Reviewerly/exHarmony huggingface dataset:
Artex (Art Teacher Exhibition) 2019

Artex is an art exhibition organized by Galleria Wesberly House, Kuching that showcases 40 art teachers' work including UNIMAS, UiTM, Swinburne and several schools in Sarawak.
Training Data Clustering for Key-Sound Estimation in Rhythm Action Games

A rhythm action game is a genre of video game in which the player acts in accordance with visual symbols (charts) representing the rhythm of music. This work aims at generating a chart from the music automatically to realize easy development of rhythm action games. A key-sound is a sound object which is played in response to the player's operation. To generate a chart, it is necessary to classify the sound objects into key-sounds and the other sounds. This process can be done by machine learning, but the training data have a wide variety due to the individuality of creators, and the optimal chart is not unique. Accordingly, we divided the training data into clusters based on the classification tendency and prepared multiple models. Classification experiments using multiple models suggested the possibility of improvement, but further investigation is necessary for the selection of the optimum cluster.
Testing AI-democratization : What are the lower limits of textgeneration using artificial neural networks?

Articial intelligence is an area of technology which is rapidly growing. Considering it'sincreasing inuence in society, how available is it? This study attempts to create a web contentsummarizer us ...
Analysis of Fraudulent Transaction Detection Techniques based on Customer Behavioural Patterns

Rapid Growth of internet and E-Commerce has opened a new Marketing Phenomenon i.e Online Shopping. However, the problem of same being illegally used by Intruders for Fraudulent activities has become increasingly prominent, and the security of user account is not guaranteed. On the other side, the e-commerce website keeps all the data record related to transaction so there is tremendous amount of data identifying human behaviours. This paper is based on study of different methodology used for identifying the fraudulent transaction with different attributes of users based on their historical transaction. Each historical transaction consists of various transaction attributes which can be used to detect whether the transaction is fraud or genuine. In each of the methodology Initial step is to create a behavioural profiling model by assigning score and then classify the transaction as fraudulent or nonfraudulent transaction.
Coding to Decipher Linear A

This paper discusses the program logic for an attempt at using coding to aid in the decipherment of Linear A, the writing system of the Ancient Minoan Civilization. Using Python, a system can be created to compare Linear A strings to lexical lists and dictionaries of languages from a compatible time period.
Jigsaw Puzzle Solver to Locate Piece Position

Jigsaw puzzles present an interesting form of problem solving based around techniques of matching and feature recognition. This paper presents a method to find the location of jigsaw pieces relative to the completed puzzle using an ORB feature detector alongside a FLANN based matcher. An image or frame containing a jigsaw piece is reduced to its region of interest through thresholding and contour finding before being passed through the feature matching process. Matches are then filtered using Lowe’s ratio test and outliers are found and removed using a homography transformation matrix. Evaluation of the proposed method saw 81.2% accuracy of a jigsaw puzzle with 24 pieces which was significantly more accurate than shape detecting methods and more robust than methods which required specialised jigsaw pieces.
Fuzzy Logic: A Technique for Assessing Students’ Learning Performance

Learning assessments of students are very vital in assessing the overall learning performance of students.  Knowing the learners capabilities are always part of a teacher’s job to fully understand the learner’s need. Hence, the researchers were motivated to conduct this study on the purpose of evaluating and identifying the performance of students using the fuzzy inference.  The research is focusedonthe private university students.Results of examinations from both the lecture and laboratory classes are evaluated to determine the how the student performs in a class.  The exam scores of lecture and laboratory class serves as input to the fuzzification technique to determine which among the two class types would the students perform well.  In this paper, fuzzy logic basic concepts are applied on theexam results.  The authors then concluded that students are performing better during laboratory examinations.
A Novel Approch for Binary Elliptic Curve Cryptosystem Implementation Over GF(2^409)

Security in embedded systems are presented in this work based on Elliptic Curve crypto-processor (ECCP). Comparison with state-of-the-art algorithms that have been used to implement ECCP are also presented. It proves that Montgomery ladder algorithm is preferred one when the speed is needed. Optimization problem for general ECCP architecture are started from the selected algorithm and ends with liveness analysis and forward path. The inversion operation based on Itoh-Tsujii GF(2409) is discussed in this work. The proposed ECCP is implemented for GF(2163) and GF(2409) where the execution time are 8 μs and 61.2 μs respectively in sequentially design.  The design and  results are implemented using Xilinx ISE Virtex6.
Save Your Grades! A 3D Mobile Educational-Entertainment Game Using Ant Colony Algorithm

