Chloroplast is a type of subcellular organelle in green plants and algae. It is the main subcellular organelle for conducting photosynthetic process. The proteins, which localize within the chloroplast, are responsible for the photosynthetic process at molecular level. The chloroplast can be further divided into several compartments. Proteins in different compartments are related to different steps in the photosynthetic process. Since the molecular function of a protein is highly correlated to the exact cellular localization, pinpointing the subchloroplast location of a chloroplast protein is an important step towards the understanding of its role in the photosynthetic process. Experimental process for determining protein subchloroplast location is always costly and time consuming. Therefore, computational approaches were developed to predict the protein subchloroplast locations from the primary sequences. Over the last decades, more than a dozen studies have tried to predict protein subchloroplast locations with machine learning methods. Various sequence features and various machine learning algorithms have been introduced in this research topic. In this review, we collected the comprehensive information of all existing studies regarding the prediction of protein subchloroplast locations. We compare these studies in the aspects of benchmarking datasets, sequence features, machine learning algorithms, predictive performances, and the implementation availability. We summarized the progress and current status in this special research topic. We also try to figure out the most possible future works in predicting protein subchloroplast locations. We hope this review not only list all existing works, but also serve the readers as a useful resource for quickly grasping the big picture of this research topic.We also hope this review work can be a starting point of future methodology studies regarding the prediction of protein subchloroplast locations.
Unaided authentication services provide the flexibility to login without being dependent on any additional device. The power of recording attack resilient unaided authentication services (RARUAS) is undeniable as, in some aspects, they are even capable of offering better security than the biometric based authentication systems. However, high login complexity of these RARUAS makes them far from usable in practice. The adopted information leakage control strategies have often been identified as the primary cause behind such high login complexities. Though recent proposals havemade some significant efforts in designing a usable RARUAS by reducing its login complexity, most of them have failed to achieve the desired usability standard. In this paper, we have introduced a new notion of controlling the information leakage rate. By maintaining a good security standard, the introduced idea helps to reduce the login complexity of our proposed mechanism − named as Textual-Graphical Password-based Mechanism or TGPM, by a significant extent. Along with resisting the recording attack, TGPM also achieves a remarkable property of threat detection. To the best of our knowledge, TGPM is the first RARUAS, which can both prevent and detect the activities of the opportunistic recording attackers who can record the complete login activity of a genuine user for a few login sessions. Our study reveals that TGPM assures much higher session resiliency compared to the existing authentication services, having the same or even higher login complexities. Moreover, TGPM stores the password information in a distributed way and thus restricts the adversaries to learn the complete secret from a single compromised server. A thorough theoretical analysis has been performed to prove the strength of our proposal from both the security and usability perspectives. We have also conducted an experimental study to support the theoretical argument made on the usability standard of TGPM.
Blockchain has recently emerged as a research trend, with potential applications in a broad range of industries and context. One particular successful Blockchain technology is smart contract, which is widely used in commercial settings (e.g., high value financial transactions). This, however, has security implications due to the potential to financially benefit froma security incident (e.g., identification and exploitation of a vulnerability in the smart contract or its implementation). Among, Ethereum is the most active and arresting. Hence, in this paper, we systematically review existing research efforts on Ethereum smart contract security, published between 2015 and 2019. Specifically, we focus on how smart contracts can be maliciously exploited and targeted, such as security issues of contract program model, vulnerabilities in the program and safety consideration introduced by program execution environment. We also identify potential research opportunities and future research agenda.
If an adversary tries to obtain a secret s in a (t, n) threshold secret sharing (SS) scheme, it has to capture no less than t shares instead of the secret s directly. However, if a shareholder keeps a fixed share for a long time, an adversary may have chances to filch some shareholders’ shares. In a proactive secret sharing (PSS) scheme, shareholders are supposed to refresh shares at fixed period without changing the secret. In this way, an adversary can recover the secret if and only if it captures at least t shares during a period rather than any time, and thus PSS provides enhanced protection to long-lived secrets. The existing PSS schemes are almost based on linear SS but no Chinese Remainder Theorem (CRT)-based PSS scheme was proposed. This paper proposes a PSS scheme based on CRT for integer ring to analyze the reason why traditional CRT-based SS is not suitable to design PSS schemes. Then, an ideal PSS scheme based on CRT for polynomial ring is also proposed. The scheme utilizes isomorphism of CRT to implement efficient share refreshing.
