotoxicity of abamectin on hepatopancreas cells of E. sinensis and this in vitro cell culture model could be used for further assessment of pesticide toxicity.

With the advent of IoT technology, the dynamic nature of IoT devices has introduced new obstacles to access control. It is essential to consider the security requirements of the actual physical environment, rendering the traditional access control approach centered on the information space. In the IoT ecosystem, there are several issues such as the dynamics of devices frequently entering and leaving, the lack of computing and storage capacity, and distributed deployment. To address these challenges, this paper proposes the Domain Attribute Based Access Control(DABAC) that incorporates domain elements to implement the physical location limitation of dynamic devices. Moreover, an intelligent gateway is utilized to divide the physical area and act as a proxy to achieve regional device management, automatic networking of devices in the domain, and the dynamic expansion of the sensor network resulting from device entry or exit. Then, given the distributed deployment of devices, smart contracts are employed to deploy access control mechanisms and construct a trusted environment to mitigate threats such as single points of failure. Finally, the DABAC is implemented on the Ethereum platform, simulating a smart medical situation. The experimental results demonstrate that the proposed solution effectively addresses the problem of access control of device dynamics in an untrusted IoT environment while maintaining system security.

Nitrite toxicity poses a significant threat to aquatic organisms, including largemouth bass (LMB) and Micropterus salmoides. This study aimed to elucidate the role of bZIP transcription factors in mediating the molecular responses to nitrite stress in the LMB spleen. We identified 120 bZIP genes in the LMB genome using bioinformatics analysis and divided them into 11 subgroups based on phylogenetic relationships. Under nitrite stress, the bZIP_XI subgroup was upregulated, suggesting the activation of the stress response in the LMB spleen. Cellular pathway analysis revealed enrichment of pathways related to stress response, DNA repair, apoptosis, and autophagy. Co-expression network analysis highlighted bZIP_XI members such as msabZIP_49, msabZIP_12, msabZIP_39, and msabZIP_116 as potential key regulators. These transcription factors likely modulated the expression of stress-related genes like VCAM1, POLE3, and BMP1. Conserved binding motifs in the promoters of these genes may support regulation by bZIP_XI. Furthermore, bZIP_XI members correlated with immune cell infiltration in the spleen, potentially regulating immune-related genes like BCL2L1 and SELE. Homologs of bZIP_XI in other fish species exhibited similar expression patterns under stress. Overall, this study implicates the bZIP transcription factor family, notably the bZIP_XI subgroup, in orchestrating the molecular response of the LMB spleen to nitrite toxicity by regulating stress response pathways and immune function. These findings provide insights into nitrite stress adaptation in fish.

The performance of software defect prediction (SDP) models determines the priority of test resource allocation. Researchers also use interpretability techniques to gain empirical knowledge about software quality from SDP models. However, SDP methods designed in the past research rarely consider the impact of data transformation methods, simple but commonly used preprocessing techniques, on the performance and interpretability of SDP models. Therefore, in this paper, we investigate the impact of three data transformation methods (Log, Minmax, and Z-score) on the performance and interpretability of SDP models. Through empirical research on (i) six classification techniques (random forest, decision tree, logistic regression, Naive Bayes, K-nearest neighbors, and multilayer perceptron), (ii) six performance evaluation indicators (Accuracy, Precision, Recall, F1, MCC, and AUC), (iii) two interpretable methods (permutation and SHAP), (iv) two feature importance measures (Top-k feature rank overlap and difference), and (v) three datasets (Promise, Relink, and AEEEM), our results show that the data transformation methods can significantly improve the performance of the SDP models and greatly affect the variation of the most important features. Specifically, the impact of data transformation methods on the performance and interpretability of SDP models depends on the classification techniques and evaluation indicators. We observe that log transformation improves NB model performance by 7%–61% on the other five indicators with a 5% drop in Precision. Minmax and Z-score transformation improves NB model performance by 2%–9% across all indicators. However, all three transformation methods lead to substantial changes in the Top-5 important feature ranks, with differences exceeding 2 in 40%–80% of cases (detailed results available in the main content). Based on our findings, we recommend that (1) considering the impact of data transformation methods on model performance and interpretability when designing SDP approaches as transformations can improve model accuracy, and potentially obscure important features, which lead to challenges in interpretation, (2) conducting comparative experiments with and without the transformations to validate the effectiveness of proposed methods which are designed to improve the prediction performance, and (3) tracking changes in the most important features before and after applying data transformation methods to ensure precise and traceable interpretability conclusions to gain insights. Our study reminds researchers and practitioners of the need for comprehensive considerations even when using other similar simple data processing methods.

