Reviewerly/exHarmony huggingface dataset:
Improvement of Degraded Object Shapes Based on Skeletonizing and Recognizing Using Geometry

Remote sensing technology normally uses for the provision of regional information to support decision making in area utilization, regional boundary determination, infrastructure mapping, etc. Through satellite imagery where it very sensitive to noise causing a shape building objects are degraded and cannot be recognized clearly caused by other objects covering observed building objects and there are holes due to pixel identification errors due to color differences so that the shape of the building object produced is inaccurate and only visible objects in certain parts. Methodology of this research is divided into two stages, the first stage of improvement of degraded building object forms, including separation of buildings with other objects using the K-Means Clustering algorithm (k = 2), Filling Region to cover holes, and Skeletonizing morphology to produce the framework used to search for endpoints that are lost when the indentation leads in, so that in the end the segmentation of the building object line will be formed perfectly through these endpoints; the second stage of the introduction of the shape of the building object using the Harris Corner Detector method to recognize shapes based on the total vertex. The results showed that using Skeletonizing, degraded building objects were able to be refined and can be identified based on angles with relatively high accuracy in 71%.
A Novel Probabilistic Matrix F actorization for Personal Recommendation

Matrix Factorization (MF) is a well-known method which is to predict users' preference in recommender systems. This method has the advantage of reducing data sparsity and cold start problem. Probabilistic matrix factorization (PMF) is an improved method of matrix factorization. Recommender system can efficiently reduce the information overload problem. However, it is bound by the recommended accuracy. In this paper we introduce Bayesian distribution to improve accuracy and design our novel probabilistic matrix factorization (NPMF) proved to have better performance. In addition, we also use K nearest neighbor method and stochastic gradient descent to further improve our method. The experimental studies using two different datasets including MovieLens 100k and MovieLens 1M, show that the proposed methods already have comparable and better performance.
Designing for Fallible Humans

Security and privacy solutions today are designed with an assumption of a rational user. System designers assume that the user is able to review all information shown to them, consider it along with other information they have, and user priorities, and make a conscious, rational decision in their best interest. We all know that these assumptions are wrong. Even worse, they are simply excuses for technology-centric, best-effort design. This paper argues for designing for fallible humans, taking into account human cognitive limitations, human bias and human preferences. Such design means anticipating human error and compensating for it with built-in safeguards, it means presenting information in a way palatable to humans, it means soliciting user input and working collaboratively with the user's cognitive biases and preferences. It means helping users weave security and privacy into their daily routine, and not view them as obstacles or overhead to other, more desirable tasks.
Shadow based Time Prediction in Video Sequences using Hough Transform

Identifying informative objects under the presence of shadows is a difficult step in computer vision applications. While capturing images and videos cameras plays a vital role in gathering information in the absence of paid persons. But in low light conditions camera can result in poor quality videos. And also low light can lead to camera shake and blurred images and videos. Forensic Identification Services (FIS) face difficulty in case of emergency conditions for finding the time of the incidents.In this paper, we give more importance to shadow part of an image/video. Initially detects the part of the image that gives object and the related shadow by using background subtraction method. To detect the shadow part YCbCr color spaces are employed. Morphological operations are used to extract the detected shadow regions and remove the unwanted tine objects. Using Hough Transformation line drawn over the object and the associated shadow to extract the length, angle between the object and the shadow. Time estimation is done based on the features like length and angle.
Adaptive Path Planning for Unmanned Planetary Rover with Dynamic Obstacle

More and more wheeled planetary probes have been applied to exploration missions in order to realize the exploration of extraterrestrial planets. In order to ensure the ability to perform tasks in complex environments, planetary vehicles need the ability of autonomous path planning and obstacle avoidance. In this paper, RRT* and dynamic window approach are combined to complete autonomous path planning and dynamic obstacle avoidance under the dynamic environment with some prior map information. The simulation and physical verification under ROS development platform and Jackal unmanned vehicle platform are realized. It provides ideas for path planning and obstacle avoidance of unmanned planetary vehicles in static and dynamic environments.
Forecasting for Photovoltaic System Based on 6-Dimension Extreme Leaning Machine