The availability of pulmonary nodules in CT scan image of lung does not completely specify cancer. The noise in an image and morphology of nodules, like shape and size has an implicit and complex association with cancer, and thus, a careful analysis should be mandatory on every suspected nodules and the combination of information of every nodule. In this paper, we introduce a “denoising first” two-path convolutional neural network (DFD-Net) to address this complexity. The introduced model is composed of denoising and detection part in an end to end manner. First, a residual learning denoising model (DR-Net) is employed to remove noise during the preprocessing stage. Then, a two-path convolutional neural network which takes the denoised image by DR-Net as an input to detect lung cancer is employed. The two paths focus on the joint integration of local and global features. To this end, each path employs different receptive field size which aids to model local and global dependencies. To further polish our model performance, in different way from the conventional feature concatenation approaches which directly concatenate two sets of features from different CNN layers, we introduce discriminant correlation analysis to concatenate more representative features. Finally, we also propose a retraining technique that allows us to overcome difficulties associated to the image labels imbalance. We found that this type of model easily first reduce noise in an image, balances the receptive field size effect, affords more representative features, and easily adaptable to the inconsistency among nodule shape and size. Our intensive experimental results achieved competitive results.
Optimal location query in road networks is a basic operation in the location intelligence applications. Given a set of clients and servers on a road network, the purpose of optimal location query is to obtain a location for a new server, so that a certain objective function calculated based on the locations of clients and servers is optimal. Existing works assume no labels for servers and that a client only visits the nearest server. These assumptions are not realistic and it renders the existing work not useful in many cases. In this paper, we relax these assumptions and consider the k nearest neighbours (KNN) of clients. We introduce the problem of KNN-based optimal location query (KOLQ) which considers the k nearest servers of clients and labeled servers. We also introduce a variant problem called relocation KOLQ (RKOLQ) which aims at relocating an existing server to an optimal location. Two main analysis algorithms are proposed for these problems. Extensive experiments on the real road networks illustrate the efficiency of our proposed solutions.
State machine replication has been widely used in modern cluster-based database systems. Most commonly deployed configurations adopt the Raft-like consensus protocol, which has a single strong leader which replicates the log to other followers. Since the followers can handle read requests and many real workloads are usually read-intensive, the recovery speed of a crashed follower may significantly impact on the throughput. Different from traditional database recovery, the recovering follower needs to repair its local log first. Original Raft protocol takes many network round trips to do log comparison between leader and the crashed follower. To reduce network round trips, an optimization method is to truncate the follower’s uncertain log entries behind the latest local commit point, and then to directly fetch all committed log entries from the leader in one round trip. However, if the commit point is not persisted, the recovering follower has to get the whole log from the leader. In this paper, we propose an accurate and efficient log repair (AELR) algorithm for follower recovery. AELR is more robust and resilient to follower failure, and it only needs one network round trip to fetch the least number of log entries for follower recovery. This approach is implemented in the open source database system OceanBase. We experimentally show that the system adopting AELR has a good performance in terms of recovery time.
This paper is devoted to the investigation of the evaluation and query algorithm problem for the influence of spatial location based on RkNN (reverse k nearest neighbor). On the one hand, an object can make contribution to multiple locations. However, for the existing measures for evaluating the influence of spatial location, an object only makes contribution to one location, and its influence is usually measured by the number of spatial objects in the region. In this case, a new measure for evaluating the influence of spatial location based on the RkNN is proposed. Since the weight of the contribution is determined by the distance between the object and the location, the influence weight definition is given, which meets the actual applications. On the other hand, a query algorithm for the influence of spatial location is introduced based on the proposed measure. Firstly, an algorithm named INCH (INtersection’s Convex Hull) is applied to get candidate regions, where all objects are candidates. Then, kNN and Range-k are used to refine results. Then, according to the proposed measure, the weights of objects in RkNN results are computed, and the influence of the location is accumulated. The experimental results on the real data show that the optimized algorithms outperform the basic algorithm on efficiency. In addition, in order to provide the best customer service in the location problem andmake the best use of all infrastructures, a location algorithm with the query is presented based on RkNN. The influence of each facility is calculated in the location program and the equilibrium coefficient is used to evaluate the reasonability of the location in the paper. The smaller the equilibrium coefficient is, the more reasonability the program is. The actual application shows that the location based on influence makes the location algorithm more reasonable and available.
