Reviewerly/exHarmony huggingface dataset:
Automatic Method for Measuring Object Size Using 3D Camera

This paper proposes a method to automatically measure object size with high accuracy by a structure-light based stereo camera system. The approach includes four steps namely preprocessing, object detection, key points extraction and depth interpolation before size calculation. The RGB and depth frames are firstly aligned in the preprocessing step. Then, the object is detected by depth threshold and key points are extracted by Shi-Tomasi corner detector combined with our proposed key point extraction algorithm. An interpolation algorithm is developed to handle incorrect depth at the edge of objects. Last but not least, the object dimensions are calculated by Euclidean distance through the 3D coordinate of the key points. Experimental results of three-object measurement show that average accuracy is about 99% which is close to those of manual and theoretical methods. These also outperform the results of recently published papers.
A Hybrid Task Scheduling Algorithm Combining Symbiotic Organisms Search with Fuzzy Logic in Cloud Computing

As one of the significant IT technologies, cloud computing attracts more and more attention thanks to its unique business mode. In cloud computing, cloud service providers distribute computing capabilities through virtualization, then cloud users submit the tasks to remote datacenters for processing. In this process, task scheduling plays a pivotal role in improving the overall performance, it is also helpful to optimize the load between virtual machines, enhance quality of service and minimize the completion time. This paper provides a hybrid meta-heuristic algorithm using fuzzy logic to calculate the fitness of organisms by combining Symbiotic Organisms Search algorithm with Fuzzy Logic, which is called FLSOS. The objectives of this method are the minimization of the makespan and improvement of the degree of balance between virtual machines. The simulation experiment was conducted by Cloudsim, an outstanding simulation platform. The result shows our proposed method can achieve better performance, reflecting a certain degree of practicality.
PPDB Online As Innovation of Education Services

Abstract Education is the most important aspect of the process of empowering and developing the quality of the State. Innovations in education services must be made to keep up with technological developments and the demands of society. One of the innovations in education services is PPDB Online. PPDB online is an electronic-based service system used in the process of admitting new students in high school. This paper explains the application of PPDB Online as an educational service innovation in East Java Provincial Education Office, Surabaya Branch. The application of PPDB Online in East Java Provincial Education Office, Surabaya Branch, as innovation in education service defined through the five e-service dimensions there is ease of use, e-scape, customization, responsiveness, and assurance. Ease of use means that the application of PPDB online makes it easy to register online. E-scape means that the application design created by East Java Provincial Education Office, Surabaya Branch provides comfort and convenience for PPDB users online. Customization means that the application makes it easy for users to convey constraints regarding PPDB online. Responsiveness means that the consumer inquiries and requests can be responded to quickly. Assurance means the data of the user is safe because to access the user must register and have a password to access PPDB online.
Opinion mining from student text review for choosing better online courses

Abstract The process of online assessment has utilized get increased gradually as an evaluating tool in order to measure the lectures performance over Institution of Higher Learning (IHLs). However, these approaches generally have questionnaires sets consists of both quantitative and qualitative whereas the teaching assessment systems for several online lecture have concentrated on quantitative questions part due to its quick analysis. Contrarily, the part of qualitative has needed opinion from students that frequently ignored or misplaced and the opinion results level are omitted. This is due to student’s opinion which is generally in term of unstructured text that made hard to analyze the feedback manually. Moreover, the enormous data amount has produced on daily basis which can be utilized to opinion mining for extracting the student’s opinion in this exact online education area that has not widely applied in all educational system. Therefore, this paper has designed a framework by applying opinion mining concept to the student of coursera online machine learning course as the dataset whereas the comparison is accomplished using opinion mining framework to extract. The unstructured text gets processed and classification results based on polarity to obtain better outcome. Hence, this framework maintain several steps namely opinion extraction, unstructured text processing and polarity classification using NLP. Moreover, the performance of the outcome from the Opinion Mining (OM) gets evaluated using machine learning algorithms.
Deduplication of Advertisement Assets Using Deep Learning Ensembles

Creative image deduplication is the process of detecting similar images among digital advertisement assets. We evaluated a novel ensemble of DenseNet based Siamese Network and Scale Invariant Feature Transformation (SIFT) which is able to outperform both individually. Our approach also outperforms a state-of-the-art model on the desired metrics in the adtech world. The evaluation was carried upon the advertisement assets of a major ad-network. Due to the difficulty in acquiring labelled images for the training dataset, we also present a novel strategy for semi-automatically generating labels.
Bichromatically-controlled entanglement between asymmetric quantum dots in a photonic cavity

