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Organized event is an important form of human activity. Nowadays, many digital platforms offer organized events on the Internet, allowing users to be organizers or participants. For such platforms, it is beneficial to predict potential event participants. Existing work on this problem tends to borrow recommendation techniques. However, compared to e-commerce items and purchases, events and participation are usually of a much smaller frequency, and the data may be insufficient to learn an accurate prediction model. In this paper, we propose to utilize social media retweeting activity to enhance the learning of event participant prediction models. We create a joint knowledge graph to bridge the social media and the target domain, assuming that event descriptions and tweets are written in the same language. Furthermore, we propose a learning model that utilize retweeting information for the target domain prediction more effectively. We conduct comprehensive experiments in two scenarios with real-world data. In each scenario, we set up training data of different sizes, as well as warm and cold test cases. The evaluation results show that our approach consistently outperforms several baseline models in both warm and cold tests. [ABSTRACT FROM AUTHOR]

Given the recent availability of large volumes of social media discussions, finding temporal unusual phenomena, which can be called events, from such data is of great interest. Previous works on social media event detection either assume a specific type of event, or assume certain behavior of observed variables. In this paper, we propose a general method for event detection on social media that makes few assumptions. The main assumption we make is that when an event occurs, affected semantic aspects will behave differently from their usual behavior, for a sustained period. We generalize the representation of time units based on word embeddings of social media text, and propose an algorithm to detect durative events in time series in a general sense. In addition, we also provide an incremental version of the algorithm for the purpose of real-time detection. We test our approaches on synthetic data and two real-world tasks. With the synthetic dataset, we compare the performance of retrospective and incremental versions of the algorithm. In the first real-world task, we use a novel setting to test if our method and baseline methods can exhaustively catch all real-world news in the test period. The evaluation results show that when the event is quite unusual with regard to the base social media discussion, it can be captured more effectively with our method. In the second real-world task, we use the event captured to help improve the accuracy of stock market movement prediction. We show that our event-based approach has a clear advantage compared to other ways of adding social media information. [ABSTRACT FROM AUTHOR]

Twitter is one of the largest online platforms where people exchange information. In the first few years since its emergence, researchers have been exploring ways to use Twitter data in various decision making scenarios, and have shown promising results. In this review, we examine 28 newer papers published in last five years (since 2016) that continued to advance Twitter-aided decision making. The application scenarios we cover include product sales prediction, stock selection, crime prevention, epidemic tracking, and traffic monitoring. We first discuss the findings presented in these papers, that is how much decision making performance has been improved with the help of Twitter data. Then we offer a methodological analysis that considers four aspects of methods used in these papers, including problem formulation, solution, Twitter feature, and information transformation. This methodological analysis aims to enable researchers and decision makers to see the applicability of Twitter-aided methods in different application domains or platforms. [ABSTRACT FROM AUTHOR]

Temporary deals such as flash sales nowadays are popular strategies in retail business for cleaning out excessive inventories. It is known that the success of a temporary deal is related to product quality, promotion, and discount rates. In this paper, we look at another more obscure factor, that is the timing in the market, and we argue that such timing can be learned from social media. For example, the trending of words "summer" and "ice cream" in social media may indicate successful sales of air conditioners. We propose an approach to detect emerging words in social media as timing signals, and associate them with successful temporary deals. More specifically, the words that tend to emerge just before successful deals are considered as effective timing signals. We obtain a real-world temporary deal dataset from an industry partner and collect a social media datasets from Twitter for experiments. With experimental evaluation, we show and discuss the discovered timing signals. Furthermore, we propose a prediction framework and show that using social media timing signals can achieve better accuracy for predicting temporary deal success, comparing to internal deal information. [ABSTRACT FROM AUTHOR]

In many fields, e.g., data mining and machine learning, distance-based outlier detection (DOD) is widely employed to remove noises and find abnormal phenomena, because DOD is unsupervised, can be employed in any metric spaces, and does not have any assumptions of data distributions. Nowadays, data mining and machine learning applications face the challenge of dealing with large datasets, which requires efficient DOD algorithms. We address the DOD problem with two different definitions. Our new idea, which solves the problems, is to exploit an in-memory proximity graph. For each problem, we propose a new algorithm that exploits a proximity graph and analyze an appropriate type of proximity graph for the algorithm. Our empirical study using real datasets confirms that our DOD algorithms are significantly faster than state-of-the-art ones. [ABSTRACT FROM AUTHOR]

