Reviewerly/exHarmony huggingface dataset:
Students’ Perception of Distributed Teaching Presence in Discussion Forums. A Case Study

The Information and Communication Technologies (ICT) have been implemented regularly in faculty classes in the last two decades. ICT—mediated learning environments have the potential to transform educational practices as well as students’ learning experiences. Online discussion forums are useful to share graduate students’ opinions and the social construction of knowledge. The objective of this chapter is to analyse the distributed teaching presence as reflected in university teachers’ contributions through their inputs in the forums of a faculty teaching course. A qualitative research study was conducted to analyse two online virtual courses at the Technological University of Pereira (Colombia). Main results indicate that participants’ tended to solely present their own visions, ideas and understandings, but did not compare them with other participants, which had the effect of missing the communicative and interactive potential of e- and b-learning. Trascending the traditional teaching approaches based on transmission of content and entering into a more constructive and interpersonal communication is key for a proper use of this type of teaching.
SMT Solver-Based Cryptanalysis of Block Ciphers

Satisfiability modulo theories (SMT) is a powerful framework for solving constraint satisfaction problem expressed in first-order logic and mainly used for software and hardware verification. In this article, we demonstrate the power of SMT solvers in cryptanalysis. We propose an algorithm for cryptanalysis of block ciphers using SMT solvers. In the cryptanalytic attack, we represent a block cipher in terms of Boolean equations and convert them into a suitable format (i.e. SMT-LIB). Finally, we use SMT solvers to find the key. An important feature of our attack is that it requires a few plaintext-ciphertext pairs to recover the secret key. We use the propose algorithm to demonstrate the cryptanalysis of International Data Encryption Algorithm (IDEA). We use various serial and parallel SMT solvers to apply known plaintext attack on IDEA and compare their performances. SMT solver can recover full key for three round of IDEA and 32 unknown key bits for full IDEA cipher, assuming 96 key bits are known. Furthermore, we compare our results with existing attacks on IDEA.
2D Visual Servoing meets Rapidly-exploring Random Trees for collision avoidance

Visual Servoing is a well-known subject in robotics. However, there are still some challenges on the visual control of robots for applications in human environments. In this article, we propose a method for path planning and correction of kinematic errors using visual servoing. 3D information provided by external cameras will be used for segmenting the environment and detecting the obstacles in the scene. Rapidly-exploring Random Trees are then used to calculate a path through the obstacles to a given, previously calculated, end-effector goal pose. This allows for model-free path planning for cluttered environments by using a point cloud representation of the environment. The proposed path is then followed by the robot in open-loop. Error correction is performed near the goal pose by using real-time calculated image features as control points for an Image-Based Visual Servoing controller that drives the end-effector towards the desired goal pose. With this method, we intend to achieve the navigation of a robotic arm through a cluttered environment towards a goal pose with error correction performed at the end of the trajectory to mitigate both the weaknesses of Image Based Visual Servoing and of open-loop trajectory following. We made several experiments in order to validate our approach by evaluating each individual main component (environment segmentation, trajectory calculation and error correction through visual servoing) of our solution. Furthermore, our solution was implemented in ROS using the Baxter Research Robot.
A Novel Local Invariant Feature Extraction Method for High-dynamic Range Images

In order to perform target recognition under realistic conditions, it is necessary to extract local invariant features of a real scene. This paper proposes a novel local invariant feature extraction method for high dynamic range images. Firstly, based on the multi-exposure fusion HDR image calculation model, this paper introduces the perceptual model of human eye vision for scene irradiance, and establishes a complete theoretical model of HDR imaging and mapping. Then, this paper proposes a new LIFT extraction method, which performs LIFT detection on the scene reflection layer and LIFT description on the scene illumination layer. Finally, experiments have shown that this method can increase the number of correct matching of feature points of machine vision wide baseline images. The research results in this paper combine the invariant feature matching technology with the HDR image technology that can record the irradiance value of the real scene, thus expanding the application background of the image LIFT extraction technology.
Capsule Networks With Word-Attention Dynamic Routing for Cultural Relics Relation Extraction