Abstract Entanglement generation and control of two spatially separated asymmetric quantum dots with broken inversion symmetry and mediated by a photonic cavity is studied using a quantum master equation formalism. The quantum dots are coherently driven by a bichromatic laser consisting of a strong optical field nearly resonant with the optical transition of each quantum dot, and a low frequency field. The optical field dresses each quantum dot, and due to the presence of large permanent dipole moments in the quantum dots they are coupled by the low frequency field. We make use of the generated dressed-state scheme for entanglement control. The master equation which describes the interaction with the cavity modes and the coherent fields is numerically solved. In order to gain some insight on the role of the external parameters on entanglement, an effective Hamiltonian for the atomic subsystem is derived in the dressed state representation by adiabatically eliminating the cavity field operators. It is found that steady-state entanglement can be controlled by means of the amplitude and frequency of the low frequency field.
Reducing Makespans of DAG Scheduling through Interleaving Overlapping Resource Utilization

As data center clusters need to process quintillion bytes of data per day, it becomes a critical problem that efficiently scheduling jobs to improve resource utilization. However, the data analysis job usually contains multiple stages with dependent relationships, which brings challenges for scheduling. Those stages are modeled as Directed Acyclic Graphs (DAGs) and the general DAG scheduling problem is NP-hard. In this paper, we notice that in some parallel computing frameworks such as Spark, the execution of each stage could be divided into multiple phases that use different resources. We observe that interleaving different resources in a pipelined manner could improve resource utilization. Based on this observation, we propose to minimize the job makespan by exploiting resource pipeline. We first theoretically analyze the scheduling for perfectly parallel stages. In this case, our scheduling problem is equivalent to a DAG shop problem which is NP-hard. A contention-free scheduler is proposed and its approximation properties are analyzed. Stages of real-world jobs are usually not perfectly parallel. For general jobs, a reinforcement learning (RL) based scheduler is proposed to adaptively adjust the resource contention. We evaluate our contention-free and RL-based schedulers on a Spark cluster deployed on the Amazon EC2. Experiments on real-world and synthetic datasets show our RL-based scheduler can improve the CPU and network utilization by 33.0% and 29.7%, respectively.
Towards Atmospheric Interfaces

This paper introduces a preliminary taxonomy to bring the condition of air into the foreground of human perception. To create this taxonomy, we drew on the foundations of atmospheric research and studies in the field of human-computer interaction to provide an overview of different inputs and outputs that enable an interaction with the air. In addition, we present a potential use case that could benefit from a taxonomy to allow the development of atmospheric interfaces and empower the transfer of knowledge. We discuss our findings and conclude with challenges that can be addressed in future research.
An Algorithms for Finding the Cube Roots in Finite Fields

Let Fq be a finite field with q elements. Quadratic residues in number theory and finite fields is an important theory that has many applications in various aspects. The main problem of quadratic residues is to find the solution of the equation x2 = a, given an element a. It is interesting to find the solutions of x3 = a in Fq. If the solutions exist for a we say that a is a cubic residue of Fq and x is a cube root of a in Fq. In this paper we examine the solubility of x3 = a in general finite fields. Here, we give some results about the cube roots of cubic residue, and we propose an algorithm to find the cube roots using primitive elements.
Reconstructing 3-D Graphical Model Using an Under-Constrained Cable-Driven Parallel Robot

In this paper, a novel application of an under-constrained cable-driven parallel robot is introduced. By this application, the 3D model of any object with any size limited to the workspace of the robot can be reconstructed, which is a definite asset for creating the 3D model in such an easy and cost-efficient way despite other methods like a laser scanner. The most significant problem of an under-constrained cable-driven robot is the orientations of the end-effector which cannot be controlled regardless of its position, i.e., the orientation and position of the end-effector are not independent. In this paper, the foregoing problem was solved by using a stabilizer to capture a video in prescribed orientations. The results demonstrate that the end-effector can be controlled with high precision by a PID controller to reconstruct the 3D model of an object based on the captured videos of a camera which is mounted on the end-effector of the robot. In this paper, the object is a social robot called Sanbot that has a shiny surface, which decreases the 3D model reconstruction precision. However, the results reveal that the 3D model of the Sanbot robot has a very clear view, and the experimental results confirm the efficiency of the proposed method. This method provides an easy way to reconstruct the 3D model of an object which cannot be reconstructed by other methods.
Predicting User Performance in Augmented Reality User Interfaces with Image Analysis Algorithms