GRB 221009A is the brightest gamma-ray burst (GRB) ever detected. To probe the very-high-energy (VHE; >100 GeV) emission, the High Energy Stereoscopic System (H.E.S.S.) began observations 53 hr after the triggering event, when the brightness of the moonlight no longer precluded observations. We derive differential and integral upper limits using H.E.S.S. data from the third, fourth, and ninth nights after the initial GRB detection, after applying atmospheric corrections. The combined observations yield an integral energy flux upper limit of ${{\rm{\Phi }}}_{\mathrm{UL}}^{95 \% }=9.7\times {10}^{-12}\,\mathrm{erg}\,{\mathrm{cm}}^{-2}\,{{\rm{s}}}^{-1}$ above E _thr = 650 GeV. The constraints derived from the H.E.S.S. observations complement the available multiwavelength data. The radio to X-ray data are consistent with synchrotron emission from a single electron population, with the peak in the spectral energy distribution occurring above the X-ray band. Compared to the VHE-bright GRB 190829A, the upper limits for GRB 221009A imply a smaller gamma-ray to X-ray flux ratio in the afterglow. Even in the absence of a detection, the H.E.S.S. upper limits thus contribute to the multiwavelength picture of GRB 221009A, effectively ruling out an IC-dominated scenario.

Abstract Source code suggestion is the utmost helpful feature in the integrated development environments that helps to quicken software development by suggesting the next possible source code tokens. The source code contains useful semantic information but is ignored or not utilised to its full potential by existing approaches. To improve the performance of source code suggestion, the authors propose a deep semantic net (DeepSN) that makes use of semantic information of the source code. First, DeepSN uses an enhanced hierarchical convolutional neural network combined with code‐embedding to automatically extract the top‐notch features of the source code and to learn useful semantic information. Next, the source code's long and short‐term context dependencies are captured by using long short‐term memory. We extensively evaluated the proposed approach with three baselines on ten real‐world projects and the results are suggesting that the proposed approach surpasses state‐of‐the‐art approaches. On average, DeepSN achieves 7.6% higher accuracy than the best baseline.

Under the protection of the patent system, an inventor can patent his or her invention, to make an economic profit from the patent, thus encouraging further invention. However, a patent is territorial. To get an international patent, the invention should be patented several times in all target patent offices, even with the help of international conventions like PCT, leading to inefficiency, expensiveness, and uncertainty. Therefore, we construct an international patent application system based on a permissioned blockchain named PABC. Patent applications can be approved or rejected without relying on one patent office. Each patent office in a country or region manages a peer node of the system, reaching agreements of all the applications. First, we build the model of the system and detail the three-layer architecture of PABC Core. Then, we implement PABC on a permissioned blockchain platform - Hyperledger Fabric. In addition, experiments about its throughput, transaction latency, and failure rate were carried out. The results show that PABC can meet current needs.

Informative identifiers are crucial for the comprehensibility and maintainability of programs. Exploring properties of identifiers and investigating their impact on software artifacts have been an important research focus. However, to enable such capabilities, fundamentally we need to have comprehensive understanding on the main characteristics of identifiers at the first place, which is unfortunately not sufficiently studied. For example, it remained unclear what Part of Speech (POS) tags that developers commonly use to define identifiers. To answer such open issues, we conducted a large-scale empirical study on the naturalness of identifiers, based on 5,000 open source Java and Android projects, concerning five dimensions of identifiers: distributions, compositions, POS tags, lengths, and initializations of identifiers. Results of the empirical study contain five key findings for identifiers in programs, including, e.g., the observation that the three POS tags (i.e., nouns, verbs, and adjectives) are the most commonly used ones when developers define identifiers. Furthermore, based on our findings, we provide implications and insights for developers, researchers, and Integrated Development Environments (IDEs) in the context that identifier-related activities are performed or functionalities are enabled.

