Reviewerly/exHarmony huggingface dataset:
Recommendation of Student Admission Priorities Using K-Means Clustering

This study aims to investigate student's characteristics based on three variables, namely grade point average (GPA), period of study, and administrative obedience in order to draw a recommendation for student admission priorities at Mulawarman University.This recommendation will be used as one of reference variable on new student recruitment.The 8.741 records of student data sourced from the university data warehouse were mined using K-Means clustering.This mining process produced three clusters, cluster-1 includes 1,758 students with centroid {0.158,0.694,0.663},while cluster-2 embraces 4,928 students with centroid {0.970,0.700,0.675},and cluster-3 with centroid {0.953,0554,0.386}covers 2.055 students.This result shows that cluster-2 has the best combination of centroid values, implied that new students from schools where students in cluster-2 graduated from are recommended as the high priority students to be admitted at Mulawarman University
Word-Conditioned Image Style Transfer

In recent years, deep learning has attracted attention not only as a method on image recognition but also as a technique for image generation and transformation. Above all, a method called Style Transfer is drawing much attention which can integrate two photos into one integrated photo regarding their content and style. Although many extended works including Fast Style Transfer have been proposed so far, all the extended methods including original one require a style image to modify the style of an input image. In this paper, we propose to use words expressing photo styles instead of using style images for neural image style transfer. In our method, we take into account the content of an input image to be stylized to decide a style for style transfer in addition to a given word. We implemented the propose method by modifying the network for arbitrary neural artistic stylization. By the experiments, we show that the proposed method has ability to change the style of an input image taking account of both a given word.
Human Action Imitation System Based on Nao Robot

A human action imitation system based on Nao robot is proposed in this paper. A new strategy, combining Momentum with Stochastic gradient descent(SGD), is designed to accelerate the training process of Convolutional Neural Networks(CNN). The CNN is used to recognize the 2D coordinate of joints of a human body. This data is translate to Nao Robot and make it to imitate human action. The experiments show that the training rate is faster than traditional training method. The Robot can imitate the human action accurately.
Application of Continuous Time Quantum Walks to Image Segmentation

This paper provides the algorithm that applies concept of continuous time quantum walks to image segmentation problem. The work, inspired by results from its classical counterpart [9], presents and compares two versions of the solution regarding calculation of pixel-segment association: the version using limiting distribution of the walk and the version using last step distribution. The obtained results vary in terms of accuracy and possibilities to be ported to a real quantum device. The described results were obtained by simulation on classical computer, but the algorithms were designed in a way that will allow to use a real quantum computer, when ready.
An Optimal Light Weight Cryptography—SIMON Block Cipher for Secure Image Transmission in Wireless Sensor Networks

With rapid growth of multimedia applications, secure image transmission over Wireless Sensor Network (WSN) is a challenging task. So, image encryption techniques are used to meet the demand for real-time image security over wireless networks. In the proposed research, the security of Digital Images (DI) in wireless sensor network is enhanced using Light Weight Ciphers (LWC) which encrypts the input image through encryption process. To improve DI privacy and confidentiality, an innovative security model is proposed i.e. Lightweight SIMON block cipher. The proposed LWC encrypted the image along with optimal key selection and enhanced the image security level in the cloud. For key optimization, a meta-heuristic algorithm called Opposition-based Particle Swarm Optimization (OPSO) algorithm was presented. The proposed SIMON-OPSO achieved the minimum time in generating key value to decrypt the image. The simulation result demonstrated that the SIMON-OPSO algorithm improves the accuracy of DI security for all input images (Lena, Barbara, Baboon, and House) compared to existing algorithms.
L2 Learners’ Preferences of Dialogue Agents: A Key to Achieve Adaptive Motivational Support?

This study reports on differences observed among learners’ preferences of two conversational strategies embedded in a dialogue agent dedicated to enhancing their willingness to communicate (WTC) in a second language. We found that the combination of both strategies is, in general, the most preferred by learners. However, perception, as well as effects of the support provided by these strategies seem to vary according to learners’ level of willingness to communicate. Lower WTC learners tended to prefer affective backchannels while their counterparts seem rather favor communication strategies. These results were also in line with posttest results which revealed that learners’ expected WTC tended to be higher after interacting with dialogue agents embedding their preferred strategies. In sum, these results can be viewed as preliminary evidence of the meaningfulness to account for second language learners’ preferences towards balancing adaptively the type of strategies employed by dialogue agents to motivate learners towards communication in the target second language.
Creation of Data Classification System for Local Administration