Online museums and online cultural relic information provide abundant data for relation extraction research.However, in the relation extraction task of modelling space information, spatially insensitive methods of convolutional neural networks and long short term memory network in most current works still remain challenging in rich text structures, which makes models difficult to encode effectively and lacks the ability of text expression.To address this issue, we propose a framework named WAtt-Capsnet (the capsule network with word-attention dynamic routing), which is based on capsule networks with word-attention dynamic routing for the relation extraction task of online cultural relic data for capturing richer instantiation features.We further present combination embedding for capturing the characteristic information of Chinese sentences by considering the contribution of word embedding, parts of speech, character embedding and the position of words to capture rich internal structure information of sentences.More importantly, to reduce the decay of useful information in long sentences, we propose a routing algorithm based on a word-attention mechanism to focus on informative words.The experimental results demonstrate that the proposed method achieves significant performance for the relation extraction task of online cultural relic data.
Counting and enumerating preferred database repairs

In its traditional definition, a repair of an inconsistent database is a consistent database that differs from the inconsistent one in a "minimal way." Often, repairs are not equally legitimate, as it is desired to prefer one over another; for example, one fact is regarded more reliable than another, or a more recent fact should be preferred to an earlier one. Motivated by these considerations, researchers have introduced and investigated the framework of preferred repairs, in the context of denial constraints and subset repairs. There, a priority relation between facts is lifted towards a priority relation between consistent databases, and repairs are restricted to the ones that are optimal in the lifted sense. Three notions of lifting (and preferred repairs) have been proposed: Pareto, global, and completion. In this article, we investigate the complexity of three problems on preferred repairs. The first is the problem of deciding whether the priority relation contains enough information to clean the database unambiguously, or in other words, whether there is exactly one preferred repair. We show that the different lifting semantics entail highly different complexities for this problem. Then, we study the ability to quantify ambiguity, by investigating two classes of problems. The first is that of counting the preferred repairs. We establish a dichotomy in data complexity for the entire space of (sets of) functional dependencies for all three notions. The second class of problems is that of enumerating (i.e., generating) the preferred repairs. We devise enumeration algorithms with efficiency guarantees on the delay between generated repairs, even for constraints represented as general conflict graphs or hypergraphs.
K-ONE Playground: Reconfigurable Clusters for a Cloud-Native Testbed

Cloud-native computing with edge clouds is dominating the current computing paradigm. To prepare a flexible testbed for this paradigm, the build-out of K-ONE Playground started in 2015 based on the concept of SmartXPlayground. K-ONE Playground targets a multi-site edge cloud testbed based on the concept of composable playground that can flexibly compose physical, virtual, container resources from a resource pool to user-defined infrastructure. SmartX Playground should properly handle demanding requirements for a composable playground. In this paper, we propose a unique design of reconfigurable clusters, which can provide physical and virtual resources ready for cloud-native DevOps services. We also describe a detailed implementation of the reconfigurable cluster for the real-world infrastructure of K-ONE Playground. Finally, we verify its feasibility with operations and practical examples of cloud-native service development.
Mafqudat: Arabic Smartphone Application for Reporting Lost and Found Items

Nowadays, missing or losing items has become a common problem owing to our stressful and pressured lifestyle. Losing items can be highly inconvenient and may have an effect on emotional well-being. However, technology can be employed to provide effective solutions to help people in searching, verifying and finding their missing items. In response, a solution for effectively searching for the missing items in an outdoor environment is presented. This paper proposes a mobile-based technology designed for Arabic users for a more efficient method of finding lost items easily and quickly.
A Hybrid Bi-Attention Mechanisms and Long Short-Term Memory Model for Chinese Question Classification

The question classification assigns a classify label to each question sentence. These category labels represent the answer type of the question or the intention type of user. The classification of question is not only an important part of the question answer(QA) system, but also the result of the question classification can influences the quality of the QA system directly. At early stages, scholars studied rule-based problem classification methods. Due to the rules were not universal, they were gradually transformed into methods based on machine learning and deep learning. This paper proposes a question classification model (BAL) that combines with bi-attention mechanisms and long short-term memory (LSTM) network. The model proposed in this paper can acquire multi-level text features and recognize the classification of questions better by combining with the attention mechanism of the word vector and the attention mechanism of part-of-speech. In the Chinese question classification data set, compared with common convolutional neural network, common long-short-term memory network, and convolutional neural network based on attention mechanisms, the model proposed in this paper can achieve better classification results.
Data security system using hybrid cryptosystem RC4A-RSA algorithm