The present study investigated the effects of methionine (Met) on growth, immune function, and antioxidant capacity in partridge shank broilers, which were treated with either an anticoccidial drug or a coccidia vaccine. Chickens were fed five graded levels of Met (0.33%, 0.39%, 0.45%, 0.51%, or 0.57%) for 21 days in combination with the drug or vaccine. The results revealed that an optimal level of Met supplementation (1) increased ADFI (average daily feed intake), ADG (average daily gain), and F/G values (feed-to-gain ratio), indicating improved production; (2) increased OPG levels (oocysts per gram feces), intestinal lesion scores, bursa of Fabricius and thymus indexes, and sIgA content; (3) improved GSH-Px activities, and increased content levels of T-protein, albumin, and urea nitrogen. In addition, birds in the anticoccidial drug group had higher final weights, higher ADFI and ADG values, as well as lower F/G values, compared with birds in the vaccine group, indicating that coccidia vaccine reduces the performance of broilers. In conclusion, we found that an optimal level of dietary Met improved the production of partridge shank broilers, and this result might be related to immune function and antioxidant capacity. Optimal levels of digestible Met in terms of production performance (ADG and F/G) and immune function (sIgA in ileum mucosa) in partridge shank broilers (1–21 days) were found to be 0.418, 0.451, and 0.451 of diet, respectively, when birds were given anticoccidial drug treatment, with corresponding figures of 0.444, 0.455, and 0.452% when the coccidia vaccine was administered.

This paper presents the design and implementation of a location data collecting system only using the barometer sensor on the smartphone. It is energy efficient because of using low-power barometer sensor readings to infer the location, and it protects user privacy by providing the non-sensitive location data. To design such a location data collecting system, we make some key technical contributions: 1) a curve fitting-based solution to remove the barometer sensor reading noise caused by weather change; 2) a deep learning algorithm to detect user moving activities based on the restricted Boltzmann machine, and; 3) a clustering-based extraction algorithm for signatures of different locations. The field studies show that the SELoc provides user daily locations with an accuracy of 85%; meanwhile, the average energy consumption is only about 22% compared with GPS.

Task offloading in Mobile Edge Computing (MEC) is a solution to augment resource-limited mobile devices' capabilities by migrating tasks to the edge of the network (i.e., edge servers and idle devices). At present, a lot of work is focused on optimizing policies to reduce latency or energy consumption for users. However, they mostly ignore that services are not necessarily trustworthy because the resource providers are complex, dynamic, and unreliable. The trustworthiness of a service in our paper mainly includes two aspects. One is that resource providers will not violate users' privacy. The other is that resource providers will perform well to ensure the effectiveness of services. To solve this problem, we propose a trust-aware task offloading framework. The main purpose of the framework is to select a resource provider for a user to reduce latency or energy consumption and ensure service trustworthiness at the same time. The framework can be divided into three modules (i.e., trust evaluation, filtering and selection). By combining trust evaluation and filtering modules, some resource providers that are not trusted by users are filtered out to ensure that the services provided to users are trustworthy. In the selection module, we select an appropriate provider for a user from the qualified (i.e., left after the filtering process) resource providers based on an offloading policy. The experimental results show that our framework not only reduces latency or energy consumption for users, but also reduces the failure rate of tasks.

CADS (cooperative autonomous driving systems) are software-intensive and safety-critical reactive systems and give great promise to our daily life, but system errors may not be identified in the design stage until the implement stage, and the cost to correct them will be more expensive later than the early stage. For designing trustworthy autonomous software systems, we have to deal with multiclock constraint models. SysML (System Modeling Language) meets increasing adoption in order to carry out system-level modelling and verification against abstract representations, but it suffers from semantic ambiguities in the design of safety-critical autonomous systems. The main objective is to investigate methods for coping with the design and analysis models simultaneously and to achieve semantic consistency based on mathematical foundations and formal model transformation. In this paper, we propose a method to combine the requirement modelling process with analysis process together for CADS safety and reliability guarantee. Firstly, we extend SysML metamodels and construct SysML profile for the CADS domain that could improve modelling correctness and enhance reusability. An instantiated CADS model has been designed by means of adopting a profile containing different key functional and nonfunctional attributes and behaviors. Secondly, we define formal syntax and semantic notations for modelling elements in the SysML state machine diagram and show transformation rules between the state machine diagram and the CCSL (Clock Constraint Specification Language) model. Semantic preservation is also proved using the bisimulation relation between them for rigorous mapping correctness. Thirdly, a cooperative autonomous overtaking driving case study on the highway scenario is used for illustration, and we use the tool TimeSquare to simulate CCSL specification execution traces at the system design stage.