Learning in this generation can be different nowadays, with the help of educational games learning are designed to be fun and interactive than the common way of teaching. The games are designed in a way to help people learn particular topics, that involves expanded concepts which develop skills as they learn while playing. Problem-solving is a process which playing games gives through a constant observation. The development of the game encountered problems on Artificial Intelligence. The problem needed a new way of implementing AIs into the game while optimizing the amount of time spent on each AI that were being implemented. The study utilized path finding algorithms for the Artificial Intelligence to determine or predict the exact location of the player.  The use of Ant Colony Optimization Algorithm for path finding,  helped the authors design Artificial Intelligence that would take the shortest path in searching of the player. This study optimizes each AI to find the player in shortest route.  In addition, this study concluded that integrating educational-entertainment games would help students in improving their intelligence.
Quantum Coin Flipping: and Circuit Design Problems in Distributed Quantum Computing

Quantum coin flipping is a cryptographic primitive in which two or more parties that do not trust each other want establish a fair coin flip. These parties are not physically near each other and use quantum communication channels to interact. A quality of protocols is measured by the best possible cheating strategy, which is the solution of a complex semidefinite optimization problem. In this master thesis we show new explicit bounds in multiparty quantum coin flipping, we investigate how to explicitly formulate these problem in a standard form, we show that a fair coin flip results in the lowest possible bias and we determine more measures of the quality of a protocol. Furthermore, this master thesis presents a rigorous and detailed mathematical description of semidefinite optimization, quantum information theory and quantum coin flipping. This thesis also includes an article written together with J. Mulderij, T. Attema, I. Chiscop and F. Phillipson on distributed quantum computing. In this article, we pose new questions and formulate integer linear programs that solve to find optimal assignment of qubits to computers for a given network of quantum computers and quantum algorithm.
Basic Concepts of Fuzzy Matrices

A fuzzy matrix may be a matrix with elements having values in the fuzzy interval".In this article, the unit interval [0,1] andthe interval [-1,1] are called fuzzy intervals[3].Fuzzy matrices occur within the modeling of assorted fuzzy systems, with products usually determined by the "max(min)"rule arising from fuzzy set theory.In this paper some comfortable conditions for convergence beneath "max(min)" product of the powers of a sq.fuzzy matrix and of a fuzzy state method square measure established.Convergence of powers of a fuzzy matrix
Secure cloud computing using homomorphic construction

When customers are transferring their private data to any third party, then there is much responsibility of both security and compliance. There exist hurdles in mainly two aspects – one is in the users' or customers point of view, where they want to ensure the privacy of its input parameters and results. Another is to cloud servers point of view, where cloud entity is worried about feasibleness of encrypted/transformed inputs and operating on them. Thus, users would not like to submit their private problem data inputs to the cloud. encrypting the private data prior to submission to cloud is a usual solution. Here in this paper, we have suggested a framework for cloud data security and presented the detailed construction of our proposed homomorphic encryption system. Along with that the security and correctness analysis of our proposed construction is given. The scheme satisfies both Additive and Multiplicative homomorphism properties.
Design and Evaluation of User-Centered Explanations for Machine Learning Model Predictions in Healthcare

Challenges in interpreting some high-performing models present complications in applying machine learning (ML) techniques to healthcare problems. Recently, there has been rapid growth in research on model interpretability; however, approaches to explaining complex ML models are rarely informed by end-user needs and user evaluations of model interpretability are lacking, especially in healthcare. This makes it challenging to determine what explanation approaches might enable providers to understand model predictions in a comprehensible and useful way. Therefore, I aimed to utilize clinician perspectives to inform the design of explanations for ML-based prediction tools and improve the adoption of these systems in practice. In this dissertation, I proposed a new theoretical framework for designing user-centered explanations for ML-based systems. I then utilized the framework to propose explanation designs for predictions from a pediatric in-hospital mortality risk model. I conducted focus groups with healthcare providers to obtain feedback on the proposed designs, which was used to inform the design of a user-centered explanation. The user-centered explanation was evaluated in a laboratory study to assess its effect on healthcare provider perceptions of the model and decision-making processes. The results demonstrated that the user-centered explanation design improved provider perceptions of utilizing the predictive model in practice, but exhibited no significant effect on provider accuracy, confidence, or efficiency in making decisions. Limitations of the evaluation study design, including a small sample size, may have affected the ability to detect an impact on decision-making. Nonetheless, the predictive model with the user-centered explanation was positively received by healthcare providers, and demonstrated a viable approach to explaining ML model predictions in healthcare. Future work is required to address the limitations of this study and further explore the potential benefits of user-centered explanation designs for predictive models in healthcare.This work contributes a new theoretical framework for user-centered explanation design for ML-based systems that is generalizable outside the domain of healthcare. Moreover, the work provides meaningful insights into the role of model interpretability and explanation in healthcare while advancing the discussion on how to effectively communicate ML model information to healthcare providers.
Multi-angle key point detection of face based on deep learning detector

