Reviewerly/exHarmony huggingface dataset:
Website Quality Analysis of Balai Besar Pascapanen Bogor Using 2QCV3Q Model

BB-Pascapanen website is an official website of Balai Besar Pascapanen Bogor whose mission is to be able to integrate information as a research institute producing technology and innovation of agricultural post-harvest that supports the realization of food sovereignty and farmers' welfare. Based on the regulation and to accommodate those missions the website has been developed. The purpose of this study is to analyze the quality of the BB-Pascapanen website through a 2QCV3Q model. This model was chosen because it has a multi-party approach involving three actors in the development of a website, website owner, web developer and user which will make an analysis of website quality management implemented more detail. The results of the analysis show from institution and web developer point of view that the feasibility dimension needs performance improvement. While the other dimensions from user point of view that need to be improved are the dimension of identity (attributes of website uniqueness, Impressum distinctness), content (attribute of information detail and accuracy), services (attribute of correspondence feature), locations (attribute of availability of Link, URL easy to remember, easy searching in website, addresses written in website exists in google map, hotline), and usability (attribute of layout structure, orientation navigation and response, download time).
Generating High-Quality Air-Coupled Ultrasonic Images for Wooden Material Characterization by Single Image Super-Resolution

As the practical applications in other fields, high-resolution images are usually expected to provide a more accurate assessment for the air-coupled ultrasonic (ACU) characterization of wooden materials. This paper investigated the feasibility of applying single image super-resolution (SISR) methods to recover high-quality ACU images from the raw observations that were constructed directly by the on-the-shelf ACU scanners. Four state-of-the-art SISR methods were applied to the low-resolution ACU images of wood products. The reconstructed images were evaluated by visual assessment and objective image quality metrics, including peak signal-to-noise-ratio and structural similarity. Both qualitative and quantitative evaluations indicated that the substantial improvement of image quality can be yielded. The results of the experiments demonstrated the superior performance and high reproducibility of the method for generating high-quality ACU images. Sparse coding based super-resolution and super-resolution convolutional neural network (SRCNN) significantly outperformed other algorithms. SRCNN has the potential to act as an effective tool to generate higher resolution ACU images due to its flexibility.
The Implementation of NetFlow Log System Using Ceph and ELK Stack

Each software always has log data. Log data is essential for developers and end-users. With log data, we can indicate the information of system error, warning events or whether our system processes are loading successfully. This paper aims to provide a virtualization platform for our campus NetFlow log data in order user can use for further analysis. Our system used Ceph storage system and ELK Stack technology. Ceph provides a vast amount of scalable storage and ELK consists of three components, namely: Elasticsearch, Logstash, and Kibana. Elasticsearch is a search engine in which can be used for searching all kinds of documents. Logstash is used for managing centralize, transform and stash our data to Elasticsearch and Kibana is used for visualizing Elasticsearch data and an excellent tool for a real-time data analytics and decision making. Our data is continuous data. Approximately our data growth up 38 GB per day and our data have grown more than 8TB until now.
Designing a self-aware jacket

Smart textiles and garments promise intriguing new possibilities for the wearer. Integrated interaction can create new experiences and sensors can detect relevant information about the wearer. However, this poses an additional challenge for the designer of smart garments, about how to integrate these technologies. In this work, we want to investigate how human intuition and technical knowledge feed into the design of smart garments. Using a jacket that tracks its whereabouts as a use case, we have collected a dataset from 18 test subjects with varying technical knowledge, on what sensor patterns they would create on the garment. Using a specifically created simulation framework, we have evaluated the performance of the created sensor patterns. We observed that many participants intuitively create well-working patterns, while technical knowledge does not play a significant role in the resulting performance.
Comparison between Two Motorized Wheelchair Braking Methods

Motorized wheelchairs make life easier for people with severely limited mobility. Some of them have more difficulty in using such equipment, which puts them in riskier situations. This work compares two systems for wheelchair braking (progressive deceleration and automatic braking). The task consisted in driving the chair against a wall with a 90q angle and two speeds (1.3 km/h and 2.4 km/h). An ultrasound sensor detects the minimum distance to the wall and activates the braking system that was modified electronically for the task. The automatic braking system reduced the distance between brake activation spot and total stop by up to 55.3% at 2.4 km/h.
Recurrent models and lower bounds for projective syntactic decoding

Natalie Schluter. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Design of neuron-calculators for the normalized equivalence of two matrix arrays based on FPGA for self-learning equivalently convolutional neural networks (SLE_CNNs)