Since the variables inherent to various diseases cannot be controlled directly in humans, behavioral dysfunctions have been examined in model organisms, leading to better understanding their underlying mechanisms. However, because the spatial and temporal scales of animal locomotion vary widely among species, conventional statistical analyses cannot be used to discover knowledge from the locomotion data. We propose a procedure to automatically discover locomotion features shared among animal species by means of domain-adversarial deep neural networks. Our neural network is equipped with a function which explains the meaning of segments of locomotion where the cross-species features are hidden by incorporating an attention mechanism into the neural network, regarded as a black box. It enables us to formulate a human-interpretable rule about the cross-species locomotion feature and validate it using statistical tests. We demonstrate the versatility of this procedure by identifying locomotion features shared across different species with dopamine deficiency, namely humans, mice, and worms, despite their evolutionary differences. Comparing changes in behaviour across various species is not always trivial, especial across significantly divergent species. Here, the authors develop a deep learning framework that allows them to map changes in locomotion demonstrated on dopamine-deficient humans, mice and worms. [ABSTRACT FROM AUTHOR]

Mesh network is a common topology in deploying Edge/Fog computing in IoT due to its robustness, expandability and reliability. In the Mesh topology, gateways are the key role for the entire networks to communicate with the clouds. In order to ensure network availability in a failover scenario, a router must always have backup gateways to maintain mesh robustness during primary gateway failover. Order-k Voronoi diagram is known for its capability to identify k-nearest facilities and ensure that all objects will always have k-nearest backup facilities. In this paper, we utilize order-k Voronoi diagram with sink tree to produce order-k hops Voronoi diagram to identify k-gateways coverage with minimal hops for all routers as the backup gateways. Our experiment shows that order-k hops Voronoi diagram is more effective in ensuring that all routers have backup gateways with a minimum number of hops than an ordinary order-k network Voronoi diagram, hence reduce network latency for the entire mesh networks and maintain the robustness of the mesh network. [ABSTRACT FROM AUTHOR]

In electrical and mechanical industries, forming a pure copper layer on various substrates would improve the performance of automotive motors, batteries, etc. However, the formation of a pure copper layer has yet to be reported on a pure copper substrate. Here, a pure copper layer is formed on a pure copper substrate (C10200) using a multi-beam laser cladding system with blue diode lasers. The powder mass efficiency increases with the laser intensity. These results are used to construct a powder temperature model. At a laser intensity of 6.5 × 10 4 W / cm 2 , 9.65% of the powder forms a copper layer. In the supplied powder, approximately 5% of the powder is melted on the fly. In addition, about 5% of the powder is melted after trapping on the substrate. Hence, melting powder on the fly and melting the trapped powder on the substrate form the copper layer. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) have significant limitations in terms of available bandwidth and energy. The limited bandwidth in WSNs can cause delays in message delivery, which does not suit the real-time sensing applications such as a gas leak. Moreover, in such applications, there are multi-modal sensors whose values like temperature, gas concentration, location, and CO 2 levels must be transmitted together for correct and timely detection of a gas leak. In this paper, we propose a novel Z-order based data compression scheme, Z-compression, to reduce energy consumption and save bandwidth without increasing message delivery latency. Instead of using the popular Huffman tree style based encoding, the Z-compression uses Z-order encoding to map the multidimensional sensing data into one-dimensional binary stream transmitted using a single packet. Our experimental evaluations using different real-world datasets show that the Z-compression has a much better compression ratio, energy, and bandwidth savings than known schemes like LEC, Adaptive-LEC, FELACS, and TinyPack for multi-modal sensor data. Through the extensive experiments, we show that Z-compression is suitable for real-world sensing applications requiring high-stream rate WSNs and delay-tolerant low-power listening WSNs. In high-stream rate WSNs, the Z-compression can save bandwidth and increases the throughput. In low-power listening WSNs, by concatenating the Z-compressed data at selected reporting nodes, we can reduce the duty cycles of the nodes in WSNs, thus prolong the lifetime of the network, and still maintain the low distortion rate. [ABSTRACT FROM AUTHOR]

This article addresses the top-k query processing problem on mobile ad hoc networks (MANETs). Top-k query processing is common to retrieve only highly important data items. However, methods for top-k query processing are not enough efficient and accurate in MANET environments. For improving the efficiency and accuracy, replication is a promising technique that each node in MANETs replicates data items retained by other nodes into its storage. Therefore, we fully combine the top-k query processing with data replication. We propose a framework that efficiently processes top-k queries based on a new replication strategy. We develop new replication strategy FReT (topology-Free Replication for Top-k query). FReT determines near-optimal allocations of replicas. It advantages efficient top-k query processing from limited search area without maintenance costs even if mobile nodes move. Our top-k query processing methods retrieve the exact answer with small overhead and delay by gradually increasing the search area based on FReT. We demonstrate, through extensive experiments, that FReT and query processing methods function well in terms of small delay and overhead without sacrificing exactness of the query result. [ABSTRACT FROM AUTHOR]

