Reviewerly/exHarmony huggingface dataset:
A New Technique for Estimating Fractal Dimension of Color Images

Fractal dimension (FD) effectively quantifies the roughness of the image surface, which can be applied to many image processing applications. Although a number of methods are available to calculate FD of grayscale images, limited work is done for the color images. In this paper, a new method is proposed to estimate FD of color images in CIE L*a*b* (CIELAB) color space. Firstly, the color image is converted to CIELAB space. Secondly, each of the L, a, and b components of CIELAB space are divided into overlapped grids and $$n_{r}$$ value of each corresponding grid at different sizes are obtained using the distance between the minimum and maximum (L, a, b) triplets, where $$n_{r}$$ is the number of boxes that constitute the intensity variations over a grid with scale, r. Thirdly, $$n_{r}$$ values of the same sized grids are accumulated to obtain the $$N_{r}$$ values. Finally, robust least squares regression (RLS) is applied to these $$N_{r}$$ values to obtain the FD value. Three latest methods are adopted to differentiate the performance of the proposed method using the synthesized fractal Brownian motion (FBM) and Brodatz image databases. Experimental results show that the proposed method is more accurate in estimating the FD of color images.
An Estimation Method of Visibility Level Based on Low Rank Matrix Completion Using GPV Data

This paper proposes a method for estimation of visibility level based on low rank matrix completion using GPV data. On snowy day, driving task can be hard because of poor visibility. By presenting visibility information to drivers and road manager, it will lead to safer traffic. To estimate visibility, weather data, which is significantly related to visibility, are used in the proposed method. In the proposed method, Grid point value (GPV) data and visibility data including missing values on unobserved points are arranged to matrix. The missing values are estimated based on low rank matrix completion. Experimental results show the effectiveness of our method.
Educational Software Product: Investigating the Need for a New Software Methodology

Developing software needs art in addition to the scientific knowledge. Graduation project is a compulsory course requires the students to develop a software product. Students are using the traditional software development methodologies typically designed for software oriented for business (business software), therefore it is not suitable for software developed to fulfill requirements of academic subject (educational software). These two types of software are different in many perspectives. In educational software, the purpose is to satisfy pedagogical and educational purpose. This sharp variance caused many difficulties such as loss of time, cost and quality commitment and missing the correct guidance that helps the students later in the industry. In this paper, we are targeting to identify, clarify, explore, and highlight the variances between educational and business software. To achieve this target, a comparison based on software life cycle and project knowledge areas has been prepared between educational and business software. The results that have been extracted from the comparisons proved the difficulties of using tradition methodologies to develop educational software. Finally, recommendations to develop especial methodology for educational software have been stated.
Villages Status Classification Analysis Involving K-Means Algorithm To Support Kementerian Desa Pembangunan Daerah Tertinggal dan Transmigrasi Work Programs

Abstract Data mining is a technique of extracting information that has not been known before in a collection of data in the database. Data mining has been applied in various fields that require extracting information, some of the work that can be generated with data mining is classification, prediction, and data grouping. In this study, an analysis of village data collection was carried out to explore the potential or knowledge of the data that has been presented with the main objective of producing a grouping of village status. To support clustering or grouping activities the K-means algorithm used with the general process is to carry out the modeling process without supervision and is one of the methods for grouping data with a system partition, with the principle of allocating each data to the centroid or the closest average, work steps conducted in support of this research is to collect data related to the analysis of data grouping and proceed with the calculation process in accordance with the work steps of the K-means algorithm, the amount of data used as a test of 303 villages scattered in the old Padang regency. The results of calculations by displaying a new group of Cluster 0 is occupied by 120 villages, cluster 1 with a total data of 123 villages, Cluster 2 with a total of 6 villages, cluster 3 with a total of 33 villages while cluster 4 with a total of 21.
Transfer of Inter-Robotic Inductive Classifier

In multi-robot deployments, the robots need to share and integrate their own experience and perform transfer learning. Under the assumption that the robots have the same morphology and carry equivalent sensory equipment, the problem of transfer learning can be considered incremental learning. Thus, the transfer learning problem inherits the challenges of incremental learning, such as catastrophic forgetting and concept drift. In catastrophic forgetting, the model abruptly forgets the previously learned knowledge during the learning process. The concept drift arises with different experiences between consecutively sampled models. However, state-of-the-art robotic transfer learning approaches do not address both challenges at once. In this paper, we propose to use an incremental classifier on a transfer learning problem. The feasibility of the proposed approach is demonstrated in a real deployment. The robot consistently merges two classifiers learned on two different tasks into a classifier that performs well on both tasks.
Application of Data Augmentation in Lane Line Detection