This paper deals with classification of flow of messages coming to the local government from various data sources, such as social networks, website of the government, emails, etc. The primary data, which is extracted from various sources, is stored in the NoSQL database. Further, using special methods and developed applications, the data is classified and sent to the relevant departments. The article focuses on the review of methods and the construction of the architecture of the system of data classification which retrieved from social networks, website of the local government, emails, etc.
Interference Experiment with a Transparent Mask Rejects Wave Models of Light

Young's double-slit experiment shows characteristics of light that are modeled as indicating a wave nature of light.Other experiments suggest a particle model of light.An experiment is performed with the Fraunhofer pattern from a first mask impinging on a transparent second mask with a slit.The screen pattern is an interference pattern such as produced in Young's Experiment.An opaque strip between the first and second mask blocks the light of the center maxima from the first mask.The screen interference fringes remained.Moving the glass mask so the slit is removed from light shows the necessity of a slit in the second mask.This suggests a Newtonian type of light model.This experiment rejects the wave models of light.
Arabic writer verification based on shape complexity

Writer recognition task has received a lot of interests during the last decade due to it wide range of applications. This task includes writer identification and/or verification. In this paper we propose an original approach for the Arabic writer verification based on shape complexity. In this approach two handwritten words are submitted to the system, which will proceed to a segmentation to each of them into graphemes. A dedicated segmentation module for Arabic handwriting has been developed for this purpose. All the graphemes generate are then analyzed to select the one who embed the most relevant information about the writer identity in each word. This step is performed through a shape complexity measure that determines the most complex shape (grapheme). The decision, if the two samples have been written by same writer or not is based on the similarity of the two most complex shapes extracted from each word. The approach has been evaluated on dataset of 105 and the obtained results are very promising since we succeed to accept correctly in 80 % of cases when we used the whole dataset and reject correctly in 89% of the cases when we use 20% of the dataset.
THROMBOCYTE ESTIMATION BASED ON BLOOD SMEAR AND ITS COMPARISON USING AUTOMATED HAEMATOLOGY ANALYSERS

BACKGROUNDAccurate platelet counts are very important and are essential for patient care.There are many methods for counting platelets.Study was conducted with the objectives of evaluation of thrombocyte count from peripheral blood smear (PBS) and by using Sysmex automated haematology analyser and to compare the precision of manual method in comparison to automated platelet counts.Evaluation of thrombocyte counts is very necessary for certain conditions like dengue hemorrhagic fever.Thrombocyte counts are considered as one of the most important parameters to assess the severity of the illness and also to monitor the progression of the illness and response to the treatment.In such critical situations, manual counting of thrombocytes is considered more reliable compared to automated analysers.Automated haematology analyser counters are not usually available at all centres, especially in peripheral and rural areas though thrombocytes can also be assessed from the peripheral blood smears, which can be easily and precisely done at any set up.
PC Builder Hero

In this paper, we describe PC Builder Hero, an immersive virtual reality (VR) game, which takes the user through building a custom personal computer (PC) from scratch. The main motivation is to raise awareness and inspire curiosity about the inner components and working mechanisms of computers in a safe environment where mistakes aren't costly. We aim to teach users how to build a PC experientially with a hands-on game, leveraging the immersiveness of VR. The user picks virtual computer components and monitors the price of the built computer and the accumulated static over time. The user's goal is to find a balance between the specifications of the components and the overall price, while complying with the proper handling procedures of building a PC. This paper includes the design and implementation details of the high-fidelity prototype along with the results and implications of an informal early testing.
ACTUALIZATION OF TIMBRE FEATURES OF PIANO TEXTURE IN THE PROCESS OF PERFORMANCE: SYNESTHETIC ASPECT

The article examines piano texture in the aspect of its timbre features discovered during the synesthetic analysis of N. Medtner’s “Fairy Tale” op. 20 № 2. The author focuses on identifying the mechanisms forming the timber character of piano texture in a performer’s interpretation. To achieve this objective, the researcher analyses the technology of a performer’s “adjustment” of the timber component of a musical image to make it artistically complete and multidimensional. For the first time, the paper identifies the proprio-interoceptive synesthetic mechanism of the mutual influence of a composer’s score and a performer’s consciousness, which organizes the process of timbre texture formation. Special attention is paid to agogics having its roots in the existential structures of a performer’s time perception.
The Future of the IT Workforce

The purpose of this panel is to discuss different views on the future of the IT workforce at the heart of our discipline. We will discuss how technological, organizational, and environmental trends will affect the IT workforce. Based on these changes, we explore how IT workforce research need to advance discuss fruitful directions.
Robust phase unwrapping based on non-coprime fringe pattern periods for deflectometry measurements

