pwc-archive/papers-with-abstracts huggingface dataset:
Correlated Anomaly Detection from Large Streaming Data
Authors: Zheng Chen, Xinli Yu, Yuan Ling, Bo Song, Wei Quan, Xiaohua Hu, Erjia Yan
Date: 2018-12-19 00:00:00
Tasks: Anomaly Detection, Event Detection, Group Anomaly Detection

Correlated anomaly detection (CAD) from streaming data is a type of group
anomaly detection and an essential task in useful real-time data mining
applications like botnet detection, financial event detection, industrial
process monitor, etc. The primary approach for this type of detection in
previous researches is based on principal score (PS) of divided batches or
sliding windows by computing top eigenvalues of the correlation matrix, e.g.
the Lanczos algorithm. However, this paper brings up the phenomenon of
principal score degeneration for large data set, and then mathematically and
practically prove current PS-based methods are likely to fail for CAD on
large-scale streaming data even if the number of correlated anomalies grows
with the data size at a reasonable rate; in reality, anomalies tend to be the
minority of the data, and this issue can be more serious. We propose a
framework with two novel randomized algorithms rPS and gPS for better detection
of correlated anomalies from large streaming data of various correlation
strength. The experiment shows high and balanced recall and estimated accuracy
of our framework for anomaly detection from a large server log data set and a
U.S. stock daily price data set in comparison to direct principal score
evaluation and some other recent group anomaly detection algorithms. Moreover,
our techniques significantly improve the computation efficiency and scalability
for principal score calculation.
A Fuzzy Community-Based Recommender System Using PageRank
Authors: Maliheh Goliforoushani, Radin Hamidi Rad, Maryam Amir Haeri
Date: 2018-12-18 00:00:00
Tasks: Community Detection, Recommendation Systems

Recommendation systems are widely used by different user service providers
specially those who have interactions with the large community of users. This
paper introduces a recommender system based on community detection. The
recommendation is provided using the local and global similarities between
users. The local information is obtained from communities, and the global ones
are based on the ratings. Here, a new fuzzy community detection using the
personalized PageRank metaphor is introduced. The fuzzy membership values of
the users to the communities are utilized to define a similarity measure. The
method is evaluated by using two well-known datasets: MovieLens and FilmTrust.
The results show that our method outperforms recent recommender systems.
Decision Support System for Renal Transplantation
Authors: Ehsan Khan, Avishek Choudhury, Amy L Friedman, Daehan Won
Date: 2018-12-11 00:00:00

The burgeoning need for kidney transplantation mandates immediate attention.
Mismatch of deceased donor-recipient kidney leads to post-transplant death. To
ensure ideal kidney donor-recipient match and minimize post-transplant deaths,
the paper develops a prediction model that identifies factors that determine
the probability of success of renal transplantation, that is, if the kidney
procured from the deceased donor can be transplanted or discarded. The paper
conducts a study enveloping data for 584 imported kidneys collected from 12
transplant centers associated with an organ procurement organization located in
New York City, NY. The predicting model yielding best performance measures can
be beneficial to the healthcare industry. Transplant centers and organ
procurement organizations can take advantage of the prediction model to
efficiently predict the outcome of kidney transplantation. Consequently, it
will reduce the mortality rate caused by mismatching of donor-recipient kidney
transplantation during the surgery. Keywords
Classification of Cervical Cancer Dataset
Authors: Avishek Choudhury, Y. M. S Al Wesabi, Daehan Won
Date: 2018-12-11 00:00:00
Tasks: Classification, Dimensionality Reduction, feature selection, General Classification, Missing Values

