Reviewerly/exHarmony huggingface dataset:
High Utility Co-location Patterns Mining from Spatial Dataset with Time Interval

A spatial co-location is a set of spatial events being frequently observed together in nearby geographic space. The existing high utility co-location patterns mining algorithm are based on idealized data, without considering time interval of spatial instance in real-world. In order to address the problem, firstly, this paper redefines the concepts of spatial instance, the minimum time interval overlap rate and neighbor relationship, etc. Secondly, a basic algorithm and a pruning algorithm of high utility co-location patterns mining with time interval are proposed. Since high utility co-location patterns mining do not satisfy the anti-monotonic property, a pruning strategy based on feature utility participation weight is exploited for computational efficiency. Finally, experimental results are provided to show the effective, efficient, and scalability of the basic and pruning algorithm. In addition, the algorithm proposed in this paper is also compared with high utility patterns mining algorithm without time interval.
Habit-Based and Goal-Directed Systems: Knowledge Transfer in Individual and Social Learning

The present chapter discusses value-based habitual and goal-directed systems as studied in the animal and human learning literature. It focuses on the means by which these two systems might interact in knowledge transfer, particularly as it applies to social learning. Knowledge is conceived here in terms of types of logic computations as implemented by neural networks. A discussion of dual-process type structures in the brain is provided as well as neural-dynamic implementations thereof and considerations for how a perspective of the brain as carrying out logic computations might be useful for developing the general cognitive capacities of artificial agents.
Unbiased Mortality Prediction for Unbalanced Data Using Machine Learning

Mortality defines `to be in the state of being prone to death'. The mortality rate is the measure of the frequency of occurrence of death in a defined population during a specified interval. The major causes of death in the recent decade is due to the Cardiovascular diseases, Cancers and Respiratory diseases. The problem is hosted on CodaLab as an ongoing competition. In this work, we have been provided with a data set that contains 342 features. The training data consists of 80,000 patient's records which are highly biased as 90 percent of the labels belong to the class `0'. Due to its skewed nature, various oversampling techniques were tried. Certain Machine learning techniques were then applied to detect the death chances of a person during their stay at the hospital based on their medical records. Accuracy of 76.68% percent was achieved on the testing data (20,000 records).
Extending the Recognition Limit of Two-Dimensional Bar Code

In order to extend the recognition range of QR codes in practical applications, we have designed an identification method that recognizes the two-dimensional codes with large linear size transformations. Through the pixel-by-pixel scaling method, we examine the limit case of two-dimensional code recognition. According to our experiments, when the linear variation of the two-dimensional code is between ± 5 times the original width, it can be identified correctly. Besides, the calibration pattern of the two-dimensional code shows no specific influence on the recognition after the deformation of the two-dimensional code. Finally, the number of pixels with zero color gradation is studied. It is found that the two-dimensional code can be correctly identified when the proportion of pixels with zero gray color level is between 20% and 30%.
Clustering of Districts in Indonesia using the 2015 High School Social Sciences National Examination Results

This study aims to cluster 513 districts in Indonesia using the results of High School National Examination or “Ujian Nasional (UN)” in Indonesian language majoring in social sciences to map the learning outcomes in the districts. The attributes consist of 6 subjects which are Bahasa Indonesia, English, Mathematics, Economics, Sociology, and Geography. The clustering methods used are Complete-linkage and K-Means. The clustering results are compared with the District Human Development Index (HDI) of the clusters. The results show that the districts in Indonesia are grouped into 5 clusters and there is a slight dissimilarity between the scores of UN and HDI.
Feature Selection Method Based on Maximum Conditional and Joint Mutual Information

In multidimensional time series data analysis, redundant features in samples can add to the complexity of problem analysis. Feature selection methods based on mutual information (MI) can effectively reduce data dimensionality and provide more accurate analysis results. Unfortunately, existing methods do not involve reasonable consideration to the relevance between time series features and rely on only one or two criteria to assess whether a feature is redundant. Given these problems, a time series feature selection method based on Maximum Conditional and Joint Mutual Information (MCJMI) is presented. It separates each time series into discrete ones. Two factors, overall Joint Mutual Information (JMI) and Conditional Mutual Information (CMI), are integrated in the selection of features. The experimental results demonstrate that MCJMI is both effective and useful for time series feature selection and can improve the accuracy and stability of feature selection.
Solar Forecasting for Low Voltage Network Operations: Selected Case Studies in Australia

