Parsing Time Research Articles

MB-MaaS: Mobile Blockchain-based Mining-as-a-Service for IIoT environments

In this paper, we propose a mobile blockchain (MB) based mining-as-a-service (MaaS) scheme, MB-MaaS for resource-constrained industrial internet-of-things (IIoT) environments. The scheme addresses the research gaps of fixed static allocation for miners to perform computationally intensive mining tasks through a multi-hop computational offloading (CO) scheme and addresses an auction mechanism for a fair bidding process among the miner nodes. The scheme operates in three phases. In the first phase, a multi-hop CO scheme with a fair incentive policy is formulated for miners. The CO schemes offer guaranteed offloading services to mobile devices from far-edge systems through a chain of neighbor nodes. Then, in the second phase, MaaS is proposed to leverage expensive mining tasks through 5G-enabled pico/femtocells. Integration of 5G allows massive end-to-end device and service connectivity. As IIoT ecosystems have limited memory and compute requirements, MaaS assures that the proposed consensus has a responsive validation and mining time. To make the data exchange in the consensus process lightweight, and allow a large number of sensors to share the data in a lightweight manner, an effective consensus mechanism Lightweight Proof-of-Proximity (LPoP), is proposed that forms group validations instead of single block validation. The data is exchanged through javascript object notation (JSON) format, maintaining a steady transaction rate. MB-MaaS is compared against the existing scheme for parameters bid thresholds and request servicing times, and mining and consensus formation. For example, the request serving time at 12 requests is improved by 56.78%, and a significant improvement of 26.47% is observed for processed blocks; parsing time on average is improved by 7.89%. The comparative analysis suggests that the scheme is more efficient than other competing approaches.

IMPLEMENTASI LEAST SIGNIFICANT BIT (LSB) DAN ALGORITMA VIGENERE CIPHER PADA AUDIO STEGANOGRAFI

This study discusses the implementation of the Least Significant Bit (LSB) method and the Vigenere Cipher algorithm to maintain the confidentiality of information or data from unauthorized parties by hiding messages on media audio. LSB is used to insert messages by replacing the lowest bit in a byte of the carrier media. Vigenere Cipher cryptographic algorithm to increase message security by encoding the alphabetic text using a Caesar cipher based on the letters in the password. Testing based on steganographic criteria, namely changes in file size, fidelity, and robustness. Based on tests carried out on several mp3 audio files that have been inserted with messages with variations of 50 to 1000 characters, the results obtained that the file size does not change after the message is inserted and can be extracted again. The number of characters in a message affects the parsing time of the message. In terms of fidelity, character insertion does not change the bit rate of audio stega. Meanwhile, in terms of robustness, stega audio is not resistant to manipulation. These results indicate that the LSB method and the Vigenere Cipher algorithm on audio media can insert messages and decipher messages that have been encrypted properly without damaging the audio components.

Collaborative Packet Header Parsing in NetFPGA-Based High Speed Switches

Packet parsing is an important task performed by network elements, that involves traversal of a complex parser graph. At very high link bandwidths, the parsing time has to be reduced significantly. This letter proposes a collaborative approach, where the ingress switch parses the header stack and passes an unique parser code (UPC) to downstream routers, that use this UPC to compute the header offsets without parsing. This approach was implemented on a Xilinx NetFPGA based programmable data plane switch, and was shown to reduce packet processing time by nearly 8%.

Optimizing Frameworks’ Performance Using C++ Modules Aware ROOT

ROOT is a data analysis framework broadly used in and outside of High Energy Physics (HEP). Since HEP software frameworks always strive for performance improvements, ROOT was extended with experimental support of runtime C++ Modules. C++ Modules are designed to improve the performance of C++ code parsing. C++ Modules offers a promising way to improve ROOT’s runtime performance by saving the C++ header parsing time which happens during ROOT runtime. This paper presents the results and challenges of integrating C++ Modules into ROOT.

