Reviewerly/exHarmony huggingface dataset:
From Monolingual to Multilingual Ontologies: The Role of Cross-Lingual Ontology Enrichment

While the multilingual data on the Semantic Web grows rapidly, the building of multilingual ontologies from monolingual ones is still cumbersome and hampered due to the lack of techniques for cross-lingual ontology enrichment. Cross-lingual ontology enrichment greatly facilitates the semantic interoperability between different ontologies in different natural languages. Achieving such enrichment by human labor is a time-consuming and error-prone task. Thus, in this paper, we propose a fully automated ontology enrichment approach using cross-lingual matching (OECM) approach, which builds a multilingual ontology by enriching a monolingual ontology from another one in a different natural language using a cross-lingual matching. OECM selects the best translation among all available translations of ontology concepts based on their semantic similarity with the target ontology concepts. We present a use case of our approach for enriching English Scholarly Communication Ontologies using German and Arabic ontologies from the MultiFarm benchmark. We have compared our results with the results from the Ontology Alignment Evaluation Initiative (OAEI 2018). Our approach has higher precision and recall in comparison to five state-of-the-art approaches. Additionally, we recommend some linguistic corrections in the Arabic ontologies in Multifarm which have enhanced our cross-lingual matching results.
Fuzzy granularity neighborhood extreme clustering

Clustering is an important method for data analysis. Up to now, how to develop an efficient clustering algorithm is still a critical issue. Unsupervised extreme learning machine is an effective neural network learning method which has a fast training speed. In this paper, a fuzzy granularity neighborhood extreme clustering algorithm which is based on extreme learning machine is proposed. We use fuzzy neighborhood rough set to develop a new feature selection method to eliminate redundant attributes and introduce the adaptive adjustment mechanism to solve the parameters of unsupervised extreme learning machine. Different from the existing clustering algorithms, the proposed algorithm can obtain a clustering result with minimum intra-cluster distance and maximum inter-cluster distance. The proposed algorithm and comparison algorithms are executed on the synthetic data sets and real data sets. The experimental results show that the proposed algorithm outperforms the comparison algorithms on the most data sets and the proposed algorithm is effective for clustering task.
Hardware Design of a Context-Preserving Filter-Reorganized CNN for Super-Resolution

This paper presents a hardware design of a CNN for single image super-resolution (SISR) processing. Very deep convolutional network for image super-resolution (VDSR) is a promising algorithm for SISR generating high-quality images but it is too complex to be implemented in hardware for commercial products. The paper presents a novel hardware design that is applicable to commercial products with low hardware resource usage while avoiding a significant degradation of image quality. The proposed design employs sub-pixel convolution layers to support various scale factors. A context-preserving 1D reorganization scheme is proposed to reduce the number of multipliers. Additional optimization technique is proposed to reduce the internal SRAM to store the input feature map. Thanks to the proposed optimizations along with additional techniques popular in hardware design, the number of multipliers and the size of SRAMs are reduced to 1/118.27 and 1/47.59, respectively, in comparison with a straightforward implementation of VDSR. The proposed CNN hardware synthesized with TSMC 65-nm CMOS technology, and it requires 8153.1K gate counts and 333.1kB SRAM to process a full-HD image. The proposed context-preserving 1D reorganization and memory reduction optimizations are also applicable to denoising and image classification without a significant performance degradation.
Restorying a “Newbie” Teacher’s 3D Virtual Teaching Trajectory, Resilience, and Professional Development Through Action Research: A Narrative Case Study

Unique affordances of 3D multi‐user virtual environments (e.g., immersive simulation, avatar tele/copresence) have attracted language teachers and researchers to explore the effects of Second Life (SL) on learners’ language outcomes and perceptions. Research on such a synergy of language education and virtual learning has suggested learners’ heightened motivation, improved communication skills, boosted confidence, and developed avatar identity. Nevertheless, the target population in prior research was predominately language learners; teachers’ professional development and beliefs in 3D virtual teaching are relatively under‐researched. Motivated by action research, this case study explores how an ESL teacher switched her role from an experienced classroom‐based teacher to an SL “newbie” teacher, thus reconstructing her teacher identity and fostering professional growth. Coinciding with narrative inquiry, her verbatim account was documented in her critical reflections in blogging, shadowed and interviewed by the researcher as her mentor. Her story epitomises an online teacher’s resilience in striving to equip herself with a new skill set and new understandings of online teaching vis‐à‐vis challenges encountered, strategies employed, and lessons learned through critical reflections in dialogue blogging. These aspects open a new avenue for research and pedagogy in virtual teacher training and professional development through action research in the 3D virtual environment.
Deep Learning for Semantic Segmentation of UAV Videos

