With the rapid development of quantitative trading, stock selection is an ongoing task that requires consideration of the characteristics of stocks and investment strategies. Fuzzy set theory is excellent for modeling and describing abstract characteristics to establish the link between stocks and strategies. In this article, we present our idea for a stock selection system called TripleS. It is based on the suitability index (SI) derived from fuzzy-set theory. The TripleS relies on the position size to extract the stock characteristics SI that describes not only the characteristics of each stock but also the extent to which they are suitable for certain strategies. Then, the SI undergoes a transformation process in the hands of the developed fuzzy-set modules for stock selection and investment. There are some other proposed methods for stock selection respectively called TripleS-T1, TripleS-T2, TripleS-T2U, TripleS-EF, and TripleS-EFT. Experimental results show that the proposed TripleS has minor improvement in precision but significant improvement in investment performance. The TripleS-EFT has outstanding profitability, the highest annual return in all strategies, and the highest Sharpe ratio in two of the four strategies, and achieves an annual return of 10.01% and a Sharpe ratio of 1.032, which significantly outperform the benchmark system. The TripleS-EFT also significantly outperforms the state-of-the-art fuzzy-based system in measuring annual return and even has double the annual return in several strategies.

We argue that the capital expenditures made by an individual mobile network operator is extremely high and risky. Also, radio spectrum sharing still lacks intelligence in the current architecture of mobile networks and needs to be rethought. We propose that a goal for the future mobile network architecture that shall be able to free mobile network operators from having to hold spectrum licenses and natively enable intelligent radio spectrum sharing among multiple mobile network operators. On the basis of the design principles, the duty of a single mobile network operator is split into two roles, one focuses on infrastructure development, the other only contains authorizations on the radio spectrum usage. We introduce a new role to the mobile network architecture, named Spectrum Trader, is a primary broker for spectrum trading, and it is used to coordinate with the demand-side requests and the supply-side resources to drive demand in a real-time bidding manner. We also introduce a concept of spectrum embedding that shall enable efficient and intelligent spectrum allocation by recommending the right spectrum bands based on user scenario. Finally, several challenges that need to be addressed in practical deployment are investigated.

With the rapid development of quantitative trading, stock selection is an enduring task, that requires consideration of the characteristic of stocks and investment strategies. Fuzzy-set theory is outstanding in modeling abstract characteristics, which is suitable for building the connection between stocks and strategies. In this paper, we propose a stock selection system (SSS) based on the suitability index (SI) and fuzzy-set theory. SSS utilizes position sizing to extract stock characteristics, SI, which expresses not only the characteristics of stocks but also the suitability for strategies. The SI is then transformed by the designed fuzzy-set modules for stock selection and investment. Several stock selection mechanisms are proposed, denoted as SSS-T1, SSS-T2, SSS-WT1, and SSS-WT2. Experimental results show that SSS has a slight improvement in precision, but leads to a significant improvement in investment. The proposed SSS-WT1 and SSS-WT2 systems achieve the highest annual returns of 6.2% and the highest Sharpe ratios of 0.924, outperforming the benchmark with the annual return of 1.9% and the Sharpe ratio of 0.201.

In this paper, the algebraic constructions of quasi-cyclic low-density parity-check (QC-LDPC) codes with the unequal error protection (UEP) property are considered. A criterion for constructing such codes via the masking technique is proposed, based on which explicit conditions on the base parity-check matrices and masking matrices to achieve UEP are provided. We also give three specific constructions of UEP QC-LDPC codes. Furthermore, a sufficient condition to ensure strict UEP is presented. Simulation results demonstrate the superiority of our constructed codes over time-sharing schemes. The constructed codes also have competitive error performance against randomly constructed equal-error-protection LDPC codes and irregular UEP LDPC codes designed based on the degree distribution.

Big-data computing is a new critical challenge for the ICT industry. Engineers and researchers are dealing with data sets of petabyte scale in the cloud computing paradigm. Thus, the demand for building a service stack to distribute, manage, and process massive data sets has risen drastically. In this paper, we investigate the Big Data Broadcasting problem for a single source node to broadcast a big chunk of data to a set of nodes with the objective of minimizing the maximum completion time. These nodes may locate in the same datacenter or across geo-distributed datacenters. This problem is one of the fundamental problems in distributed computing and is known to be NP-hard in heterogeneous environments. We model the Big-data broadcasting problem into a LockStep Broadcast Tree (LSBT) problem. The main idea of the LSBT model is to define a basic unit of upload bandwidth, r, such that a node with capacity c broadcasts data to a set of [c/r] children at the rater. Note that r is a parameter to be optimized as part of the LSBT problem. We further divide the broadcast data into m chunks. These data chunks can then be broadcast down the LSBT in a pipeline manner. In a homogeneous network environment in which each node has the same upload capacity c, we show that the optimal uplink rate r* of LSBT is either c/2 or c/3, whichever gives the smaller maximum completion time. For heterogeneous environments, we present an O(nlog2n) algorithm to select an optimal uplink rater* and to construct an optimal LSBT. Numerical results show that our approach performs well with less maximum completion time and lower computational complexity than other efficient solutions in literature.