The use of .NET Core in web applications development

The article presents the use of .NET Core in web applications development. The analysis covers tests performed on a test application based on the .NET Core framework. The following benchmarking criteria were selected for the framework analysis: pattern search in text, counting parsing time, rendering operations on graphical files, adding files to archives, CRUD operation speed. These criteria were compared on two different operating systems - Windows, Linux. Additionally, the paper presents results obtained after the tests and their analysis. The hypothesis that tests will be executed faster on Windows than on Linux has only been partially confirmed.

Function Recognition in Stripped Binary of Embedded Devices

Function recognition is the preliminary step of the reverse analysis. It is the premise of binary reuse, generating control flow graphs, and performing semantic analysis. There are still many shortcomings in the application of current function identification tools, including cross-platform support, efficiency, and resource usage. To improve the accuracy and efficiency of function recognition in embedded system firmware, this paper proposes a new function recognition algorithm FRwithMBA based on the structure information. With the help of function call resolve, FRwithMBA identifies functions by determining the termination position of the function based on branches instruction and termination signatures of the function. In the experiment, we evaluated FRwithMBA against IDA Pro, radare2, and angr with the router binary Cisco IOS and found that FRwithMBA is more effective in identifying functions in the stripped binary under PPC and MIPS than the other tools. In terms of recognition effectiveness, FRwithMBA performs a little better than IDA Pro, but the results of radare2 and angr are poor. For a 14-MB binary, the parsing time of IDA Pro is 178 s, radare2 uses 376 s, angr uses 1896 s, and FRwithMBA uses 25 s. When processing 220-MB Executable and Linkable Format files, FRwithMBA can identify nearly 420 000 functions in about 240 s, IDA takes 2754 s, and both radare2 and angr get a fault. In the experiment, the recall of FRwithMBA reached 99.99% and the precision reached 99.7%. It is better than the other tools. In other words, FRwithMBA has more speed, higher accuracy, and less resource occupation.

A Computational Investigation of Sources of Variability in Sentence Comprehension Difficulty in Aphasia

We present a computational evaluation of three hypotheses about sources of deficit in sentence comprehension in aphasia: slowed processing, intermittent deficiency, and resource reduction. The ACT-R based Lewis and Vasishth (2005) model is used to implement these three proposals. Slowed processing is implemented as slowed execution time of parse steps; intermittent deficiency as increased random noise in activation of elements in memory; and resource reduction as reduced spreading activation. As data, we considered subject vs. object relative sentences, presented in a self-paced listening modality to 56 individuals with aphasia (IWA) and 46 matched controls. The participants heard the sentences and carried out a picture verification task to decide on an interpretation of the sentence. These response accuracies are used to identify the best parameters (for each participant) that correspond to the three hypotheses mentioned above. We show that controls have more tightly clustered (less variable) parameter values than IWA; specifically, compared to controls, among IWA there are more individuals with slow parsing times, high noise, and low spreading activation. We find that (a) individual IWA show differential amounts of deficit along the three dimensions of slowed processing, intermittent deficiency, and resource reduction, (b) overall, there is evidence for all three sources of deficit playing a role, and (c) IWA have a more variable range of parameter values than controls. An important implication is that it may be meaningless to talk about sources of deficit with respect to an abstract verage IWA; the focus should be on the individual's differential degrees of deficit along different dimensions, and on understanding the causes of variability in deficit between participants.

An on-the-fly grammar modification mechanism for composing and defining extensible languages

Adaptable Parsing Expression Grammar (APEG) is a formal method for defining the syntax of programming languages. It provides an on-the-fly mechanism to perform modifications of the syntax of the language during parsing time. The primary goal of this dynamic mechanism is the formal specification and the automatic parser generation for extensible languages. In this paper, we show how APEG can be used for the definition of the extensible languages SugarJ and Fortress, clarifying many aspects of the syntax of these languages. We also show that the mechanism for on-the-fly modification of syntax rules can be useful for defining grammars in a modular way, implementing almost all types of language composition in the context of specification of extensible languages.

