Reviewerly/exHarmony huggingface dataset:
A Convenient Calibration Method for LRF-Camera Combination Systems Based on a Checkerboard

In this paper, a simple and easy high-precision calibration method is proposed for the LRF-camera combined measurement system which is widely used at present. This method can be applied not only to mainstream 2D and 3D LRF-cameras, but also to calibrate newly developed 1D LRF-camera combined systems. It only needs a calibration board to record at least three sets of data. First, the camera parameters and distortion coefficients are decoupled by the distortion center. Then, the spatial coordinates of laser spots are solved using line and plane constraints, and the estimation of LRF-camera extrinsic parameters is realized. In addition, we establish a cost function for optimizing the system. Finally, the calibration accuracy and characteristics of the method are analyzed through simulation experiments, and the validity of the method is verified through the calibration of a real system.
Privacy Preservation of Social Network Users Against Attribute Inference Attacks via Malicious Data Mining

Online social networks (OSNs) are currently a popular platform for social interactions among people.Usually, OSN users upload various contents including personal information on their profiles.The ability to infer users' hidden information or information that has not been even uploaded (i.e.private/sensitive information) by an unauthorised agent is commonly known as attribute inference problem.In this paper, we propose 3LP+, a privacy-preserving technique, to protect users' sensitive information leakage.We apply 3LP+ on a synthetically generated OSN data set and demonstrate the superiority of 3LP+ over an existing privacy-preserving technique.
On Target Shift in Adversarial Domain Adaptation

Discrepancy between training and testing domains is a fundamental problem in the generalization of machine learning techniques. Recently, several approaches have been proposed to learn domain invariant feature representations through adversarial deep learning. However, label shift, where the percentage of data in each class is different between domains, has received less attention. Label shift naturally arises in many contexts, especially in behavioral studies where the behaviors are freely chosen. In this work, we propose a method called Domain Adversarial nets for Target Shift (DATS) to address label shift while learning a domain invariant representation. This is accomplished by using distribution matching to estimate label proportions in a blind test set. We extend this framework to handle multiple domains by developing a scheme to upweight source domains most similar to the target domain. Empirical results show that this framework performs well under large label shift in synthetic and real experiments, demonstrating the practical importance.
Application of DIC to deformation measurement around damages in CFRP cross-ply laminates

Digital Image Correlation (DIC) is a non-contact method to analyse the deformation of materials that can measure displacement or strain behaviour during tensile loading. In this study, DIC is used to measure the deformation of cross-ply carbon fibre reinforced plastics (CFRP) laminates and for the detection of damages in them. The objective of this study is to measure the deformation around the damages in cross-ply CFRP laminates by using the DIC system from both width and thickness directions. For this purpose, thick CFRP [04/9024]s and thinner [0/906]s laminates were tested. In cross-ply laminates, usually a straight transverse crack will initially occur in 90 degree ply followed by the following damages such as delamination, fiber fracture, and etc. To start, straight transverse cracks are induced in the laminates by using an artificial crack method. Then, X-ray radiography is used to detect the location of the straight cracks in order to be used for DIC observation to examine the strain distribution around the existed cracks. The DIC observation from the surface of the laminate around the cracks area clearly showed how strain is distributed from one crack to another adjacent crack. From the result, secondary mode damages of matrix cracking such as oblique and curved cracks can also be observed and are being discussed in this paper.
Upcycling Tree Branches as Architectural Elements through Collaborative Design and Fabrication

While tree trunks are standardized as lumber, branches are typically chipped or burned. This paper proposes a workflow to upcycle such mundane and diverse natural material to architectural elements. Introducing an online design interface, we let users participate in the design and fabrication workflow from collecting branches to CNC milling. The branches are first scanned, and then key geometrical features are extracted and uploaded to the online game "BranchConnect". This application lets multiple non-expert users create 2D-layouts. At the point of intersection between two branches, the geometry of a lap joint and its cutting path are calculated on-the-fly. A CNC router mills out the joints accordingly, and the branches are assembled manually. Through this workflow, users go back-and-forth between physical and digital representations of tree branches. The process was validated with two case studies.
Optimizing the Hyperparameter of Feature Extraction and Machine Learning Classification Algorithms