The last article titled "Secure Data Sharing Framework via Hierarchical Greedy Embedding in Darknets", the authors propose a Secure Data Sharing framework (SeDS) for future darknets via hierarchical greedy embedding. SeDS adopts a hierarchical topology and uses a set of secure nodes to protect the whole topology. To support geometric routing in the hierarchical topology, a two-level bit-string prefix embedding approach (Prefix-T) is first proposed, and then a greedy forwarding strategy and a data mapping approach are combined with Prefix-T for data sharing. [Extracted from the article]

Top-k queries, which retrieve k data items with the best score, have been receiving considerable attention because they can support many real life applications. In this paper, we propose an efficient query routing scheme in mobile ad hoc networks (MANETs), namely CTR. CTR enables top-k data retrieval by only necessary nodes by employing a new clustering framework for top-k query processing. In this framework, nodes holding high rank data items become ClusterHeads (CHs), and top-k queries are transmitted between CHs via gateway nodes which belong to multiple clusters. Each CH maintains a set of hop counts between itself and high rank data items, so that it can judge whether or not to transmit a query on the fly. We further propose a query routing method, CTR $$^{2}$$ , which integrates the clustering framework of CTR and a data replication approach. CTR $$^{2}$$ improves the performance by retrieving the top-k data from nearby nodes. Extensive experiments have demonstrated that the proposed approaches function well in terms of accuracy of the query result, traffic, and delay. [ABSTRACT FROM AUTHOR]

With the increase of data generation in distributed fashions such as peer-to-peer systems and sensor networks, top- k query processing which is a way to aggregate only a small set of data that sufficiently satisfies many users' preferences, becomes a substantial issue. When data are periodically updated in each epoch e.g., weather information, without any techniques, a naive solution is to aggregate all data and their updates to ensure the completeness of final answers, however, it is too costly in terms of data transfer especially for data aggregator nodes (intermediate nodes). In this paper, we propose a top- k monitoring query processing method in 2-tier distributed systems based on a publish-subscribe scheme. A set of top- k subscriptions specifying summary scope of users' interests is informed to aggregators to limit the number of transferred data records for each epoch. In addition, instead of issuing subscriptions of all queries, our method identifies a small set of minimal subscriptions as well as utilizes some adaptive heuristic rules to efficiently maintain those subscriptions resulting in lower communication overhead. Our experiments through both synthetic and real datasets show that our technique is efficient and outperforms other comparative reactive methods. [ABSTRACT FROM AUTHOR]

Preference query processing is important for a wide range of applications involving distributed databases, such as network monitoring, web-based systems, and market analysis. In such applications, data objects are generated frequently and massively, which presents an important and challenging problem of continuous query processing over distributed data stream environments. A top-k dominating query, which has been receiving much research attention recently, returns the k data objects that dominate the highest number of data objects in a given dataset, and due to its dominance-based ranking function, we can easily obtain superior data objects. An emerging requirement in distributed stream environments is an efficient technique for continuously monitoring top-k dominating data objects. Despite of this fact, no study has addressed this problem. In this paper, therefore, we address the problem of continuous top-k dominating query processing over distributed data stream environments. We present two algorithms that monitor the exact top-k dominating data and efficiently eliminate unqualified data objects for the result, which reduces both communication and computation costs. In addition to these algorithms, we present an approximate algorithm that further reduces both communication and computation costs. Extensive experiments on both synthetic and real data have demonstrated the efficiency and scalability of our algorithms. [ABSTRACT FROM AUTHOR]

Due to the recent massive data generation, preference queries are becoming an increasingly important for users because such queries retrieve only a small number of preferable data objects from a huge multi-dimensional dataset. A top-k dominating query, which retrieves the k data objects dominating the highest number of data objects in a given dataset, is particularly important in supporting multi-criteria decision making because this query can find interesting data objects in an intuitive way exploiting the advantages of top-k and skyline queries. Although efficient algorithms for top-k dominating queries have been studied over centralized databases, there are no studies which deal with top-k dominating queries in distributed environments. The recent data management is becoming increasingly distributed, so it is necessary to support processing of top-k dominating queries in distributed environments. In this paper, we address, for the first time, the challenging problem of processing top-k dominating queries in distributed networks and propose a method for efficient top-k dominating data retrieval, which avoids redundant communication cost and latency. Furthermore, we also propose an approximate version of our proposed method, which further reduces communication cost. Extensive experiments on both synthetic and real data have demonstrated the efficiency and effectiveness of our proposed methods. [ABSTRACT FROM AUTHOR]

