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Anomaly detection plays a crucial role in many Internet of Things (IoT) applications such as traffic anomaly detection for smart transportation and medical diagnosis for smart healthcare. With the explosion of IoT data, anomaly detection on data streams raises higher requirements for real-time response and strong robustness on large-scale data arriving at the same time and various application fields. However, existing methods are either slow or application-specific. Inspired by the edge computing and generic anomaly detection technique, we propose an isolation forest based framework with dynamic Insertion and Deletion schemes (IDForest), which can incrementally update the forest to detect anomalies on data streams. Besides, IDForest is deployed on edge servers in parallel through packing each tree into a subtask, which facilitates the fast anomaly detection on data streams. Extensive experiments on both synthetic and real-life datasets demonstrate the efficiency and robustness of our framework for anomaly detection. [ABSTRACT FROM AUTHOR]

In this paper, a Gaussian high-dimensional random matrix approach is used to conduct an in-depth study and analysis of the detection of abnormal student behaviour in Chinese language classrooms, and a corresponding model is designed for practical application. The Gaussian high-dimensional random matrix technique and recurrent neural network technique are applied to build a basic technical framework for intelligent proctoring. An innovative method of human posture estimation is used to complete the proctoring task, and the structure of the Gaussian high-dimensional random matrix method is simplified and the model is compressed to achieve real-time and parallelism of the method. A PolSAR sparse representation classification (OGRM_SRC) algorithm based on orthogonal Gaussian random matrices (OGRM) is proposed to construct an observation dictionary based on polarisation features and OGRM to select typical ground target samples in polarized feature images. The system not only functions as a psychometric assessment of students but also is capable of detecting and analysing abnormal student behaviour based on the improved forest algorithm. The residuals of the pixel to be classified are calculated relative to each atom in the observation dictionary, and the minimum reconstruction residual is used as the classification criterion. The OGRM_SRC algorithm is applied to classify the pixel, and the final classification results are obtained and evaluated for accuracy. We propose a fine-grained action recognition optimisation method for recurrent neural networks, fusing temporal attention information and spatial attention information to improve the action recognition method and improve the model's ability to recognise fine-grained abnormal behaviours such as looking left, looking right, and copying with head down. Based on this, we conducted some comparative experiments to validate the effectiveness of our work and verified that the system achieved a recognition efficiency of 76.8 FPS and a recognition accuracy of 98.5% in a graphical computing processor. [ABSTRACT FROM AUTHOR]

Bifunctional electrocatalysts are key for achieving efficient energy conversion in Oxygen Reduction Reaction (ORR) and Oxygen Evolution Reaction (OER), essential processes in renewable energy applications. Despite their critical role, achieving optimal activity and stability in these catalysts remains a formidable challenge, hindering the full realization of sustainable energy systems. Within the confines of this study, the catalytic efficiencies pertaining to ORR/OER of 22 distinct CoN 4 C-X catalysts, encompassing both unadulterated CoN 4 C and those axially coordinated, were meticulously evaluated employing density functional theory (DFT). The outcomes of our investigation reveal that CoN 4 C-O emerges as a promising bifunctional catalyst, characterized by its notably low overpotential (0.36 V/0.34 V). Furthermore, a linear relationship involving the d-band center, ICOHP, and adsorbed intermediates was elucidated, substantiating their efficacy as accurate descriptors of catalytic performance. Subsequent analysis of electronic structural properties unveiled the manifestation of pronounced peaks at the Fermi energy level, indicative of a robust interaction between Co 3d orbitals and N 2p orbitals with the oxygen ligand, substantially enhancing the catalyst's activity. This body of research contributes significantly to the deepened comprehension of five-coordinated CoN 4 C catalysts and furnishes a robust theoretical foundation for the methodical design of high-efficiency electrocatalysts utilizing Co-based carbon materials. [Display omitted] • CoN 4 C-X axial ligand catalysts exhibit good structural stability, excellent electrical conductivity, and improved stiffness. • CoN 4 C-O was identified as a qualified ORR/OER bifunctional electrocatalyst. • The optimal binding strength with the oxygenated intermediates endow CoN 4 C-X good catalytic performance. [ABSTRACT FROM AUTHOR]

Some examples are how such convergency incurs security and privacy concerns in machine/deep learning models, and how to design a more suitable cloud/edge computing platformer deployment for a specific AI algorithms or models. After the big success of cloud computing technology in industry, edge computing is now extending the various cloud services from central server farms to the venues close to applications. The cloud/edge computing infrastructure can provide powerful, flexible, and pervasive computation and storage capabilities which have been fostering many big data applications. [Extracted from the article]

Because of the advantages of simplicity, cost-effectiveness and visibility, lateral-flow immunoassays (LFAs) have been widely used in the food safety field. However, the low sensitivity of LFAs needs to be solved. Nanozymes have amazing potential for application in biosensors due to their excellent and abundant enzyme-like characteristics. In this study, an Au@Pt nanozyme synthesized by a one-step method showed the higher affinity with TMB/H 2 O 2 and higher catalytic efficiency than that of horseradish peroxidase (HRP). For the detection of streptomycin (STR), a typical aminoglycoside antibiotic, a novel LFA based on Au@Pt as a visual tag and an enhanced LFA based on the enzyme-like activity of Au@Pt by addition of the chromogenic substrate 3-amino-9-ethyl-carbazole (AEC) were established and compared with conventional LFA based on AuNPs. The qualitative limit of detection (LOD) was 1 ng mL−1 for the LFA based on Au@Pt as the visual tag and 0.1 ng mL−1 for the enhanced LFA based on the activity of Au@Pt, in comparison to 8 ng mL−1 for LFA based on AuNPs. Furthermore, the level of streptomycin in milk samples from Zhenjiang City was successfully evaluated by the novel LFA based on Au@Pt nanozyme. These results suggest that LFAs based on nanozymes are a promising and effective tool for food safety. Image 1 • Au@Pt nanozyme with higher peroxidase-like activity was synthesized by a simple one-step method. • Lateral-flow immunoassays based on Au@Pt as tag and enzyme mimic showed higher sensitivity. • Lateral-flow immunoassay based on catalytic activity of Au@Pt was used to detect STR in milk. [ABSTRACT FROM AUTHOR]