As one of the key problems in both remote sensing and computer vision, video semantic segmentation has been attracting increasing amounts of attention.Using video segmentation technique for Unmanned Aerial Vehicle (UAV) data processing is also a popular application.Previous methods extended single image segmentation approaches to multiple frames.The temporal dependencies are ignored in these methods.This paper proposes a novel segmentation method to solve this problem.Combining the fully convolutional networks (FCN) and the Convolution Long Short Term Memory (Conv-LSTM) together, we segment the sequence of the video frames instead of segmenting each individual frame separately.FCN serves as the frame-based segmentation method.Conv-LSTM makes use of the temporal information between consecutive frames.Experimental results show the superiority of this method especially in some classes compared to the single image segmentation model using video dataset from UAV.
A project based innovative approach to an embedded systems course laboratory in software engineering education

Abstract In the software development process, which must be coded depending on the platform, software engineering students should have the optimum hardware competence. In this study, the embedded systems course in the software engineering curriculum is replanned based on the project development process. Sixty software engineering students formed the sample of the research. The sample was divided into two groups, as study and control groups. Presentations of the projects developed by taking into account the multivariate project development stages were realised. A measurement instrument was developed to determine whether there is a change in the attitudes of software engineering students towards embedded systems and related technologies ( α = 0.829). At the end of the course, it was determined that the attitudes of the students (survey group) who took the embedded systems course and those who did not take this course (control group) differed significantly. This differentiation is in favour of the survey group.
Balancing Security and Privacy in Genomic Range Queries

Recent spectacular advances in genome sequencing coupled with greatly reduced storage and computation costs, make genomic testing increasingly accessible to individuals. Today one can easily get his/her DNA digitized by a sequencing lab and store the result on a local device before performing a range of tests by engaging with a testing facility. Due to the inherent sensitivity of genetic material and often proprietary tests, privacy is the natural and key issue. However, heretofore insufficient attention paid to genomic security might have grave consequences, such as incorrect drug prescriptions or erroneous parentage outcomes.
More Complete Resultset Retrieval from Large Heterogeneous RDF Sources

Over the last years, the Web of Data has grown significantly. Various interfaces such as LOD Stats, LOD Laudromat, SPARQL endpoints provide access to the hundered of thousands of RDF datasets, representing billions of facts. These datasets are available in different formats such as raw data dumps and HDT files or directly accessible via SPARQL endpoints. Querying such large amount of distributed data is particularly challenging and many of these datasets cannot be directly queried using the SPARQL query language. In order to tackle these problems, we present WimuQ, an integrated query engine to execute SPARQL queries and retrieve results from large amount of heterogeneous RDF data sources. Presently, WimuQ is able to execute both federated and non-federated SPARQL queries over a total of 668,166 datasets from LOD Stats and LOD Laudromat as well as 559 active SPARQL endpoints. These data sources represent a total of 221.7 billion triples from more than 5 terabytes of information from datasets retrieved using the service "Where is My URI" (WIMU). Our evaluation on state-of-the-art real-data benchmarks shows that WimuQ retrieves more complete results for the benchmark queries.
Identification of Conversational Intent Pattern Using Pattern-Growth Technique for Academic Chatbot

This paper describes the development work of our Academic Chatbot model in identifying the user's conversational intents patterns. We experimented using social conversation log data from WhatsApp Messenger by the academic coordinator and students during the student's internship period. The discovered conversational patterns are used as a heuristic in building the knowledge base for the intent and entity of our Academic Chatbot model. Our preliminary findings depicted that related conversational intent patterns named Frequent Intent Pattern (FIP) was discovered with confidence value as high as 0.9 using the Sequential Pattern-Growth technique. The basis of using a Pattern-Growth pattern representation has given an insight where the chatbot can learn over time and new information can be added based on the intent pattern discovery. The outcome of this project is a customized Academic Chatbot (AcaBot) model that will be able to assist academicians and students in Academic Institution automatically and instantly 24/7 regarding relevant academic topics.
IoT based Irrigation and Water Logging monitoring system using Arduino and Cloud Computing