With the continuous development of deep learning technology and the deepening of neural network research, more and more traditional problems have begun to seek new solutions by constructing deep neural networks, and breakthroughs have been made in many fields. Deep learning is a data-driven method, and huge neural network models require massive data support. However, as the most important part of deep learning, data has many problems such as difficulty in data collection, data labeling consumes a lot of manpower, and uneven data distribution. Expanding the original data can not only save a lot of resources but also effectively improve network performance. This article selects a specific problem for data augmentation research. As a very important part of autonomous driving technology, lane line detection is also a hot spot in computer vision research. We took the lane line detection problem as an entry point, conducted data augmentation experiments on the public road data set, and verified that the data augmentation methods used in this article improved network performance by 5.5 percent through comparative experiments.
Group Strategy-proof Mechanisms for Shuttle Facility Games

We study the game-theoretical structure of a scenario where a decision maker has to determine locations of stations in a transportation system.We introduce a new model on facility games, called the "shuttle facility game."A facility F is defined to be an interval with two stations over a transportation line.Then, the decision maker wishes to design a mechanism that given as input a set of intervals reported by each player, where I i represents the commuting route of player i, determines a location for F. The profit of a facility location is defined based on the "convenience" to each player, such as the distance to the facility.A player i may try to manipulate the output of the mechanism by strategically misreporting I i to get a higher profit.We formulate two shuttle facility games: the fixed-length and the flexible-length shuttle facility game; and prove that each admits a group strategy-proof mechanism.We prove that the social profit is also maximized by a location of F determined by our group strategy-proof mechanism, that is, a decision maker can find a location of F so that the social profit is maximized and group strategy-proofness is attained at the same time.
EDA for autonomous behavior assurance

Autonomous systems are self-governed and self-adaptive systems that must additionally comply with high assurance correctness and safety criteria. Such autonomous systems cannot be tested and verified in the traditional design process. While all systems hardware and software components can be implemented as usual, test and verification only cover the autonomous system functionality, but do not include the goal-driven autonomous behavior in all possible circumstances. This autonomous behavior is a primary design target. Thus, autonomous systems pose a number of emerging challenges and opportunities to the field of electronic design automation (EDA). Examples include specification of (evolving) requirements involving components and their interaction, defining different assurance levels for bounded operational environments, synthesis of mechanisms to guideline diagnosis and rigorously monitor systems integration after deployment.
Artificial Potential Field Based Distributed Cooperative Collision Avoidance for UAV Formation

This paper designs a distributed cooperative controller based on artificial potential function, which is used for communication maintenance, formation configuration and collision avoidance among unmanned aerial vehicles (UAVs). Firstly, the quaternion dynamics mathematical model of the single quadrotor UAV is established and the leader-follower formation method is selected to establish the UAVs distributed communication topology graph. Then, an artificial potential function formation cooperative controller is designed to complete the distributed communication maintenance, formation configuration and collision avoidance of four quadrotor UAVs. Finally, numerical simulation verifies the effectiveness of the designed controller.
A Methodology and Experiments towards Autonomous Decision Making

One of the key challenges to AI is autonomous Decision Making (DM). This paper strives towards a formal approach to model cognitive DM in order to enable AI machines to conduct autonomous DM. On the basis of the theoretical models for rational decisions and rigorous DM developed in our Lab, we implement a set of algorithms for autonomously handling primitive, single, and serial decisions rigorously. An autonomous DM supporting tool is designed and implemented based on the algorithms. Experiments on various decision problems have demonstrated the capability of the tool and the methodology in many cases beyond those of human DM outcomes in both complexity and speed.
Use of classification trees and rule-based models to optimize the funding assignment to research projects: A case study of UTPL