This paper presents the design and implementation of BTrack, a new approach for location tracking on mountain roads. It is as accurate as the GPS on common smartphones, but costs much less energy. To design such a location system, we carried out a nation-wide online survey to confirm the desirability for low power location tracking system, and the result is positive. Then, we proposed BTrack, and it makes two key technical contributions. The first is to propose a dynamic transition matrix hidden Markov model to combine the barometer and accelerometer reading hints for estimating the location of the user. The second is to design some novel techniques for parameter estimation, and proposed an adaptive algorithm to reduce the computational complexity. The field studies show that the accuracy of BTrack is no worse than GPS in 56% cases, meanwhile, the energy consumption is only about 30%. Compared with the existing works, BTrack is more suitable for location tracking on mountain roads.

Spectrum-based fault localization (SFL) is a popular lightweight automatic software fault localization technique that uses coverage information of program execution to compute the likelihood of root cause of failure(s) for each program component and ranks them descending by their suspiciousness scores. However, some recent studies indicate an SFL technique to be useful only if the root cause(s) of failures is ranked at top k. Due to the nature of the SFL technique, it is impossible that the root fault(s) is always ranked at top k, which may interfere with the usefulness of SFL in practice. To solve this issue, an SFL technique via enlarging the non-fault region to further improve fault absolute ranking was proposed. The idea behind this is that we can intuitively improve fault absolute ranking for an SFL technique if some non-fault components ranked higher were excluded from the fault ranking list. In the approach, we enlarge the non-fault region iteratively to narrow down the suspicious region based on two scenarios, and then rank those components in the suspicious region using existing SFL techniques. The empirical results indicate that our approach significantly helps existing SFL techniques to further improve their usefulness.

Cross-project defect prediction (CPDP) is a field of study where a software project lacking enough local data can use data from other projects to build defect predictors. To support CPDP, the cross-project data must be carefully filtered before being applied locally. Researchers have devised and implemented a plethora of various data filters for the improvement of CPDP performance. However, it is still unclear what data filter strategy is most effective, both generally and specifically, in CPDP. The objective of this paper is to provide an extensive comparison of well-known data filters and a novel filter devised in this paper. We perform experiments on 44 releases of 14 open-source projects, and use Naive Bayes and a support vector machine as the underlying classifier. The results demonstrate that the data filter strategy improves the performance of cross-project defect prediction significantly, and the hierarchical select-based filter proposed performs significantly better. Moreover, when using appropriate data filter strategy, the defect predictor built from cross-project data can outperform the predictor learned by using within-project data.

As a new software paradigm, cloud computing provides services dynamically according to user requirements. However, it tends to disclose personal information due to collaborative computing and transparent interactions among software as a service (SaaS) services. We propose a private data disclosure checking method that can be applied to the collaboration interaction process. First, we describe the privacy requirement with ontology and description logic. Second, with dynamic description logic, we validate whether SaaS services are authorized to obtain a user's privacy attributes, to prevent unauthorized services from obtaining their private data. Third, we monitor authorized SaaS services to guarantee privacy requirements. Therefore, we can prevent users' private data from being used and propagated illegally. Finally, we propose privacy disclosure checking algorithms, and demonstrate their correctness and feasibility by experiments.