Performance Enhancement of XML Parsing By Using Artificial Neural Network

XML is used for data representation and exchange. XML data processing becomes more and more important for server workloads like web servers and database servers. One of the most time consuming part is XML document parsing. Parsing is a core operation performed before an XML document can be navigated, queried, or manipulated. Recently, high performance XML parsing has become a topic of considerable interest. This paper proposes a mechanism for efficiently processing XML documents with the help of Artificial Neural Network (ANN). Provide the set of XML documents to the system and parsing results will store in the database. With the help of Artificial Neural Network (ANN) the performance of XML parsing will improve by reducing parsing time of XML document. Levenberg-Marquardt algorithm (LMA) is used to train the neurons in the Artificial Neural Network (ANN) to recognize XML document pattern. This proposed system will improve the performance of xml parsing by reducing parsing time of XML document with the help of Artificial Neural Network (ANN).

Distributed and Parallel Big Textual Data Parsing for Social Sensor Network

Recently, due to the popularization of the smartphone and social network service (SNS), many SNS users write their opinions for social events. According to these social phenomena, social sensor network which analyzes social events by utilizing those users' text data is proposed. Parsing is essential module to analyze user's text contents because it gives the understanding of semantics by extracting the words and their classes from texts. However, parsing requires much time because it needs to analyze all context information from the users' text. In addition, as users' text data are generated and transferred in streaming, the required parsing time increases too. This situation occurs that it is hard to parse the text on the single machine. Therefore, to drastically enhance the parsing speed, we propose distributed and parallel parsing system on the MapReduce. It applies the legacy parser to the MapReduce through loose coupling. Also, to reduce communication overheads, the statistical model used by the parser is resided on local cache in each mapper. The experimental result shows that the speed of proposed system is 2–19 times better than that of the legacy parser. As a result, we prove that the proposed system is useful for parsing text data in social sensor network.

COMPARATIVE ANALYSIS OF XML AND JSON USING PHP APPLICATION PLATFORM WITH REPRESENTATIONAL STATE TRANSFER ( REST ) ARCHITECTURAL

Data communication in the internet today is so complex as an example of the speed factor becomes very important in communicating, everyone wants fast data communication services provided in order to maximum. In relation to the application as a communication liaison with client server applications, web service using a data serialization format to transmit the data. Before the data is sent, either fromthe client to the server or vice versa, should be modified in a specific data format beforehand according to the web service is used. Types of data serialization format used in the web service such as XML and JSON. The method used for testing include data serialization method, data measurement method and data parsing method. Data serialization method is used to calculate the time serialization of data from the database to the form of XML and JSON in applications with PHP platform. Data measurement method used to measure the size of the XML and JSON data which based on many fields of data serialization process. Data parsing method is used to calculate the processing time and JSON parsing XML data. Results o f comparative analysis o f XML and JSON in PHP applications using thearchitecture Rest can be concluded that the test result o f the difference in time and time serialization and JSON parsing XML data is influenced by the number o f records, if the number of records the greater the difference in eating time data serialization and parsing the data the greater the time also itcan be concluded that the faster the process JSON serialization and parsing XML data is compared. Testing results o f the JSON data size is smaller than the size of XML. Data exchange using XML format has a size limit of up to 31456.31 KB while JSON XML exceeds the size limit. Testing results on the Internet when the number o f records up to 50,000 data when the data serialization and parsing time data can not be detected in the database.