Social network applications are becoming increasingly popular on mobile devices. A mobile presence service is an essential component of a social network application because it maintains each mobile user's presence information, such as the current status (online/offline), GPS location and network address, and also updates the user's online friends with the information continually. If presence updates occur frequently, the enormous number of messages distributed by presence servers may lead to a scalability problem in a large-scale mobile presence service. To address the problem, we propose an efficient and scalable server architecture, called PresenceCloud, which enables mobile presence services to support large-scale social network applications. When a mobile user joins a network, PresenceCloud searches for the presence of his/her friends and notifies them of his/her arrival. PresenceCloud organizes presence servers into a quorum-based server-to-server architecture for efficient presence searching. It also leverages a directed search algorithm and a one-hop caching strategy to achieve small constant search latency. We analyze the performance of PresenceCloud in terms of the search cost and search satisfaction level. The search cost is defined as the total number of messages generated by the presence server when a user arrives; and search satisfaction level is defined as the time it takes to search for the arriving user's friend list. The results of simulations demonstrate that PresenceCloud achieves performance gains in the search cost without compromising search satisfaction.

Scholars usually spend great deal of time on searching and reading papers of key researchers. However, to objectively determine key researcher of a topic relies on several measurements, such as publication, citation, recent academic activities. In this paper, a prototype of scholars searching and recommendation system based on a web mining approach in expert finding system is proposed. The system gives and recommends the ranking of scholars and turns out top-k scholars. A new ranking measure is designed, namely p-index, to reveal the scholar ranking of a certain field. We use a real-world dataset to test the robustness, the experiment results show our approach outperforms other existing approaches and users are highly interested in using the system again.

In this paper, a new construction of quasi-cyclic low-density parity-check (QC-LDPC) codes for unequal error protection (UEP) is proposed. We first give a new class of UEP block codes. QC-LDPC codes with binomial-term parity-check matrices which can avoid girths less than 8 and achieve an enlarged upper bound on the minimum distance are also introduced. An effective UEP scheme based on the proposed UEP block codes and QC-LDPC codes which can provide flexible choice of protection levels is then presented. Simulation results show that the new QC-LDPC codes can achieve good performance and low error floors. The bits with different designed protection levels can indeed have unequal bit-error-rate performance.

In this paper, a new construction of irregular low-density parity-check convolutional codes (LDPC-CCs) is presented. Both upper and lower bounds on the free distance are derived as well. Compared with previously constructed irregular LDPC-CCs, our design can not only avoid a girth less than 8 but also provide an enlarged free distance. Since some undesired cycles occur due to the binomial terms in the parity-check matrices of general LDPC-CCs, we provide a specific procedure to remove this kind of length-8 cycles. It can also be applied to our irregular construction. Simulation results show that the codes based on the new construction and the cycle-removal procedure can achieve better bit-error-rate performance and a lower error-floor.

How to rapidly disseminate a large-sized file to many recipients is a fundamental problem in many applications, such as updating software patches and distributing large scientific data sets. In this paper, we present the Bee protocol, which is a cooperative peer-to-peer data dissemination protocol aiming at minimizing the maximum dissemination time for all peers to obtain time-critical data, such as critical patch updates. Bee is a decentralized protocol that organizes peers into a randomized mesh-based overlay and each peer only works with local knowledge. We devise a slowest peer first strategy to boost the speed of dissemination, and a topology adaptation algorithm that provides the most efficient utilization of the network capacity. Bee is designed to support network heterogeneity and deal with the flash crowd arrival pattern without sacrificing the dissemination speed. We present experimental results on the performance of Bee in terms of dissemination time and show that its performance can approach lower bound of the maximum dissemination time.

In this paper, we propose a new construction of (3, t)-regular low-density parity-check convolutional codes by properly including binomial entries in the parity-check matrices. Both of the upper and lower bounds on free distance are derived for the new codes. Compared with previous constructions, our design can not only avoid codes of girth less than 8 but also provide enlarged free distances for some code rates. Simulation results show that the codes based on the new construction can achieve better bit-error-rate performance and lower error floor.

The content distribution techniques have recently started embracing peer-to-peer system as an alternative to the client-server architecture, such as BitTorrent system. BitTorrent system offers a scale mechanism for distributing a large volume of data to a set of peers over the Internet, but it is not designed for minimizing the time taken for all peers to receive the file. As a result, the peers of BitTorrent system may suffer a long download time, specifically the narrow-band peers. In this paper, in order to reduce the download time of BitTorrent, we propose a weighty piece selection strategy instead of the local rarest first strategy in BitTorrent. The proposed strategy is based on the greedy concept that a peer assigns each missing piece a weight according to total number of neighbor's downloaded pieces. The peer selects the missing piece with the highest priority for next download. This strategy can speed up the cooperation between heterogeneous peers while making the BitTorrent more efficient in terms of the average download time and the total elapsed time. The simulation results show that weighty piece selection strategy can improve more than 15% average download time and reduce in average 60% total elapsed time than the BitTorrent system.

The most prevalent peer-to-peer (P2P) application till today is file sharing, and unstructured P2P networks can support inherent heterogeneity of peers, are highly resilient to peers’ failures, and incur low overhead at peer arrivals and departures. Dynamic querying (DQ) is a new flooding technique which could estimate a proper time-to-live (TTL) value for a query flooding by estimating the popularity of the searched files, and retrieve sufficient results under controlled flooding range for reducing network traffic. Recent researches show that a large amount of peers in the P2P file sharing system are the free-riders, and queries are seldom hit by those peers. The free-riders problem causes a large amount of redundant messages in the DQ-like search algorithm. In this paper, we proposed a new search algorithm, called "AntSearch", to solve the problem. In AntSearch, each peer maintains its hit rate of previous queries, and records a list of pheromone values of its immediate neighbors. Based on the pheromone values, a query is only flooded to those peers which are not likely to be the free-riders. Our simulation results show that, compared with DQ and its enhanced algorithm DQ+, the AntSearch algorithm averagely reduces 50% network traffic at almost the same search latency as DQ+, while retrieving sufficient results for a query with a given required number of results.