Phase-measuring deflectometry is a technique for non-contact inspection of reflective surfaces. A camera setup captures the reflection of a sine-modulated fringe pattern shifted across a screen; the location-dependent measured phase effectively encodes the screen coordinates. As the used fringe patterns are much narrower than the screen dimension, the resulting phase maps are wrapped. The number-theoretical solution uses the Chinese remainder theorem to calculate an unwrapped phase map from repeated measurements with coprime fringe widths. The technique is highly susceptible to phase noise, i.e. small deviations of the measured phase values generally lead to unwrapped phase values with large errors. We propose a modification and show how non-coprime period widths make phase unwrapping robust against phase noise. Measurements with two non-coprime fringe period widths introduce the opportunity to discriminate between “legal” measured phase value pairs, that potentially originate from noise-free measurements, and “illegal” phase value pairs, that necessarily result from noise-affected measurements. Arranged as a matrix, the legal measurements lie on distinct diagonals. This insight not only allows to determine the legality of a measurement, but also to provide a correction by looking for the closest legal matrix entry. We present an experimental comparison of the resulting phase maps with reference phase maps. The presented results include descriptive statistics on the average rate of illegal phase measurements as well as on the deviation from the reference. The measured mean absolute deviation decreases from 1.99 pixels before correction to 0.21 pixels after correction, with a remaining maximum absolute deviation of 0.91 pixels.
An Integrated System for Mixed-Initiative Planning of Manned Spaceflight Operations

Manned spaceflight in outer/deeper space will require crew operations that are independent of ground support. This requires the crew to re-plan day-to-day activities, particularly in the case of unforeseen circumstances. To support these planning duties, we are developing a mixed-initiative planning tool that optimizes schedules in collaboration with astronauts. This paper highlights the tool's planning algorithm. The planning algorithm has two closely-coupled components: first, an optimization algorithm (optimizer) based on local search heuristics and, secondly, a computational model of the work that is to be performed. In this framework, the optimizer acts as a surrogate model of the more detailed computational models, such that new solutions can be efficiently explored. The computational work model is capable of simulating a plan through time, and can account for dynamic interactions between activities and work environment that are not modeled in the optimizer. Moreover, the computational model returns to the optimizer metrics that reflect required teamwork to coordinate activities between astronauts. The paper includes a description of the optimizer and computational simulation models as well as a case study with activities, agents and resources that are representative of a typical manned mission.
Identification of Diseases in Rice Plants Using the Gray Level Co-Occurrence Matrix Method

Abstract Rice plants (Oryza sativa, sp) are a staple food raw material that is vital for the people of Indonesia. Rice plants are included as a year / annual plant group. The management of rice plants is an innovative and dynamic approach in increasing the production and opinion of farmers through assembling technological components in a participatory manner with farmers. For this reason, appropriate technology facilities are needed that can be used by farmers in determining the type of leaf disease that burns during the growing period of rice so that it can increase production. This study aims to apply the Gray level Co-Occurrence Matrix method to produce feature values on rice leaves. Differences in the patterns of rice leaves are used by identifying each rice leaf pattern, which results in the type of leaf disease burning through the rice leaf pattern. In this study, the accuracy of this system was obtained at 72.5%.
UGV Direction Control by Human Arm Gesture Recognition via Deterministic Learning

In this paper, we present a novel method for controlling an unmanned ground vehicle (UGV) by using a new machine learning technique, called deterministic learning [1], to learn and recognize four specifically designed body languages, which represent four corresponding moving directions (i.e., left, right, up, and down) of the controlled UGV. The Microsoft Kinect sensor is employed to collect the human body skeleton data, from which four specifically-designed features are extracted for neural network training. The discrete-time deterministic learning algorithm is then utilized to train the radial basis function neural networks (RBFNNs). The dynamics of the human arm waving motion is guaranteed to be accurately identified, represented, and stored as a RBF NN model with converged constant NN weights. In the testing phase, a set of estimators are built based on the database established in the learning phase, so as to conduct real-time rapid recognition of new in-coming gesture commands. The smallest error principle is used to decode the human intention, the decoded result will then be sent to the UGV through TCP/IP to control the UGV's moving directions. A full-integrated graphical user interface (GUI) has been developed based on Matlab to demonstrate effectiveness of the proposed approach and visualize the experimental results.
Optical Sources Optimization for 3D Reconstruction Based on Underwater Vision System

Retracted.
Improved Competitive Swarm Optimization Algorithms for Feature Selection