The environmental concentration of tetrabromobisphenol A bis(2-hydroxyethyl) ether (TBBPA-DHEE) and tetrabromobisphenol A mono(hydroxyethyl) ether (TBBPA-MHEE), as some of the main derivatives and byproducts of tetrabromobisphenol A (TBBPA), are obscure due to lacking available analytical methods. In the present study, a multiplexed competitive chemiluminescent imaging immunoassay (CLIIA) based on specific monoclonal antibody respectively against TBBPA-MHEE and TBBPA-DHEE has been developed for the simultaneous detection of the two targets. To improve the sensitivity of immunoassay, Au nanoparticles (AuNPs) were utilized as solid support for loading horseradish peroxidase, labeled to obtain the multi-enzyme particles. Under the optimized conditions, the limits of detection (LODs) for TBBPA-MHEE and TBBPA-DHEE were 1.85 ng/mL and 2.05 ng/mL, respectively. After estimating its accuracy and precision, the established method was applied for investigation of the contaminants at the inner rivers in Zhenjiang, Jiangsu Province. Our results indicated that no TBBPA-MHEE was found in all 11 water samples and two samples of TBBPA-DHEE were detected up to 6.34 ng/mL, possibly ascribed to the dense population and slow flow rate around the sampling site. [ABSTRACT FROM AUTHOR]

Summary: A simple and rapid multiplexed direct competitive chemiluminescent (CL) imaging immunoassay had been developed for the simultaneous detection of kanamycin (KAN) and streptomycin (STR), which were widely used against bacterial infections in animals. To achieve the multiplexed detection of the two targets, a microarray format based on nitrocellulose membranes (NCMs) has been designed. Then, the peroxidase activity of the captured HRP‐labelled immunocomplex in each channel was measured by an enhanced luminol‐based chemiluminescent solution using a cooled low‐light CCD with high resolution. For qualitative analysis, the limit of detection (LOD) was 0.5 ng mL−1 for KAN and 3.13 ng mL−1 for STR by the naked eye. Through the CL data collected, KAN and STR could be detected quantitatively at the LOD of 0.03 and 0.33 ng mL−1, respectively. This method can be utilised as a screening test to evaluate the presence of antibiotics in milk samples. [ABSTRACT FROM AUTHOR]

Cyberspace faces a series of threats that can spread rapidly across distributed systems. Many such transmissible cyber threats aim to damage users’ data. In recent years, the popularity of cloud computing has driven a lot of users to save their data in the cloud. The centralization of users’ data in the cloud has created new opportunities and incentives for transmissible cyber threats targeting users’ data. In this context, in addition to cloud vendor’s security mechanisms, it is important to allow users to efficiently verify the integrity of their data saved in the cloud. The seminal sampling based PDP (Provable Data Possession) scheme can attain effective probabilistic verification with high computational efficiency for the integrity of users’ data saved in the cloud by use of a set of randomly sampled data blocks. By integrating with the forward error correcting (FEC) technique, a recently-proposed robust PDP scheme can protect against arbitrarily small amounts of data corruption and has therefore been widely adopted in practice. It is a core task to determine the number of sample blocks in this scheme because this parameter plays a fundamental role in balancing the security of the scheme and the computational cost. A smaller value can comprise the security while a larger one can incur extra high computational cost. Existing work mainly leverages the Monte Carlo methods to estimate this parameter. However, these methods suffer from heavy computational cost. In this paper, we propose a method to determine this parameter based on the curve fitting technique. Specifically, we formally analyze the parameter selection process of the robust PDP scheme, and leverage the curve fitting technique to improve the efficiency of parameter selection while ensuring the optimality of the number of samples for the robustness of the scheme. Extensive experimental results demonstrate the effectiveness and efficiency of our approach. Specifically, it can be 25 times faster than the existing solution for 1, 000, 000 times simulated attacks. [ABSTRACT FROM AUTHOR]

Cloud computing provides promising scalable IT infrastructure to support various processing of a variety of big data applications in sectors such as healthcare and business. Data sets like electronic health records in such applications often contain privacy-sensitive information, which brings about privacy concerns potentially if the information is released or shared to third-parties in cloud. A practical and widely-adopted technique for data privacy preservation is to anonymize data via generalization to satisfy a given privacy model. However, most existing privacy preserving approaches tailored to small-scale data sets often fall short when encountering big data, due to their insufficiency or poor scalability. In this paper, we investigate the local-recoding problem for big data anonymization against proximity privacy breaches and attempt to identify a scalable solution to this problem. Specifically, we present a proximity privacy model with allowing semantic proximity of sensitive values and multiple sensitive attributes, and model the problem of local recoding as a proximity-aware clustering problem. A scalable two-phase clustering approach consisting of a t-ancestors clustering (similar to k-means) algorithm and a proximity-aware agglomerative clustering algorithm is proposed to address the above problem. We design the algorithms with MapReduce to gain high scalability by performing data-parallel computation in cloud. Extensive experiments on real-life data sets demonstrate that our approach significantly improves the capability of defending the proximity privacy breaches, the scalability and the time-efficiency of local-recoding anonymization over existing approaches. [ABSTRACT FROM AUTHOR]