First, in the introduction, we will show the urgent need to create neuron-calculators (NCs) for the normalized equivalence of two matrix arrays for self-learning equivalently-convolutional neural networks (SLE_CNNs), video processors for parallel image processing with enhanced functionality. Consider promising areas of application of such single and multichannel neuron-calculators as high-precision, high-speed and high-performance accelerators for hardware systems and architectures for recognition, classification, image categorization, in particular for 2D-image space-invariant associative memory structures, SLE_CNNs based on the equivalence paradigm. Next, we will consider and analyze the theoretical foundations, the mathematical apparatus of matrix and continuous logic, and their basic operations, show their functional completeness, evaluate their advantages and prospects for application in the design of biologically inspired devices and systems for processing and analyzing signal arrays. We will show that some functions of continuous logic, including operations of normalized equivalence of vector and matrix signals, operation of the bounded difference of continuous logic, are the powerful basis for designing advanced accelerator calculators, microcells for hybrid (mixed) analog-to-digital transformations, comparisons and calculations of characteristics. Next, we will consider in more detail the design and simulation aspects of such digital neuron-calculators for the normalized equivalence of two matrix arrays based on FPGA, including their various modifications, depending on the dimension and the number of compared arrays. We will propose our approach for calculation of the normalized equivalence functions comparing current fragments of images and filters with dimensions 3&times;3, 7&times;7, 15&times;15, and others. The project is executed on FPGA ALTERA MAXII. The simulation was done with Intel Quartus Prime 17 and showed that in a single chip it is possible to place four parallel working neuron calculators processing 4 filters with a size of 15&times;15. The approximate processing time is less than 5&mu;s. Power consumption is 50mW for supply voltage of 2.5V and clock frequency equals to 50MHz. We will also consider modifications that improve performance for different filter size. We show the results of modeling the proposed new implementations of NCs, we estimates and compare them.
Sharing More Information in Visual Cryptography Scheme with Block Expansion

Visual cryptography scheme (VCS) shares secret image into shadows. Without computing, stacking shadows can reveal the secret image. In this paper, we propose a VCS with block expansion. Each shadow pixel is expanded into a 4-pixel block with extra secret information embedded in. In the revealing process, the secret image can be revealed by stacking shadows. In the meanwhile, we can reveal the extra shared secret information by computation.
Fine-grained Sentiment Analysis of Foreign Exchange News

Foreign exchange (Forex) news plays a significant role in asset pricing, risk assessment and exchange rate forecasting in Forex markets. In this work, we leverage machine learning algorithms to probe the sentiment orientation and sentiment intensity of Forex news. In the aspect of sentiment orientation, two cases (text embedding to the vector and fusion of sentiment words' weights) are evaluated to investigate the sentiment orientation. Moreover, confusion matrix is adopted to further analyze the classification results. In terms of sentiment intensity, three categories of words are considered and grid search is applied to seek the weights of words. Experiments indicate that this paper has achieved relatively good results in sentiment analysis.
Meta-Modelling Ontology Design Pattern

In the last decades, the meta-modelling problem has received increasing attention in the conceptual modelling and semantic web communities. We have proposed a solution to this problem in the context of ontological modelling which consists in extending a fragment of the Web Ontology Language OWL with a meta-modelling constructor to equate instances to classes. Even though there are methodologies and patterns that help the ontology engineer to conceptualize a domain using ontologies, there is a lack of such guides for the meta-modelling approaches that extend OWL. In this work we introduce a design pattern that guides in the conceptualization of domains for which there are requirements at different knowledge levels, in particular for different user perspectives.
Automated Large Geographic Ontologies Generation Method from Spatial Databases

Ontologies have emerged as an important component in Information Systems and, specifically, in Geographic Information Systems, where they play a key role. However, the creation and maintenance of geographic ontologies can become an exhausting work due to the rapid growth and availability of spatial data, which are provided through relational databases most times. For this reason there has been an increasing interest in the automatic generation of geographic ontologies from relational databases in recent years. This work describes an automatic method to generate a geographic ontology from the spatial data provided by a relational database. The importance and originality of this study lie in that it is able to model two main aspects of a spatial database in the generated ontology: (1) The three main types of spatial data (point, line and polygon) are modelled as a data property and not as an object property. (2) Four data integrity constraints: First Normal Form, Not Null, Unique and Primary Key. Another contribution of our proposal is related to the support for generating large ontologies, which are not usually supported by traditional tools of ontological engineering such as Protégé or OWL API. Finally, some experiments were conducted in order to show the effectiveness of the proposed method.
Underwater image quality improvement approach based on an adapted Gabor multi-channels filtering