The utilization of solar forecasting data for the proactive management of low voltage (LV) networks is in its infancy. This paper presents three cases studies from Australian low voltage network operators (DNSPs - distribution network service providers) who have been given access to high resolution solar forecasting data for the purposes of evaluating the use of the technology for LV network management. The first of the selectes use cases focus on how solar forecasting data can aid decision making in day to day LV network operations. The second and third use case are closely related, and demonstrate how behind-the-meter solar forecasts through Solcast Grid Aggregations allows LV network operators to generate more accurate gross load profiles, so that the underlying true demand can be estimated at the zone substation level, by combining these data with SCADA measurements. Broadly, we conclude that these use cases demonstrate that there is significant value for LV network operators to utilize solar forecasting data in their network management and planning activities.
An Efficient Scheme to Combine the User Demographics and Item Attribute for Solving Data Sparsity and Cold-start Problems

This paper investigates several schemes to combine the user demographic information and item attribute data that respectively beneficial to solve the data sparsity and cold-start problems in recommendation systems. We propose four schemes that are varied based on how the combination of the two data can be constructed. To test and evaluate the concept, we implement the schemes on a probabilistic-attribute method adapted to suit our attribute model. Compared to the benchmark methods, experiment results show that our approach is superior in solving the data sparsity and cold-start problems. In general, the scheme that combines the item attribute data with a partial user demographic information performs better than the other variations of the combined-attribute scheme. This finding confirms that combining both the user demographic information, though not all of them, and the item attribute can efficiently solve the data sparsity and cold-start problems.
Towards Efficient Information-Theoretic Function Secret Sharing

A (t, r)-function secret sharing ((t, r)-FSS) scheme allows a dealer to secret-share a functionf among r parties as r secret keys k <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , ... , k <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> such that for any input x the parties can compute r output shares that allow the reconstruction of f (x) but any ≤ t of the parties cannot learn any information aboutf. FSS schemes for point functions have been constructed under the name of distributed point functions (DPFs). The existing DPFs are computationally secure and based on the existence of PRGs or OWFs. As a result, the protocols where DPFs work as building blocks are computationally secure as well. In this paper, we study information-theoretically secure (t, r)-FSS (called (t, r)-itFSS) and propose a generic transformation from information-theoretic private information retrieval (PIR) schemes to itFSS schemes for point functions. We measure the efficiency of itFSS with its communication complexity, which can be defined as the total length of the secret keys and the output shares, maximized over the choices off and x. By instantiating the generic transformation, we obtain (t, r)-itFSS schemes for a variety of choices of (t, r), which have sublinear (in the functions' domain size) communication complexity. How to make sure that the parties' shares of f (x) do not reveal more information than what needed to computef(x) is an interesting problem. An itFSS with this property is called function-private. In this paper, we also define a parameter called the mutual rate of itFSS in order to measure the amount of information that will be leaked by the parties' output shares. We calculate the mutual rates for several specific itFSS schemes. We also define computational function privacy and propose a 2-party itFSS scheme with computational function privacy.
Online Motion Planning For Mobile Robot

This paper presents an online motion planning system for mobile robot with a simulator based technique. The simulator is made based on the self-build platform on the mobile robot by using Python language. The collecting data as an information in the simulator is built base on holonomic wheeled type of robot. The specification and configuration of this mobile robot follows the guidelines issued by Middle Sized League Robocup competition. The purpose of this paper to make simulator software as a visualization widget that makes a representation of the coordinate position of the robot and the soccer field condition. The position and orientation of the robot information are gated in this visualization widget. The soccer field representation in a two dimensional condition as a game field of the robot. The robot coordinate as an object with magenta color with have each number of the robot. This object is enable to move in any direction as represent whatever operator want to change the position and orientation of the robot. These condition purpose was used and it can increasing usability of the system. The testing condition between real testing and simulation testing was implemented. Based on the experimental result, the data was conducted we get root mean square error around 1.29 percent of X direction, 1.65 percent of Y direction and around 1.06 percent of the symmetrical trajectory along X and Y coordinate. The result shows that the online motion planning with simulator based can be directly implemented to the soccer robot in the real time condition.
Implementation of Hybrid Password Authentication Scheme Based on Shape-Text on Raspberry Pi as a Client-Server-Based Access Control System to Overcome Shoulder Surfing Attack

