Miracle-dz/UniFAR-FTUs huggingface dataset:
DeepTrip: Adversarially Understanding Human Mobility for Trip Recommendation

In this work we propose DeepTrip -- an end-to-end method for better understanding of the underlying human mobility and improved modeling of the POIs' transitional distribution in human moving patterns. DeepTrip consists of: a Trip Encoder to embed a given route into a latent variable with a recurrent neural network (RNN); and a Trip Decoder to reconstruct this route conditioned on an optimized latent space. Simultaneously, we define an Adversarial Net composed of a generator and critic, which generates a representation for a given query and uses a critic to distinguish the trip representation generated from Trip Encoder and query representation obtained from Adversarial Net. DeepTrip enables regularizing the latent space and generalizing users' complex check-in preference. We demonstrate the effectiveness and efficiency of the proposed model, and the experimental evaluations show that DeepTrip outperforms the state-of-the-art baselines on various evaluation metrics.
FAGON: Fake News Detection Model Using Grammatical Transformation on Deep Neural Network

As technology advances, the amount of fake news is increasing more and more by various reasons such as political issues and advertisement exaggeration. However, there have been very few research works on fake news detection, especially which uses grammatical transformation on deep neural network. In this paper, we shall present a new Fake News Detection Model, called FAGON(Fake news detection model using Grammatical transformation On deep Neural network) which determines efficiently if the proposition is true or not for the given article by learning grammatical transformation on neural network. Especially, our model focuses the Korean language. It consists of two modules: sentence generator and classification. The former generates multiple sentences which have the same meaning as the proposition, but with different grammar by training the grammatical transformation. The latter classifies the proposition as true or false by training with vectors generated from each sentence of the article and the multiple sentences obtained from the former model respectively. We shall show that our model is designed to detect fake news effectively by exploiting various grammatical transformation and proper classification structure.
A Near Real-Time Approach for Sentiment Analysis Approach Using Arabic Tweets

: Big data storage and real time data analysis are major challenges for IT researchers. The recent massive increase in data has not been accompanied by adequate storage technology and data processing algorithms. Understanding what people think about an idea, a product, a service or a policy is important for individuals, companies and governments. Sentiment analysis process can be used to identify opinions expressed in text on certain subjects. The result accuracy has a direct effect on decision making in both business and government. Our focus in this paper is first to identify the critical issues associated with real-time big data analysis and then to develop a new paradigm on Hadoop Ecosystem with real-time stream data processing to analyze Arabic tweet sentiment on Twitter. To perform real-time analytics, data collection should be performed using Apache Flume in order to move and aggregate all tweets received online (near real-time) to pre-defined locations through a channel called Sinks to the Hadoop distributed file system (HDFS). In addition, due to the serious challenges in Arabic text and speech and the high speed with which tweets arrive, we designed a complex sentiment analysis (SA) module to process each incoming tweet in such a way that no tweets are lost without being analyzed. Also, a sentiment analysis approach to Arabic text was developed using multiple Hive User Defended Functions (UDF). Finally, to guarantee a varied data collection, we proposed a Java MapReduce program for lexicon-based Arabic sentiment analysis, which supports n-gram search in the lexicon. Our approach was applied to determining opinions about MERS virus in the Kingdom of Saudi Arabia on Twitter Public Stream API and the results are discussed.
Unsupervised Deep Feature Extraction for Neonatal Sleep Stage Classification

Preterm birth carries many risks for an infant. These infants are accomodated in a specialized unit of the hospital: the Neonatal Intensive Care Unit, NICU. The NICU utilizes many tools to provide care and assess maturation of these infants. Sleep stage monitoring is such an important tool. EEG provides a non-invasive way of performing the monitoring task. However, correctly identifying sleep stages from EEG recordings is a challenging and time-consuming task even for experts.
DuStt - A Speech-to-Text Engine for Dutch

We develop and demonstrate a speech-to-text engine for Dutch, starting from the open-source project DeepSpeech and using the Corpus Gesproken Nederlands. The DuStt engine provides models targeted towards Dutch, Flemish or speakers from both Belgium and The Netherlands. Users can upload or record their own input as well as load prerecorded samples and obtain a transcription on the spot. The demonstration is video available at: https://youtu.be/DtTK0uo5W7s.
What If We Encoded Words as Matrices and Used Matrix Multiplication as Composition Function?

