Publications are essential for scientific communication. Access to publications is provided by conventional libraries, digital libraries operated by learned societies or commercial publishers, and a huge number of web sites maintained by the scientists themselves or their institutions. Comprehensive meta-indices for this increasing number of information sources are missing for most areas of science. The DBLP Computer Science Bibliography of the University of Trier has grown from a very specialized small collection of bibliographic information to a major part of the infrastructure used by thousands of computer scientists. This short paper first reports the history of DBLP and sketches the very simple software behind the service. The most time-consuming task for the maintainers of DBLP may be viewed as a special instance of the authority control problem; how to normalize different spellings of person names. The third section of the paper discusses some details of this problem which might be an interesting research issue for the information retrieval community.

The DBLP Computer Science bibliography: Evolution, research issues, perspectives

Survey of machine learning techniques for malware analysis

The rise of machine learning for detection and classification of malware: Research developments, trends and challenges

Data mining techniques have been concentrated for malware detection in the recent decade. The battle between security analyzers and malware scholars is everlasting as innovation grows. The proposed methodologies are not adequate while evolutionary and complex nature of malware is changing quickly and therefore turn out to be harder to recognize. This paper presents a systematic and detailed survey of the malware detection mechanisms using data mining techniques. In addition, it classifies the malware detection approaches in two main categories including signature-based methods and behavior-based detection. The main contributions of this paper are: (1) providing a summary of the current challenges related to the malware detection approaches in data mining, (2) presenting a systematic and categorized overview of the current approaches to machine learning mechanisms, (3) exploring the structure of the significant methods in the malware detection approach and (4) discussing the important factors of classification malware approaches in the data mining. The detection approaches have been compared with each other according to their importance factors. The advantages and disadvantages of them were discussed in terms of data mining models, their evaluation method and their proficiency. This survey helps researchers to have a general comprehension of the malware detection field and for specialists to do consequent examinations.

/pdf/a-state-of-the-art-survey-of-malware-detection-approaches-1112xq1dou.pdf

A state-of-the-art survey of malware detection approaches using data mining techniques

https://cyberleninka.org/article/n/693122.pdf

Survey of main challenges (security and privacy) in wireless body area networks for healthcare applications

In this paper, we propose a parallel QRDM encoder (PQRDME) for multi-user MIMO (MU-MIMO) systems. The proposed algorithm transforms the full tree-search problem of the conventional QRDME algorithm into parallel partial trees that are processed in parallel, leading to a tremendous increase in the encoding throughput. The proposed algorithm outperforms the fixed-complexity sphere encoder (FSE) and performs close to the optimum performance for several scenarios. For instance, in a 4x4 MUM-MIMO system, the proposed PQRDME lags the optimum performance by 0.3 dB while outperforming the FSE by 2.3dB at a target BER of $10^{-4}$. In this case, the proposed algorithm also doubles the encoding throughput of the conventional QRDME algorithm.

Parallel QRD-M Encoder for Decentralized Multi-User MIMO Systems

Connectivity and trust in social networks have been exploited to propose applications on top of these networks, including routing, Sybil defenses, and anonymous communication systems. In these networks, and for such applications, connectivity ensures good performance of applications while trust is assumed to always hold, so as collaboration and good behavior are always guaranteed. In this paper, we study the impact of differential behavior of users on performance in typical social network-enabled routing applications. We classify users into either collaborative or rational (probabilistically collaborative) and study the impact of this classification and the associated behavior of users on the performance of such applications, including random walk-based routing, shortest path based routing, breadth-first-search based routing, and Dijkstra routing. By experimenting with real-world social network traces, we make several interesting observations. First, we show that some of the existing social graphs have high routing costs, demonstrating poor structure that prevents their use in such applications. Second, we study the factors that make probabilistically collaborative nodes important for the performance of the routing protocol within the entire network and demonstrate that the importance of these nodes stems from their topological features rather than their percentage of all the nodes within the network.

Characterizing Collaboration in Social Network-enabled Routing

In this paper, we propose a fixed-complexity sphere encoder (FSE) for multi-user MIMO (MU-MIMO) systems. The Proposed FSE accomplishes a scalable tradeoff between performance and complexity. Also, because it has a parallel tree-search structure, the proposed encoder can be easily pipelined, leading to a tremendous reduction in the precoding latency. The complexity of the proposed encoder is also analyzed, and we propose two techniques that reduce it. Simulation and analytical results demonstrate that in a MU-MIMO system, the complexity of the proposed FSE is 16% that of the conventional QRD-M encoder (QRDM-E). Also, the encoding throughput of the proposed endoder is 7.5 times that of the QRDM-E with tolerable degradation in the BER performance, while achieving the optimum diversity order.

Fixed-complexity Sphere Encoder for Multi-user MIMO Systems

In this paper, we propose an adaptive coordinated Tx-Rx beamforming scheme for inter-user interference cancellation, when a base station (BS) communicates with multiple users that each has multiple receive antennas. The conventional coordinated Tx-Rx beamforming scheme transmits a fixed number of data streams for each user regardless of the instantaneous channel states, that is, all the users, no matter they are with ill-conditioned or well-conditioned channels, have the same number of data streams. However, in the proposed adaptive coordinated Tx-Rx beamforming scheme, we adaptively select the number of streams per user to solve the inefficient problem of the conventional coordinated Tx-Rx beamforming scheme. As a result, the BER performance is improved. Simulation results show that the proposed algorithm outperforms the conventional co-ordinated Tx-Rx beamforming algorithm by 2.5dB at a target BER of 10-2.

Coordinated Transmit and Receive Processing with Adaptive Multi-Stream Selection

In the context of multi-user precoding, the idea behind vector perturbation (VP) lies in adding an integer vector to the data vector such that the overall transmit power is reduced, where the performance at the users end is consequently improved. In the literature, several techniques have been proposed to find a quasi-optimum perturbing vector, where this process was represented as an integer lattice search problem. In this paper, we propose a parallel QRD-M encoder (PQRDME) that, besides attaining a quasi-optimum diversity order, leads to tremendous reduction in the latency of the vector perturbation stage. Based on the set grouping, the proposed encoder transforms the full tree-search of the conventional QRDME into partial trees that can be pipelined to increase the encoding throughput. We evaluate the proposed algorithm under several scenarios with both perfect channel state information (PCSI) and imperfect CSI (ICSI) at the transmitter side, where simulation results show robust performance when compared to the optimum encoder.

/pdf/parallel-qrd-m-encoder-for-multi-user-mimo-systems-4l2sj43eqr.pdf

Manar Mohaisen

Papers

Parallel QRD-M Encoder for Decentralized Multi-User MIMO Systems

Characterizing Collaboration in Social Network-enabled Routing

Fixed-complexity Sphere Encoder for Multi-user MIMO Systems

Coordinated Transmit and Receive Processing with Adaptive Multi-Stream Selection

Parallel QRD-M encoder for multi-user MIMO systems