Cloud computing promises a scalable infrastructure for processing big data applications such as medical data analysis. Cross-cloud service composition provides a concrete approach capable for large-scale big data processing. However, the complexity of potential compositions of cloud services calls for new composition and aggregation methods, especially when some private clouds refuse to disclose all details of their service transaction records due to business privacy concerns in cross-cloud scenarios. Moreover, the credibility of cross-clouds and on-line service compositions will become suspicional, if a cloud fails to deliver its services according to its “promised” quality. In view of these challenges, we propose a privacy-aware cross-cloud service composition method, named HireSome-II (History record-based Service optimization method) based on its previous basic version HireSome-I. In our method, to enhance the credibility of a composition plan, the evaluation of a service is promoted by some of its QoS history records, rather than its advertised QoS values. Besides, the k-means algorithm is introduced into our method as a data filtering tool to select representative history records. As a result, HireSome-II can protect cloud privacy, as a cloud is not required to unveil all its transaction records. Furthermore, it significantly reduces the time complexity of developing a cross-cloud service composition plan as only representative ones are recruited, which is demanded for big data processing. Simulation and analytical results demonstrate the validity of our method compared to a benchmark. [ABSTRACT FROM AUTHOR]

Abstract: In big data applications, data privacy is one of the most concerned issues because processing large-scale privacy-sensitive data sets often requires computation resources provisioned by public cloud services. Sub-tree data anonymization is a widely adopted scheme to anonymize data sets for privacy preservation. Top–Down Specialization (TDS) and Bottom–Up Generalization (BUG) are two ways to fulfill sub-tree anonymization. However, existing approaches for sub-tree anonymization fall short of parallelization capability, thereby lacking scalability in handling big data in cloud. Still, either TDS or BUG individually suffers from poor performance for certain valuing of k-anonymity parameter. In this paper, we propose a hybrid approach that combines TDS and BUG together for efficient sub-tree anonymization over big data. Further, we design MapReduce algorithms for the two components (TDS and BUG) to gain high scalability. Experiment evaluation demonstrates that the hybrid approach significantly improves the scalability and efficiency of sub-tree anonymization scheme over existing approaches. [Copyright &y& Elsevier]

A large number of cloud services require users to share private data like electronic health records for data analysis or mining, bringing privacy concerns. Anonymizing data sets via generalization to satisfy certain privacy requirements such as $(k)$-anonymity is a widely used category of privacy preserving techniques. At present, the scale of data in many cloud applications increases tremendously in accordance with the Big Data trend, thereby making it a challenge for commonly used software tools to capture, manage, and process such large-scale data within a tolerable elapsed time. As a result, it is a challenge for existing anonymization approaches to achieve privacy preservation on privacy-sensitive large-scale data sets due to their insufficiency of scalability. In this paper, we propose a scalable two-phase top-down specialization (TDS) approach to anonymize large-scale data sets using the MapReduce framework on cloud. In both phases of our approach, we deliberately design a group of innovative MapReduce jobs to concretely accomplish the specialization computation in a highly scalable way. Experimental evaluation results demonstrate that with our approach, the scalability and efficiency of TDS can be significantly improved over existing approaches. [ABSTRACT FROM AUTHOR]

Abstract: Cloud computing provides massive computation power and storage capacity which enable users to deploy applications without infrastructure investment. Many privacy-sensitive applications like health services are built on cloud for economic benefits and operational convenience. Usually, data sets in these applications are anonymized to ensure data ownersʼ privacy, but the privacy requirements can be potentially violated when new data join over time. Most existing approaches address this problem via re-anonymizing all data sets from scratch after update or via anonymizing the new data incrementally according to the already anonymized data sets. However, privacy preservation over incremental data sets is still challenging in the context of cloud because most data sets are of huge volume and distributed across multiple storage nodes. Existing approaches suffer from poor scalability and inefficiency because they are centralized and access all data frequently when update occurs. In this paper, we propose an efficient quasi-identifier index based approach to ensure privacy preservation and achieve high data utility over incremental and distributed data sets on cloud. Quasi-identifiers, which represent the groups of anonymized data, are indexed for efficiency. An algorithm is designed to fulfil our approach accordingly. Evaluation results demonstrate that with our approach, the efficiency of privacy preservation on large-volume incremental data sets can be improved significantly over existing approaches. [Copyright &y& Elsevier]

Cloud computing provides massive computation power and storage capacity which enable users to deploy computation and data-intensive applications without infrastructure investment. Along the processing of such applications, a large volume of intermediate data sets will be generated, and often stored to save the cost of recomputing them. However, preserving the privacy of intermediate data sets becomes a challenging problem because adversaries may recover privacy-sensitive information by analyzing multiple intermediate data sets. Encrypting ALL data sets in cloud is widely adopted in existing approaches to address this challenge. But we argue that encrypting all intermediate data sets are neither efficient nor cost-effective because it is very time consuming and costly for data-intensive applications to en/decrypt data sets frequently while performing any operation on them. In this paper, we propose a novel upper bound privacy leakage constraint-based approach to identify which intermediate data sets need to be encrypted and which do not, so that privacy-preserving cost can be saved while the privacy requirements of data holders can still be satisfied. Evaluation results demonstrate that the privacy-preserving cost of intermediate data sets can be significantly reduced with our approach over existing ones where all data sets are encrypted. [ABSTRACT FROM AUTHOR]

