Im Jahre 1977 wurde der „Data Encryption Algorithm“ (DEA) vom „National Bureau of Standards“ (NBS, später „National Institute of Standards and Technology“ – NIST) zum amerikanischen Verschlüsselungsstandard für Bundesbehörden erklärt [NBS_77]. 1981 folgte die Verabschiedung der DEA-Spezifikation als ANSI-Standard „DES“ [ANSI_81]. Die Empfehlung des DES als StandardVerschlüsselungsverfahren wurde auf fünf Jahre befristet und 1983, 1988 und 1993 um jeweils weitere fünf Jahre verlängert. Derzeit liegt eine Neufassung des NISTStandards vor [NIST_99], in dem der DES für weitere fünf Jahre übergangsweise zugelassen sein soll, aber die Verwendung von Triple-DES empfohlen wird: eine dreifache Anwendung des DES mit drei verschiedenen Schlüsseln (effektive Schlüssellänge: 168 bit) [NIST_99]. Der DES basiert auf einer von Horst Feistel bei IBM entwickelten Blockchiffre („Lucipher“) mit einer Schlüssellänge von 128 bit. Da die amerikanische „National Security Agency“ (NSA) dafür gesorgt hatte, daß der DES eine Schlüssellänge von lediglich 64 bit besitzt, von denen nur 56 bit relevant sind, und spezielle Substitutionsboxen (den „kryptographischen Kern“ des Verfahrens) erhielt, deren Konstruktionskriterien von der NSA nicht veröffentlicht wurden, war das Verfahren von Beginn an umstritten. Kritiker nahmen an, daß es eine geheime „Trapdoor“ in dem Verfahren gäbe, die der NSA eine OnlineEntschlüsselung auch ohne Kenntnis des Schlüssels erlauben würde. Zwar ließ sich dieser Verdacht nicht erhärten, aber sowohl die Zunahme von Rechenleistung als auch die Parallelisierung von Suchalgorithmen machen heute eine Schlüssellänge von 56 bit zum Sicherheitsrisiko. Zuletzt konnte 1998 mit einer von der „Electronic Frontier Foundation“ (EFF) entwickelten Spezialmaschine mit 1.800 parallel arbeitenden, eigens entwickelten Krypto-Prozessoren ein DES-Schlüssel in einer Rekordzeit von 2,5 Tagen gefunden werden. Um einen Nachfolger für den DES zu finden, kündigte das NIST am 2. Januar 1997 die Suche nach einem „Advanced Encryption Standard“ (AES) an. Ziel dieser Initiative ist, in enger Kooperation mit Forschung und Industrie ein symmetrisches Verschlüsselungsverfahren zu finden, das geeignet ist, bis weit ins 21. Jahrhundert hinein amerikanische Behördendaten wirkungsvoll zu verschlüsseln. Dazu wurde am 12. September 1997 ein offizieller „Call for Algorithm“ ausgeschrieben. An die vorzuschlagenden symmetrischen Verschlüsselungsalgorithmen wurden die folgenden Anforderungen gestellt: nicht-klassifiziert und veröffentlicht, weltweit lizenzfrei verfügbar, effizient implementierbar in Hardund Software, Blockchiffren mit einer Blocklänge von 128 bit sowie Schlüssellängen von 128, 192 und 256 bit unterstützt. Auf der ersten „AES Candidate Conference“ (AES1) veröffentlichte das NIST am 20. August 1998 eine Liste von 15 vorgeschlagenen Algorithmen und forderte die Fachöffentlichkeit zu deren Analyse auf. Die Ergebnisse wurden auf der zweiten „AES Candidate Conference“ (22.-23. März 1999 in Rom, AES2) vorgestellt und unter internationalen Kryptologen diskutiert. Die Kommentierungsphase endete am 15. April 1999. Auf der Basis der eingegangenen Kommentare und Analysen wählte das NIST fünf Kandidaten aus, die es am 9. August 1999 öffentlich bekanntmachte: MARS (IBM) RC6 (RSA Lab.) Rijndael (Daemen, Rijmen) Serpent (Anderson, Biham, Knudsen) Twofish (Schneier, Kelsey, Whiting, Wagner, Hall, Ferguson).

Advanced Encryption Standard (AES).

https://lexitron.nectec.or.th/KM_HL5001/file_HL5001/Paper/Inter%20Journal/krrn_11652.pdf

Thai speech processing technology: A review

In this study, we apply data mining tools to generate interesting patterns for predicting box office performance of movies using data collected from multiple social media and web sources including Twitter, YouTube and the IMDb movie database. The prediction is based on decision factors derived from a historical movie database, followers count from Twitter, and sentiment analysis of YouTube viewers' comments. We label the prediction in three classes, Hit, Neutral and Flop, using Weka's K-Means clustering tool. Interesting patterns for prediction are generated by Weka's J48. Since our prediction is for movies yet to be released in summer 2013, the performance of the final results will be validated by a follow-up study.