The process of assigning a quantitative value to a piece of text expressing a mood or effect is called Sentiment analysis.Comparison of several machine learning, feature extraction approaches, and parameter optimization was done to achieve the best accuracy.This paper proposes an approach to extracting comparison value of sentiment review using three features extraction: Word2vec, Doc2vec, Terms Frequency-Inverse Document Frequency (TF-IDF) with machine learning classification algorithms, such as Support Vector Machine (SVM), Naive Bayes and Decision Tree.Grid search algorithm is used to optimize the feature extraction and classifier parameter.The performance of these classification algorithms is evaluated based on accuracy.The approach that is used in this research succeeded to increase the classification accuracy for all feature extractions and classifiers using grid search hyperparameter optimization on varied pre-processed data.
Asymmetric collaboration in virtual reality

It has been established that Virtual Reality (VR) possesses certain qualities for educational purposes. These include the ability to place the learner at the location or in the perspective that the desired knowledge exists (e.g., travelling to another planet or shrinking to miniature size to observe internal anatomy). VR is also considered to contribute with enriching the curricular content, promoting active forms of learning, performance assessment of high validity, and provide the opportunity to teach applied academic knowledge in life-like situations. In regards to teaching mathematics and geometry, three key affordances have been identified; interactive manipulation and construction of three-dimensional geometry, comprehension of spatial relationships, and rectification of abstract problems. Safety protocols and practical guidelines from classroom experimentation have also been formulated by various research projects.&#x0D; In this manuscript, asymmetrical immersive VR in education will be reviewed, as it is relevant for the narrative of learning situations where multiple students use the technology together. As an example, in mathematics, asymmetric VR could be used in contexts where unknown variables must be found in collaboration. The purpose of the narrative literature review is to gain a greater understanding of how asymmetric game mechanics has influence on communication and collaboration between learners. To map the dynamics of this type of learning activity, a taxonomy will be presented. Since VR is still under development in terms of hardware and software, it is important that the current and future technical possibilities are described in a conceptual manner, as well as conclude on optimal coupling between communication dynamics and collaboration mechanics.
A Visual Neural Network for Robust Collision Perception in Vehicle Driving Scenarios

This research addresses the challenging problem of visual collision detection in very complex and dynamic real physical scenes, specifically, the vehicle driving scenarios. This research takes inspiration from a large-field looming sensitive neuron, i.e., the lobula giant movement detector (LGMD) in the locust’s visual pathways, which represents high spike frequency to rapid approaching objects. Building upon our previous models, in this paper we propose a novel inhibition mechanism that is capable of adapting to different levels of background complexity. This adaptive mechanism works effectively to mediate the local inhibition strength and tune the temporal latency of local excitation reaching the LGMD neuron. As a result, the proposed model is effective to extract colliding cues from complex dynamic visual scenes. We tested the proposed method using a range of stimuli including simulated movements in grating backgrounds and shifting of a natural panoramic scene, as well as vehicle crash video sequences. The experimental results demonstrate the proposed method is feasible for fast collision perception in real-world situations with potential applications in future autonomous vehicles.
High performance frequent subgraph mining on transaction datasets: A survey and performance comparison

Graph data mining has been a crucial as well as inevitable area of research. Large amounts of graph data are produced in many areas, such as Bioinformatics, Cheminformatics, Social Networks, etc. Scalable graph data mining methods are getting increasingly popular and necessary due to increased graph complexities. Frequent subgraph mining is one such area where the task is to find overly recurring patterns/subgraphs. To tackle this problem, many main memory-based methods were proposed, which proved to be inefficient as the data size grew exponentially over time. In the past few years, several research groups have attempted to handle the Frequent Subgraph Mining (FSM) problem in multiple ways. Many authors have tried to achieve better performance using Graphic Processing Units (GPUs) which has multi-fold improvement over in-memory while dealing with large datasets. Later, Google's MapReduce model with the Hadoop framework proved to be a major breakthrough in high performance large batch processing. Although MapReduce came with many benefits, its disk I/O and noniterative style model could not help much for FSM domain since subgraph mining process is an iterative approach. In recent years, Spark has emerged to be the De Facto industry standard with its distributed in-memory computing capability. This is a right fit solution for iterative style of programming as well. In this survey, we cover how high-performance computing has helped in improving the performance tremendously in the transactional directed and undirected aspect of graphs and performance comparisons of various FSM techniques are done based on experimental results.
A new public key cryptosystem based on Edwards curves

The elliptic curve cryptography plays a central role in various cryptographic schemes and protocols. For efficiency reasons, Edwards curves and twisted Edwards curves have been introduced. In this paper, we study the properties of twisted Edwards curves on the ring $${\mathbb {Z}}/n{\mathbb {Z}}$$ where $$n=p^rq^s$$ is a prime power RSA modulus and propose a new scheme and study its efficiency and security.
Students’ attitudes towards blended language courses: A case study