Depression is the second leading-cause of disability in China. Although studies have shown that more than 80% of patients with advanced schistosomiasis (AS) suffer from anxiety and depression, these study groups are all hospitalized patients with AS and do not represent the extent of the disease in the whole group. To our knowledge, there are no studies assessing the severity of depression in patients with other forms of schistosomiasis japonicum. Therefore, it is necessary to evaluate the occurrence and potential risk factors of depression in the schistosomiasis endemic population. This cross-sectional study was conducted in Jiangxi Province, where schistosomiasis is relatively common in China, as the investigation site. The Patient Health Questionnaire-9 (PHQ-9) scale was selected to assess the depressive symptoms in the study population. At the same time, basic personal information of the research subjects and relevant socio-economics and schistosomiasis endemic area indicators were collected. The survey results show that AS has the highest incidence of depression (34.35%), while non-advanced schistosomiasis (N-AS) and control group (CG) have 22.35% and 22.24% respectively; the incidence of depression in AS is significantly higher than in N-AS and CG, while there is no statistical significance in the comparison between N-AS and CG; the incidence of mild depressive symptoms accounts for 61.08%-75.54% of the total incidence of depression in different groups; multivariate analysis shows that the occurrence of moderate/severe depressive symptoms in the AS group was significantly related to above 60 years old, male, the combination of other serious diseases, personal financial difficulties, and marshland and lake endemic areas. In the N-AS group, the occurrence of moderate/severe depressive symptoms was significantly related to the combination of other serious diseases, personal financial difficulties, significant correlation between marshland and lake endemic areas and the level of control of schistosomiasis epidemics. In conclusion, depression is still relatively common among patients with schistosomiasis patients, especially those with AS. It is recommended that the government and relevant departments consider mental health care when developing prevention and control work in schistosomiasis-endemic areas, in order to effectively protect the physical and mental health of schistosomiasis patients and residents in endemic areas. [ABSTRACT FROM AUTHOR]

As the 5G rolls out around the world, many edge applications will be deployed by app vendors and accessed by massive end-users. Efficient detection of malicious network behavior is paid more and more attention. The current traffic detection work is still stuck on the analysis of high-dimensional data. It will restrict the improvement of threat monitoring and network governance when facing massive network flows. Characterization of network flows within simple domains is required to simplify the process of network analysis. Traffic characterization is a key task that allows service providers to detect and intercept anomalous traffic, such that high QoS (Quality of Service) and service availability are maintained and spread of malicious content is prevented. Unfortunately, there is still a lack of research on the concrete characterization of network data. Analogous to spectrum, in this paper, we proposed the concept of FlowSpectrum for the first time in order to represent the network flow, concretely. In the FlowSpectrum, network flow is represented as a spectral line rather than the raw data or a feature vector of the network flow. All flows are able to be mapped as spectral lines, and traffic identification is achieved by analyzing the positions of spectral lines. FlowSpectrum can significantly reduce the complexity of network traffic behavior analysis while enhancing the interpretability of detection and facilitating cyberspace behavior management. We designed a neural network structure based on semi-supervised AutoEncoder for decomposition and dimensionality reduction of network flows in FlowSpectrum. The characterization capability of FlowSpectrum is proved by thorough experiments. Moreover, we realized the correspondence between network behaviors and intervals of spectral lines, preliminarily. Generally speaking, FlowSpectrum can provide new ideas for the field of network traffic analysis. [ABSTRACT FROM AUTHOR]

We are pleased to announce the publication of the special issue focusing on the convergent study of big data processing, cloud, and Internet of Things (IoT). Big data, cloud/edge computing, and IoT have become the cornerstones that define and uphold our data-oriented, interconnected, and internet-driven reality. Scalable big data analytics for IoT with the cloud/edge computing infrastructure is the right answer. [Extracted from the article]

• Co-Cu catalysts break the limit of conventional AEM sites. • Path II (H 2 O → *OH → *2OH → *OOH → O 2) was identified as the most efficient Co-Cu OER pathway. • The synergistic effect of Co and Cu optimizes the electronic structure and reduces the overpotential. The development of highly active electrocatalysts for Oxygen Evolution Reactions (OER) is critical in the field of energy conversion and storage. Among potential candidates, diatomic catalysts have demonstrated the potential to outperform monoatomic counterparts, though comprehensive studies on their reaction mechanisms remain limited. In this study, a Co-Cu diatomic catalyst was computationally designed using density functional theory (DFT), and four reaction pathways involving multiple intermediates (*O, *OH, *OOH, *2OH, *O + *OH) were calculated. The results indicate that the Co-Cu diatomic catalyst exhibits superior catalytic performance on pathway II (H 2 O → *OH → *2OH → *OOH → O 2) with an overpotential of 0.27 V, overcoming the limitations imposed by the active site of conventional anion exchange membrane (AEM) catalysts. The high catalytic activity is attributed to the synergistic interaction between the metal atoms, bypassing the high-energy barrier step typically observed in conventional pathways. In this mechanism, the Cu d-band center is close to the Fermi energy level, enhancing electron transfer, while Co provides a stable adsorption site and effectively regulates the adsorption and conversion of reaction intermediates. These findings offer new strategies for the rational synthesis of bimetallic catalysts. [ABSTRACT FROM AUTHOR]

Collaborative filtering-based recommender systems are regarded as an important tool to predict the items that users will appreciate based on the historical usage of users. However, traditional recommendation solutions often pay more attentions to the accuracy of the recommended items while neglect the diversity of the final recommended list, which may produce partial redundant items in the recommended list and as a result, decrease the satisfaction degree of users. Moreover, historical usage data for recommendation decision-makingoften update frequently, which may lead to low recommendation efficiency as well as scalability especially in the big data environment. Considering these drawbacks, a novel method called DivRec_LSH is proposed in this paper to achieve diversified and efficient recommendations, which is based on the historical usage records and the Locality-Sensitive Hashing (LSH) technique. Finally, we compare our method with existing methods on the MovieLens dataset. Experiment results indicate that our proposal is feasible in addressing the triple dilemmas of recommender systems simultaneously, i.e., high efficiency, accuracy and diversity. [ABSTRACT FROM AUTHOR]