In general, the authentication method that is regularly utilized to increase the security factor on a client-server based access control system communication network is to use a password. Password is commonly used because it has advantages, which is easy to memorize by users and is simple. However, password authentication techniques on client-server-based access control systems have the disadvantage of being vulnerable to Shoulder Surfing Attack. In this study, an experimental verification was carried out on a client-server-based access control system that has implemented the Hybrid Password Authentication Scheme Based on Shape-Text in dealing with Shoulder Surfing Attack. The client used is the Raspberry Pi while the server used is a Personal Computer (PC). Implementing a Hybrid Password Authentication Scheme Based on Shape-Text on an access control system can reduce the average success rate of an attacker guessing the correct password. The average correctness of passwords guessed by attackers on access control systems that do not apply the Hybrid Password Authentication Scheme Based on Shape-Text is 0.34 digits (with a password length of 6 digits of decimal numbers), while the average correctness of passwords guessed by the attacker on an access control system that has implemented a Hybrid Password Authentication Scheme Based on Shape-Text is 0.19 points (with a password length of 6 points from a 5x5 binary number matrix).
A Constructive Multilevel Security System with Cryptographic Techniques by using Cyber-Physical System in the Space/Defense Applications

Protecting sensitive data and maintaining the confidentiality is the most important role in this world. Especially many areas like Space, Defense, Education, Finance, Communications, international affairs sectors. The Multilevel Security (MLS) approach provides a relevant framework for the analysis of security systems regarding cross-level approach through the cyber-physical system. We propose a new key management system for multilevel security in the threat model security policies- security mechanism. The new approach is to construct the MLS through Cyber Physical Systems (CPS) in association with advanced cryptographic tools to protect the defense confidential data from unauthorized, and also to make sure that the data is not released to any counterfeit users. In this paper, a prototype is selected to explain the security of cryptographic strategy like symmetric encryption, message authentication codes, public key encryption, key agreement protocols, and digital signature schemes.
Using Naïve Bayes Classifier for Application Feedback Classification and Management in Bahasa Indonesia

The world keeps moving, software products too. An application's objectives, structures, requirements, and assumptions that have been elicited and analyzed previously may need to be reassessed and updated. In order to fully understand these requirements evolutions, what changes are necessary, and why those changes are needed, one essential source of requirements is user feedback. However, handling and analyzing so many user feedbacks can be time-consuming. Using natural language processing tools for Bahasa Indonesia and Naive Bayes classifier, this research aims to develop a tool to process natural language and classify user feedbacks. The developed tool is expected to make feedback classification less time-consuming so that developers can project their energy to more productive and creative works. The machine learning models are built using the feedback dataset taken from an up-and-running university e-learning system and show promising results. The highest confusion matrix scores are 92.5% for accuracy, 85.6% precision, 85.1% recall, and lastly, 85.4% for the F-measure score. The resulting web application for feedback management is then evaluated to the users, and even though it still needs to be further polished and improved for real industrial use, it is perceived to be useful and easy to use.
Lightweight Involutive Components for Symmetric Cryptography

Lightweight components are used in the design of modern lightweight ciphers. In the current paper, firstly we investigate a new family of lightweight 4×4 almost MDS diffusion layers, mathematically, and determine when they are involutive. The proposed diffusion layers are suitable for software applications and have less fixed points, compared with the existent ones. Then, we examine the cryptographic properties of nonlinear components constructed by a 2-round Feistel scheme, theoretically, and propose some concrete examples of them, including lightweight hardware-oriented 8×8 S-boxes. We compare the presented S-boxes with the ones in Midori-128.
Embodying Meaningful Digital Media

Technological products have become central to the ways in which many people communicate with others, conduct business and spend their leisure time. Despite their prevalence and significance in people's lives, these devices are often perceived to be highly replaceable. From a sustainability perspective, there is value in creating technological products with meaning directly associated with their materiality to reduce the rate of product consumption. We set out to explore the potential for design to promote the formation of product attachment by creating technological devices with meaningful materiality, closely integrating the physical form with the significance of its digital contents. We used the life stories and ongoing input of our intended user as inspiration for the creation of Melo, a bespoke music player. The evaluation and critical reflection of our design process and resulting artefact are used to propose a design strategy for promoting product attachment within the growing sector of technological devices.
An Efficient Image Retrieval System for Remote Sensing Images Using Deep Hashing Network