In an effort to create enriched language learning environments, language teachers, program and course developers, and publishing companies have long sought novel ideas and alternatives.Thus, a wide range of information and communications technologies, which have manifested themselves in the form of virtual learning environments, web-based platforms, or mobile-assisted applications, have been embraced in the field.With the introduction of new means and platforms for delivering instruction outside the classroom, blended learning, combining e-learning and conventional classroom methods as well as providing room for student control over the learning process, has gained popularity as a promising option.In this case study, which focuses on a group of second-year English language students at a School of Foreign Languages, the goal was to investigate students' attitudes towards blended learning with reference to six learning aspects: learning-flexibility, online-learning, study-management, technology-use, classroom-learning, and online-interaction (Tang & Chaw, 2013).Students' responses to the attitude questionnaire were subjected to descriptive statistical analysis as well as internal consistency tests among subscales.A subsequent focus group meeting was held with a group of 15 students to attain qualitative data on their views and attitudes towards blended learning with respect to their experiences and expectations.Highachievers had a tendency to hold positive attitudes towards all learning aspects while low-achievers required more face-to-face in-class time, interaction and study management support, pointing to possible links among such variables as language proficiency, learner autonomy and attitudes towards blended learning and highlighting how critical student characteristics and attitudes could prove in the implementation process.
Discrete Complex Fuzzy Transform based Face Image Recognition Method

In this paper, a novel discrete complex fuzzy transform (DCFT) and the proposed DCFT based facial image recognition method is presented.The presented DCFT consists of histogram extraction, peak points of histogram calculation and images construction.3 real and 3 complex images are constructed using DCFT.Also, 3 angular images and 3 vector image are calculated using the real and complex images.To create real and complex images, polynomial and smith fuzzy sets are used in this paper.Briefly, 12 image are constructed using DCFT.In order to demonstrate effect of the proposed DCFT, face images data sets and local binary pattern (LBP) are used to create facial image recognition method.In this method, LBP is applied on the each DCFT image and 12 x 256 size of feature are extracted.Also, maximum pooling is applied on this feature set to obtain 256 size of feature.In the classification phase, support vector machine (SVM) and k nearest neighborhood (KNN) classifiers are used.The comparisons clearly demonstrate that the proposed DCFT is increased facial image recognition capability.
Distributed Optimization of Multi-Robot Motion with Time-Energy Criterion

This paper is an application of a special case of distributed optimization problem.It is applied on optimizing the motion of multiple robot systems.The problem is decomposed into L subproblems with L being the number of robot systems.This decomposition reduces the problem to solving a single robot problem.The optimization problem is solved via a distributed algorithm, utilizing subgradient method.A global objective function is set as the sum of individual robot objectives in time and energy.Constraints are divided into two sets, namely, robot-individual constraints and robots' interactions (collision) constraints.The approach is applied for the case of wheeled mobile robots: we are able to generate in parallel for each robot an optimized control input trajectory and then illustrate it in simulation examples.
Semi-supervised Learning with Generative Adversarial Networks for Arabic Dialect Identification

Dialect Identification (DID) refers to the process of identifying different dialects within the same language class. Compared with more general language identification (LID), DID is a more challenging task because of the substantial similarity between dialects. For an i-vector based LID/DID, prior studies have shown advancements with deep neural networks (DNNs) over Gaussian Mixture Models (GMMs) in acoustic modeling. In this study, a novel i-vector representation which is based on unsupervised bottleneck features is examined as the feature to identify dialects from Arabic broadcast speech. To utilize the unlabeled training data, semi-supervised learning with generative adversarial networks (GANs) are incorporated in the back-end classifier development. Experiments with the proposed method in the third release version of the Multi-Genre Broadcast (MGB-3) Challenge yields the best single system performance among all submitted systems. An overall classification accuracy of 73.8% achieves a +28.8% relative improvement over the MGB-3 baseline with an accuracy of 57.3%, which is the state-of-the-art performance in this DID task. The fused system further achieves an improvement of +39.4% in accuracy.
Using a Deep Learning Model on Images to Obtain a 2D Laser People Detector for a Mobile Robot

This work has been supported by the Spanish Government TIN2016-76515-R Grant, supported with Feder funds.
Evaluation of Heavy Metals Pollution by using pollution indices in the Soil of Assiut District, Egypt