Photovoltaic power generation technology is greatly influenced by weather. The higher the accuracy of power prediction is, the more beneficial it is. A 6-dimension one-hidden-layer neural network of Extreme Leaning Machine (ELM) method was proposed. A model considering solar irradiance, wind power, wind speed, temperature, humidity and pressure was established for predicting the output power of photovoltaic power generation system. Then, the model was predicted and verified by the data from a photovoltaic power station in Beijing. Finally, by comparing with BP neural network, it shows that the performance of extreme learning machine based on sigmoid function is optimal.
A Proposal of HR System's Visualization Based on Harvard Model, Life Cycle, and Organization Strategy and Management Type

Rapid economic changes have recently been requesting human resource departments in Japanese firms to transform their roles and services. [1] No paper refers a method to map the themes they talk about and to discuss these issues explicitly by using the Harvard Model and Organization Strategy and Management Type. In this paper, we propose a method using the Harvard Model and Organization Strategy and Management Type. The method is divided into Visualization Map of HR Systems based on Life Cycle and Organization Strategy and Management Type. We asked employees of Human Resource Departments to use worksheets of "visualization map of HR systems based on life cycle" and "Organization Strategy and Management Type". Then, we evaluated the two points on whether they thought the HR System of their company was appropriate and whether they could write down and explain the HR System to other people. As a result, we confirmed that we achieved the goal of identifying issues of the firm and facilitating discussions with management and HR employees of other companies. In addition, this method could play a role in a training program for people who have a little experience as employees of Human Resource Departments.
Digital Technology Integration in Different Educational Fields

The perception of learning and teaching in the educational universities have been affected by digital technology. With the industrial concern over sustainability of resources and efficiency in operation in a digital environment, the need arises to implement digital technologies in the educational setting so that digital competence of the future workforce can be elevated, and better industrial output-based education is provided. In this chapter, an attempt has been made to describe and discuss the current scenario of digital integration in higher educational disciplines. The issues concerning this integration include teacher inability to incorporate digital thinking into student learning, student non-adaptability to modern technologies, unreliability of digital educational resources, and lack of infrastructure/power supply in most of the educational institutes. An active digital learning approach in students and extensive training sessions for digital utilization excellence in teachers and educators are a few ways to solve issues regarding the above-mentioned integration.
OTPAF: A Security Requirement Conceptual Model of SaaS for Malaysian Government based on Common Criteria

The aim of this study is to define security requirements (SR) of Information Technology (IT) product that is deployed on Cloud platform as Software as a Service (SaaS) for Malaysian government. This is critical in order to secure the product from information security threats such as malware attack, account hijacking, data leakage and at the same time, in line with government policy. It is important to address the SR as early as before the product acquisition to avoid any security incidents happen that will affect the government IT ecosystem. Hence, to help government officer from IT and procurement department in preparing security specification for acquisition or procurement exercise, we introduce OTPAF model, a novel approach for defining SR by connecting security components which are security objective (O), threat (T), policy (P), assumption (A) and functionality (F) in deriving a relational statement. First we acquire the government information security objectives and policies. Then cloud top threats and controls are referred to map altogether. Following that, we elicit the security functionality using Common Criteria (CC) approach and combines the components to become SR. Result presents how the conceptual model OTPAF and the values of the security components deriving to a relational statement that becoming SR.
Classification Performance Analysis of Printed Odia Alphabets using Neural Network based Approach

Character recognition is an active and pivotal area in image processing which is the decision making phase of recognition system. In this paper, an effort is made for the classification of the printed odia alphabets without feature extraction and using neural network based approach. The main objective is based on the concept of implementing and analyzing the classification using back-propagation and layer recurrent neural network. The training dataset consists of 112 odia characters and the testing dataset comprises of testing tuples with their class labels. The accuracy of the classifier is measured depending on the percentage of test data that are correctly classified according to the target classes. Also, the two above mentioned neural networks are trained through different training functions and their efficiency is compared based on their classification accuracy. The illustrated procedure is implemented through MATLAB and the output is measured in terms of classification accuracy, and root mean square error. The obtained results suggests that the layer recurrent neural network based system yields good accuracy rate in classifying the data sets.
"Revisiting information retrieval tasks with user behavior models" by Yiqun Liu and Jiaxin Mao with Martin Vesely as coordinator