In the process of funding research projects, two important factors must be studied. First, experts judges the potential value of a project. Secondly, the research ability is judged by the applicants previous research activity. The most appropriate way to assign the appropriate amount of money to project proposals is always a difficult decision. This work focuses on the second factor based on classifying the researchers previous research activity on an automated logical classification (accepted, rejected) resolving conflicts of interests between administration and applicants and helping in the decision-making process. As the class in these kinds of studies is usually unbalanced, because there are fewer accepted projects than rejected projects, how the use of an imbalanced dataset or a balanced dataset affects to the models is investigated by using several resampling methods. Later, several trees and rule-based machine learning techniques are used to create classification models. This is based on information from the faculty members information of the "Technical Particular University of Loja (UTPL)," in cases, with balanced datasets and those with unbalanced datasets. Multivariate analysis, feature selection, algorithm parameter tuning and validation methods are used to achieve robust classification models. The most accurate results are obtained with a rules-based model and use of the C5.0 algorithm. As the latter provides acceptable accuracy, close to 95 % when predicting both classes and to 99 % when predicting the accepted projects class, both the methodology and final model are validated.
I/O-efficient data structures for non-overlapping indexing

The non-overlapping indexing problem is defined as follows: pre-process a given text T[1,n] of length n into a data structure such that whenever a pattern P[1,m] comes as an input, we can efficiently report the largest set of non-overlapping occurrences of P in T. The best-known solution is by Cohen and Porat [ISAAC 2009]. The size of their structure is O(n) words and the query time is optimal O(m+nocc), where nocc is the output size. Later, Ganguly et al. [CPM 2015 and Algorithmica 2020] proposed a compressed space solution. We study this problem in the cache-oblivious model and present a new data structure of size O(nlog⁡n) words. It can answer queries in optimal O(mB+logB⁡n+noccB) I/O operations, where B is the block size. The space can be improved to O(nlogM/B⁡n) in the cache-aware model, where M is the size of main memory. Additionally, we study a generalization of this problem with an additional range [s,e] constraint. Here the task is to report the largest set of non-overlapping occurrences of P in T, that are within the range [s,e]. We present an O(nlog2⁡n) space data structure in the cache-aware model that can answer queries in optimal O(mB+logB⁡n+nocc[s,e]B) I/O operations, where nocc[s,e] is the output size.
Cyber Threats to Information Security in the Digital Economy

The article focuses on the problem of promoting information security in the emerging digital economy. The main emphasis is placed on solving the problems of preventing threats and cybersecurity risks in the era of rapidly developing digital technologies. The basic approaches are related to the construction of an infrastructure to combat cybercrime: formation, technological improvement of IT software and hardware, regulation, the use of finance and insurance, support for the media and the development of a safety culture. The results of this study can be applied to the design of information security systems at all levels.
Hybrid No-Reference Video Quality Models for H.264 with Encrypted Payload

In this paper, we propose a hybrid no-reference video quality measurement method for H.264. Although there is a strong need from the industry for no-reference models that don’t require reference signals, developing reliable no-reference models for perceptual video quality assessment has been a challenging goal. On the other hand, in most digital video transmission, encoded video signals are transmitted in video packets, which can provide valuable information about coding parameters and transmission errors. The proposed hybrid noreference models uses both the information available from video packets and decoded signals to estimate coding artefacts and impairments caused by transmission errors. Sometime, the payloads of video packets may be encrypted to protect contents. In such case, the available information is rather limited. The proposed method was independently tested against large databases and shows good performance.
WITHDRAWN: High level montgomery modular multiplier for CSA architecture

The Montgomery modular multiplication finds its application in the area of Cryptography such as RSA algorithm where modular multiplication plays a vital role. The security of the algorithm lies in the number of bits in the multiplier and multiplicand. As the number of bits increase, delay and hardware increases which makes it impractical for the real time application. The objective of this work is to propose an efficient Montgomery algorithm without the use of hardwired multipliers embedded in the Field Programmable Gate Array and reduce the delay. The performance of the multiplier algorithm is highly dependent on the addition module. In order to improve the performance, various architectures for the addition such as carry skip adder, carry look ahead adder, carry save adder, semi and full carry save adder are used. The semi and full carry save addition along with the modification in the Montgomery algorithm helps in improving the speed of the design. The proposed algorithm was compared with the existing algorithm interms of operating frequency and area. The proposed algorithm has been implemented on the stratix FPGA and shows better performance.
Comparable Data Evaluation Method for a Radio-Nuclear Sensor When Used on an UAV

For humans it is dangerous to explore areas that are possibly contaminated with radioactive sources. We are using an unmanned aerial vehicle (UAV) equipped with a gamma radiation detector to perform an airborne localization of radiating sources. In this work we used flight patterns with different parameters (varying altitude, velocity and lateral offsets) to test the localization accuracy. We tested our system with these flight patterns and evaluated the data with a least squares method resulting in a maximum error of 3.3 meters.
Impact of Quality Of Images On Users Behavior On Social Media