CSO is an optimization algorithm based on the competition concept, which has been applied to feature selection and had a good performance on datasets with high feature dimensions. As a wrapper method, CSO is confronted with the problem of computation expensive and time consuming. To solve the problems, we proposed two improved algorithms from the perspective of reducing particle fitness calculation times: FCSO (Faster Competitive Swarm Optimization) and SFCSO (Selected Faster Competitive Swarm Optimization). FCSO shortens the time required for running by reducing the number of particles involved in each iteration, whereas SFCSO achieves the same goal by screening mechanism. SFCSO improves the stability of FCSO. We use KNN classifier to carry out experiments on four datasets with different size and dimension. The experimental results show that FCSO reduced time to one-tenth of the original, while SFCSO was half of it. FCSO has a better performance on binary classification problems so as SFCSO in multi-classification problems. Both algorithms can significantly reduce time complexity with a little decline of accuracy or even a higher accuracy, which is acceptable.
Using Cognitive Walkthrough and Hybrid Prototyping to Gather User Requirements in Early Design Virtual Reality Prototypes

To evaluate Virtual Reality (VR) prototypes usability involves a variety of single-perspective or Hybrid methods. The latter has being suggested by literature as offering a more complete sets of requirements highlighting both ‘in-world’ and user interface problems. This paper describes our experiences in using a single-perspective method for gathering user requirements in the REVERIE (Real and Virtual Engagement In Realistic Immersive Environment) project. The study reports results involving nine evaluators who reviewed two hybrid VR prototypes with educational context. It was found that this approach was effective in highlighting a plethora of usability problems covering all aspects of the two VR prototypes. The performance of our approach was similar to the literature. Although additional validation work is required, we can conclude that our approach may provide a viable option to evaluate early design VR prototypes when required (e.g., when the expertise needed to use a hybrid method is not available). Future work aims to compare the performance of our approach with two-stage and multiple stage hybrid methods.
Exploring an Extension to the Foresight Measure: A Mixed-Mode Study of Individual Managerial Employees

Foresight is the ability to visualize alternate futures and shape the direction of future events. Generally considered as an organizational capability, foresight can be the basis of sustained competitive advantage, and needs to be fostered by enabling employees to acquire, assimilate, and analyze information with a future orientation. However, research on what constitutes foresight at the employee (i.e., individual) level is sparse. This paper fills this gap by using a five-stage mixed-mode process to propose an extended foresight measure. This paper has implications for research on the individual aspect of microfoundations of capability building, organizational measures development, and foresight literature.
Dehazing and Road Feature Extraction from Satellite Images

Image captured by satellite will be degraded due to scattering of the light by the atmospheric particles under challenging environmental conditions like fog, haze, smoke, etc. Hence this will seriously affect the performance of computer vision system. In this paper an image dehazing based on Quad tree subdivision and convolution neural network(CNN) transmission map is developed to provide end to end dehazing. This algorithm will help to recover the image clearly and accurately. Road extraction plays a significant role in traffic management, city planning road monitoring map updating, GPS navigation, etc. After analyzing various road models and features, this paper also presents an effective method for road extraction based on morphological operation and canny edge detection from the dehazed image. Hence provide a fast, simple and accurate method of dehazing and road extraction.
Motion capture data segmentation using Riemannian manifold learning

Abstract Due to the inherent nonlinear nature of data, traditional linear methods have some limitations in finding the intrinsic dimensions of motion capture (Mo‐cap) data. Mo‐cap data are more in line with the characteristics of the manifold. Assuming that the data are initially a low‐dimensional manifold and uniformly sampled in high‐dimensional Euclidean space, manifold learning recovers low‐dimensional manifold structures from high‐dimensional sampled data. This paper proposes an automatic segmentation method based on geodesics by introducing a Riemannian manifold. We convert Mo‐cap data from Euler angles into quaternions, calculate the intrinsic mean of the motion sequence, hemispherize quaternions, and use logarithmic and exponential mapping to calculate geodesic distances instead of quaternions. The experimental results show that the algorithms can achieve automatic segmentation and have a better segmentation effect.
On the first JND and break in presence of 360-degree content

Unlike traditional planar 2D visual content, immersive 360-degree images and videos undergo particular processing steps and are intended to be consumed via head-mounted displays (HMDs). To get a deeper understanding on the perception of 360-degree visual distortions when consumed through HMDs, we perform an exploratory task-based subjective study in which we have asked subjects to define the first noticeable difference and break-in-presence points when incrementally adding specific compression artifacts. The results of our study: give insights on the range of allowed visual distortions for 360-degree content; show that the added visual distortions are more tolerable in mono than in stereoscopic 3D; and identify issues with current 360-degree objective quality metrics.