In a peninsular country like India, agriculture contributes about 16% of total GDP, so irrigation system has been given a high priority in economic development. With the advancement of wireless sensor network, cloud computing and IoT in the field of irrigation, this paper aims at conventional usage of water by providing adequate amount of water to the crops at the right time and conserving the excess run-off water from the farmlands. The excess runoff water will be saved in a tank for future use. As in coastal belts of India there remains a heavy scarcity of water during the month of March to May the farmlands remains as barren lands. By using this method, crop rotation becomes possible, as water will be supplied from the water stored from run-off water. It aims at automation the irrigation process by surveillance of soil moisture, humidity and temperature. Arduino Uno is the micro-controller, which monitors the sustainability conditions of the crops ensuring adequate water supply and better crop yield. It also helps the farmer to control the farmland remaining in far away from that place also. By using IoT the farmer can also make a predictive study of the amount of water requirement in the upcoming days. Humidity sensor, temperature sensor and moisture sensor are deployed in the peripherals of the field which will sense the environmental conditions and send those data to an local wireless server and the recorded data will be stored on cloud.
In2Rec

Interpretability of recommender systems has caused increasing attention due to its promotion of the effectiveness and persuasiveness of recommendation decision, and thus user satisfaction. Most existing methods, such as Matrix Factorization (MF), tend to be black-box machine learning models that lack interpretability and do not provide a straightforward explanation for their outputs. In this paper, we focus on probabilistic factorization model and further assume the absence of any auxiliary information, such as item content or user review. We propose an influence mechanism to evaluate the importance of the users' historical data, so that the most related users and items can be selected to explain each predicted rating. The proposed method is thus called Influencebased Interpretable Recommendation model (In2Rec). To further enhance the recommendation accuracy, we address the important issue of missing not at random, i.e., missing ratings are not independent from the observed and other unobserved ratings, because users tend to only interact what they like. In2Rec models the generative process for both observed and missing data, and integrates the influence mechanism in a Bayesian graphical model. A learning algorithm capitalizing on iterated condition modes is proposed to tackle the non-convex optimization problem pertaining to maximum a posteriori estimation for In2Rec. A series of experiments on four real-world datasets (Movielens 10M, Netflix, Epinions, and Yelp) have been conducted. By comparing with the state-of-the-art recommendation methods, the experimental results have shown that In2Rec can consistently benefit the recommendation system in both rating prediction and ranking estimation tasks, and friendly interpret the recommendation results with the aid of the proposed influence mechanism.
Autonomous Visual Navigation using Deep Reinforcement Learning: An Overview

Reinforcement Learning (RL) algorithm with deep learning techniques helps to solve many complex problems of today's world, such as to play a video game and autonomous navigation in the robots using the raw image as an input. Deep learning provides the mechanism to RL which enables the agent to solve the human level task. The rise of RL begins when a computer player beat the human expert in the most difficult game Go [6]. In this paper, we discuss some important topics such as the general view of reinforcement learning, methods, and algorithms of reinforcement learning and challenges which reinforcement learning is facing. Finally, we discussed a survey of implemented algorithms of RL in the field of robotics for autonomous visual navigation.
Linguistically Informed Relation Extraction and Neural Architectures for Nested Named Entity Recognition in BioNLP-OST 2019

Named Entity Recognition (NER) and Relation Extraction (RE) are essential tools in distilling knowledge from biomedical literature.This paper presents our findings from participating in BioNLP Shared Tasks 2019.We addressed Named Entity Recognition including nested entities extraction, Entity Normalization and Relation Extraction.Our proposed approach of Named Entities can be generalized to different languages and we have shown it's effectiveness for English and Spanish text.We investigated linguistic features, hybrid loss including ranking and Conditional Random Fields (CRF), multi-task objective and tokenlevel ensembling strategy to improve NER.We employed dictionary based fuzzy and semantic search to perform Entity Normalization.Finally, our RE system employed Support Vector Machine (SVM) with linguistic features.Our NER submission (team:MIC-CIS) ranked first in BB-2019 norm+NER task with standard error rate (SER) of 0.7159 and showed competitive performance on PharmaCo NER task with F1-score of 0.8662.Our RE system ranked first in the SeeDev-binary Relation Extraction Task with F1-score of 0.3738.
Prediction Residue Analysis in MPEG-2 Double Compressed Video Sequences

In video forensics, the study of the prediction residue across successive frames is key to verify the integrity of digital videos. Focusing on an MPEG-2 double compression scheme, we analyze how the variance of the prediction residue evolves during the second compression depending on the type of frame (either I or P) employed in the first encoding and exploring different compression strengths and deadzone widths for quantization. This analysis reveals that the width of the quantizer deadzones actually affects the performance of existing methods based on the Variation of Prediction Footprint (VPF) for double compression detection and Group Of Pictures (GOP) size estimation. The predicted behavior from the theoretical characterization of the prediction residue is confirmed through experimental results with real video sequences.
A Novel HRRP Target Recognition Method Based on LSTM and HMM Decision-making