Prediction of movies box office performance using social media

An algorithm for the assembly of multiple test forms is proposed in which the multiple-form problem is reduced to a series of computationally less intensive two-form problems. At each step one form is assembled to its true specifications; the other form is a dummy assembled only to maintain a balance between the quality of the current form and the remaining forms. It is shown how the method can be implemented using the technique of 0-1 linear programming. Two empirical examples using a former item pool from the Law School Admission Test (LSAT) are given - one in which a set of parallel forms is assembled and another in which the targets for the information functions of the forms are shifted systematically.

Simultaneous assembly of multiple test forms

In this paper, we propose a new approach to construct a system of transformation rules for the Part-of-Speech (POS) tagging task. Our approach is based on an incremental knowledge acquisition method where rules are stored in an exception structure and new rules are only added to correct the errors of existing rules; thus allowing systematic control of the interaction between the rules. Experimental results on 13 languages show that our approach is fast in terms of training time and tagging speed. Furthermore, our approach obtains very competitive accuracy in comparison to state-of-the-art POS and morphological taggers.

A robust transformation-based learning approach using ripple down rules for part-of-speech tagging

The paper describes the Thai Intonation Analysis System (TIAS) for speaker-independent recognition that achieves 90-95% word accuracy by applying two techniques: intonation flow analysis and voice identity calculation. The intonation flow analysis considers spectrograms emphasizing their modification styles for the frequency peaks. The five styles of modification mapped to Thai tones are low, medium, falling, high, and rising respectively. However, there are some words where tones cannot be labeled correctly by the former because of speakers' amplitude, frequency, and uttering period differences. These limitations can be cured by applying the voice identity calculation that applies correlative coefficient relation to find voice identity of each compared spectrogram. The paper also reported experimental results for the task of intonation analysis with ten Thai numeral words uttered with three-time repetitions by ten male speaker. In this paper, the speech waveforms are adapted from time-domain signals to harmonic-frequency domain.

Thai intonation analysis in harmonic-frequency domain

The natural language processing (NLP) for Thai language is rather complicated using in the real tasks because it has a complex sequential structure of the sentence. The POS tagging can improve the accuracy of syntactic analysis so it can support the improvement of many NLP tasks. We present the supervised machine learning that is suitable for annotate the POS type for Thai language by comparison between the Support Vector Machine (SVM) and the Conditional Random Fields (CRFs). The BEST 2012 News and Entertainments corpus is utilized in our experiments. However, the sequential characteristic of Thai language is the interesting point and we use it as our feature in training set. Our sequential features contain forward 3-gram, backward 3-gram and 5-gram. The best accuracy of our experiments is 93.638% from SVMs POS tagging that learning by word of forward 3-gram when the size of training data is ten thousand tokens. Moreover, with the same training data, the best accuracy of CRFs is very close with SVM that is 93.254% when the learning form is the word with POS of 5-gram.

A comparative study on different techniques for Thai part-of-speech tagging

Today the movie classification also known as“Movie Rating” still does not has the computerized systems to support and still using human help to classify as a censorship. This movie classification process also takes long time and may have some conflicts among the committee because of their background and experience. The committee also needs to use the vote for helping then consents all their conflicts. By applying the computerized systems in the movie classification will help to improve the correctness, speed up the classified process and eliminate the conflicts that may happen among the censorial committee. This paper will apply the image processing and the speech data processing to the computerized systems. The proposed system can classify the video movies by using 2 interested criteria which are violence and language. The system was developed for video movie rating between 13+ through 18+. The accuracy rate of this system is around 66.67%.

Automatic Movie Rating Using Visual and Linguistic Information

Unknown Keyphrase Extracting by Verifying their Sentence-Based Semantics

Within an organization, different information systems, e.g. Enterprise Resource Planning, use their own, specific, and proprietary data model. Enterprise data management enables a 360-degree view of customers, products and other data elements but a holistic approach is needed: All the different data models need to be “mapped” to one consolidated, normalized enterprise data model in order to enable a real-time transformation, i.e. data elements of the information systems need to be converted, mapped, merged, and stored in an enterprise database. In the era of Big Data, IoT, and Industry 4.0, EDM becomes an even more important task in the context of Digitalization. This paper describes a meta-model-based approach that lets the data scientist map elements of different data models to elements of an enterprise data model using UML class diagrams, the UML Profile mechanism, OCL, and model transformations. This leads to an executable data mapper for Enterprise data management that transfers and consolidated data from operational information systems into an enterprise database.

Rachada Kongkachandra

Papers

Thai intonation analysis in harmonic-frequency domain

A comparative study on different techniques for Thai part-of-speech tagging

Automatic Movie Rating Using Visual and Linguistic Information

Unknown Keyphrase Extracting by Verifying their Sentence-Based Semantics

Towards Enterprise Data Management: Data Model Mapping