In order to meet the speed and accuracy requirements of face key point detection (face alignment) in application scenarios, firstly, cascaded prediction is carried out on the basis of SSD (single shot multibox detector), which combines more uniformly distributed feature layers to form MR-SSD (more robust SSD), a deep learning detector with more robust response to multi-scale faces. Secondly, based on the cascade shape regression method of local binary feature (LBF), a multi-angle initialization algorithm based on the difference between the facial pixels is proposed. Five groups of feature points in the 90 degree inclination range of positive and negative face are initialized to achieve excellent fitting effect for inclined face under multi angles. The mean square deviation of each group of feature points after regression is calculated and the maximum corresponding shape is used as the final regression shape. The optimal architecture proposed in this paper can obtain robust face bounding box and face alignment schemes against multi-angle tilt in real time.
Multilingual term extraction from comparable corpora : informativeness of monolingual term extraction features

Most research on bilingual automatic term extraction (ATE) from comparable corpora focuses on both components of the task separately, i.e. monolingual automatic term extraction and finding equivalent pairs cross-lingually. The latter usually relies on context vectors and is notoriously inaccurate for infrequent terms. The aim of this pilot study is to investigate whether using information gathered for the former might be beneficial for the cross-lingual linking as well, thereby illustrating the potential of a more holistic approach to ATE from comparable corpora with re-use of information across the components. To test this hypothesis, an existing dataset was expanded, which covers three languages and four domains. A supervised binary classifier is shown to achieve robust performance, with stable results across languages and domains.
FKASA holds 3rd National Conference on Wind & Earthquake Engineering

The Faculty of Civil Engineering & Earth Resources (FKASA), Universiti Malaysia Pahang (UMP) continues its legacy of being the pioneer in the East Coast of Malaysia by organizing the third annual National Conference on Wind & Earthquake Engineering (NCWE) at Impiana KLCC Hotel in Kuala Lumpur on July 12, 2019.
Pengembangan Kain Tenun Gedog Tuban Bertekstur Dengan Teknik Manipulating Fabric

Abstract The author sees the potential of the textured Tubanâ€™s Gedog woven fabric that exists in Fajar Ciptandi's research in 2017 to be developed. Then the authors develop the fabric by providing more value through the stages of literature review, observation, interviews and exploration to find the right technique, namely manipulating fabric with embroidery that utilizes existing textures. Then the design process is carried out until the creation of a ready-to-wear deluxe fashion collection by raising a rustic theme which is related to the philosophy of life and the characteristics of the Tubanâ€™s Gedog woven fabric, namely beauty in simplicity and imperfection. Keywords: Gedog Tuban Woven Fabric, Textured Gedog Tuban Woven Fabric, Textile.
Classifying Documents using both Textual and Visual Features

With the increasing importance of Customer Due Diligence, _nancial institutions are forced to digitize and categorize their paper archives. Currently, document classi_cation is primarily realized using textual information. This paper proposes to complement textual features with visual features, using a convolutional neural network and transfer learning. The proposed approach is tested on both a small real-world data set for a large Dutch bank, and on the larger academic RVL-CDIP data set. It is found that using the combined approach yields better classi_cation performance than using only textual or visual features. For the RVL-CDIP data set the proposed method achieves state-of-the-art accuracy of 93.51%, exceeding previous results based on solely visual features. For the smaller real-world data set, the combined method scores marginally better than the benchmark set using only textual features, while being computationally much more expensive. Therefore, it is concluded that adding visual features using deep learning is a favorable approach to increase document classi_cation performance, given that the data and computational resources are available.
THE EFFECT OF SYSTEM QUALITY AND INFORMATION QUALITY ON THE SATISFACTION OF USER ENTERPRISE RESOURCE PLANNING-SYSTEM APPLICATION AND PRODUCT IN DATA PROCESSING (ERP-SAP) WITH PERCEIVED USEFULNESS AS A VARIABLE MODERATING IN PTPN III (PERSERO) UNIT OF LABUHAN