• Peritoneal dialysis patients with periodontitis exhibited different microbial community structure compared with control group • There are specific microbes enriched in the subgingival flora of peritoneal dialysis patients with periodontitis • Prevotellaceae, Selenomonas, Aggregatibacter, Anaeroglobus, TM7 [G-5], and Centipeda are the specific microbes. Periodontitis is an oral chronic inflammatory disease caused by dental plaque. It is comorbid with numerous systemic diseases and associated with several predisposing factors, such as chronic kidney disease (CKD). Peritoneal dialysis is one of the ultimate treatments for patients with severe CKD. However, peritoneal dialysis patients with periodontitis often will be accompanied with more poor oral hygiene and periodontal clinical indexes. This study aimed to compare the microbial flora of periodontitis patients with or without peritoneal dialysis. Sixteen peritoneal dialysis patients with periodontitis (P group) and 16 patients with periodontitis only (C group) were selected. Subgingival plaque samples of them were processed for bioinformatics analysis by 16S rDNA gene sequencing. The diversity indices and species richness in the P group were insignificantly higher than that in the C group (P > 0.05). The two groups exhibited different microbial community structure. At Genus level, Prevotellaceae, Selenomonas, Aggregatibacter, Anaeroglobus, TM7_[G-5], and Centipeda were significantly enriched in the P group than those in the C group. This study demonstrated that specific microbes enriched in the subgingival flora of peritoneal dialysis patients with periodontitis. [ABSTRACT FROM AUTHOR]

g-C3N4 is a highly promising green semiconductor photocatalyst, but its bandgap is wide and its utilization efficiency for visible light is limited. The photocatalytic performance of g-C3N4 can be effectively improved through element doping. This paper uses first principles methods to study the mechanism of the influence of non-metallic element B and metallic element Cu co doping on the electronic structure and optical properties of g-C3N4. The results indicate that the most stable localization point for B, Cu co-dopped g-C3N4, (001) surface is that B occupies the H site, while Cu occupies the N2 site. B. Compared with single B element doping, co doping with Cu can further reduce the energy gap and work function of g-C3N4 (001) surface. The addition of Cu mainly improves the electronic conductivity and light capture ability of B-doped g-C3N4 (001) surface, thereby enhancing photocatalytic activity. [ABSTRACT FROM AUTHOR]

With the popularization of smart devices and the rapid development of Internet of Things (IoT), location-based social networks (LBSNs) are growing rapidly. As a crucial personalized location service of LBSNs, point-of-interest (POI) recommendation has become a research hotspot. However, due to the use of personal information, POI recommendation system brings serious risks of privacy disclosure. Existing studies mainly focused on improving recommendation performance while ignoring privacy issues. To cope with the challenges, we propose a POI recommendation framework with users' privacy preserving in an IoT environment based on local differential privacy (LDP). We first design an LDP-friendly POI recommendation method based on improved Hawkes process (HawkesRec) to integrate users' long-term static and time-varying preferences. Then we put forward a privacy preserving recommendation framework based on HawkesRec and local differential privacy to protect the visited POIs and recommendation results of users. Experimental results over three real-world datasets demonstrate that the proposed solution achieves better performance than other baselines and has a good capability of privacy preserving. [ABSTRACT FROM AUTHOR]

Edge Computing provides mobile and Internet-of-Things (IoT) app vendors with a new distributed computing paradigm which allows an app vendor to deploy its app at hired edge servers distributed near app users at the edge of the cloud. This way, app users can be allocated to hired edge servers nearby to minimize network latency and energy consumption. A cost-effective edge user allocation (EUA) requires maximum app users to be served with minimum overall system cost. Finding a centralized optimal solution to this EUA problem is NP-hard. Thus, we propose EUAGame, a game-theoretic approach that formulates the EUA problem as a potential game. We analyze the game and show that it admits a Nash equilibrium. Then, we design a novel decentralized algorithm for finding a Nash equilibrium in the game as a solution to the EUA problem. The performance of this algorithm is theoretically analyzed and experimentally evaluated. The results show that the EUA problem can be solved effectively and efficiently. [ABSTRACT FROM AUTHOR]

The first-principle pane wave pseudopotential method in GGA-PBE generalized gradient approximation is aphid to study the lattice parameters, electronic properties and mechanical properties of hydrogen storage alloy LaNi4Ge under different pressures. The results show that the lattice parameters of LaNi4Ge compound decrease with the increase of pressure, and is a-axles is more easily compressed than taxes. There are no signal cant trend changes in the total density of states, the Fermi energy level decreases with the increase of pressure, and is hardness increases with the increase of pressure. The elastic properties show that the material is decisive in the pressure range, and the elastic constant Cu and elastic modulus E increase further with the increase of pressure, and maintain the mechanical stability in a certain pressure range. [ABSTRACT FROM AUTHOR]

With the rapid growth of data in size and complexity, that are available on shared cloud computing platform, the threat of malicious activities and computer crimes has increased accordingly. Thus, investigating efficient data visualization techniques for visual analytics of such big data and visual intrusion detection over data intensive cloud computing is urgently required. In this paper, we first propose a new parallel coordinates visualization method that uses arced-axis for high-dimensional data representation. This new geometrical scheme can be efficiently used to identify the main features of network attacks by displaying recognizable visual patterns. In addition, with the aim of visualizing the clear and detailed structure of the dataset according to the contribution of each attribute, we propose a meaningful layout for the new method based on singular value decomposition algorithm, which possesses statistical property and can overcome the curse of dimensionality. Finally, we design a prototype system for network scan detection, which is based on our visualization approach. The experiments have shown that our approach is effective in visualizing multivariate datasets and detecting attacks from a variety of networking patterns, such as the features of DDoS attacks. [ABSTRACT FROM AUTHOR]