: WesummarizeourcontributiontotheInternationalConferenceonLearningRepresentations CBOW Is Not All You Need: Combining CBOW with the Compositional Matrix Space Model , 2019. We construct a text encoder that learns matrix representations of words from unlabeled text, while using matrix multiplication as composition function. We show that our text encoder outperforms continuous bag-of-word representations on 9 out of 10 linguistic probing tasks and argue that the learned representations are complementary to the ones of vector-based approaches. Hence, we construct a hybrid model that jointly learns a matrix and a vector for each word. This hybrid model yields higher scores than purely vector-based approaches on 10 out of 16 downstream tasks in a controlled experiment with the same capacity and training data. Across all 16 tasks, the hybrid model achieves an average improvement of 1.2%. These results are insofar promising, as they open up new opportunities to efficiently incorporate order awareness into word embedding models.
Semantic-Enhanced Learning (SEL) on Artificial Neural Networks Using the Example of Semantic Location Prediction

Recent machine learning models find a widespread use whether in respect of data mining and forecasting or in the classification domain. However, real-world situations comprise complex estimation tasks that carry a certain semantic load and bring a certain degree of fuzziness with them. This is a fuzziness which humans, due to their common sense knowledge and their personal experience, can easily understand by linking the underlying concepts together, while machines may from scratch not. A vast amount of both training data and time are necessary in order for a computational model to be capable of learning such kind of relations and adapting to new situations. In this work, we show that letting explicit semantic knowledge flow into a predictive model leads to an improved performance with regard to training time, accuracy and robustness. In particular, we propose adding an auxiliary semantic layer to the model, whose role is to provide it with information about the semantic interrelation of the treated classes creating in this way shortcuts and saving valuable training time while improving its quality at the same time. We explore several versions of our approach and we illustrate their functionality in a semantic location prediction scenario using 2 different real-world datasets.
Antimicrobial Potential of Bioactive Bone Cements

Poly(methyl methacrylate) (PMMA) based bone cement is widely used to anchor artificial joints. In recent years, antibiotics have been incorporated in bone cements and administered systemically to either prevent or reduce the severity of infections. Certain antibiotics, such as tobramycin or vancomycin antibiotics have been incorporated at high concentrations into PMMA bone cements when dealing with infected hip joints, however, the inclusion of some antibiotics adversely affect the mechanical properties of the cement. Furthermore, studies have indicated that the incorporation of gentamicin in PMMA cements does not have a statistically significant effect on the biofilm formation of species such as Staphylococcus aureus (S. aureus). Bioactive glasses have been shown to have numerous applications in the biomedical field because of their properties in bonding to both hard and soft tissues. Bioglass® undergoes surface dissolution in a physiological environment forming a hydroxycarbonate apatite layer and, in addition, exhibits the anti-inflammatory properties. Recent studies have shown that two paste bioactive bone cement systems that use Bioglass® as filler exhibit physical and mechanical properties comparable to PMMA cements with low polymerization exotherm and better mechanical properties with improved adhesion to bone and implant surfaces. In this study, Bioglass® containing two paste bone cements were investigated for their potential bacteriostatic properties and compared with PMMA cements with and without antibiotics. The results of this study indicated that the PMMA cement containing the antibiotic, gentamycin and the Bioglass® containing cements both preand post-immersion in simulated body fluid (SBF) for brief periods showed the inhibition zones were not statistically significantly different in their average size for any of the three bacterial species, namely Pseudomonas aeruginosa, S. aureus and Staphylococcus epidermidis. However, the PMMA cement without any antibiotic, namely gentamycin (CMW1) did not show any inhibition zones around the specimens for any of the three bacterial species. It was also noted that both Bioglass® filled bioactive cements immersed in SBF showed statistically significant increases in inhibition zones for all three specimens compared to specimens that were not immersed (p<0.001). (Journal of Applied Biomaterials & Biomechanics 2008; 6: 16-22)
Kakao Deep Reading Index: Consumption Time as a Key Factor in News Curation Algorithm