This study aims to determine the Effect of System Quality and Information Quality on User Satisfaction of Enterprise Resource Planning-Systems Application and Product In Data Processing (ERP-SAP) with Perceived Usefulness as Moderating Variables in PTPN III (Persero) Unit of Labuhan Batu II District. This type of research is causal research. This study took a sample of 110 respondents with the census method, questionnaires were given to implementing employees who use ERP-SAP at PTPN III (Persero) (Persero) Unit of Labuhan Batu II District. The type of data in this study is primary data obtained from the answers to the questionnaire. Testing the research hypothesis using multivariate with Structural Equation Modeling (SEM) and moderating testing using the interaction test. The test results indicate that the quality of the system and the quality of information has a positive and significant effect on SAP user satisfaction. The moderating variable perceived usefulness does not significantly moderate the effect of system quality and information quality on SAP user satisfaction.
Collective Creativity as a driver for innovation : A qualitative multiple case study analysis of the phenomena of Collective Creativity within innovation-driven environments

In our today’s fast-paced and rapidly changing world, innovation became a crucial factor for economic growth and organizational effectiveness [35,37]. Several factors promoting innovation have been ...
A Crypto-Blocking Approach for the Security Paradigm for Aadhar Towards Privacy Preservation on Cloud Infrastructure

In the world various methods are being adopted to create a systematic identification for their citizens. In year 2009, UIDAI a government body of India initiated a 12-digit number called Aadhar number, which is generated out of biometric and demographic fusion of an individual and their identity. In order to ensure a highest level of data protection as well as privacy preservation of the Aadhar card propagation through a network, it requires an efficient model of security that becomes synchronous with the cloud infrastructure. This paper initially investigates the existing approaches and their limitations towards the Aadhar card security proposes a security model namely ECrypto-AaDhaar based on the cryptography approach synchronous to the cloud architecture. The privacy preservation of the AAdhar card associative demographic and biometric information is performed considering a statistical crypto-blocking operation prior propagating it through the network in the context of cloud infrastructure. The study later also presented an experimental analysis to demonstrate the performance of ECrypto-AaDhaar technique from a time complexity perspective.
Relations Between Evaluations of NIST Tests and Lyapunov Exponents of Sequences Generated by the Piecewise Logistic Map over Integers

In this paper, we focus on binary sequences obtained from piecewise logistic map over integers, and show the relation between the Lyapunov exponents of the maps and results of the NIST tests for the sequences. When the Lyapunov exponent is a negative value, we confirm that the branch diagram is sparse and the bit occurrence rate is also greatly biased. We also give a sample of that the positive/negative signs of the Lyapunov exponents mostly coincide on the evaluations of the NIST tests. From the branching diagram of the piecewise logistic map, we can moreover find that there are many good pseudorandom sequences generated by the individual control parameters of the map.
Improving the Detection Rate of Forgery JPEG Images based on Combining Histogram Features and Discrete Wavelet Transform (DWT) with the use of Support-Vector Machine

Manipulating digital images is not often a difficult task due to the rapid development of software and image manipulation techniques. Hence, there is no need for professional skills or training. When used as an artistic tool, it is completely harmless, but when these images can be presented in judicial system as evidence or for the creation of political associations, as well as, using them in legal documents, electronic money circulation or press; in these cases, the distinction between an original image and a forgery image is very important. In order to solve the problem in this research using a Discrete Wavelet Transform (DWT), which is performed by decomposing a signal into smaller and smaller details, as well as, the use of periodic patterns in the histogram generated by double compression with different coefficients, significant improvements were made in terms of reducing computations and increasing the detection rate of forging areas. Many of the proposed methods for detecting image forgery use a feature extraction model from a valid and manipulated dataset and then classify them using machine learning with the aim of optimizing the accuracy. In this research, following the extraction of features in the proposed method, using the SVM classification identifies image forgery and then identifies the forging area after it detects the falsification or originality of the image. The results of this study indicate 97.98% accuracy in the Columbia database and 98.1% in the IFS-TC database.
Sensor-Fusion Based Navigation for Mobile Robot in Outdoor Environment

Autonomous navigation of the vehicles or robots is very challenging and useful task used by many scientists and researchers these days. By keeping this fact in mind, an algorithm for autonomous navigation of mobile robot in outdoor environment is proposed. This navigation track consists of colored border containing obstacles and some unplanned surfaces along with some specific points for GPS (Global Positioning System) alarms. The main goal is to avoid colored border and obstacles. For the vision problem webcam is used. First the colored border is detected by using OpenCV library by following HSI (Hue Saturation Intensity)technique. The Canny edge algorithm is used to find both edges of the border, then for detecting straight lines on both sides of track, Hough transformation is used. Finally, the closest border line is detected and its center point is calculated for which the mobile robot has to steer to avoid it. Second step is to avoid the obstacles, which is done by LRF (Laser Range Finder), first the range of LRF is defined, because not all obstacles have to be avoided, only those obstacles are detected and avoided which are in specified range as defined before, and finally GPS receiver is used to make alarms at some specific points. As a result, a successful navigation of the mobile robot in the outdoor environment is implemented.