Translating linear temporal logic (LTL) formulas into Büchi automata is one of the most important aspects of LTL model checking. Certain successful algorithms, such as LTL2BA and SPOT, first translate an LTL formula into a transition-based generalized Büchi automaton (TGBA) and then degeneralize it into a Büchi automaton. This paper focuses on achieving a better translation from LTL to TGBA and analyzing the performance of every step of the algorithm. We decompose the translation into three steps to give a step-wise description and improve all three steps. The first step is the basic translation without acceptance conditions and simplifications, which combines the advantages of both LTL2BA and SPOT. Second, we introduce a new definition of acceptance conditions. Our proofs and experiments have shown that our technique is more efficient and improves the degeneralization ability. Finally, we introduce the simplifications of our algorithm. We focus on not only producing better final Büchi automata but also minimizing intermediate automata, which can reduce the execution time.

Avermectin is commonly used in aquaculture systems for pest control in recent decades in China. However, no information is provided for the toxic effect to the important commercial species, Chinese mitten crab, Eriocheir sinensis. To investigate the aquatic toxicity of avermectin, an acute toxic test was performed in this study. The results showed that the 48 h- and 96 h- LC50 were 1.663 and 0.954 mg/L, respectively. For further research, crabs were exposed to sublethal concentrations of 0.03, 0.06, 0.12, 0.24 and 0.48 mg/L. Levels of antioxidants, including superoxide dismutase (SOD), catalase (CAT) and total antioxidant capacity (T-AOC) were significantly (P

GPS datasets in the big data regime provide rich contextual information that enable efficient implementation of advanced features such as navigation, tracking, and security in urban computing systems. Understanding the hidden patterns in large amount of GPS data is critically important in ubiquitous computing. The quality of GPS data is the fundamental key problem to produce high quality results. In real world applications, certain GPS trajectories are sparse and incomplete; this increases the complexity of inference algorithms. Few of existing studies have tried to address this problem using complicated algorithms that are based on conventional heuristics; this requires extensive domain knowledge of underlying applications. Our contribution in this paper are two-fold. First, we proposed deep learning based bidirectional convolutional recurrent encoder-decoder architecture to generate the missing points of GPS trajectories over occupancy grid-map. Second, we interfaced attention mechanism between enconder and decoder, that further enhance the performance of our model. We have performed the experiments on widely used Microsoft geolife trajectory dataset, and perform the experiments over multiple level of grid resolutions and multiple lengths of missing GPS segments. Our proposed model achieved better results in terms of average displacement error as compared to the state-of-the-art benchmark methods.

Due to the limited scalability of DRAM,it is hard to optimize the performance of big data analysis and the big data applications.The new non-volatile memory (NVM) brings the opportunity to improve the performance and efficiency for big data applications,which benefits by the advantages of NVM,including its non-volatile,high storage density,and low power consumption.The PCM/DRAM hybrid memory architecture based on the non-volatile memory was analyzed.The feasibility and advantages of hybrid memory for big data applications through the analysis on the optimization of performance,energy consumption and memory management strategies for hybrid memory architecture were demonstrated.The defects in existing work were summarized and the potential research field in PCM/DRAM hybrid memory architecture was discussed.

Cloud computing has provided services for users as a software paradigm. However, it is difficult to ensure privacy information security because of its opening, virtualization, and service outsourcing features. Therefore how to protect user privacy information has become a research focus. In this paper, firstly, we model service privacy policy and user privacy preference with description logic. Secondly, we use the pellet reasonor to verify the consistency and satisfiability, so as to detect the privacy conflict between services and user. Thirdly, we present the algorithm of detecting privacy conflict in the process of cloud service composition and prove the correctness and feasibility of this method by case study and experiment analysis. Our method can reduce the risk of user sensitive privacy information being illegally used and propagated by outsourcing services. In the meantime, the method avoids the exception in the process of service composition by the privacy conflict, and improves the trust degree of cloud service providers.

With the development of web services technology, web services have changed from single to composite services. Privacy protection in composite services is becoming an important issue. P3P (platform for privacy preferences) is a privacy policy language which was designed for single web services. It enables service providers to express how they will deal with the privacy information of service consumers. In order to solve the problem that P3P cannot be applied to composite services directly, we propose a method to obtain P3P privacy policies for composite services. In this method, we present the definitions of Purpose, Recipient, and Retention elements as well as Optional and Required attributes for P3P policies of composite services. We also provide an instantiation to illustrate the feasibility of the method.