Cervical cancer is the leading gynecological malignancy worldwide. This paper
presents diverse classification techniques and shows the advantage of feature
selection approaches to the best predicting of cervical cancer disease. There
are thirty-two attributes with eight hundred and fifty-eight samples. Besides,
this data suffers from missing values and imbalance data. Therefore,
over-sampling, under-sampling and embedded over and under sampling have been
used. Furthermore, dimensionality reduction techniques are required for
improving the accuracy of the classifier. Therefore, feature selection methods
have been studied as they divided into two distinct categories, filters and
wrappers. The results show that age, first sexual intercourse, number of
pregnancies, smokes, hormonal contraceptives, and STDs: genital herpes are the
main predictive features with high accuracy with 97.5%. Decision Tree
classifier is shown to be advantageous in handling classification assignment
with excellent performance.
Identification of Cancer -- Mesothelioma Disease Using Logistic Regression and Association Rule
Authors: Avishek Choudhury
Date: 2018-12-11 00:00:00
Tasks: regression

Malignant Pleural Mesothelioma (MPM) or malignant mesothelioma (MM) is an atypical, aggressive tumor that matures into cancer in the pleura, a stratum of tissue bordering the lungs. Diagnosis of MPM is difficult and it accounts for about seventy-five percent of all mesothelioma diagnosed yearly in the United States of America. Being a fatal disease, early identification of MPM is crucial for patient survival. Our study implements logistic regression and develops association rules to identify early stage symptoms of MM. We retrieved medical reports generated by Dicle University and implemented logistic regression to measure the model accuracy. We conducted (a) logistic correlation, (b) Omnibus test and (c) Hosmer and Lemeshow test for model evaluation. Moreover, we also developed association rules by confidence, rule support, lift, condition support and deployability. Categorical logistic regression increases the training accuracy from 72.30% to 81.40% with a testing accuracy of 63.46%. The study also shows the top 5 symptoms that is mostly likely indicates the presence in MM. This study concludes that using predictive modeling can enhance primary presentation and diagnosis of MM.
Sequential model aggregation for production forecasting
Authors: Raphaël Deswarte, Véronique Gervais, Gilles Stoltz, Sébastien da Veiga
Date: 2018-11-30 00:00:00
Tasks: model, Production Forecasting, regression

Production forecasting is a key step to design the future development of a reservoir. A classical way to generate such forecasts consists in simulating future production for numerical models representative of the reservoir. However, identifying such models can be very challenging as they need to be constrained to all available data. In particular, they should reproduce past production data, which requires to solve a complex non-linear inverse problem. In this paper, we thus propose to investigate the potential of machine learning algorithms to predict the future production of a reservoir based on past production data without model calibration. We focus more specifically on robust online aggregation, a deterministic approach that provides a robust framework to make forecasts on a regular basis. This method does not rely on any specific assumption or need for stochastic modeling. Forecasts are first simulated for a set of base reservoir models representing the prior uncertainty, and then combined to predict production at the next time step. The weight associated to each forecast is related to its past performance. Three different algorithms are considered for weight computations: the exponentially weighted average algorithm, ridge regression and the Lasso regression. They are applied on a synthetic reservoir case study, the Brugge case, for sequential predictions. To estimate the potential of development scenarios, production forecasts are needed on long periods of time without intermediary data acquisition. An extension of the deterministic aggregation approach is thus proposed in this paper to provide such multi-step-ahead forecasts.
Extraction of Behavioral Features from Smartphone and Wearable Data
Authors: Afsaneh Doryab, Prerna Chikarsel, Xinwen Liu, Anind K. Dey
Date: 2018-12-18 00:00:00

The rich set of sensors in smartphones and wearable devices provides the
possibility to passively collect streams of data in the wild. The raw data
streams, however, can rarely be directly used in the modeling pipeline. We
provide a generic framework that can process raw data streams and extract
useful features related to non-verbal human behavior. This framework can be
used by researchers in the field who are interested in processing data from
smartphones and Wearable devices.
Machine Learning in Official Statistics
Authors: Martin Beck, Florian Dumpert, Joerg Feuerhake
Date: 2018-12-13 00:00:00
Tasks: BIG-bench Machine Learning