This paper presents a new approach to improve the identification of underwater fiducial markers for camera pose estimation. The use of marker detection is new in the underwater field. Hence, it requires a new image preprocessing to reach the same performance as in onshore environment. This is a challenging task due to the poor quality of underwater images. Images captured in highly turbid environment are strongly degraded by light attenuation and scattering. In this context, dehazing methods are increasingly used. However, they are less effective because the scattering of light in the water is different from the atmosphere. Therefore, the estimation of dehazing parameters on the target image can lead to a bad image restoration. For this reason, an objectoriented dehazing method is proposed to optimize the contrast of markers. The proposed system exploits the texture features derived by multi-channel filtering for image segmentation. To achieve this, saliency detection is applied to estimate the visually salient objects in the image. The generated saliency map is passed through a Gabor filters bank and the significant texture features are clustered by K-means algorithm to produce the segmented image. Once different objects of the image are separated, an optimized Dark Channel Prior (DCP) dehazing method is applied to optimize the contrast of each individual object. The implemented system has been tested on a large image dataset taken during night offshore experiments in turbid waters at 15 meters depth. Results showed that the object-oriented dehazing improves the successful of markers identification in underwater environment.
LODBookRec

In this paper, we present the LODBookRec application. LODBookRec builds on top of Linked Open Data (LOD) knowledge about literature domain and provides information retrieval GUI to conveniently present this knowledge to the end users. As such, LODBookRec aims to contribute towards better utilization of LOD and provide a suitable platform for on-line evaluation of information retrieval methods, especially recommender systems. LODBookRec contains a basic search GUI and several recommendation methods, with the primary focus on item-based recommendations.
The Structural Relationship among Smartphone Dependency, Teaching Presence, Deep Approach to Learning and Satisfaction in Online Deeper Learning

The purpose of this study is to investigate the structural relationships among smartphone dependency, teaching presence and a deep approach to learning on satisfaction with courses. Based on the result, this study verified the outcome model and suggested the strategies for improving the quality of online deeper learning in higher education. This study employed structural equation modeling, data were collected from a 4-years college offering online courses in Korea. The results indicated that smartphone dependency and teaching presence had significant effects on deep approach to learning. In addition, teaching presence and deep approach to learning had significant effects on satisfaction with courses.
Auto Evaluation of Syntactic Analysis via Blending Word Similarity and Tree Status

With the extensive application of natural language processing technology, the importance of syntactic analysis is highlighted. It has very important application value in the fields of Machine Translation, Information Retrieval and Automatic Question and Answering system. In the past few decades, many influential syntactic analysis algorithms have been proposed, such as Line Graph Analysis Algorithms, CKY Analysis Algorithms, GLR Analysis Algorithms and so on. With people’s continuous efforts, these algorithms tend to improve and have played an important role in larger application areas. However, there isn’t a good standard in the evaluation of the results obtained through these algorithms. Based on the Tree Isomorphism Algorithm, this paper calculates the similarity between the same sentence’s syntactical tree and the syntactical tree obtained through human tagging with the help of the node information of the tree, and analyzes the performances of various algorithms. This algorithm creatively proposes the method of calculating the similarity of trees and formulate the similarity evaluation standard.
Research on Knowledge Element Relation and Knowledge Service for Agricultural Literature Resource

The volume of publicly available data in agriculture literature resource is constantly increasing. For the problem, we provide an effective method that help the user get a desired integrated knowledge unit with minimal time and energy. The method mainly implements knowledge relation and knowledge service based knowledge element with standardized structure and knowledge level relation label. The proposed method can integrate knowledge element from diverse knowledge sources and produce new knowledge for user based on the knowledge extraction and classification. At last, we will provide knowledge service application cases based on knowledge element model.
Classifying Vehicle Traffic Messages from Twitter to Organize Traffic Services

Communication in social networks as Twitter, Facebook, and Line application is a channel telling a traffic status by a text message. Traffic services organization by peoples monitors messages from the social network in order to take actions when having the accident. This research focuses on creating a system that classify the text-message from social networks, to get the status of traffic, type of accident, level of accident and GPS location in order to consider and take action by the traffic services organization. The designed system in this phase uses text analysis by considering structure and message styles and the fully design applies hidden Markov models to classify the type of the message. Short message from the social network came from Twitter, JS100-radio and FM91.
Three-Materials Image Recover from Value Range Projection Data

In order to figure out the number of the tooth’s roots and subroots, only the bone, air and blood to be separated is enough. This paper proposed a new algorithm denoted Value Range Transform which inherits the idea of the Mojette transform to recover the value range of each pixel, instead of the exact value. According to the Hounsfield scale of the bone, air and blood, the value range of each pixel can easily tell the exact material of the pixel belong to. The Value Range Transform included the direct transform and the inverse transform. The value range projection data produced from the direct value range transform. From the value range projection data, the inverse transform can recover the three different materials of the scene. After examied by the experiments, the materials can be recovered from the projection data sets which Katz’s criterion are far to be checked.
Teaching Philosophy: An Optimization-Liquidation or a New Start?