This paper introduces the structure and effects of Kakao’s news curation algorithm, which is created based on the Deep Reading Index (DRI). The DRI examines the extent of deep reading through content reading time, that is, the duration of reader engagement with an article. Current news curation algorithms focus on reader choice, with the click-through rate or pageviews as the gauge for consumption frequency. DRI is a product of the challenge of introducing and adopting a new factor called 'consumption time' instead of 'frequency of consumption', which is the basis of existing curation algorithms. The analysis of DRI-based services proves that the new algorithm can act as a curation system that is more effective in providing in-depth and quality news reports.
Proceedings of TIAD-2019 Shared Task - Translation Inference Across Dictionaries co-located with the 2nd Language, Data and Knowledge Conference (LDK 2019), Leipzig, Germany, May 20, 2019

The second shared task for Translation Inference Across Dictionaries (TIAD 2019) is aimed at exploring methods and techniques for automatically generating new bilingual (and multilingual) dictionaries from existing ones. Although considerable work has been done to this end in the past, it was usually conducted on different types of datasets and evaluated in different ways, applying various algorithms that are often not comparable. Thus, the main aim of TIAD is to support a coherent experiment framework that enables reliable validation of results and solid comparison of the processes used. This initiative also aims to enhance further research on the topic of inferring translations across languages, and continues the first TIAD edition, that took place in the first Language Data and Knowledge (LDK-2017) conference on June 18, 2017 in Galway, Ireland (​https://tiad2017.wordpress.com/​).
The prognostic value of NT-proBNP in chronic heart failure

blockers, beta-adrenergic blocking agents and antialdosterone agents. Measurement of circulating biomarkers of cardiovascular disease has been proposed as a surrogate end-point of clinical outcome in trials of such drugs in patients with HF [2]. First isolated from rat atria and porcine brain, natriuretic peptides have been shown to reduce the vasoconstriction that is seen in patients with HF; in other words they may be considered as "beneficial cardiac hormones". Secreted mainly by ventricular and atrial myocytes in response to increased mechanical stress, they act on peripheral receptors and cause diuresis and vasodilatation. They also decrease renin, aldosterone and endothelin secretion, and have direct antifibrotic effects. The plasma concentration of various fragments of cardiac natriuretic peptides has been shown to be elevated in patients with HF in contrast to that in apparently healthy volunteers. The determination of such plasma concentrations of certain natriuretic peptides, or their precursors, can therefore be helpful in the diagnosis of HF. These biomarkers perform particularly well in excluding (ruling out) HF in patients presenting with symptoms of dyspnea. Measurement of the plasma concentration of cardiac natriuretic peptides has been included in the guidelines for the diagnosis and treatment of chronic HF as proposed by the European Society of Cardiology [3].
Interactive Virtual-Reality Fire Extinguisher with Haptic Feedback

We present an interactive virtual-reality (VR) fire extinguisher that provides both realistic viewing using a head-mounted display (HMD) and kinesthetic experiences using a pneumatic muscle and vibrotactile transducer. The VR fire extinguisher is designed to train people to use a fire extinguisher skillfully in real fire situations. We seamlessly integrate three technologies: VR, object motion tracking, and haptic feedback. A fire scene is immersed in the HMD, and a motion tracker is used to replicate a real designed object into the virtual environment to realize augmented reality. In addition, when the handle of the fire extinguisher is squeezed to release the extinguishing agent, the haptic device generates both vibrotactile and air flow tactile feedback signals, providing the same experience as that obtained while using a real fire extinguisher.
Iterative Model-Based Transfer in Deep Reinforcement Learning