The last decade has witnessed one of the fastest growth in Web 2.0 technology with the diversity of channels, structure and content of user generated content. Such data can be textual, visual or multimedia. This created valuable source of data for users, professionals and research community. This paper explores potential correlation that may exist between images and the associated textual content. Especially, we explore the influence of the image quality on the nature of the users' content. For this purpose, Brisque image quality index has been performed. The quantification between the image quality and the users' textual generated content is evaluated using both the number of comments generated by users and overall sentiment score associated to the given image. The results of such analysis confirms the hypothesis of positive correlation between the quality of posted image and the associated user's content in the above sense. A new tourism dataset Tourism48 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> constructed from IMGUR web service was also introduced and made public.
Investigating Possibilites for Protecting and Hardening Installable FaaS Platforms

Function as a Service is a popular trend in the area of cloud computing and also for IoT use cases. Thus, in addition to cloud services, installable open source platforms for FaaS have recently emerged. To deploy such an installable FaaS platform in production, the security aspect needs to be considered which has not been investigated in detail yet. Therefore, this work presents possible security threats and recommended security measures for protecting and hardening installable FaaS platforms. Currently available FaaS platforms are analyzed according to the possibilities they offer to implement such security measures. Although most platforms provide necessary security measures, there is still potential to improve the platforms by offering advanced measures and facilitate a secure deployment.
Contrast Enhancement of Mobile Phone Camera Using Multi-Scale Feature Map

This paper presents a contrast enhancement method using encoder-decoder network. To prevent the imbalanced amplification among color channels, the proposed method incorporates multi-scale feature maps estimated from an input low-contrast image in an encoder network. Experimental results show that the proposed method can enhance a low-contrast image without brightness saturation, color distortion, and noise amplification than existing methods.
MOSDA: On-Chip Memory Optimized Sparse Deep Neural Network Accelerator With Efficient Index Matching

The irregular data access pattern caused by sparsity brings great challenges to efficient processing accelerators. Focusing on the index-matching property in DNN, this article aims to decompose sparse DNN processing into easy-to-handle processing tasks to maintain the utilization of processing elements. According to the proposed sparse processing dataflow, this article proposes an efficient general-purpose hardware accelerator called MOSDA, which can be effectively applied for operations of convolutional layers, fully-connected layers, and matrix multiplications. Compared to the state-of-art CNN accelerators, MOSDA achieves 1.1× better throughput and 2.1× better energy efficiency than Eyeriss v2 in sparse Alexnet in our case study.
Further factorization of xn − 1 over a finite field (II)

Let [Formula: see text] be a finite field with [Formula: see text] elements. Let [Formula: see text] be a positive integer with radical [Formula: see text], namely, the product of distinct prime divisors of [Formula: see text]. If the order of [Formula: see text] modulo [Formula: see text] is either 1 or a prime, then the irreducible factorization and a counting formula of irreducible factors of [Formula: see text] over [Formula: see text] were obtained by Martínez, Vergara and Oliveira, Explicit factorization of [Formula: see text], Des. Codes Cryptogr. 77(1) (2015) 277–286 and Wu, Yue and Fan, Further factorization of [Formula: see text] over a finite field, Finite Fields Appl. 54 (2018) 197–215. In this paper, we explicitly factorize [Formula: see text] into irreducible factors in [Formula: see text] and calculate the number of the irreducible factors when the order of [Formula: see text] modulo [Formula: see text] is a product of two primes.
Maximum A Posteriori Approximation of Dirichlet and Beta-Liouville Hidden Markov Models for Proportional Sequential Data Modeling

Hidden Markov models (HMM) have recently risen as a key generative machine learning approach for time series data study and analysis. While early works focused only on applying HMMs for speech recognition, HMMs are now prominent in various fields such as stock market forecasting, video classification, and genomics. In this paper, we develop a Maximum A Posteriori (MAP) framework for learning the Dirichlet and Beta-Liouville HMMs that have been proposed recently as an efficient way for modeling sequential proportional data. In contrast to the conventional Baum Welch algorithm, commonly used for learning HMMs, the proposed algorithm places priors for the learning of the desired parameters; hence, regularizing the estimation process. We validate our proposed approach on two challenging real applications; namely, dynamic texture classification and infrared action recognition.