Assiut District represents an important part of central Nile Valley. It is a complex district containing many big industries, urbanization and agricultural activities. These activities can lead to the pollution of agricultural soil with toxic heavy metals. The aim of this work is the assessment of potential risks of these metals in soils using pollution indices; Enrichment Factor (EF), Index of Geoaccumulation (Igeo), Contamination Factor (CF), Ecological Risk Factor (Er), Degree of Contamination (Cd), Pollution Load Index (PLI) and Potential Ecological Risk Index (PERI). The present study calculated the mentioned indices for eight heavy metals; As, Cd, Co, Cr, Cu, Ni, Pb, and Zn. Pollution indices calculations revealed that the investigated area can considered generally moderately polluted with the studied heavy metals. The emission of heavy metals from the factories as well as agricultural practices, urban runoff and sewage stations in the zone may be the source of that pollution. Consequently, control methods must be functional to the area adjacent to the factories to diminish the pollution. Finally, the studied cultivated soils showed low pollution than other cultivated soils in Australia, Canada, Germany, Tanzania, Netherlands, USA, as well as other areas in Egypt.
Business intelligence analytics using sentiment analysis-a survey

Sentiment analysis (SA) is the study and analysis of sentiments, appraisals and impressions by people about entities, person, happening, topics and services. SA uses text analysis techniques and natural language processing methods to locate and extract information from big data. As most of the people are networked themselves through social websites, they use to express their sentiments through these websites.These sentiments are proved fruitful to an individual, business, government for making decisions. The impressions posted on different available sources are being used by organization to know the market mood about the services they are providing. Analyzing huge moods expressed with different features, style have raised challenge for users. This paper focuses on understanding the fundamentals of sentiment analysis, the techniques used for sentiment extraction and analysis. These techniques are then compared for accuracy, advantages and limitations. Based on the accuracy for expexted approach, we may use the suitable technique.
An investigation into the attraction and completion rates of MOOCs

Many studies are dedicated to MOOCs, however there was no clear answer to what makes a particular MOOC popular. This study was an attempt to answer this question. Data about MOOC distribution was collected for this research, including category data, university types and MOOC platforms, data regarding MOOC popularity in different time periods, and regarding MOOC completion rates. It was determined that university type and MOOC category did not influence the number of enrolled students. However, Coursera and edX attracted many more students than the other MOOC platforms. Besides these facts, the number of students who really completed the course was much higher for the MOOCs created by top universities. Thus, courses by top universities did not have higher enrolment, however they became more well-known because of the number of students who really took them. The assessment format had a high influence on the completion rates as well. A traditional MOOC format and auto grading caused higher completion rates than other formats. Thus, popular MOOCs could be created in any category by each university, however Coursera and edX’s courses attracted more students, and an auto grading course format involved them in studying the course.
OpinionML—Opinion Markup Language for Sentiment Representation

It is the age of the social web, where people express themselves by giving their opinions about various issues, from their personal life to the world’s political issues. This process generates a lot of opinion data on the web that can be processed for valuable information, and therefore, semantic annotation of opinions becomes an important task. Unfortunately, existing opinion annotation schemes have failed to satisfy annotation challenges and cannot even adhere to the basic definition of opinion. Opinion holders, topical features and temporal expressions are major components of an opinion that remain ignored in existing annotation schemes. In this work, we propose OpinionML, a new Markup Language, that aims to compensate for the issues that existing typical opinion markup languages fail to resolve. We present a detailed discussion about existing annotation schemes and their associated problems. We argue that OpinionML is more robust, flexible and easier for annotating opinion data. Its modular approach while implementing a logical model provides us with a flexible and easier model of annotation. OpinionML can be considered a step towards “information symmetry”. It is an effort for consistent sentiment annotations across the research community. We perform experiments to prove robustness of the proposed OpinionML and the results demonstrate its capability of retrieving significant components of opinion segments. We also propose OpinionML ontology in an effort to make OpinionML more inter-operable. The ontology proposed is more complete than existing opinion ontologies like Marl and Onyx. A comprehensive comparison of the proposed ontology with existing sentiment ontologies Marl and Onyx proves its worth.
Identify Abusive and Offensive Language in Indonesian Twitter using Deep Learning Approach

Indonesia has a huge number of Twitter users and a lot of them often communicate using abusive language. Not only in the context of jokes, many Indonesian netizens often use abusive language to curse (offense) someone. Research about abusive language detection in Indonesian Twitter has been done using classical machine learning approach. However, the performance was still not too good, especially in differentiating whether the tweet is an abusive but not offensive or an offensive language. This paper implements a deep learning approach to enhance the performance when identifying abusive but not offensive or an offensive language. We use Long Short-Term Memory (LSTM) with word embedding because our literature study found that LSTM with word embedding is good for text classification (both for English or Indonesian text classification). The experiment result shows that LSTM with word embedding can increase the F1-Score from the previous work until 19.44%, from 70.06% to 83.68%.