18 research outputs found
1D-Touch: NLP-Assisted Coarse Text Selection via a Semi-Direct Gesture
Existing text selection techniques on touchscreen focus on improving the
control for moving the carets. Coarse-grained text selection on word and phrase
levels has not received much support beyond word-snapping and entity
recognition. We introduce 1D-Touch, a novel text selection method that
complements the carets-based sub-word selection by facilitating the selection
of semantic units of words and above. This method employs a simple vertical
slide gesture to expand and contract a selection area from a word. The
expansion can be by words or by semantic chunks ranging from sub-phrases to
sentences. This technique shifts the concept of text selection, from defining a
range by locating the first and last words, towards a dynamic process of
expanding and contracting a textual semantic entity. To understand the effects
of our approach, we prototyped and tested two variants: WordTouch, which offers
a straightforward word-by-word expansion, and ChunkTouch, which leverages NLP
to chunk text into syntactic units, allowing the selection to grow by
semantically meaningful units in response to the sliding gesture. Our
evaluation, focused on the coarse-grained selection tasks handled by 1D-Touch,
shows a 20% improvement over the default word-snapping selection method on
Android
Quantum algorithms for community detection and their empirical run-times
We apply our recent work on empirical estimates of quantum speedups to the practical task of community detection in complex networks. We design several quantum variants of a popular classical algorithm -- the Louvain algorithm for community detection -- and first study their complexities in the usual way, before analysing their complexities empirically across a variety of artificial and real inputs. We find that this analysis yields insights not available to us via the asymptotic analysis, further emphasising the utility in such an empirical approach. In particular, we observe that a complicated quantum algorithm with a large asymptotic speedup might not be the fastest algorithm in practice, and that a simple quantum algorithm with a modest speedup might in fact be the one that performs best. Moreover, we repeatedly find that overheads such as those arising from the need to amplify the success probabilities of quantum sub-routines such as Grover search can nullify any speedup that might have been suggested by a theoretical worst- or expected-case analysis
Design and Management of Manufacturing Systems
Although the design and management of manufacturing systems have been explored in the literature for many years now, they still remain topical problems in the current scientific research. The changing market trends, globalization, the constant pressure to reduce production costs, and technical and technological progress make it necessary to search for new manufacturing methods and ways of organizing them, and to modify manufacturing system design paradigms. This book presents current research in different areas connected with the design and management of manufacturing systems and covers such subject areas as: methods supporting the design of manufacturing systems, methods of improving maintenance processes in companies, the design and improvement of manufacturing processes, the control of production processes in modern manufacturing systems production methods and techniques used in modern manufacturing systems and environmental aspects of production and their impact on the design and management of manufacturing systems. The wide range of research findings reported in this book confirms that the design of manufacturing systems is a complex problem and that the achievement of goals set for modern manufacturing systems requires interdisciplinary knowledge and the simultaneous design of the product, process and system, as well as the knowledge of modern manufacturing and organizational methods and techniques
Review of Low Voltage Load Forecasting: Methods, Applications, and Recommendations
The increased digitalisation and monitoring of the energy system opens up
numerous opportunities to decarbonise the energy system. Applications on low
voltage, local networks, such as community energy markets and smart storage
will facilitate decarbonisation, but they will require advanced control and
management. Reliable forecasting will be a necessary component of many of these
systems to anticipate key features and uncertainties. Despite this urgent need,
there has not yet been an extensive investigation into the current
state-of-the-art of low voltage level forecasts, other than at the smart meter
level. This paper aims to provide a comprehensive overview of the landscape,
current approaches, core applications, challenges and recommendations. Another
aim of this paper is to facilitate the continued improvement and advancement in
this area. To this end, the paper also surveys some of the most relevant and
promising trends. It establishes an open, community-driven list of the known
low voltage level open datasets to encourage further research and development.Comment: 37 pages, 6 figures, 2 tables, review pape
Passenger BIBO detection with IoT support and machine learning techniques for intelligent transport systems
The present article discusses the issue of automation of the CICO (Check-In/Check-Out) process for public transport fare collection systems, using modern tools forming part of the Internet of Things, such as Beacon and Smartphone. It describes the concept of an integrated passenger identification model applying machine learning technology in order to reduce or eliminate the risks associated with the incorrect classification of a smartphone user as a vehicle passenger. This will allow for the construction of an intelligent fare collection system, operating in the BIBO (Be-In/Be-Out) model, implementing the "hands-free" and "pay-as-you-go" approach. The article describes the architecture of the research environment, and the implementation of the elaborated model in the Bad.App4 proprietary solution. We also presented the complete process of concept verification under real-life conditions. Research results were described and supplemented with commentary
Sharia and Democracy: Efforts to Synergize the Demands of Faith with the legal System in Indonesia
Since the fall of the New Order's authoritarian regime, Indonesia as a country with the largest Muslim population in the world is often praised as a country that has proven that Islam, democracy and modernity can grow and develop together. However, democracy in Indonesia does not escape the challenges associated with the return of the spirit of religion in political life. The problem is the return of religion to politics – and to public life in general – is a serious
