Reviewerly/exHarmony huggingface dataset:
Human and Technology Factors in the Readiness to Use Smart Mobile Devices in Learning Activities

The use of smart mobile devices in the learning process is one of the factors that can influence the success of the learning process. Lecturers are a key factor in the success of this implementation. How lecturers can be ready to accept the use of a smart mobile device in the middle of the learning process that is still considered a nuisance. The purpose of this study is to analyze what factors affect lecturers' readiness to use smart mobile devices into learning activities and also provide an overview of how IT Governance implementation can help the implementation of smart mobile devices. The researcher took a sample of lecturers who actively taught at the Information Systems Department, BINUS University in the even semester 2018 period. Authors used PLS-SEM to analyze data. The results of this study show whether computer self-efficacy, subjective norms and relative benefits have a significant impact on the willingness of teachers to integrate smart mobile devices into the learning process.
Design and Simulation of a Tank Floor Cleaning Mechanism for Mobile Robots used in Storage Tanks

Oily sludge on the floor of the tank is a significant problem for petrochemical industries and floor inspection robots. Oily sludge is a hazardous material containing a complex mixture of hydrocarbon, water, sand, and minerals deposited on the floor of the oil storage tanks. Sludge accelerates corrosion, reduces storage capacity, sticks to floor inspection robots and disrupts further tank operations Industries have started deploying robots in a tank to automate and replace the hazardous manual tank tasks. This paper presents the design of a screw conveyor based sludge cleaning mechanism to clean the sticky sludge from the floor of aboveground oil storage tanks and interface effectively with tank inspection robots to perform cleaning and inspection synchronously. The cleaning mechanism consists of a screw conveyor mounted on a 'C' shaped case with a bearing on both sides, a waterproof motor connected to the screw conveyor with a worm-wheel gear. A Rheometer is used for measuring sludge properties to understand its flow behavior. Computational fluid dynamics (CFD) based numerical simulation is performed to visualize the flow of oily sludge through the proposed cleaning mechanism.
Skeleton-based Human Action Recognition Using Spatio-Temporal Geometry (ICCAS 2019)

In this paper, we propose a temporal action recognition algorithm in a sequence image. Our methods consists of three main parts. First, it extracts the 3D human pose from 2D skeletal joints from a single image. Second, we investigate a spatio-temporal structure using the extrinsic and intrinsic connectivity that correspond to joints in a human action. We extract key joints defined as endpoints of the skeleton in each frame in order to reflect temporal variation of the joints. Due to these key joints, we can consider relative position variances of corresponding points in single frames. Finally, we exploit these motion vector to infer the spatio-temporal structure of human actions. We will compare the accuracy and efficiency of this approach with our method.
A Contour-Based Multi-scale Vision Corner Feature Recognition Using Gabor Filters

This paper proposes a corner detection algorithm based on the correlation matrices, in which the combination of edge shapes and gray variations in multiple scalars are used. First, a Canny edge detector is used to detect the edge contours of an input image. In each scale, the direction derivative of each pixel on the edge curves and its surrounding pixels are extracted by using Gabor filters with imaginary parts (IPGFs), which are further used to construct the correlation matrices. Then, the sum of normalized eigenvalues of the correlation matrices at different scales is computed to extract potential corners. Finally, non-maximum suppression and a threshold are used to extract final corners. The experimental results show that the proposed method improves the detection accuracy and is noise resistance compared with Harris algorithm.
Ensemble Learning via Extreme Learning Machines for Imbalanced Data

Ensembles are known to reduce the risk of selecting the wrong model by aggregating all candidate models. Ensembles are known to be more accurate than single models. Accuracy has been identified as an important factor in explaining the success of ensembles. Several techniques have been proposed to improve ensemble accuracy. But, until now, no perfect one has been proposed. The focus of this research is on how to create accurate ensemble learning machine (ELM) in the context of classification to deal with supervised data, noisy data, imbalanced data, and semi-supervised data. To deal with mentioned issues, the authors propose a heterogeneous ELM ensemble. The proposed heterogeneous ensemble of ELMs (AELME) for classification has different ELM algorithms, including regularized ELM (RELM) and kernel ELM (KELM). The authors propose new diverse AdaBoost ensemble-based ELM (AELME) for binary and multiclass data classification to deal with the imbalanced data issue.
Trajectory Tracking for Cooperative Transportation with T<sup>3</sup>-multirotor