Service recommender systems have been shown as valuable tools for providing appropriate recommendations to users. In the last decade, the amount of customers, services and online information has grown rapidly, yielding the big data analysis problem for service recommender systems. Consequently, traditional service recommender systems often suffer from scalability and inefficiency problems when processing or analysing such large-scale data. Moreover, most of existing service recommender systems present the same ratings and rankings of services to different users without considering diverse users’ preferences, and therefore fails to meet users’ personalized requirements. In this paper, we propose a Keyword-Aware Service Recommendation method, named KASR, to address the above challenges. It aims at presenting a personalized service recommendation list and recommending the most appropriate services to the users effectively. Specifically, keywords are used to indicate users’ preferences, and a user-based Collaborative Filtering algorithm is adopted to generate appropriate recommendations. To improve its scalability and efficiency in big data environment, KASR is implemented on Hadoop, a widely-adopted distributed computing platform using the MapReduce parallel processing paradigm. Finally, extensive experiments are conducted on real-world data sets, and results demonstrate that KASR significantly improves the accuracy and scalability of service recommender systems over existing approaches. [ABSTRACT FROM AUTHOR]

To gain and retain competitive advantages in a competitive business arena, a business cloud-computing platform should continuously strive to offer new services and remain competitive. Unfortunately, it becomes more and more recognized by the industry that a cloud-computing platform could not cover all aspects of IT layers engaged in infrastructure, platform and application. In practice, end users' requests are nearly unlimited; while the services held by a cloud-computing platform is relatively limited, no matter in service category or in service capacity. In view of this challenge, an elastic cloud platform is investigated by recruited outside services that are absent from the cloud platform. Concretely, through dynamically hiring a qualified service on Internet to replace the absent service inside a cloud platform, an elastic cloud platform could nearly provide unlimited capabilities in an outsourcing service way, e.g., computing power, storage, application functions, etc. At last, the validity of the method is evaluated by a case study. [ABSTRACT FROM AUTHOR]

Abstract: With the increasing popularity of cloud computing technologies, more and more service composition processes are enacted and executed in could environment. Compared with the various and approximately infinite application requirements from end users, the web services held by a cloud platform are usually limited. Therefore, it is often a challenging effort to develop a service composition, in such a situation that only part of the functional qualified candidate services could be found inside a cloud platform. In this situation, the absent services will be invocated in a cross-platform way outside the cloud platform. In view of this challenge, a QoS-aware composition method is investigated for supporting cross-platform service invocation in cloud environment. Furthermore, some experiments are deployed to evaluate the method presented in this paper. [Copyright &y& Elsevier]

Currently, self-driving, emerging as a key automatic application, has brought a huge potential for the provision of in-vehicle services (e.g., automatic path planning) to mitigate urban traffic congestion and enhance travel safety. To provide high-quality vehicular services with stringent delay constraints, edge computing (EC) enables resource-hungry self-driving vehicles (SDVs) to offload computation-intensive tasks to the edge servers (ESs). In addition, caching highly reusable contents decreases the redundant transmission time and improves the quality of services (QoS) of SDVs, which is envisioned as a supplement to the computation offloading. However, the high mobility and time-varying requests of SDVs make it challenging to provide reliable offloading decisions while guaranteeing the resource utilization of content caching. To this end, in this paper we propose a collaborative computation offloading and content caching method, named CoPace, by leveraging deep reinforcement learning (DRL) in EC for self-driving system. Specifically, we first introduce OSTP to predict the future time-varying content popularity, taking into account the temporal-spatial attributes of requests. Moreover, a DRL-based algorithm is developed to jointly optimize the offloading and caching decisions, as well as the resource allocation (i.e., computing and communication resources) strategies. Extensive experiments with real-world datasets in Shanghai, China, are conducted to evaluate the performance, which demonstrates that CoPace is both effective and well-performed. [ABSTRACT FROM AUTHOR]

Nowadays, with the development of cyber-physical systems (CPS), there are an increasing amount of applications deployed in the CPS to connect cyber space with physical world better and closer than ever. Furthermore, the cloud-based CPS bring massive computing and storage resource for CPS, which enables a wide range of applications. Meanwhile, due to the explosive expansion of applications deployed on the CPS, the energy consumption of the cloud-based CPS has received wide concern. To improve the energy efficiency in the cloud environment, the virtualized technology is employed to manage the resources, and the applications are generally hosted by virtual machines (VMs). However, it remains challenging to meet the Quality-of-Service (QoS) requirements. In view of this challenge, a QoS-aware VM scheduling method for energy conservation, named QVMS, in cloud-based CPS is designed. Technically, our scheduling problem is formalized as a standard multi-objective problem first. Then, the Non-dominated Sorting Genetic Algorithm III (NSGA-III) is adopted to search the optimal VM migration solutions. Besides, SAW (Simple Additive Weighting) and MCDM (Multiple Criteria Decision Making) are employed to select the most optimal scheduling strategy. Finally, simulations and experiments are conducted to verify the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

• This paper discusses the weighted tensor nuclear norm in the framework of tensor-SVD. • The subgradient of the weighted tensor nuclear norm is studied. • The KKT condition of the proposed nonsmooth nonconvex problem is proved. In this paper, we consider the tensor completion problem, which aims to estimate missing values from limited information. Our model is based on the recently proposed tensor-SVD, which uses the relationships among the color channels in an image or video recovery problem. To improve the availability of the model, we propose the weighted tensor nuclear norm whose weights are fixed in the algorithm, study its properties and prove the Karush-Kuhn-Tucker (KKT) conditions of the proposed algorithm. We conduct extensive experiments to verify the recovery capability of the proposed algorithm. The experimental results demonstrate improvements in computation time and recovery effect compared with related methods. [ABSTRACT FROM AUTHOR]