A nearest neighbor (NN) query, which returns the most similar object to a user-specified query object, plays an important role in a wide range of applications and hence has received considerable attention. In many such applications, e.g., sensor data collection and location-based services, objects are inherently uncertain. Furthermore, due to the ever increasing generation of massive datasets, the importance of distributed databases, which deal with such data objects, has been growing. One emerging challenge is to efficiently process probabilistic NN queries over distributed uncertain databases. The straightforward approach, that each local site forwards its own database to the central server, is communication-expensive, so we have to minimize communication cost for the NN object retrieval. In this paper, we focus on two important queries, namely top-k probable NN queries and probabilistic star queries, and propose efficient algorithms to process them over distributed uncertain databases. Extensive experiments on both real and synthetic data have demonstrated that our algorithms significantly reduce communication cost. [ABSTRACT FROM AUTHOR]

Traffic jam has grown to be a more and more difficult problem to be solved in big cities around the world, and people are getting less chance to take taxis. In order to remit this issue, we propose a recommendation strategy based on taxi traces data for passenger by the roads to make it easier. Considering the scale of taxi traces data, Hadoop is employed to handle the traces data, whose tasks include filtering and cleaning of the data, mapping taxi traces, and computing the average passage time and empty taxi arrival rate on the roads. When a user uploads his position and the time, assisted by weather condition gain from the Internet, we get the very model that corresponds to the date and the weather; the time interval is thought to be the expected waiting time between the moment when user requests the service and the moment when the cumulative number of empty taxi is greater than or equal to 1 after adding the time the taxi spends on the road, and is pushed to the user. The experiment is conducted on the base of a real-world dataset generated by over 12,000 taxis over a period of 3 months in Beijing. Experimental results demonstrate that the processing speed of Hadoop is nine times faster than serial's, which displays the feasibility of Hadoop in the application of massive traces data. In addition, the accuracy of the recommendation strategy reaches up to 88.75 %, and it meets the demand of real-time service. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various topics within the issue including popularization of smart phones, technological advancements in Internet of Things (IoT), machine-to-machine (M2M), cloud computing, and Vehicular Networks reliability.

People routinely carry mobile devices in their daily lives and obtain a variety of information from the Internet in many different situations. In searching for information (content) with a mobile device, a user's activity (e.g., moving or stationary) and context (e.g., commuting in the morning or going downtown in the evening) often change, and such changes can affect the user's degree of concentration on his or her mobile device's display and information needs. Therefore, a search system should provide the user with an amount of information suitable for the current activity and a type of information suitable for the current context. In this study, we present the design and implementation of a content search system that considers a mobile user's activity and context, with the goal of reducing the user's operation load for content search. The proposed system switches between two kinds of content search systems according to the user's activity: the location- based content search system is activated when the user is stationary (e.g., standing and sitting), while a menu- based content search system is activated when the user is moving (e.g., walking). Both systems present information according to user context. The location-based system presents detailed information via menus and a map according to location-based categories. The menu-based system presents only a few options to enable users to get content easily. Through user experiments, we confirmed that participants could get desired information more easily with this system than with a commercial search system. [ABSTRACT FROM AUTHOR]

In Wireless Sensor Networks (WSNs), localization is one of the most important technologies since it plays a critical role in many applications, e.g., target tracking. If the users cannot obtain the accurate location information, the related applications cannot be accomplished. The main idea in most localization methods is that some deployed nodes (landmarks) with known coordinates (e.g., GPS-equipped nodes) transmit beacons with their coordinates in order to help other nodes localize themselves. In general, the main localization algorithms are classified into two categories: range-based and range-free. In this paper, we reclassify the localization algorithms with a new perspective based on the mobility state of landmarks and unknown nodes, and present a detailed analysis of the representative localization algorithms. Moreover, we compare the existing localization algorithms and analyze the future research directions for the localization algorithms in WSNs. [ABSTRACT FROM AUTHOR]

It has been shown recently that using Mobile Agents (MAs) in wireless sensor networks (WSNs) can help to achieve the flexibility of over-the-air software deployment on demand. In MA-based WSNs, it is crucial to find out an optimal itinerary for an MA to perform data collection from multiple distributed sensors. However, using a single MA brings up the shortcomings such as large latency, inefficient route, and unbalanced resource (e.g. energy) consumption. Then a novel genetic algorithm based multi-agent itinerary planning (GA-MIP) scheme is proposed to address these drawbacks. The extensive simulation experiments show that GA-MIP performs better than the prior single agent algorithms in terms of the product of delay and energy consumption. [ABSTRACT FROM AUTHOR]