In the first half of 2018, the Federal Statistical Office of Germany
(Destatis) carried out a "Proof of Concept Machine Learning" as part of its
Digital Agenda. A major component of this was surveys on the use of machine
learning methods in official statistics, which were conducted at selected
national and international statistical institutions and among the divisions of
Destatis. It was of particular interest to find out in which statistical areas
and for which tasks machine learning is used and which methods are applied.
This paper is intended to make the results of the surveys publicly accessible.
Studying oppressive cityscapes of Bangladesh
Authors: Halima Akhter, Nazmus Saquib, Deeni Fatiha
Date: 2018-12-10 00:00:00

In a densely populated city like Dhaka (Bangladesh), a growing number of
high-rise buildings is an inevitable reality. However, they pose mental health
risks for citizens in terms of detachment from natural light, sky view,
greenery, and environmental landscapes. The housing economy and rent structure
in different areas may or may not take account of such environmental factors.
In this paper, we build a computer vision based pipeline to study factors like
sky visibility, greenery in the sidewalks, and dominant colors present in
streets from a pedestrian's perspective. We show that people in lower economy
classes may suffer from lower sky visibility, whereas people in higher economy
classes may suffer from lack of greenery in their environment, both of which
could be possibly addressed by implementing rent restructuring schemes.
Efficient Nonlinear Transforms for Lossy Image Compression
Authors: Johannes Ballé
Date: 2018-01-31 00:00:00
Tasks: Image Compression

We assess the performance of two techniques in the context of nonlinear
transform coding with artificial neural networks, Sadam and GDN. Both
techniques have been successfully used in state-of-the-art image compression
methods, but their performance has not been individually assessed to this
point. Together, the techniques stabilize the training procedure of nonlinear
image transforms and increase their capacity to approximate the (unknown)
rate-distortion optimal transform functions. Besides comparing their
performance to established alternatives, we detail the implementation of both
methods and provide open-source code along with the paper.
Context-sensitive Natural Language Generation for robot-assisted second language tutoring
Authors: Bram Willemsen, Jan de Wit, Emiel Krahmer, Mirjam de Haas, Paul Vogt
Date: 2018-11-01 00:00:00
Proceeding: WS 2018 11
Tasks: Text Generation

This paper describes the L2TOR intelligent tutoring system (ITS), focusing primarily on its output generation module. The L2TOR ITS is developed for the purpose of investigating the efficacy of robot-assisted second language tutoring in early childhood. We explain the process of generating contextually-relevant utterances, such as task-specific feedback messages, and discuss challenges regarding multimodality and multilingualism for situated natural language generation from a robot tutoring perspective.
Learning from limited datasets: Implications for Natural Language Generation and Human-Robot Interaction
Authors: Jekaterina Belakova, Dimitra Gkatzia
Date: 2018-11-01 00:00:00
Proceeding: WS 2018 11
Tasks: One-Shot Learning, Spoken Dialogue Systems, Text Generation

One of the most natural ways for human robot communication is through spoken language. Training human-robot interaction systems require access to large datasets which are expensive to obtain and labour intensive. In this paper, we describe an approach for learning from minimal data, using as a toy example language understanding in spoken dialogue systems. Understanding of spoken language is crucial because it has implications for natural language generation, i.e. correctly understanding a user{'}s utterance will lead to choosing the right response/action. Finally, we discuss implications for Natural Language Generation in Human-Robot Interaction.
Shaping a social robot's humor with Natural Language Generation and socially-aware reinforcement learning
Authors: Hannes Ritschel, Elisabeth Andr{\'e}
Date: 2018-11-01 00:00:00
Proceeding: WS 2018 11
Tasks: multimodal interaction, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Text Generation

Humor is an important aspect in human interaction to regulate conversations, increase interpersonal attraction and trust. For social robots, humor is one aspect to make interactions more natural, enjoyable, and to increase credibility and acceptance. In combination with appropriate non-verbal behavior, natural language generation offers the ability to create content on-the-fly. This work outlines the building-blocks for providing an individual, multimodal interaction experience by shaping the robot{'}s humor with the help of Natural Language Generation and Reinforcement Learning based on human social signals.
From sensors to sense: Integrated heterogeneous ontologies for Natural Language Generation
Authors: Mihai Pomarlan, Robert Porzel, John Bateman, Rainer Malaka
Date: 2018-11-01 00:00:00
Proceeding: WS 2018 11
Tasks: Text Generation