VOCs (volatile organic compounds) have an important impact on the atmospheric environment, climate and human health. In a typical chemical industrial development zone in eastern China, 123 kinds of VOCs were on-line monitored simultaneously at three sites using the GC-FID/MS system for one year. The seasonal and diurnal variations of composition and concentration of VOCs were compared and analyzed systematically. The VOCs features existed significant differences at the three sites, which were greatly affected by the types and emissions of surrounding enterprises. The alkanes appeared as the major pollutants at the site adjacent to petrochemical processing enterprises (site A), while OVOCs were the majority at the other two sites, which were around by the fine chemical enterprises (site B and C). The TVOC concentrations presented seasonal differences at the three sites, which showed higher values in summer (468.6 μg/m3) at site A, in winter (409.2 μg/m3) at site B，and in autumn (298.9 μg/m3) at site C, respectively. The isopentane (16.8%) and ethylene oxide (17.9%) made up the largest proportion of the VOCs at site A and B, respectively. While it was more susceptible to the effects of dichloromethane, 1,2-dichloroethane and chloromethane at site C. Three pollution events of ethylene oxide, isopentane and dichloromethane were analyzed and traced based on the prevailing wind and the change of concentration at the three sites. Further analysis of the impact of VOCs on the formation of secondary pollutants of O 3 and PM 2.5 found that the contribution of local O 3 (28.1 %) in summer was significantly larger in the study area than that in non-industrial area. The multiphase chemical reactions were more likely to occur in autumn, which promoted the formation of secondary PM 2.5 , showing the correlation coefficient of 0.73 between the secondary organic aerosol formation potential of VOCs and PM 2.5. • VOCs Characteristics was presented at three sites in a chemical industrial development zone. • Characteristics and sources of VOCs were presented for different types of enterprises. • The effects of VOCs on the formation of O 3 and PM 2.5 in industrial areas were analyzed. [ABSTRACT FROM AUTHOR]

• MWCNTs were conjugated with HRP by physical absorbance and covalent linking. • The multi-enzyme particle based on MWCNTs with physical absorbance showed the highest catalytic efficiency and that based on AuNPs lowest. • Using the particles based on MWCNTs with physical absorbance and covalent linking, the LODs of IMB-based method were improved 37-fold and 15-fold. Various nanoparticles provide new nanoscaffolds for enzymes to immobilize and develop a series of multi-enzyme probes for sensitive assays. In this experiment, gold nanoparticles (AuNPs) and multi-well carbon nanotubes (MWCNTs) were adopted as solid support and different conjugation methods with horseradish peroxidase (HRP) were explored. Kanamycin (KAN), one of amino glycoside antibiotics, was adopted as the target to develop the enhanced MPs-based competitive immunoassays. Three multi-enzyme probes, Au/KA N- HRP/HRP, MWCNTs/KA N- HRP/HRP- p and MWCNTs/KAN-HRP/HRP- c , were synthesized. MWCNTs/KAN-HRP/HRP- p showed highest catalytic efficiency, following MWCNTs/KAN-HRP/HRP- c , and Au/KAN-HRP/HRP lowest. With the optimal condition, enhanced competitive MPs immunoassays with these multi-enzyme probes were established to detect KAN. The value of LOD were 128 pg/mL, 17 pg/mL and 6.99 pg/mL based on Au/KAN-HRP/HRP, MWCNTs/KAN-HRP/HRP- c and MWCNTs/KAN-HRP/HRP- p respectively, which improved 2-fold, 15-fold and 37-fold respectively compared with that of conventional IMBs assay. With the aid of multi-enzyme probes based on nanoparticles, it is promising for sensitive detection and screening of molecules from biological samples. [ABSTRACT FROM AUTHOR]

Smart home automation enables the users to realize the access control of the in-home appliances by the mobile devices. With the rapid development of mobile cloud computing, offloading computation workloads of the home automation applications to nearby cloudlets has been treated as a promising approach to overcoming inherent flaws of portable devices, such as low battery capacity. The computing capacity of cloudlet is limited compared with the distant public cloud whose elastic computation resources are almost infinite. Therefore, some mobile services should wait for the occupied computation resources in the cloudlet to get released, which is less energy-efficient. In view of this challenge, we model the waiting time spending in the cloudlet as a M/M/m/∞ queue and propose a hybrid computation offloading algorithm for home automation applications to minimize the total energy consumption of the mobile devices within a given constant deadline. The proposed algorithm combines cloudlet with public clouds, providing a more energy-efficient offloading strategy for home automation applications. Technically, a particle swarm optimization (PSO)-based heuristic algorithm is implemented to schedule mobile services. Comprehensive experiments are conducted to demonstrate the effectiveness and efficiency of our proposed algorithm. [ABSTRACT FROM AUTHOR]

Based on magnetoelectric effect, an angle sensor consisting of a magnetostrictive/piezoelectric laminate composite (MPLC), a multi-polar magnetic ring (MPMR), a modulation coil, and a shaft is presented. The MPLC and the modulation coil winded around it are the immobile parts of the angle sensor. The shaft and the MPMR fixed on it are the moving parts of the angle sensor. The modulation coil pro-applies an AC magnetic field to the MPLC which works at its resonance state. So the MPLC can dynamical and static detects the DC magnetic field produced by the MPMR. The theoretical analysis and experimental results demonstrated that the output signal of the angle sensor is influenced by the DC magnetic field. Thus, the amplitude of the output signal is used to measure the rotational angle, and the varying frequency of the amplitude has a linear relationship with the rotational speed. A resolution of 0.1° at a rotational speed of 10 rpm and the distance between the MPLC and the MPMR of 0.5 mm is achieved from this sensor, so a small step-change rotational angle of 0.1° can be clearly distinguished. In addition, for enhancing the performance of the angle sensor, the distance is much smaller much better. These characteristics show that the magnetoelectric effect can be successfully used in rotational parameters testing and make the angle sensor as a promising candidate device for rotational applications, such as robots, motors, revolving stage, etc. [ABSTRACT FROM AUTHOR]