Due to the huge increase in volumes of remote sensing images, there is a requirement for retrieval systems which maintain the retrieval accuracy and efficiency which requires better learning of features and the binary hash codes which better discriminate the images of different classes of images. The existing retrieval systems for remote sensing images use CNNs for feature learning which fails to preserve the spatial properties of an image which in turn affect the quality of binary hash code and the retrieval performance. This Paper tries to address the above goals by using (1) Extracting Hierarchical features of convolutional neural network and using them to sequential learning to better learn the features preserving spatial and semantic properties. (2) Use lossless triplet loss with two more loss functions to generate the binary hash codes which better discriminate the images of different classes. The proposed architecture consists of three phases: (1) Fine-tuning a pre-trained model. (2) Extracting the hierarchical features of convolutional neural network. (3) Using those features to train the deep learning-based hashing network. Experiments are conducted on a publicly available dataset UCMD and show that when hierarchial convolutional features are considered there is a significant improvement in performance.
A Reliable Hand Tracking Method Using Kinect

Hand tracking has been focused over years in the field of human-computer interaction. But there are still some challenges such as reliability and accuracy due to the highly articulated and deformable of hand. In this paper we propose a real time hand tracking method with the depth information of the Kinect sensor. In this method, we preprocessed the depth information with a pixel filter to deal with the noise. Then we isolated the hand region from the depth image by using the KCF (Kernelized Correlation Filters) to the confined minimum depth area, which can release the limitation of hand movement range. Then the forearm region is segmented with assistance of PCA (Principle Component Analysis). And, to accurately locate the fingertip position, we integrate the maximum geodesic distance to hand centroid into EIC (Extended Inscribed Circle). And by labelling each finger to avoid mistaken fingertip determination. Experimental results verify the accuracy and reliability of our solution.
Partial phase reconstruction for zero optical path difference determination using a chirp Z-transform-based algorithm

A new adaptive algorithm for partial phase reconstruction using a chirp Z-transform-based algorithm is proposed. In this algorithm, a digital interference fringe signal is transformed into the frequency domain using a linear transformation. After selecting the spectral components of the signal in the frequency domain, the partially wrapped phase signal is selected and reconstructed in the time domain using chirp Z-transformation, which is utilized to determine the zero phase-crossing point near the point of interest, and the point is used to determine the relative position zero optical path length difference between the two mirrors in the interferometer. Both the qualitative and quantitative results clearly demonstrate the superiority of our method over the standard discrete Fourier transform pair algorithm.
A image-upscaling engine for 1080p to 4k using gradient-based interpolation

Image upscaling is an efficient solution to eliminate the gap between image/video sizes and display resolutions. Generally, image-upscaling algorithms use computation-intensive edge detection and filtering to improve their performance with inefficient hardware performances. In this study, an image-upscaling algorithm for 1080p to 4K using gradient-based interpolation technique is proposed. It simply adopts the horizontal and vertical gradients among source pixels to consider the texture content among them. This technique inherits the textures from source pixels and further extends them to target pixels using spatial-correlation. Experiment results demonstrate that this study has better average peak-to-noise-ratio (PSNR) and structural similarity (SSIM). The hardware architecture is realised using TSMC CMOS 0.18 μm technology. It comprises two core techniques: bubble-eliminating data scheduling (BEDS) and memory-efficient gradient generator (MEGG). BEDS can efficiently remove bubble cycles to improve hardware performances. MEGG can use compact memory capacity to produce gradient information. Its working frequency is 178 MHz with a power consumption of 9.43 mW. The maximum throughput is as high as 712 Mpixels/sec, which can sufficiently support 4K@60 fps. This study presents higher hardware efficiencies with better visual quality and object completeness in image upscaling for 1080p to 4K applications.
Mobile Robot Routing with Energy Consumption Optimization

We consider scheduling mobile robots which are used to carry materials for intelligent manufacturing. Battery-powered mobile robots transport materials from warehouse to a set of demand points, and their power varies during a trip as the weight of loads varies whenever unloading some materials at a demand point. In most of literature about vehicle routing problems, researchers only consider minimizing travel distance, without explicitly considering the effect of different loads on the energy consumption, which is critical for many real world applications. To address this problem, we develop an innovative mixed integer linear programming model for scheduling robots with energy consumption optimization. Numerical results show that our model can significantly reduce energy consumption with slightly increased traveling distance, comparing with the traditional routing models which just minimize the total travel distance. The model can also accurately predict the energy consumption of trips in serving demand points, hence increasing the reliability of scheduling and reducing the times of charging. The results demonstrate the importance of considering energy consumption in some scheduling problems where the weight variation of loads cannot be ignored. This optimization model can also be used in other areas, such as city logistics where vehicles are used to deliver goods to customers at different locations in a city.