Feature learning is a key step of target recognition for high-resolution range profile (HRRP).In traditional methods, Hidden Markov Model (HMM) can learn the features of HRRP target aspect information.However, the contextual correlations of HRRPs are ignored due to the independence assumptions in HMM, which brings many limitations to feature learning and weakens the generalization performance.On the contrary, the Long Short-Term Memory (LSTM) network can learn the contextual correlations of HRRPs, but not the target aspect information.To overcome the limitations in these feature learning methods, a new HRRP target recognition method that combines LSTM with HMM decision-making is proposed in this paper.The method consists of two branches: one is target recognition based on HMMs directly; the other is that the latent correlations of HRRPs are extracted by LSTM network and then use HMMs to do target recognition.Finally, the recognition result is obtained by making joint decisions between the two branches.The HRRPs are generated by the inversion of real radar images and the experimental results show that the proposed algorithm outperforms both the HMM and LSTM method.
Research of Servers and Protocols as Means of Accumulation, Processing and Operational Transmission of Measured Information

The article describes approaches to the system of data accumulation and storage. The analysis of the possibility of accumulation and processing of data on the local server, as well as in the cloud. The study of the dependence of file transfer time on buffer size for cloud computing and processing technology has been carried out.
Modeling of Complex System Phenomena via Computing With Words in Fuzzy Cognitive Maps

Fuzzy cognitive maps (FCMs) play an important role in high-level reasoning but are limited in their ability to model complex systems with singularities. We are interested in systems that exhibit discontinuous behaviors as one or more of their internal node states approach a threshold. In a new approach to FCM dynamics, we define general classes of aggregation functions which “jump” to a boundary value when any input crosses a threshold, or when all inputs do. The threshold value is a context-dependent parameter which can be readily understood by subject matter experts. Aggregation functions are applied separately to positively and negatively causal antecedents to each node then combined to form the nodal state. This modeling is applied in Computing with Words (CWW) settings, in which link strengths and activation levels are elicited using vocabulary words represented by interval type-2 fuzzy membership functions. We illustrate the behaviors of these novel FCM systems in comparison with their nonsingularity versions.
Combination of Recurrent Neural Network and Deep Learning for Robot Navigation Task in Off-Road Environment

SUMMARY This paper tackles the challenge of the necessity of using the sequence of past environment states as the controller’s inputs in a vision-based robot navigation task. In this task, a robot has to follow a given trajectory without falling in pits and missing its balance in uneven terrain, when the only sensory input is the raw image captured by a camera. The robot should distinguish big pits from small holes to decide between avoiding and passing over. In non-Markov processes such as the abovementioned task, the decision is done using past sensory data to ensure admissible performance. Applying images as sensory inputs naturally causes the curse of dimensionality difficulty. On the other hand, using sequences of past images intensifies this difficulty. In this paper, a new framework called recurrent deep learning (RDL) with combination of deep learning (DL) and recurrent neural network is proposed to cope with the above challenge. At first, the proper features are extracted from the raw image using DL. Then, these represented features plus some expert-defined features are used as the inputs of a fully connected recurrent network (as target network) to generate command control of the robot. To evaluate the proposed RDL framework, some experiments are established on WEBOTS and MATLAB co-simulation platform. The simulation results demonstrate the proposed framework outperforms the conventional controller based on DL for the navigation task in the uneven terrains.
Unexpected Health Issues Prediction In Medical Data Using Apriori Rare Based Outlier Detection Method

People affected by hazardous diseases arise in recent years. The growth of the fatal diseases is at an alarming rate. Initial detection can be helpful to get relieved from the disease. Thus the techniques for the early stage identification is much required. Existing classification algorithms employed tend to distinguish normal and disease affected people and the limitation involves no beforehand awareness about the occurrence of the disease. Outlier detection has vital application in the medical research domain for alarming the patients priorly. Exploring outliers from the medical domain gives useful and effective information to the domain experts. Hence, we propose an Apriori-rare based Associative Classification technique to predict unexpected issues in the medical domain, which is accomplished by the discovering the outliers. Healthcare datasets like Mammography mass data, Chronic kidney disease and Heart disease from UCI repository are considered for diagnosis and prognosis. The built classifier by the suggested method, enhances the accuracy and prediction rate of the diseases by detecting the presence of outliers. The outcomes are compared with a Classification Based on Association rules (CBA) algorithm in R Tool. The proposed Apriori rare base associative classification algorithm excels than the existing CBA algorithm by 15% more accurate.