challenge to the rule of democratically enacted law and the civil liberties that go with it. Islamic activism or Islamism although they use freedom provided by democracy, actually rejects the principles of democracy and human rights which they see as contrary to the sharia and the absolute sovereignty of God. In the past thirteen years there has been a tendency for rising aspirations for Indonesia to be regulated by sharia law. The purpose of this research is to look for the meaning of sharia and democracy for Muslims, the theological foundations for Muslim to support democracy, and the challenges and alternative solutions that can be offered so that sharia can be transformed to Indonesia legal system. By assuming that sharia has a purpose and that Islamic law can change, evolve in line with developments and challenges of the times, the author argues that the synergy between sharia and democracy can occur in Indonesia as long as Muslims in Indonesia can accept plurality in understanding the sharia and are not bound to one model in understanding sharia. The author believes that sharia can be applied in democratic countries such as Indonesia, because the purpose of the sharia and the purpose of the state are the same, namely the achievement of social justice for all without discrimination
Publishing Activities of Shiites and Democratization of Islamic Thought in Indonesia
This paper examines the pattern of publication in a mass Islamic organization that is a minority
in Indonesia, namely those originating from the Shia Islamic School. The publication process
itself is inseparable from the position of an organization which is one of the centers of Shia
community activities in Indonesia in giving and receiving knowledge and information. The
study on the Indonesian Ahlulbait Jamaat Association (IJABI) which was founded in Bandung
uses qualitative methods with data collection techniques through observation, interviews,
documentation studies, and literature studies. The results of the study show that there is a model
of publication activity which is characterized by the presence of managers, participants, and
supporters of publication activities based on the role of communication among the very
dominant Shia citizens. This needs to be exemplified by other organizations, in order to
strengthen the character, intelligence and skills of the community in facing the fast, effective
and efficient development of the ag
Klasyfikacja danych niekompletnych w oparciu o komitet klasyfikatorów
Thesis:
It is possible to maintain the accuracy of classification on incomplete data by selecting a committee of classifiers based on
pre-selected features.
The purpose of the work was to develop a classification committee, designed to classify data in which there are features
that do not have defined values. The classifier would be able to process incomplete feature vectors without the need to pre-fill
them, and the classification would be based on pre-selected features. Partial objectives have been specified in the work:
1. Estimation of the impact of missing or removed features of the object on the quality of classification.
2. Developing the structure of the classification committee.
3. Selection of classifiers operating in the committee.
4. Developing a decision-making algorithm (fuser) for the classification committee.
5. Selection of distinctive features for individual object classes.
6. Testing the developed system on real data.
7. Verification of the usefulness of the developed classifier for the construction of the system for assessment of liver
fibrosis in patients with hepatitis C based on the analysis of peripheral blood parameters.
The dissertation investigated the influence of the presence of null values in the data on the formation of incomplete
reference (training) vectors depending on the size of the subspace of features on which the component classifiers of the
committee work. The impact of the missing values on the quality of the classification has also been confirmed experimentally.
Based on the conclusions regarding the distribution of missing values of features among reference vectors, the structure of
the classification committee was proposed, based on the division of feature space into one-element vectors.
For the proposed structure of the classification committee, a number of conventional classifiers were tested as component
classifiers of the committee. As a component classifier of the committee being developed, the only classifier which benefited
from such a committee structure, has been chosen - the fc-NN classifier.
The Bayesian averaging, supplemented by the weighting factor for individual classes of reference objects, aimed at
improving the quality of classification in relation to objects that are not very numerous in the reference set, has been proposed
as the method of evaluating the classification committee decision.
A committee with such a structure performs initial, dynamic filtering of features, based on the vector of classified data.
Features that do not have a defined value in this vector are ignored in the classification process. In order to improve the quality
of classification, a method for pre-selection of features has been proposed, based on the component classifier of the proposed
committee. This method uses a ranking of distinctive features for individual classes from the reference set, to indicate a
suboptimal subset of the features on the basis of which the classification will be conducted.
The proposed SFfc-NN/C committee classifier has been tested on a number of benchmark databases containing full real
data. In order to determine the impact of the missing values of random features, in both classified and reference data, on the
quality of classification, null values were artificially introduced into the data, replacing the existing values of randomly selected
features. The tests were carried out without and with the initial selection of features.
Ultimately, the classifier was used to classify actual medical data - blood analysis of HCV infected patients. The undefined
values in this data set occurred naturally. The test results were consistent with previously obtained results on data from which
some values were artificially removed. Thus, the usefulness of the proposed SFfc-NN/C classifier for the construction of the liver
fibrosis assessment system in patients with hepatitis C has been confirmed.
The implementation of the partial objectives made it possible to confirm the thesis of the work. This confirmation is
experimental, and supported by the results of statistical tests