In the article, I analyze the current situation in the field of teaching philosophy and also other social sciences and humanities. The point is that in higher school, under various pretexts, many departments and philosophy councils are closed, hours for teaching philosophical courses and seminars are reduced. Teachers of philosophy and other disciplines in higher education, in addition to being overwhelmed, have to spend a lot of time on unnecessary reporting, which for the most part does not reflect the essence of the matter. The policy of education officials and their belief in the need for such reporting and reduction of hours are critically reviewed. I show that in addition to the well-known functions of philosophy, teaching philosophy is of crucial importance today is due to three important circumstances: we live, firstly, in a period of double transition (of the whole civilization and of Russia) and, secondly, in a period of an anthropological crisis and a crisis of reality, thirdly, due to a crisis of morality and ethics. Only philosophy, based on the methodology, as well as the humanities and social sciences can comprehend modern conditions. The question is about a new start to the practice of teaching philosophy. I propose to introduce a temporary moratorium on the compilation of new teaching programs and start a broad discussion in the philosophical and pedagogical community of the goals and content of the teaching of philosophy. In this regard, I offer my own understanding of the problems and the essence of the teaching of philosophy. I also share my personal experience in teaching philosophy, which includes lecturing and organizing the creative work of small groups of students.
A Brief Overview of Dead-Zone Pattern Matching Algorithms

Within the last decades, the dead-zone algorithms have emerged as being highly performant on certain types of data. Such algorithms solve the keyword exact matching problem over strings, though extensions to trees and two-dimensional data have also been devised. In this short paper, we give an overview of such algorithms.
Design of an Optimized CMOS ELM Accelerator

In the last decade, artificial intelligence (AI) has emerged at the forefront of driving many technological innovations. A variety of algorithms have been proposed as possible alternatives to implement AI in computing systems. Extreme learning machine (ELM) has emerged as one of the most effective training algorithms for simple applications based on single layer feed-forward neural networks (SLFN) because of its unique training method. Hardware implementation of neural network algorithms is a critical requirement for deploying them in timesensitive applications. In this paper, we present a simplified AI accelerator based on CMOS technology that implements an ELM based inference engine. We present analysis of implementing such an accelerator on different technology nodes with a comparative analysis to analyze the impact of technology node scaling on performance of the proposed accelerator in terms of power and area. For the analysis, the workload used was a network of dimensions 81x18x1. We observed a remarkable benefit in speed (1.3x), area (14x) and power (7x) by scaling the design from 180 nm to 45 nm. Further, we present an analysis showing benefits of introducing emerging non-volatile memory (NVM) technologies like RRAM as the primary memory technology for the accelerator. The analysis shows that replacing the conventional CMOS with RRAM would give significant benefits in leakage (4.5x) and area (33x).
Topic-Specific Natural Language Chatbot as General Advisor for College

Chatbot is a computer program that leads to a discussion by means of sound-related or transcript methods. It is specific to a topic, that is, it answers queries for a particular organization. Experimental analysis suggests that topic-specific dialogues coupled with conversational knowledge yield maximum dialogue session than general conversational dialogues. The design discussed in the following chapters can be used for implementing chatbots for any type of organization. Students and teachers can use chatbot to ask queries related to college. Using a chatbot for trivial queries can reduce teachers’ and students’ dependency on each other and hence save their time. The system consists of a user interface, a web server, a database, and a Dialogflow (Api.ai) agent. Dialogflow agent can be trained to understand user intentions. Cloud Firestore, which is a real-time NOSQL database, is used as the knowledge base. The web server is used to generate response and an Android chat app is developed for user interface and authentication. General Terms Chatbot
Noise-Contrastive Estimation Based on Relative Neighbour Sampling for Unsupervised Image Embedding Learning

Many unsupervised learning algorithms have been proposed to avoid the inconvenience of data labeling. The nature of neural networks is also gradually being explored by unsupervised learning. In this paper, we focus on the sampling strategy for unsupervised images embedding learning via instance discrimination. A new sampling method is proposed based on the observation that different samples contribute unequally to training, which pays more attention to the neighbours. The proposed sampling method is beneficial and efficient for images embedding learning. The results on benchmark data show that the proposed sampling method is robust and outperforms the compared method.