We propose the combination of a robotics ontology (KnowRob) with a linguistically motivated one (GUM) under the upper ontology DUL. We use the DUL Event, Situation, Description pattern to formalize reasoning techniques to convert between a robot{'}s beliefstate and its linguistic utterances. We plan to employ these techniques to equip robots with a reason-aloud ability, through which they can explain their actions as they perform them, in natural language, at a level of granularity appropriate to the user, their query and the context at hand.
A farewell to arms: Non-verbal communication for non-humanoid robots
Authors: Aaron G. Cass, Kristina Striegnitz, Nick Webb
Date: 2018-11-01 00:00:00
Proceeding: WS 2018 11
Tasks: Position, Text Generation

Human-robot interactions situated in a dynamic environment create a unique mix of challenges for conversational systems. We argue that, on the one hand, NLG can contribute to addressing these challenges and that, on the other hand, they pose interesting research problems for NLG. To illustrate our position we describe our research on non-humanoid robots using non-verbal signals to support communication.
Being data-driven is not enough: Revisiting interactive instruction giving as a challenge for NLG
Authors: Sina Zarrie{\ss}, David Schlangen
Date: 2018-11-01 00:00:00
Proceeding: WS 2018 11
Tasks: Text Generation, Visual Grounding

Modeling traditional NLG tasks with data-driven techniques has been a major focus of research in NLG in the past decade. We argue that existing modeling techniques are mostly tailored to textual data and are not sufficient to make NLG technology meet the requirements of agents which target fluid interaction and collaboration in the real world. We revisit interactive instruction giving as a challenge for datadriven NLG and, based on insights from previous GIVE challenges, propose that instruction giving should be addressed in a setting that involves visual grounding and spoken language. These basic design decisions will require NLG frameworks that are capable of monitoring their environment as well as timing and revising their verbal output. We believe that these are core capabilities for making NLG technology transferrable to interactive systems.
Parameter Transfer across Domains for Word Sense Disambiguation
Authors: Sallam Abualhaija, Nina Tahmasebi, Diane Forin, Karl-Heinz Zimmermann
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Lexical Simplification, Machine Translation, Semantic Similarity, Semantic Textual Similarity, Word Sense Disambiguation

Word sense disambiguation is defined as finding the corresponding sense for a target word in a given context, which comprises a major step in text applications. Recently, it has been addressed as an optimization problem. The idea behind is to find a sequence of senses that corresponds to the words in a given context with a maximum semantic similarity. Metaheuristics like simulated annealing and D-Bees provide approximate good-enough solutions, but are usually influenced by the starting parameters. In this paper, we study the parameter tuning for both algorithms within the word sense disambiguation problem. The experiments are conducted on different datasets to cover different disambiguation scenarios. We show that D-Bees is robust and less sensitive towards the initial parameters compared to simulated annealing, hence, it is sufficient to tune the parameters once and reuse them for different datasets, domains or languages.
What Sentence are you Referring to and Why? Identifying Cited Sentences in Scientific Literature
Authors: Ahmed Abura{'}ed, Luis Chiruzzo, Horacio Saggion
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Sentence

In the current context of scientific information overload, text mining tools are of paramount importance for researchers who have to read scientific papers and assess their value. Current citation networks, which link papers by citation relationships (reference and citing paper), are useful to quantitatively understand the value of a piece of scientific work, however they are limited in that they do not provide information about what specific part of the reference paper the citing paper is referring to. This qualitative information is very important, for example, in the context of current community-based scientific summarization activities. In this paper, and relying on an annotated dataset of co-citation sentences, we carry out a number of experiments aimed at, given a citation sentence, automatically identify a part of a reference paper being cited. Additionally our algorithm predicts the specific reason why such reference sentence has been cited out of five possible reasons.
Persian-Spanish Low-Resource Statistical Machine Translation Through English as Pivot Language
Authors: Benyamin Ahmadnia, Javier Serrano, Gholamreza Haffari
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Machine Translation, Sentence, Translation

