Az IKT-eszközökkel kapcsolatos preferenciák és használatuk előrejelzése (Preferences and usage forecast of ICT tools)

A szerzők ebben a tanulmányukban az információs és kommunikációs technológiai (továbbiakban IKT) eszközök közül az asztali számítógépekkel (desktopok), laptopokkal (notebookok és netbookok), táblagépekkel és okostelefonokkal foglalkoznak. Az IKT-eszközök elterjedtségének vizsgálatánál meghatározó szerepet játszik a technológia jelenléte mellett a társadalom befogadóképessége. A technológia és társadalom kapcsolatát különböző módszerekkel és modellekkel mutatják be, melyek indokolják ezen eszközök növekvő használatának szükségességét. Ebben a tanulmányban a modellekből és a felmérésekből összeállított tényezők beépítésével és az általuk feldolgozott kérdőívek elemzése által kirajzolódnak minták és olyan összefüggések, amelyek magyarázatot adhatnak a különböző eszközhasználat okainak megértésére. _____ In their study the authors deal with the desktop computers (Desktop), laptops (notebooks and netbooks), smartphones and tablet machines among of information and communication technology (hereinafter referred to as ICT) tools. The capacity of society is one of the key elements in the examination of spread of ICT. The relationship between technology and society is presented with different methods and models that are justified by the need for increasing the use of these devices. In this paper such samples and correlations are emerged of the models and surveys, which may explain the reasons for understanding of the different tool use

Quality Assurance of Learning Assessments in Large Information Systems and Decision Analysis Courses

As Information Systems courses have become both more data-focused and student numbers have increased, there has emerged a greater need to assess technical and analytical skills more efficiently and effectively. Multiple-choice examinations provide a means for accomplishing this, though creating effective multiple-choice assessment items within a technical course context can be challenging. This study presents an iterative quality improvement framework based on Plan-Do-Study-Act (PDSA) quality assurance cycle for developing and improving such multiple-choice assessments. Integral to this framework, we also present a rigorous, reliable, and valid measure of assessment and item quality using discrimination efficiency and the KR-20 assessment reliability measure. We demonstrate the effectiveness of our approach across exams developed and administered for two courses — one, a highly technical Information Systems introductory course and the other, an introductory data analytics course. Using this approach, we show that assessment quality iteratively improves when instructors measure items and exams rigorously and apply this PDSA framework

SPIE: A Framework For Advanced Database Topics

In the ever expanding universe of database skills and knowledge we propose a framework that can be used to classify advanced database topics. We use the framework to present five advanced database modules that can be successfully incorporated in an advanced database course. These modules were built to illustrate advanced topics and were tested and refined in advanced database courses over several semesters. The skills demonstrated in the modules go beyond what is typically taught in an the introductory level database course but are important in today’s highly demanding business environment

PoS-tagging and lemmatization with a deep recurrent neural network

Neural networks have been shown to successfully solve many natural language processing tasks previously tackled by rule-based and statistical approaches. We present a deep recurrent network with long short-term memory, identical to engines used in machine translation, to solve the problem of joint PoS-tagging and lemmatization in Hungarian and German. Our model achieves comparable or superior results to a state-of-the-art statistical PoS tagger. We are able to enhance the Hungarian model’s performance, as measured on a manually annotated sample unrelated to the initial training corpus, through an additional synthesized dataset

Etudes in Chinese-Hungarian corpus-based lexical acquisition

The paper reports on a series of experiments to extract matching lexical items from a 6.1 million segment corpus of movie subtitles in Mandarin Chinese and Hungarian, with the aim of expanding an existing bilingual dictionary. The challenges of data cleansing and tokenization are outlined, and the outcome of word alignment, vector space embeddings, neural machine translation and two standard statistical approaches is presented. A bilingual concordance tool for end users, based on word alignments, is introduced. A quantitative and qualitative evaluation of the results finds that the new methods drastically outperform simple collocation extraction, but also shows that human judgement is indispensable before including vocabulary in a published dictionary

Egy vakmerő digitális lexikográfiai kísérlet : a CHDICT nyílt kínai-magyar szótár

A CHDICT-tel egy nyílt, közösségileg szerkesztett kínai-magyar szótár indul hamarosan útjára. A cikk az előképek kontextusába helyezve mutatja be a munkát, illetve beszámol a kiinduló lexikai tartalom kiválasztásáról, az alkalmazott szótárfordítási eljárásról és közzététel módjáról. A szerző kísérletként tekint a projektre, melyből kiderülhet, származtatható-e kis nyelvpárokra kielégítő minőségű új szótár az elérhető nyílt forrásokból, és élteképes-e a kollaboratív modell ilyen tartalmakra