This study determines how to apply existing image analysis measures of visual clutter to augmented reality user interfaces, in conjunction with other factors that may affect performance such as the percentage of virtual objects compared to real objects in an interface, and the type of object a user is searching for (real or virtual). Image analysis measures of clutter were specifically chosen as they can be applied to complex and naturalistic images as is common to experience while using an AR UI. The end goal of this research is to develop an algorithm capable of predicting user performance for a given AR UI. In this experiment, twelve participants performed a visual search task of locating a target object in an array of objects where some objects were virtual, and some were real. Participants completed this task under three different clutter levels (low, medium, high) against five different levels of virtual object percentage (0%, 25%, 50%, 75%, 100%) and two types of targets (real, virtual) with repetition. Task performance was measured through response time. Results show significant differences in response time between clutter levels and between virtual object percentage, but not target type. Participants consistently had more difficulty finding objects in more cluttered scenes, where clutter was determined through image analysis methods, and had more difficulty in finding objects when the virtual of objects was at 50% as opposed to other scenarios. Response time positively correlated to measures of combined clutter (virtual and real) arrays but not for measures of clutter taken of the individual array components (virtual or real), and positively correlated with the clutter scores of the target objects themselves.
Improved Twitter Sarcasm Detection by Addressing Imbalanced Class Problem

Sarcasm detection is one of the challenging tasks in the domain of text mining. With the advancements in machine learning reasonable accuracy have been achieved in sarcasm detection. Studies have revealed that highly imbalanced classes affect the performance of the classifiers in sarcasm detection. The current study addresses the class imbalance problem in the sarcasm detection in tweets. Synthetic Minority Oversampling Technique (SMOTE) has been used to mitigate the imbalanced class problem present in the data collected from tweeter. Two well-known classifiers have been trained and tested with varying degrees of balance ratio of SMOTE. Results have indicated that the SMOTE is highly successful in addressing the imbalanced class problem in sarcasm detection by improving the classifier performance to a greater extent.
Application of the Simple Multi Attribute Rating Technique (SMART) Method in the selection of thrush medicine products based on consumers

Abstract The purpose of the research is to analyze marketing strategies in the selection of Mouth sores products based on consumers. The data source was obtained by observing, interviewing and giving questionnaires to 375 respondents randomly by using Google Form. The output of the research is the ranking of Mouth sores products. The technique used is utilizing one of the decision support system methods, namely Simple Multi Attribute Rating Technique (SMART) in providing recommendations for results. The alternative thrush medicine products that are used are: Adem Sari (A1), Jesscool (A2), Alangsari (A3), Larutan Penyegar cap Kaki Tiga (A4) and Larutan Penyegar cap Badak (A5). The rating criteria given are Composition (C1), Packaging (C2), Taste (C3), Price (C4) and How to use (C5). The results of the SMART method calculation give the first recommendation is Alangsari (A3) with a final value = 0.84 and Jesscool (A2) as the second recommendation with a final value = 0.45.
Adaptive Meek Technology for Anti-Traffic Analysis

With the rapid increase in the demand of users for communication security, Tor is widely used as the most reliable anonymous communication system. Meek is used as a pluggable transport for Tor, which protects Tor users from traffic analysis by disguising the traffic to the Tor network as the traffic to the cloud server. However, in the face of machine-learning attacks that use side-channel information of traffic, Meek still exposes its lack of obfuscation ability. In this research, we propose an adaptive Meek technology (AM) that resists statistical analysis of traffic to disguise Tor-based Meek traffic as the target traffic to meet the minimum cost of improving indistinguishability. First, this technology selects the target traffic, which can be constantly updated to minimize the overhead of obfuscation. Then, it provides a reliable obfuscation solution to maximize the mimicry effect by focusing on side-channel information. Throughout the obfuscation process, our technology can make obfuscation of Meek traffic achieve dynamics and diversity. We use the traffic dataset of the real scenario, then conduct an experimental evaluation through our well-trained classifiers. After our defense, the effect of the classifier was successfully reduced while the overhead is within an acceptable range.
HTDA: Hierarchical time-based directional attention network for sequential user behavior modeling