In recent years, advances in the field of Deep Reinforcement Learning (DRL) have enabled artificial agents to obtain superhuman performance on various tasks, given enough interactions with the environment [7,4,12]. While the state of the art in DRL keeps improving rapidly, most algorithms result in agents that generalize badly, performing well only on the single task they were trained on [9]. Simultaneously, while most existing DRL transfer learning literature considers model-free RL algorithms [15,10,11,9], its counterpart model-based RL, in which agents explicitly model their environment rather than directly predicting stateaction values or policies, has shown great successes, especially in recent years [3,2,8,1,5]. Despite these successes, however, the feasibility of transfer learning with these approaches remains relatively under-explored. As it follows from intuition that representing multiple environments in a single model may help express their similarities and differences and may therefore benefit transfer learning, this thesis explicitly investigates the feasibility of model-based DRL as a basis for (life-long) transfer learning.
Adversarial Perturbations for Joint Entity and Relation Extraction

The goal of the entity recognition and relation extraction task is to discover relational structures of entity mentions from unstructured texts. It is a central problem in information extraction since it is critical for tasks such as knowledge base population and question answering. In this work, we focus on extending the training procedure of our newly proposed general purpose joint model [4] for entity recognition and relation extraction with adversarial training (AT) [2]. Our model performs the two tasks of entity recognition and relation extraction simultaneously. It achieves state-of-the-art performance in a number of different contexts (i.e., news, biomedical, real estate) and languages (i.e., English, Dutch) without relying on any manually engineered features nor additional NLP tools. In summary, our proposed model: (i) does not rely on external NLP tools nor hand-crafted features, (ii) entities and relations within the same text fragment (typically a sentence) are extracted simultaneously, where (iii) an entity can be involved in multiple relations at once. To evaluate the proposed AT method, we perform the same set of experiments while we apply AT on top of our joint model. Compared to the baseline model, applying AT during training leads to a consistent additional increase in joint extraction effectiveness.
IoT-based floodgate control system

It is flood damage that causes high percentage of natural disasters that occur in South Korea. Due to the climatic characteristics, rainfall is concentrated during the summer months, causing flooding as excessive water is temporarily discharged smoothly or is not acceptable. As asphalt and cement areas have become larger on the soil due to urbanization, the area of water-proof terrain has also increased proportionately, and Heavy rains are causing more flooding in urban areas. The purpose of this paper is to prevent damage to the underground facilities (Parking lot, etc.) of apartments and buildings through the differential problem language and to remotely control the floodgates by utilizing IoT and wireless communication technology.
Improving Zero-Intelligence Plus for Call Markets

Double auctions have been widely employed and studied throughout history. Two particular variants are most commonly employed: The Call Market (CALL), also known as the Periodic Double Auction, and the Continuous Double Auction (CDA). While numerous automated trading strategies exist for the Continuous Double Auction, there is a lack of high performing strategies for CALL. The former auction variant is becoming increasingly popular in the context of energyrelated auctions in Smart Grids. Therefore, there is a need for efficient trading strategies. This paper explores whether a well-performing trading strategy designed for CDA, namely Zero-Intelligence Plus (ZIP) can be used in CALL. We first study the performance of the ZIP trader in CALL without any modifications. We then design several strategies and demonstrate that we can significantly improve the performance of ZIP in CALL while retaining the market’s high efficiency. As a result, our modified ZIP trader can be employed by autonomous agents, e.g. for trading energy in a CALL in the Smart Grid domain.
Time Shifted Pilot Signal Transmission With Pilot Hopping To Improve The Uplink Performance of Massive MIMO System For Next Generation Network