Abstract Data confidentiality is an important aspect of information systems. In case of that we need an application to maintain data confidentiality. This study aims to compare two methods of data security systems, namely using the RC4A algorithm which is a symmetric key cryptographic algorithm and the RSA algorithm which is an asymmetric algorithm with a hybrid process on the RC4A and RSA algorithms to secure the secret key of RC4A and speed up the encryption process of RSA. The study uses a comparative test on two algorithm methods, according to data security system parameters. Data is encrypted with the RC4A algorithm and the RC4A key obtained from KSA RC4A will be encrypted with RSA before sending to the receiver. The test results get Hybrid cryptosystem RC4A-RSA faster in completing the encryption process than RSA. Furthermore, the RC4A key can be secured by being encrypted using the RSA algorithm.
Word Clustering on Small Corpuses

Word clustering is a method of grouping set of words such that words in the similar groups are semantically similar to the words of other groups. This problem is most studied in Natural Language Processing and becomes a challenging, especially corpus with small size. The essence of clustering the small sized corpuses is to uncover the topics and summarization. The challenge lies in extracting the semantics and statistics from the insufficient content of the corpus. In this work, we developed a framework that streamlines a set of preprocessing activities on the corpus to represent the data better for clustering. We adapt Markov clustering algorithm to infer the similarity among the words and group them. We also demonstrate the validation of the formed clusters. The experiment carried out on synthetic data and clusters are shown in results.
Intrusion Detection Method of Industrial Control System Based on RIPCA-OCSVM

In view of the problem that the intrusion detection method based on One-Class Support Vector Machine (OCSVM) could not detect the outliers within the industrial data, which results in the decision function deviating from the training sample, an anomaly intrusion detection algorithm based on Robust Incremental Principal Component Analysis (RIPCA) -OCSVM is proposed in this paper. The method uses RIPCA algorithm to remove outliers in industrial data sets and realize dimensionality reduction. In combination with the advantages of OCSVM on the single classification problem, an anomaly detection model is established, and the Improved Particle Swarm Optimization (IPSO) is used for model parameter optimization. The simulation results show that the method can efficiently and accurately identify attacks or abnormal behaviors while meeting the real-time requirements of the industrial control system (ICS).
Automatic farmer pest repellent with Arduino ATmega2560 based on sound displacement technique

Abstract Increasing pest population is the main cause of reduced crop yields and causes farmers to suffer heavy losses. The purpose of this study is to realize a tool that can help repel bird and mouse pests automatically. The prototype of this tool is controlled using the Arduino ATmega 2560 which will regulate the performance of sensors and servo motors as a mechanical system that pulls bells producing sound to repel birds and controls the buzzer to repel rice field pests. This tool works if the ultrasonic sensor or PIR sensor detects the presence of pests. The power source used is in the form of an accumulator with an output voltage of 12 volts, a servo motor as a motor driver for bells, and a buzzer to repel bird and mouse pests. The testing process carried out on this prototype shows that overall this tool can perform its functions properly.
Semantic Network Based Approach to Compute Term Semantic Similarity

Measuring semantic similarity between two terms is essential for a variety of text analytics and understanding applications.This paper presents a approach for measuring the semantic similarity between terms. Previous work on semantic similarity methods have focused on either the structure of the semantic network between terms, or only on the Information Content (IC) of terms. However, existing approaches are limited by the size of the knowledge base and corpus. We propose an efficient and effective approach for computing semantic similarity using a large scale semantic network. This approach base on Probase, which is a big graph of concepts. Knowledge in Probase is harnessed from billions of web pages and years' worth of search logs. Through experiments performed on well known word similarity datasets, we show that our approach is much more efficient than all competing algorithms.
Supervised mixture of experts models for population health

We propose a machine learning driven approach to derive insights from observational healthcare data to improve public health outcomes. Our goal is to simultaneously identify patient subpopulations with differing health risks and to find those risk factors within each subpopulation. We develop two supervised mixture of experts models: a Supervised Gaussian Mixture model (SGMM) for general features and a Supervised Bernoulli Mixture model (SBMM) tailored to binary features. We demonstrate the two approaches on an analysis of high cost drivers of Medicaid expenditures for inpatient stays. We focus on the three diagnostic categories that accounted for the highest percentage of inpatient expenditures in New York State (NYS) in 2016. When compared with state-of-the-art learning methods (random forests, boosting, neural networks), our approaches provide comparable prediction performance while also extracting insightful subpopulation structure and risk factors. For problems with binary features the proposed SBMM provides as good or better performance than alternative methods while offering insightful explanations. Our results indicate the promise of such approaches for extracting population health insights from electronic health care records.
A Robust Structured Tracker Using Local Deep Features

