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Publication date: December 2018
Source:Computer Languages, Systems & Structures, Volume 54

Author(s): Michal Sičák, Ján Kollár

We present an algorithm that transforms context-free grammars into a non-redundant set of supercombinators. This set contains interconnected lambda calculus’ supercombinators that are enriched by grammar operations. The resulting set is scalable and it can be extended with new supercombinators created from grammars. We describe this algorithm in detail and then we apply it on 62,008 grammar samples in order to find out the properties and limits of acquired supercombinator set. We show that this set has a maximum theoretical limit of possible supercombinators. That limit is the sequence of Catalan numbers. We show that in some cases we are able to reach that limit if we use large enough input data source and we limit the size of supercombinators permitted into the final set. We also describe another benefit of our algorithm, which is the identification of most reoccurring structures in the input set.





Publication date: December 2018
Source:Computer Languages, Systems & Structures, Volume 54

Author(s): Gennaro Costagliola, Mattia De Rosa, Vittorio Fuccella

In general, visual languages need to be simple in order to be easily used and understood. As a result, many of them have simple constructs that can be defined by simply describing local constraints on the constituent elements. Based on this assumption, in a previous research, we developed a local context methodology for the specification of the syntax of simple visual languages such as flowcharts, entity-relationship diagrams, use-case diagrams. In this paper, we extend the methodology by defining a new technique for a local context based semantic translation of a visual language. The technique uses XPath-like expressions, called SGPath, together with a data flow model of execution. As for the case of local syntax checks, attributes and rules to calculate them are defined for each element of the language. For a given element in the abstract sentence graph, the SGPath expressions are used to gather values from its neighbors in order to allow the rules to calculate its semantic attributes. The new methodology has been implemented in the tool LoCoMoTiVe and been tested on visual languages such as entity-relationship diagrams, flowcharts, trees.





Publication date: December 2018
Source:Computer Languages, Systems & Structures, Volume 54

Author(s): Tian Zhao, Xiaobing Huang

Deep Learning (DL) has found great success in well-diversified areas such as machine vision, speech recognition, and multimedia understanding. However, the state-of-the-art tools (e.g. Caffe, TensorFlow, and CNTK), are programming libraries with many dependencies and implemented in languages such as C++ that need to be compiled to a specific runtime environment and require users to install the entire tool libraries for training or inference, which limits the portability of DL applications. In this work, we introduce DeepDSL, a domain specific language (DSL) embedded in Scala, that compiles DL networks encoded with DeepDSL to efficient, compact, and portable Java source programs for DL training and inference. DeepDSL represents DL networks as abstract tensor functions, performs symbolic gradient derivations to generate Intermediate Representation (IR), optimizes the IR expressions, and translates the optimized IR expressions to Java code that runs on GPU without additional dependencies other than the necessary GPU libraries and the related invocation interfaces: a small set of JNI (Java Native Interface) wrappers. Our experiments show DeepDSL outperforms existing tools in several benchmark programs adopted from the current mainstream Deep Neural Networks (DNNs).






Publication date: June 2018
Source:Computer Fraud & Security, Volume 2018, Issue 6



A malware campaign designed to compromise network and storage equipment has infected as many as 500,000 devices worldwide, according to research by Cisco Talos. The attack appears to have started with targets in Ukraine, and several sources, including the FBI, have placed the blame on Russia.





Publication date: June 2018
Source:Computer Fraud & Security, Volume 2018, Issue 6

Author(s): Steve Mansfield-Devine

So the EU’s General Data Protection Regulation (GDPR) is finally here. And we no longer have to guess who will be the first organisations to be caught in its net – Facebook and Google are already being sued, with writs having been issued the day the regulation came into force.





Publication date: June 2018
Source:Computer Fraud & Security, Volume 2018, Issue 6



The global levels of fraud are increasing, with the majority of cases being enabled by technology, while mobile fraud appears to be rising out of control. Social media is also playing a key role in this worsening picture, according to new reports.