The paucity of pilot signals in Massive MIMO system is a vital issue. To accommodate substantial number of users, pilot signals are reused. This leads to interference, resulting in pilot contamination and degrades channel estimation at the Base Station (BS). Hence, mitigation of pilot contamination is exigency in Massive MIMO system. The proposed Time Shifted Pilot Signal Transmission with Pilot signal Hopping (TSPTPH), addresses the pilot contamination issue by transmitting pilot signals in non-overlapping time interval with hopping of pilot signals in each transmission slot. Hopping is carried by switching user to new a pilot signal in each transmission slot, resulting in random change of interfering users. This contributes to the change in channel coefficient, which leads to improved channel estimation at the BS and therefore enhances the efficiency of Massive MIMO system. In this system, Uplink Signal Power to Interference plus Noise Power Ratio (SINR) and data-rate are calculated for pilot signal reuse factor 1 and 3, by estimating the channel with Least Square estimation. The proposed system also reduces the uplink Signal power for data transmission of each User Equipment for normalized spectral efficiency with rising number of antennas at the BS and thus improves battery life.
A Hybrid Method for Powdered Materials Modeling

Powdered materials, such as sand and flour, are quite common in nature, whose properties always range from granular particles to smog materials under the air friction while throwing. This paper presents a hybrid method that tightly couples APIC solver with density field to accomplish the transformation of continuous powdered materials varying among granular particles, smog, powders and their natural mixtures. In our method, a part of the granular particles will be transformed to dust smog while interacting with air and represented by density field, then, as velocity decreases the density-based dust will deposit to powder particles. We construct a unified framework to imitate the mutual transformation process for the powdered materials of different scales, which greatly enhance the details of particle-based materials modeling. We have conducted extensive experiments to verify the performance of our model, and get satisfactory results in terms of stability, efficiency and visual authenticity as expected.
Avoidant Coping with Health Problems is Related to Poorer Quality of Life among Lung Transplant Candidates

for end-stage lung disease, including chronic obstruc tive pulmonary disease, cystic fibrosis, primary pulmonary hypertension, and idiopathic pulmonary fibrosis. However, because the number of candidates for this procedure outstrips the number of available organs, patients and their families face an extended waiting period for transplants. Not only is the waiting period lengthy—median waiting period >2 years—it is also stressful for transplant candidates. Candidates face a variety of challenges including emotional concerns, financial questions, social issues, feelings of guilt, and the possibility of their own mortality. Patients’ lung disease also contributes to many ongoing health-related stressors, including decreased exercise tolerance, frequent hospitalizations, oxygen dependence, and continued weight loss. Because all of these stressors may result in lower quality of life (QOL), it is important to identify ways that patients manage their stress. There fore, an important area of research is to determine the ways that lung transplant candidates cope with stress while awaiting a transplant, and how those strategies are related to QOL. A greater understanding of coping among lung candidates may enable researchers and clinicians to develop more effective interventions aimed at improving QOL during this often prolonged waiting period. This research is also theoretically relevant to the overarching stress and health literature. Researchers from many disciplines continue to explore how stress affects people’s health, and this study has the potential to extend knowledge of how the use of different coping strategies may influence this relationship. There are 2 strands of research that are relevant to these issues in transplant populations. First, several research ers have examined the range of coping strategies that heart, liver, and bone-marrow transplant candidates and their caregivers use to manage health-related Avoidant coping with health problems is related to poorer quality of life among lung transplant candidates
Machine learning based Android malware classification

As growing Android smart-phones, malware threatens smart-phones is also increasing. There are many types of Android malwares. To detect these Android malwares effectively, first, we need to classify Android malwares. In this paper, we build a database storing Android malwares and their types and characteristics. With the database, we propose a machine learning model to classify the malwares. To evaluate the model, we conducted k-fold cross validation. Through the evaluation, our model showed over 85% accuracy in the malware classification.