Deep features extracted from convolutional neural networks have been recently utilized in visual tracking to obtain a generic and semantic representation of target candidates. In this paper, we propose a robust structured tracker using local deep features (STLDF). This tracker exploits the deep features of local patches inside target candidates and sparsely represents them by a set of templates in the particle filter framework. The proposed STLDF utilizes a new optimization model, which employs a group-sparsity regularization term to adopt local and spatial information of the target candidates and attain the spatial layout structure among them. To solve the optimization model, we propose an efficient and fast numerical algorithm that consists of two subproblems with the close-form solutions. Different evaluations in terms of success and precision on the benchmarks of challenging image sequences (e.g., OTB50 and OTB100) demonstrate the superior performance of the STLDF against several state-of-the-art trackers.
Deep Learning Based Segmentation of Body Parts in CT Localizers and Application to Scan Planning

In this paper, we propose a deep learning approach for the segmentation of body parts in computer tomography (CT) localizer images. Such images pose difficulties in the automatic image analysis on account of variable field-of-view, diverse patient positioning and image acquisition at low dose, but are of importance pertaining to their most prominent applications in scan planning and dose modulation. Following the success of deep learning technology in image segmentation applications, we investigate the use of a fully convolutional neural network architecture to achieve the segmentation of four anatomies: abdomen, chest, pelvis and brain. The method is further extended to generate plan boxes for individual as well as multiple combined anatomies, and compared against the existing techniques. The performance of the method is evaluated on 771 multi-site localizer images.
Optimization of an Electronic Signature Scheme in a Voting System in a Distributed Architecture

It was considered that the security of information is an important factor in an electoral process in a country; for the frequent problems that have been detected in the scanning of votes to expedite the count. The objective is to analyze a method of vote scanning that allows improving security, administration, and implementation so that vote counting is faster and more secure compared to the traditional voting system. The deductive method and the exploratory investigation were used to analyze the information of the articles of the reference. It turned out to be a prototype of a voting scan algorithm based on the OCR reader scanner that was proposed and analyzed; for the scanning of the votes and projection of results via the internet for the next elections to improve the integrity of the information. It was concluded that a prototype of an adequate algorithm for vote scanning is an alternative to improve a traditional system that will be integrated into a voting system.
Decision Support System in Recommending Climbing Tourism Destinations with Profile Matching Method

Abstract Climbing is one of the extreme sports categories and recreational activities that are in great demand in the last decade, especially teenagers. Climbing offers a different sensation and challenge from other sports, besides this sport also offers a beautiful view when the climbers have reached the top of the mountain that is intended, of course, also improves fitness and health. The purpose of this study is to provide climbing tourism destination recommendations to groups of organizations and individuals who have a hobby or are the first to want to do this sport. Data collection in this study was carried out by giving questionnaires to 3 groups of nature lovers organizations, each organization was given 10 questionnaires. In this study, there are 5 alternatives used, including Mount Jaya Wijaya, Mount Ijen, Mount Kerinci, Mount Rinjani and Mount Bromo with 6 Criteria, including Beauty, Altitude, Location, Cost and Pathway. Based on calculations using the Profile Matching method, the results are obtained that Mount Rinjani is a recommended climbing tourism destination.
Implementing SWOT analysis in engineering education

Abstract Information Technology-Based Education Management is an education system where the education management system includes the management of teaching and learning process data, student management of educator and education staff as well as school infrastructure is carried out using Information Technology (IT). Several studies have discussed factors about what influences teachers in using IT, which is access to resources, quality of software and hardware, ease of use, incentives to change, support, and collegiality in their school, school, and national police. Problems arise when the teaching and learning process does not change while the world of IT change very rapidly, and people automatically follow the change of IT, because of that the world of education must adapt to this change. The purpose of this research is to see how IT can be implemented in the education environment and analyzed using SWOT. The result is that the Education and School services in Bekasi Regency is in quadrant 4, this showing that education in Bekasi Regency is in a very unfortunate situation, the organization faces various internal threat and weakness. For this reason, the need for an agenda activities related to planning, organizing, implementing and controlling the academic management system for elementary and junior high school in Bekasi Regency is implemented by Information Technology-based education procedures.