This paper is an attempt to exclusively focus on investigating the pivot language technique in which a bridging language is utilized to increase the quality of the Persian-Spanish low-resource Statistical Machine Translation (SMT). In this case, English is used as the bridging language, and the Persian-English SMT is combined with the English-Spanish one, where the relatively large corpora of each may be used in support of the Persian-Spanish pairing. Our results indicate that the pivot language technique outperforms the direct SMT processes currently in use between Persian and Spanish. Furthermore, we investigate the sentence translation pivot strategy and the phrase translation in turn, and demonstrate that, in the context of the Persian-Spanish SMT system, the phrase-level pivoting outperforms the sentence-level pivoting. Finally we suggest a method called combination model in which the standard direct model and the best triangulation pivoting model are blended in order to reach a high-quality translation.
An Extensible Multilingual Open Source Lemmatizer
Authors: Ahmet Aker, Johann Petrak, Firas Sabbah
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Information Retrieval, LEMMA, Lemmatization

We present GATE DictLemmatizer, a multilingual open source lemmatizer for the GATE NLP framework that currently supports English, German, Italian, French, Dutch, and Spanish, and is easily extensible to other languages. The software is freely available under the LGPL license. The lemmatization is based on the Helsinki Finite-State Transducer Technology (HFST) and lemma dictionaries automatically created from Wiktionary. We evaluate the performance of the lemmatizers against TreeTagger, which is only freely available for research purposes. Our evaluation shows that DictLemmatizer achieves similar or even better results than TreeTagger for languages where there is support from HFST. The performance drops when there is no support from HFST and the entire lemmatization process is based on lemma dictionaries. However, the results are still satisfactory given the fact that DictLemmatizer isopen-source and can be easily extended to other languages. The software for extending the lemmatizer by creating word lists from Wiktionary dictionaries is also freely available as open-source software.
Universal Dependencies for Arabic Tweets
Authors: Fahad Albogamy, Allan Ramsay
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9

To facilitate cross-lingual studies, there is an increasing interest in identifying linguistic universals. Recently, a new universal scheme was designed as a part of universal dependency project. In this paper, we map the Arabic tweets dependency treebank (ATDT) to the Universal Dependency (UD) scheme to compare it to other language resources and for the purpose of cross-lingual studies.
Translating Dialectal Arabic as Low Resource Language using Word Embedding
Authors: Ebtesam H Almansor, Ahmed Al-Ani
Date: 2017-09-01 00:00:00
Proceeding: RANLP 2017 9
Tasks: Machine Translation, Sentiment Analysis, Text Generation, Translation, Word Embeddings

A number of machine translation methods have been proposed in recent years to deal with the increasingly important problem of automatic translation between texts of different languages or languages and their dialects. These methods have produced promising results when applied to some of the widely studied languages. Existing translation methods are mainly implemented using rule-based and static machine translation approaches. Rule based approaches utilize language translation rules that can either be constructed by an expert, which is quite difficult when dealing with dialects, or rely on rule construction algorithms, which require very large parallel datasets. Statistical approaches also require large parallel datasets to build the translation models. However, large parallel datasets do not exist for languages with low resources, such as the Arabic language and its dialects. In this paper we propose an algorithm that attempts to overcome this limitation, and apply it to translate the Egyptian dialect (EGY) to Modern Standard Arabic (MSA). Monolingual corpus was collected for both MSA and EGY and a relatively small parallel language pair set was built to train the models. The proposed method utilizes Word embedding as it requires monolingual data rather than parallel corpus. Both Continuous Bag of Words and Skip-gram were used to build word vectors. The proposed method was validated on four different datasets using a four-fold cross validation approach.