This paper suggests a new cooperative transportation system with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$T$</tex> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> -multirotors. From the promising characteristics of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$T$</tex> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> -multirotor for fully-actuated motion, the proposed system is able to track a desired payload trajectory which is impossible with a conventional underactuated multirotor. Unlike other cooperative methods such as slung-load transportation and aerial manipulator, the system can accommodate simpler mechanism by directly attaching cargo to the multirotor body. We build the dynamic model of the system and design a trajectory tracking controller considering low-level control. Dynamic equations show that the system is overactuated and it can follow arbitrary twice-differentiable trajectory if input and mechanical feasibilities are satisfied. For tracking control, linear control methods with linear quadratic regulator(LQR) and PID are sufficient for stable and efficient tracking. Control allocation is used for the sake of determining input for each actuator. Further improvement is expected by combining the proposed approach with a trajectory generation module.
Oblique Random Forest via Regularized Multisurface Proximal Support Vector Machine

We propose a novel approach to train oblique random forests using a linear classifier where the feature axis is not invariably orthogonal to the decision hyperplanes at each internal node of the base model. At each non-leaf node, for the multiclass classification problem, we group all the training samples into two groups of classes corresponding to the underlying geometric characteristics with respect to a randomly chosen feature subspace. Subsequently, two discriminatory hyperplanes are obtained, as per each class, by employing the Regularized Multisurface Proximal Support Vector Machine (RegMPSVM). While optimizing the impurity criteria, the test hyperplane of the corresponding internal node is selected from either of the bisectors of the two hyperplanes of the two groups of classes. As the tree propagates, to tackle the small sample size issue, various regularisation approaches are employed. Using this method, the oblique random forests are grown. Proposed framework's potency is exhibited by comparison with the state-of-the-art frameworks on 40 real-world benchmark classification datasets of miscellaneous research fields. In the expression of prediction accuracy, the ascendancy of our proposed framework is demonstrated by the classification results.
Generating partial civil information model views using a semantic information retrieval approach

Open data standards (e.g. LandXML, TransXML, CityGML) are a key to addressing the interoperability issue in exchanging civil information modeling (CIM) data throughout the project life-cycle. Since these schemas include rich sets of data types covering a wide range of assets and disciplines, model view definitions (MVDs) which define subsets of a schema are required to specify what types of data to be shared in accordance with a specific exchange scenario. The traditional procedure for generating and implementing MVDs is time-consuming and laborious as entities and attributes relevant to a particular data exchange context are manually identified by domain experts. This paper presents a method that can locate relevant information from a source XML data schema for a specific domain based on the user's keyword. The study employs a semantic resource of civil engineering terms to understand the semantics of a keyword-based query. The study also introduces a novel context-based search technique for retrieving related entities and their referenced objects. The developed method was tested on a gold standard of several LandXML subschemas. The experiment results show that the semantic MVD retrieval algorithm achieves a mean average precision of nearly 90%. The research is original, being a novel method for extracting partial civil information models given a keyword from the end user. The method is expected to become a fundamental tool assisting professionals in extracting data from complex digital datasets.
Elliptic Curves of Nearly Prime Order

Constructing an elliptic curve of prime order has a significant role in elliptic curve cryptography. In this paper, we propose an efficient technique to generate an elliptic curve of nearly prime order. Precisely, this algorithm produces an elliptic curve of cofactor 2. Presently, the most known working algorithms for generating elliptic curves of prime order are based on complex multiplication. The advantages of proposed algorithm are: it is relatively simple, easy to implement and produces an elliptic curve of a remarkably simple expression.
Paraphrase Detection Using Manhattan's Recurrent Neural Networks and Long Short-Term Memory

Natural Language Processing (NLP) is a part of artificial intelligence that can extract sentence structures from natural language. Some discussions about NLP are widely used, such as Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) to summarize papers with many sentences in them. Siamese Similarity is a term that applies repetitive twin network architecture to machine learning for sentence similarity. This architecture is also called Manhattan LSTM, which can be applied to the case of detecting paraphrase sentences. The paraphrase sentence must be recognized by machine learning first. Word2vec is used to convert sentences to vectors so they can be recognized in machine learning. This research has developed paraphrase sentence detection using Siamese Similarity with word2vec embedding. The experimental results showed that the amount of training data is dominant to the new data compared to the number of times and the variation in training data. Obtained data accuracy, 800,000 pairs provide accuracy reaching 99% of training data and 82.4% of new data. These results are better than the accuracy of the new data, with half of the training data only yielding 64%. While the amount of training data did not effect on training data.
Energy Data Security and Multi-Source Coordination Mechanism Based on Blockchain