Analyzing and modeling user behavior in the Information Retrieval (IR) process is important for IR research. In this paper, we propose a new research schema in IR research. In this schema, we first investigate humans' cognitive process when completing a specific IR task and build cognitive models for that task. The findings in the investigation and the proposed cognitive models are then utilized to improve machines' performance in the IR task. Through this research schema, we revisit three IR tasks. In the first study, we carefully analyze users' clicking behavior in mobile search and propose a mobile click model to extract relevance feedback from the mobile search logs. In the second study, we conduct an eye-tracking study to investigate how human assessors read a document during relevance judgment task and adopt the findings in building a novel retrieval model that can better approximate humans' relevance judgment. In the last study, we conduct another eye-tracking study to investigate humans' reading behavior when completing the reading comprehension task. We build a prediction model for user attention and leverage the predicted attention signals to improve the machine reading comprehension model. By successfully adopting this research schema to three different IR tasks, we demonstrate its effectiveness and generalizability.
Abstract: Recognition of AML Blast Cells in a Curated Single-Cell Dataset of Leukocyte Morphologies Using Deep Convolutional Neural Networks

Reliable recognition and microscopic differentiation of malignant and non-malignant leukocytes from peripheral blood smears is a key task of cytological diagnostics in hematology [1]. Having been practised for well over a century, cytomorphological analysis is still today routinely performed by human examiners using optical microscopes, a process that can be tedious, time-consuming, and suffering from considerable intra-and inter-rater variability [2]. Our work aims to provide a more quantitative and robust decision-aid for the differentiation of single blood cells in general and recognition of blast cells characteristic for Acute Myeloid Leukemia (AML) in particular.
Data Self-Portrait

The Data Self-Portrait project aims to explore the creative possibilities associated with the concept of data portrait as a mechanism of representation and expression of individual identity. The project consists in the conception and implementation of a visualization system capable of producing a self-portrait generated from personal data referring to the subject's daily activities, according to aethnographic perspective and as a reflection of his identity. This self-portrait differs from traditional portrait forms in that it is not based on a mimetic image register, and its visuality derives from the collection and representation of data, resulting from personal experiences, thus functioning as a potential biographical repository. The project results in an installation that explores different forms of visualization resulting from the same dataset as complementary expressions of the same self-portrait, comprising a dynamic visualization, interactively exploitable, a physical object and a printed publication. This project is the result of an ongoing investigation on the theme of portraits date and focused on their ability to represent the human being in time and through its digital footprint, thus exploiting a picture reconceptualization in the current context of an everyday immersed in data.
Fast Federated Kalman Filter for Nonlinear Systems with Randomly Delayed Measurements and Correlated Noises

For Gaussian approximation recursive filter (GASF) framework, the same information is used for the correction at two adjacent epochs. To reduce the complexity of processing and ensure the accuracy of the algorithm, in this paper, for nonlinear system with randomly delayed measurements and synchronizttion correlated noises, we present a fast federated Kalman filtering algorithm, where an innovation technique is used. To deal with multi-sensor measurements, a new coefficient allocation method is used to reconstruct the fusion innovation sequence. Then, a federated filtering algorithm is derived based on the optimal information sharing coefficient. Finally, we state the superiority of the algorithm in this paper.
Mapping Arbitrarily Sparse Two-Body Interactions on One-Dimensional Quantum Circuits