전술데이터링크 상호운용을 위한 XML 기반 메시징 시스템

전술데이터링크(TDL)는 전술정보 및 상황정보를 실시간으로 교환하기 위한 통신체계이며, 네트워크 중심전(NCW)에서 정보우위를 점하는데 필요한 주요 요소이다. 한국군에서도 정보우위를 위해 다양한 전술데이터링크를 사용 중이나 이러한 다양한 전술데이터링크의 사용은 전체 C4I 시스템의 통합과 다른 전술데이터링크와의 연동을 어렵게 만들어 전체 시스템의 상호운용개념에 반하게 되는 문제점을 가진다. 본 논문에서는 네트워크 중심전 환경에서 다양한 전술데이터링크간의 상호운용이 가능한 XML 기반의 메시징 시스템을 제안한다. 제안 시스템은 전술데이터링크와 C4I시스템에서 사용하는 메시지와 인터페이스를 XML로 정의하여 이용한다. 그리고 제한된 네트워크 대역폭을 가지는 군 환경을 고려하여 바이너리 XML을 통해 네트워크 부담을 줄이는 것을 제안한다. 본 논문에서는 실험결과를 통해 전술데이터링크 메시지가 XML 스키마에 정의된 대로 상호운용이 가능한 메시지 형태로 변환됨과 바이너리 XML serialization을 통해 파싱과 전송시간의 감소를 가져오는 것을 보여준다. Tactical Data Links are standardized radio communication data links for military armed forces and used to transmit and receive data. Because they are the core elements of achieving information superiority in a NCW environment, various kinds of TDL are used by the Korean armed forces. Operating various types of TDL is very helpful in getting more information; however, at the same time, it is hard to integrate and make them co-operate with other TDLs or C4ISR systems. In this paper, we propose a XML based messaging system to make various types of TDL interoperable in a NCW environment. For the messaging system between TDL and C4ISR systems, we define XML message processing interfaces and suggest binary XML for serialization to save transmission loads over constrained military communication bandwidth. Our experiment results show that the TDL message can be transformed into generalized XML data that is interoperable with other TDLs and binary XML serialization, and it saves parsing and transmission time.

An Adaptive Parser for Arabic Language Processing

In this study, we present a robust bottom-up Arabic parser that investigates the correctness of Arabic sentences by passing them through a set of predetermined states relying on their individual words. The major benefit of our approach is the reduced number of backup states tested when determining the grammatical structure of a given sentence. The proposed approach is optimized to tokenize the input sentences correctly since accurate tokenization is the essential step of the parser; this process also reduces the parsing time. Our proposed parser is extendable; hence, it allows new words to be added to the lexicon, i.e. the lexicon is built dynamically. Experimental results have demonstrated the effectiveness of our approach in checking correctly numerable sentences with different lengths. The accuracy was 85.88% when tested on a sample of 170 Arabic sentences taken from an existing Arabic text taught in k-12 grade levels.

Fast entropy decoding method for high resolution personal video recorder

In this paper, we propose a fast entropy decoding method that is well suited for personal video recorder (PVR) applications for high resolution video (HRV). In PVR applications supporting HRV, it is essential to implement a fast encoder and decoder due to the large amount of video data. The proposed bit-precision based entropy method (BPEM) provides a way to design a very simple entropy decoding process, which can substantially reduce the overall computational complexity of the decoder. For the purpose of contents protection, it is common to have a proprietary codec in PVR. In such a case, additional transcoding for storing transmitted data is required. However, this may involve a complex process that requires encoding and decoding. The proposed method is only applied to the entropy coding method of the existing standard codecs for transcoding thereby reducing the transcoding complexity down to bitstream-level symbol mapping. We implemented BPEM on an MPEG-4 simple profile (SP) codec. The experimental results showed that BPEM produced about 30 percent lower decoder complexity and 50 to 80 percent lower syntax parsing time while maintaining similar coding efficiency compared to the conventional MPEG-4 SP codec.

A psycholinguistic model of natural language parsing implemented in simulated neurons

A natural language parser implemented entirely in simulated neurons is described. It produces a semantic representation based on frames. It parses solely using simulated fatiguing Leaky Integrate and Fire neurons, that are a relatively accurate biological model that is simulated efficiently. The model works on discrete cycles that simulate 10ms of biological time, so the parser has a simple mapping to psychological parsing time. Comparisons to human parsing studies show that the parser closely approximates this data. The parser makes use of Cell Assemblies and the semantics of lexical items is represented by overlapping hierarchical Cell Assemblies so that semantically related items share neurons. This semantic encoding is used to resolve prepositional phrase attachment ambiguities encountered during parsing. Consequently, the parser provides a neurally-based cognitive model of parsing.

Haskell, do you read me?