Energy is the material basis for human society to survive and has a very important strategic position in the national economy. With the advancement of Internet technology and the extensive use of clean energy, the energy industry has demonstrated a new development trend. Based on blockchain technology, this paper analyzes energy data security and multi-source synergy mechanism, processes and classifies a large amount of energy data in energy system, and builds a blockchain-based energy data supervision and transaction model. A summary tree of energy data is proposed; a consensus mechanism based on multi-source collaboration is proposed to ensure efficient negotiation; and finally, blockchain is verified in the energy scenario. This provides reference for the application of blockchain technology in the energy industry.
Enhanced Tax Compliance for Cryptoasset Transactions

This paper discusses the challenges facing the IRS as it seeks to enforce taxpayer compliance regarding cryptocurrency transactions. For a proper analysis of these issues, we must look from the perspective of economic policy, as well as tax policy, including both domestic and global. The first part of addressing non-compliance is to abandon the idea that cryptocurrency is a currency. Instead, this paper will use “cryptoassets” to describe all types of digital and virtual currency. This broader term allows us to think beyond contemporary perspective and policy pertaining to cryptoassets and blockchain technology. Through that, the analysis will compare cryptoassets to other investments and securities, discuss the high potential for crime and fraud, examine the IRS’s current characterization of cryptoassets as property, and explore how other nations are implementing their own regulatory and tax policies for cryptoassets. Finally, this paper argues that to successfully enhance taxpayer compliance with taxable cryptoasset events in the United States, there needs to be a dual regulatory and policy shift, which can be accomplished on two fronts. First, by creating a regulatory foundation that incentivizes taxpayers to comply with tax law. And second, by carving out a special tax characterization for cryptoassets and taxable transactions related to cryptoassets.
An Android-based Hoax Detection for Social Media

Hoax is defined as a try to convince any readers to believe particular deception. A news frequently spreads on social media. Hoaxes news is engineered to meet a personal purpose and caused by predefined factors. In Indonesia, one of the top discussions is about hoax news related to natural disasters spread through social media. Some people have been sentenced to jail because of making and spreading hoax news. To date, the internet community is unable to distinguish hoax news from the actual news. This is because there is no available tool to detect hoaxes. Therefore, this paper propose an initiative to have a hoax detection application in Android-based devices by using the web scraping technique to extract data downloaded from the inputted URL with a simplified interface. The retrieved data are optimized by utilizing Nazief-Adriani stemming algorithm and analyzed by using Rabin-Karp algorithm.
Dual Sentiment Classification with Sarcasm Identification

Whenever a person wants to buy some new thing, watch a movie or go to an unknown place, he/she searches for online reviews. People who have watched that particular movie or have been to that place in the past post these reviews, which leads to huge volumes of user-oriented textual data which is rendered useless if it is not thoroughly analyzed and put into some techniques as input and derive some application-specific and meaningful results. Hence, in this paper, we propose a sentiment classification model named as Dual Sentiment Classification (DSC) with Sarcasm Identification. This model will first perform sarcasm identification on the user reviews and classify them as sarcastic and non-sarcastic and then sentiment classification will be performed using dual sentiment analysis concept, to classify the reviews as positive or negative.
A Fast Solution to the Dual Arm Robotic Sequencing Problem

Robotics applications such as spot-welding, spray-painting, drilling, and objects handling, require the robot to visit successively multiple targets. The robot travel time among targets is a significant component of the overall execution time. This travel time is in turn greatly affected by the visiting order and by the robot configurations used to reach each target. It is crucial to optimize these elements during the motion planning stage, a problem known in the literature as the Robotic Task Sequencing Problem (RTSP). This problem has been studied by the robotics community but only for single arm manipulators. Our contribution in this paper is to extend an existing RTSP algorithm to the dual arm case. The key to our approach is to exploit the classical distinction between task-space and configuration-space, which, surprisingly, has been so far overlooked in the RTSP literature. We show experimentally that our method finds high-quality motion sequences and requires a reduced amount of computation time, making it ideal for real-world applications. For a toy-example of a pick-and-place task, the proposed dual-arm RoboTSP takes less than 20 s to solve the task sequencing problem and compute the collision-free trajectories.
Designing an Interface for Trajectory Programming in Industrial Robots Using Augmented Reality