Outlier detection is an important task in data mining, with applications ranging from intrusion detection to human gait analysis. With the growing need to analyze high speed data streams, the task of outlier detection becomes even more challenging as traditional outlier detection techniques can no longer assume that all the data can be stored for processing. While the well-known Local Outlier Factor (LOF) algorithm has an incremental version, it assumes unbounded memory to keep all previous data points. In this paper, we propose a memory efficient incremental local outlier (MiLOF) detection algorithm for data streams, and a more flexible version (MiLOF_F), both have an accuracy close to Incremental LOF but within a fixed memory bound. Our experimental results show that both proposed approaches have better memory and time complexity than Incremental LOF while having comparable accuracy. In addition, we show that MiLOF_F is robust to changes in the number of data points, the number of underlying clusters and the number of dimensions in the data stream. These results show that MiLOF/MiLOF_F are well suited to application environments with limited memory (e.g., wireless sensor networks), and can be applied to high volume data streams. [ABSTRACT FROM PUBLISHER]

Cloud computing that provides elastic computing and storage resource on demand has become increasingly important due to the emergence of “big data”. Cloud computing resources are a natural fit for processing big data streams as they allow big data application to run at a scale which is required for handling its complexities (data volume, variety and velocity). With the data no longer under users’ direct control, data security in cloud computing is becoming one of the most concerns in the adoption of cloud computing resources. In order to improve data reliability and availability, storing multiple replicas along with original datasets is a common strategy for cloud service providers. Public data auditing schemes allow users to verify their outsourced data storage without having to retrieve the whole dataset. However, existing data auditing techniques suffers from efficiency and security problems. First, for dynamic datasets with multiple replicas, the communication overhead for update verifications is very large, because each update requires updating of all replicas, where verification for each update requires O(log n ) communication complexity. Second, existing schemes cannot provide public auditing and authentication of block indices at the same time. Without authentication of block indices, the server can build a valid proof based on data blocks other than the blocks client requested to verify. In order to address these problems, in this paper, we present a novel public auditing scheme named MuR-DPA. The new scheme incorporated a novel authenticated data structure (ADS) based on the Merkle hash tree (MHT), which we call MR-MHT. To support full dynamic data updates and authentication of block indices, we included rank and level values in computation of MHT nodes. In contrast to existing schemes, level values of nodes in MR-MHT are assigned in a top-down order, and all replica blocks for each data block are organized into a same replica sub-tree. Such a configuration allows efficient verification of updates for multiple replicas. Compared to existing integrity verification and public auditing schemes, theoretical analysis and experimental results show that the proposed MuR-DPA scheme can not only incur much less communication overhead for both update verification and integrity verification of cloud datasets with multiple replicas, but also provide enhanced security against dishonest cloud service providers. [ABSTRACT FROM PUBLISHER]

Cloud computing opens a new era in IT as it can provide various elastic and scalable IT services in a pay-as-you-go fashion, where its users can reduce the huge capital investments in their own IT infrastructure. In this philosophy, users of cloud storage services no longer physically maintain direct control over their data, which makes data security one of the major concerns of using cloud. Existing research work already allows data integrity to be verified without possession of the actual data file. When the verification is done by a trusted third party, this verification process is also called data auditing, and this third party is called an auditor. However, such schemes in existence suffer from several common drawbacks. First, a necessary authorization/authentication process is missing between the auditor and cloud service provider, i.e., anyone can challenge the cloud service provider for a proof of integrity of certain file, which potentially puts the quality of the so-called ‘auditing-as-a-service’ at risk; Second, although some of the recent work based on BLS signature can already support fully dynamic data updates over fixed-size data blocks, they only support updates with fixed-sized blocks as basic unit, which we call coarse-grained updates. As a result, every small update will cause re-computation and updating of the authenticator for an entire file block, which in turn causes higher storage and communication overheads. In this paper, we provide a formal analysis for possible types of fine-grained data updates and propose a scheme that can fully support authorized auditing and fine-grained update requests. Based on our scheme, we also propose an enhancement that can dramatically reduce communication overheads for verifying small updates. Theoretical analysis and experimental results demonstrate that our scheme can offer not only enhanced security and flexibility, but also significantly lower overhead for big data applications with a large number of frequent small updates, such as applications in social media and business transactions. [ABSTRACT FROM PUBLISHER]

Recent development of convolutional neural networks (CNNs) has activated a lot of studies and boosted the performance greatly on image super-resolution. This paper addresses the issue of face super-resolution, which attracts a lot of interests in the photographic industry. We propose to make use of the face-specific priors to enhance the performance of face super-resolution with the convolutional neural networks. Classical facial prior models represent the 2D facial shape in a compact low-dimensional space expressed by principal components. Here, we impose perturbations on the low-dimensional space and generate face samples with novel appearance. First, we conduct 2D facial image perturbations through 2D facial landmarks. Then, we carry on the study with perturbations on 3D facial landmarks. Facial pose and shape are perturbated to generate novel appearances of a single 2D facial image. These novel facial samples are then fed into the training process of the convolutional neural networks for face super-resolution. The experimental results demonstrate that the proposed method is adaptable to various networks and achieves superior performance for the face super-resolution task. [ABSTRACT FROM AUTHOR]