Cloud computing environment is getting more interesting as a new trend of data management. Data replication has been widely applied to improve data access in distributed systems such as Grid and Cloud. However, due to the finite storage capacity of each site, copies that are useful for future jobs can be wastefully deleted and replaced with less valuable ones. Therefore, it is considerable to have appropriate replication strategy that can dynamically store the replicas while satisfying quality of service (QoS) requirements and storage capacity constraints. In this paper, we present a dynamic replication algorithm, named hierarchical data replication strategy (HDRS). HDRS consists of the replica creation that can adaptively increase replicas based on exponential growth or decay rate, the replica placement according to the access load and labeling technique, and finally the replica replacement based on the value of file in the future. We evaluate different dynamic data replication methods using CloudSim simulation. Experiments demonstrate that HDRS can reduce response time and bandwidth usage compared with other algorithms. It means that the HDRS can determine a popular file and replicates it to the best site. This method avoids useless replications and decreases access latency by balancing the load of sites.
Crowd flows prediction is an important problem of urban computing whose goal is to predict the number of incoming and outgoing people of regions in the future. In practice, emergency applications often require less training time. However, there is a little work on how to obtain good prediction performance with less training time. In this paper, we propose a simplified deep residual network for our problem. By using the simplified deep residual network, we can obtain not only less training time but also competitive prediction performance compared with the existing similar method. Moreover, we adopt the spatio-temporal attention mechanism to further improve the simplified deep residual network with reasonable additional time cost. Based on the real datasets, we construct a series of experiments compared with the existing methods. The experimental results confirm the efficiency of our proposed methods.
Crowdsourcing has become an efficient measure to solve machine-hard problems by embracing group wisdom, in which tasks are disseminated and assigned to a group of workers in the way of open competition. The social relationships formed during this process may in turn contribute to the completion of future tasks. In this sense, it is necessary to take social factors into consideration in the research of crowdsourcing. However, there is little work on the interactions between social relationships and crowdsourcing currently. In this paper, we propose to study such interactions in those social-oriented crowdsourcing systems from the perspective of task assignment. A prototype system is built to help users publish, assign, accept, and accomplish location-based crowdsourcing tasks as well as promoting the development and utilization of social relationships during the crowdsourcing. Especially, in order to exploit the potential relationships between crowdsourcing workers and tasks, we propose a “worker-task” accuracy estimation algorithm based on a graph model that joints the factorized matrixes of both the user social networks and the history “worker-task” matrix. With the worker-task accuracy estimation matrix, a group of optimal worker candidates is efficiently chosen for a task, and a greedy task assignment algorithm is proposed to further the matching of worker-task pairs among multiple crowdsourcing tasks so as to maximize the overall accuracy. Compared with the similarity based task assignment algorithm, experimental results show that the average recommendation success rate increased by 3.67%; the average task completion rate increased by 6.17%; the number of new friends added per week increased from 7.4 to 10.5; and the average task acceptance time decreased by 8.5 seconds.
Mobile computing has fast emerged as a pervasive technology to replace the old computing paradigms with portable computation and context-aware communication. Existing software systems can be migrated (while preserving their data and logic) to mobile computing platforms that support portability, context-sensitivity, and enhanced usability. In recent years, some research and development efforts have focused on a systematic migration of existing software systems to mobile computing platforms.
To investigate the research state-of-the-art on the migration of existing software systems to mobile computing platforms. We aim to analyze the progression and impacts of existing research, highlight challenges and solutions that reflect dimensions of emerging and futuristic research.
We followed evidence-based software engineering (EBSE) method to conduct a systematic mapping study (SMS) of the existing research that has progressed over more than a decade (25 studies published from 1996–2017).We have derived a taxonomical classification and a holistic mapping of the existing research to investigate its progress, impacts, and potential areas of futuristic research and development.
The SMS has identified three types of migration namely Static, Dynamic, and State-based Migration of existing software systems to mobile computing platforms.Migration to mobile computing platforms enables existing software systems to achieve portability, context-sensitivity, and high connectivity. However, mobile systems may face some challenges such as resource poverty, data security, and privacy. The emerging and futuristic research aims to support patterns and tool support to automate the migration process. The results of this SMS can benefit researchers and practitioners–by highlighting challenges, solutions, and tools, etc., –to conceptualize the state-ofthe- art and futuristic trends that support migration of existing software to mobile computing.