Publication date: September 2018
Source:Computer Communications, Volume 127

Author(s): Aydin Rajaei, Dan Chalmers, Ian Wakeman, George Parisis

With the proliferation of smartphones and their advanced connectivity capabilities, opportunistic networks have gained a lot of traction during the past years; they are suitable for increasing network capacity and sharing ephemeral, localised content. They can also offload traffic from cellular networks to device-to-device ones, when cellular networks are heavily stressed. Opportunistic networks can play a crucial role in communication scenarios where the network infrastructure is inaccessible due to natural disasters, large-scale terrorist attacks or government censorship. Geocasting, where messages are destined to specific locations (casts) instead of explicitly identified devices, has a large potential in real world opportunistic networks, however it has attracted little attention in the context of opportunistic networking. In this paper we propose Geocasting Spray And Flood (GSAF), a simple and efficient geocasting protocol for opportunistic networks. GSAF follows an elegant and flexible approach where messages take random walks towards the destination cast. Messages that are routed away from the destination cast are extinct when devices’ buffers get full, freeing space for new messages to be delivered. In GSAF, casts do not have to be pre-defined; instead users can route messages to arbitrarily defined casts. GSAF does that in a privacy-preserving fashion. We also present DA-GSAF, a Direction-Aware extension of GSAF in which messages are forwarded to encountered nodes based on whether a node is moving towards their destination cast. In DA-GSAF only the direction of a mobile node is revealed to other devices. We experimentally evaluate our protocols and compare their performance to prominent geocasting protocols in a very wide set of scenarios, including different maps, mobility models and user populations. Both GSAF and DA-GSAF perform significantly better compared to all other studied protocols, in terms of message delivery ratio, latency and network overhead. DA-GSAF is particularly efficient in sparse scenarios minimising network overhead compared to all other studied protocols. Both GSAF and DA-GSAF perform very well for a wide range of device/user populations indicating that our proposal is viable for crowded and sparse opportunistic networks.





Publication date: September 2018
Source:Computer Communications, Volume 127

Author(s): Syed Mohammad Irteza, Hafiz Mohsin Bashir, Talal Anwar, Ihsan Ayyub Qazi, Fahad Rafique Dogar

Datacenter networks are often structured as multi-rooted trees to provide high bisection bandwidth at low cost. To utilize the available bisection bandwidth, an efficient load balancing algorithm is required. Under symmetric network conditions, packet spraying is known to perform well, as it enables fine-grained (packet-level) load balancing over equal cost paths. However, packet spraying performs poorly in asymmetric topologies. To make packet spraying effective under asymmetry while retaining its simplicity, we propose SAPS, “Symmetric Adaptive Packet Spraying”, a Software-Defined Networking (SDN) based scheme that uses packet spraying over symmetric virtual topologies. SAPS is based on the key insight that if we provide each flow with a symmetric view of the network fabric, then packet spraying can produce near-optimal performance. Through simulations and testbed experiments, we evaluate SAPS. Over a variety of application workloads and asymmetric network scenarios, including single and multiple link failures, results indicate that SAPS performs well, e.g., under single link failure, outperforming state-of-the-art load balancing schemes by up to 61% for average flow completion times.





Publication date: September 2018
Source:Computer Communications, Volume 127

Author(s): Li Guo, Zhiliang Zhu, Francis C.M. Lau, Yuli Zhao, Hai Yu

Effective relay selection is a way to achieve considerable performance gains in cooperative communication. However, in practice, the best relay may not always be selected due to the difficulty in collecting accurate network information when the network scale is very large or when the channel information is time-varying. In this paper, we propose a suboptimal relay selection scheme where the only information available to the source is the destination location. The proposed scheme consists of three phases. During the first phase, the area of relay selection is narrowed down based on the outage probability and target transmission rate. In the second phase, a node within this region is selected using the Successive Slotted Carrier Sense Multiple Access (SSCSMA) protocol. In the third and last phase, data is transmitted from the source via the relay to the destination. We present a detailed analysis of the transmission rate and the outage probability. Simulation results have shown that our method using SSCSMA outperforms Random Access based Blind Relay Selection (RABRS) in terms of transmission rate. The results have also indicated that both the outage probability constraint and the source-destination distance affect the transmission rate.