The Haskell definition and implementation of read is far from perfect. In the first place read is not able to handle the associativities defined for infix operators. Furthermore, it puts constraints on the way show is defined, and especially forces it to generate far more parentheses than expected. Lastly, it may give rise to exponential parsing times. All this is due to the compositionality requirement for read functions, which imposes a top-down parsing strategy. We propose a different approach, based on typed abstract syntax, in which grammars describing the data types are composed dynamically. Using the transformation libraries described in a companion paper these syntax descriptions are combined and transformed into parsers at runtime, from which the required read function are constructed. In this way we obtain linear parsing times, achieve consistency with the defined associativities, and may use a version of show which generates far fewer parentheses, thus improving readability of printed values. The described transformation algorithms can be incorporated in a Haskell compiler, thus moving most of the work involved to compile time.

A validating CIF parser:PyCIFRW

PyCIFRWis a general-purpose Python package providing a simple, powerful interface for working with CIF files. Objects and methods are available for reading, writing and manipulating CIF files and dictionaries. Comprehensive validation of CIF files and dictionaries against DDL1 or DDL2 dictionaries is also possible.PyCIFRWis easily included in larger projects and is portable across a large number of platforms. Although written in an interpreted language, parsing and validation times are acceptable for most applications.

Improving self-interpretation of XML-based business documents by introducing derived elements

XML is becoming the standard for document description in a B2B setting, and XML Schema is gaining wide acceptance as the schema language to define the structure of these documents. Business documents such as those currently found in B2B applications, frequently comprise derived elements, i.e. elements whose content can be calculated through a deriving function that examines the content of other elements. Based on this observation, this work advocates incorporating the notion of derived elements in XML Schema. Despite its wide presence, the notion of derived elements is not yet supported in XML Schema. To this end two issues are addressed: (1) the implicitness of current approaches to deriving function support for XML documents, and (2) the externality of some deriving data which participate in the deriving function. These aspects are respectively tackled both (1) by moving the derivation semantics to the document schema, and (2) by proposing an attachment where the deriving elements are recorded. These ideas have been realized by extending a JAXP-compliant XML parser. Now, this parser can be configured to become “derivation aware”. That is, the schemata is now enriched with deriving functions. At parsing time, these functions are interpreted, and the returned values become the content of the associated derived elements.

XML 문서 보안을 위한 새로운 XML-Signcryption scheme 설계 및 구현

As the XML is approved standard language by the UN, the progress which complemented the XML security has being processed rapidly. In this paper, we design and implement the "XML-Signcryption" as a security mechanism to protect the XML document that can operate between other platforms. The signature and encryption which is the standard specification in W3C needs to be able to proceed them separately. Generally the signature and encryption require four times modular exponential operation, however the signcryption only needed three times modular exponential operation. This will benefit overall system effectiveness in terms of cost. And this scheme offers to convenient the user, because the signature and encryption implement as a single XML format. This tool can save the parsing time as a number of tags is few within a document. And also, in this paper, based on a research of Web Services security, we can apply XML-Signcryption to the SOAP message to provide the security services. Based on the XML-Signcryption scheme which provides confidentiality, integrity, authentication and non-repudiation to the XML document and Web Service security simultaneously.

An Agent-based Parallel HPSG Parser for Shared-memory Parallel Machines.

We describe an agent-based parallel HPSG parser that operates on shared-memory parallel machines. It efficiently parses real-world corpora by using a wide-coverage HPSG grammar. The efficiency is due to the use of a parallel parsing algorithm and the efficient treatment of feature structures. The parsing algorithm is based on the CKY algorithm, in which resolving constraints between a mother and her daughters is regarded as an atomic operation. The CKY algorithm features data distribution and granularity of parallelism. The keys to the efficient treatment of feature structures are i) transferring them through shared-memory, ii) copying them on demand, and iii) writing/reading them simultaneously onto/from memory.Being parallel, our parser is more efficient than sequential parsers. The average parsing time per sentence for the EDR Japanese corpus was 78 msec and its speed-up reaches 13.2 when 50 processors were used.