Traditional programming methods of industrial robots, show different disadvantages such as the use of highly qualified personnel and long programming time for the generation of robot trajectories. The development of friendly and intuitive user interfaces that improve the human-robot interface is greatly important; so it allows generating trajectories in a natural way. To achieve this goal, tools such as augmented reality are required to enhance the robot's environment with virtual information in which the user may receive feedback. It is proposed to develop an immersive user interface using augmented reality, that allows programming trajectories for the ABB IRB 140 robot by means of a cubic marker and giving useful feedback to the user throughout the device THC VIVE. The system of augmented reality is developed with Vuforia in Unity3D, and the system of communication in ROS.
Fraud Detection During Money Transaction and Prevention

With the increased use of digital money, online financial frauds are observed to be one of the most common cyber crimes. Credit/Debit card frauds are among the easy cyber crimes targeted by fraudster. In this paper all possible online frauds of present era and likely to be occur in future have been discussed. Ignorance, greed, availability of technical tools, malafide intentions, online and offline security breaches are some reasons behind these financial cyber crimes. Most of the fraud detections techniques have been studied with their merits and constraints. Proposal of two techniques to detect and understand dynamics of evolution of fraudsters with improvement of security systems and another technique of analysis of time series data of random variables describing characteristics of online frauds have been suggested to use in future scope.
Dual Convolutional Neural Network for Image Steganalysis

In this paper, we propose a new steganalytic method that uses dual convolutional neural network (CNN) of which each has different inputs. To construct the dual CNN structure, two pairs of the preprocessing filters and the convolutional layers were brought from the conventional CNN-based steganalytic methods and the outputs of the dual CNN were concatenated and fed together into a following affine layer. Given an input image, a stego image is created by embedding some additional data into the input image using one of steganographic methods and a difference image is computed between the input and stego images. Then, the input and difference images are fed into each CNN, respectively. This indicates that the proposed method extracts /learns additional features from the difference image using the additional CNN. Experimental results demonstrated that the proposed dual CNN with additional input can identify whether the S-UNIWARD steganography was applied to the input image with an accuracy of 80.43%, and can improve the accuracy by approximately 5% when compared with the conventional CNN-based steganalytic method.
Deep Learning for motion based video aesthetics

Aesthetics is defined by the properties of arts and beauty, thus making it a very subjective domain. In our day to day lives, with the increase of multimedia requirements, the aesthetic appeal of images and videos has gained much importance in varied fields like advertising, film making, User-Interface design, social networking etc. Visual attributes greatly affect the aesthetic sense of the viewers. In this paper, to start with, we dive into the details of low level, middle level and high level image attributes that contribute towards the aesthetic appeal of images. Videos share their attributes with images except for the presence of motion in a video. Next, we proceed towards the handcrafted and deep learning techniques for assessing image and video attributes for their aesthetic appeal. Motion is an important but seldom explored visual attribute that affects video aesthetic appeal. Typically, slow motion creates an impact and appreciation amongst the viewers as they absorb the contents of the video better in comparison to faster motion in the video. Surveys conducted showcased the human inclination towards slowly paced videos in comparison to the fast-paced ones. We have experimented with the deep learning framework for detecting motion in nature based videos. Deep learning achieves an impressive performance in comparison to the handcrafted methods, thus reinforcing current trust in the deep learning frameworks for multimedia analysis.
Finding Slid Pairs for the Plantlet Stream Cipher

This paper presents the results of an investigation into relationships between (key, IV) pairs that give rise to phase shifted keystreams for the stream cipher Plantlet. We demonstrate that such slid pairs exist for Plantlet, and that very small phase shifts are possible, including shifts of length 1. We also show that the keyspace can be partitioned into sets of keys which give rise to initial states that lie on the same state cycle during keystream generation. For a given key used to generate Plantlet keystreams, we provide a method for generating all other keys that give rise to phase shifted keystreams as they move through internal states on the same state cycle.
Where Am I Looking: Localizing Gaze In Reconstructed 3D Space

We propose a method to estimate the 3D gaze of the observer onto the scene using a portable eye tracker with a monocular camera. We reconstruct the 3D scene using Structure from Motion (SfM) and use camera pose and 3D reconstruction information of the scene to localize the position and pose of the observer in the 3D reconstructed space. Along with the position, we can obtain the 2D gaze of the observer using an eye-tracker. Each person may have a different perspective of the same 3D object in the scene, observing it from different positions. We use this information to fuse these multiple perspectives in 3D space to get a better understanding of how differently each observer perceives the same scene, compared to others. In the entire system, we developed a convo-lutional neural network to detect and track eye movement and a camera re-localization method to localize the camera in 3D environment. Based on our novel eye tracking and camera re-localization methods, we can accurately localize the gaze in the 3D reconstructed environment.