Personalized recommendation is one of the most effective ways to alleviate the problem of information explosion. Recurrent Neural Network (RNN) sequential recommendation based on user preference modeling has been widely investigated recently. In this paper, a Hierarchical Time-based Directional Attention (HTDA) network is proposed to enhance sequential recommendation by applying fine-grained user intention representation and dynamic user preference representation with rich global sequential interaction features. Our analyses and results significantly differ from earlier related works in two aspects: 1) We put forward a session-level representation method, based on multi-dimensional attention mechanism, to enhance the degree of matching between the user interaction sequence and the user intention and reduce the impact of noise interaction; 2) We propose to integrate a time-based directional attention mechanism into RNN, to capture the sequential patterns of interaction sessions more effectively and improve the understanding of user dynamic preferences. The main contribution of our work is to effectively combine the accurate description of user intentions and the powerful expression of user dynamic preferences through a hierarchical attention architecture. In turn, our architecture can provide a more favorable framewrok for further enhancing the recommendation results. Extensive experiments are conducted on two public benchmark datasets, and very promising performance can be achieved with our proposed HTDA-based framework.
Comparative Study of Recent Swarm Algorithms for Continuous Optimization

Optimization is a problem of finding the best solution from various possible solutions. In solving optimization problems, collective intelligence algorithms are often used as a method for finding solutions to optimization problems. This is because collective intelligence has various algorithms to search for various types of continuous optimization problems. However, not all of these algorithms work with the same performance. An algorithm can work better for a problem than other algorithms. Therefore, the diversity of performance of this algorithm must be identified, analyzed, and compared. By knowing the strengths, weaknesses, nature, and behaviour of the algorithm in solving various problems, this is believed to be able to realize the use of more effective algorithms in solving various problems. In this paper, Dragonfly Algorithm (DA), Grey Wolf Algorithm (GWO), and Rao Algorithms are carefully investigated using nine benchmark functions. The result indicates that Rao generally performed better than GWO and DA. DA is outperformed by GWO and Rao in reaching the convergence score, although DA has an edge in searching a large search space, and in theory, given enough population, DA can perform better than GWO or Rao.
RFID multi-tag dynamic detection measurement based on conditional generative adversarial nets and YOLOv3

The quality of multi-tag imaging greatly affects the effective detection of multi-tag. When multi-tag moves rapidly, the image may have serious dynamic blur, and tags can not be detected efficiently. In previous work, it is generally assumed that blur kernel and noise stationary to improve image quality. However, the dynamic deblurring of Radio Frequency Identification (RFID) multi-tag imaging is an ill-posed inverse problem. In this paper, firstly, blur-sharp multi-tag image pairs are made by superimposing and averaging the adjoin random frames. Then, we propose blind deblurring for dynamic RFID multi-tag imaging based on conditional generative adversarial nets (CGANs), which adds perceptual loss and content loss to generator to make image sharper. Finally, tags are detected by YOLOv3 in real time in end-to-end manner. Experimental results demonstrate that PSNR is at least 0.56dB higher and speed is at least 31.25 % faster than that of the current improved convolution neural networks (CNN). CGANs can remove image blur better, which has great superiority in the field of dynamic multi-tag imaging. In addition, YOLOv3 detects multi-tag quickly, thereby improving the detection accuracy.
A Hybrid Framework for Effective Prediction of Online Streaming Data

Abstract In this paper, we present a hybrid model to perform the training and testing of prediction model with online streaming data. Prediction of online streaming data is a time critical task. Huge volume of data that is being generated online need to be ingested to a prediction model and to be used to train and test the prediction model dynamically which improves the learning rate. The existing approaches for dynamic training and testing use the local infrastructure or virtual machines from the cloud infrastructure to increase the learning rate of the prediction model with streaming data. Recently many applications prefer serverless cloud infrastructure than virtual machines. However, using the serverless infrastructure for the entire prediction process will have time and space tradeoffs due to its autonomic feature. Hence in this paper we propose a hybrid approach that uses the three different environments such as the local infrastructure, virtual machine and serverless cloud for different stages. A novel approach to select the suitable environment to train and test the LSTM based air quality prediction model with stream data is proposed with increased learning rate and reduced resource utilization.
Development of A Passive Skid for Multicopter Landing on Rough Terrain

Landing is an essential part of multicopter task operations. A multicopter has relatively stringent requirements for landing, particularly for achieving flatness. Currently, landing on rough terrain with normal skids is difficult. Therefore, research is being conducted to obtain skids capable of landing on rough terrain. In this paper, a passive skid for multicopter landing on rough terrain is proposed. The proposed device is based on an existing previous study of the multicopter carried with a electric robo-arm only for object manipulation. This innovative idea stems from the aim of giving the multicopter carried with a electric robo-arm the ability to land on various occasions and then the passive skid is designed. By using a slope to simulate a rough terrain, the range of available landing in which a multicopter can maintain its pose and the frictional torque of the passive joint are analyzed. Further, experiments are conducted to demonstrate that landing can be achieved using the skid proposed in our study.