We consider an assignment problem arising in Fermionic-swap based mapping of the one-body and two-body interaction terms in simulating time evolution of a sparse second-quantized electronic structure Hamiltonian on a quantum computer. Relative efficiency of different assignment algorithms depends on the relative costs of performing a swap and computing a Hamiltonian interaction term. Under the assumption that the interaction term cost dominates the computation, we develop an iterative algorithm that uses minimum cost linear assignment (MinLA) and matching for one-body interactions, and hypergraph optimal linear arrangement (HOLA) and partial distance-2 coloring for two-body interactions, to exploit arbitrary sparsity in the Hamiltonian for efficient computation. Using a set of 122 problems from computational chemistry, we demonstrate performance improvements up to 100% relative to the state-of-the-art approach for one-body terms and up to 86% utilization for two-body terms relative to a theoretical peak utilization. To the best of our knowledge, this is the first study to exploit arbitrary sparsity in orbital interactions for efficient computation on one-dimensional qubit connectivity layouts. The proposed algorithms lay a foundation for extension to map general k-body interactions that arise in many domains onto generalized qubit connectivity layouts available in current and future quantum systems.
An algorithmic Solution in Data Visualization for the “Hair Ball” Problem

Researching and analyzing large and complex graphs is an important aspect of data visualization research, but completely new, scalable methods and graph visualization methodologies are required [49]. Overall, this can provide more insight into this fuzzy graph's structure and function. To clarify further, in the “Hair Balls” we need to find a technique to build a solution for presenting a clean graph with the minimum overlap between edges. Despite the growing importance of researching and thoroughly examining and interpreting very large data graphs, the traditional way of viewing graphs has trouble scaling up, and usually ends up representing such large graphs as “Hair Balls.” Nevertheless, this traditional approach has a profoundly intuitive foundation [75]: nodes are represented in a form such as a circle, triangle or square, which are then bound by lines or curves representing the edges [73]. In any way, while there are many different methods of applying this fundamental underlying concept, it needs to be reconsidered in the given current and developing needs to consider the increasingly complex convergence between the edges in the graphs [55]. The Hair Ball complex, appearing as an indecipherable diagram, originated from the edge-to-edge convergence. We found the major drawback in the Hair Balls graph from our preliminary research was that it confused observers [38]-[40]. Users might feel that there are some extra nodes; but they don't actually exist. Since there are many crossovers in the Hair Balls between the edges, the impression can also affect observers ` understanding of the graph's entire structure [38] [39]. Major problem-no effective reception of information from a Hair Balls graph-meaningless to observers [64].
Convolutional Sparse Coding-based Anomalous Event Detection in Surveillance Videos

This paper presents a Convolutional Sparse Coding (CSC)-based anomalous event detection method in surveillance videos. The proposed method derives new features from reconstruction errors and sparse coefficient maps obtained by CSC, and the anomalous events are detected by a multi-layer network whose inputs are the above new features. Since such events, i.e., anomalous objects, have different characteristics in the sparse coefficient maps and their corresponding reconstruction errors, successful detection can be expected. Experimental results show high detection performance of own method.
Rocky Mountain Method

In this chapter, we get acquainted with a powerful log interpretation technique that not only helped early log analysts find water saturation from a very limited set of log measurements but also set an example for later developments both in logging tool design and in formation evaluation. The pioneer, Maurice Tixier, cleverly saw that if a calculation required an unknown parameter the need for it could be sidestepped by the simple expedient of taking ratios of known or measured parameters. The principle can even be applied to find porosity from a compensated thermal neutron log by taking the ratio of the near and far count rates. The absolute counts depend on both porosity and the unknown effects of hole size and mud weight, but the ratio of the two is practically unaffected by anything but porosity. Tixier’s seminal paper was published in 1949 and has certainly stood the test of time.
Necessity of Paradigm Shift in Criminological Theorizing

The traditional linkage of defective educational acquisition and the propensity to engage in anti-social behaviours has been accentuated in criminological theory. However, current development in ICT environment preferred for spacelessness, timelessness with its unchanging requirement for ingenuity in the use and mastery of e-interaction has consistently queried the veracity of defective education and crime participation. A survey on cyber fraud conducted in Nigeria found out that the acquisition of requisite education is central to technology-driven crime participation and guarantees continual survival and career longevity in crime business. Consequently, this article analyses the necessity of educational acquisition for crime participation and further advocated for a paradigm shift to accommodate the changing trends in the education-crime symbiotic relationship in the ICT age. This is germane to criminology of ICT in capturing and mitigating the victimization that are contactable through participation in e-relationship.
Task Allocation in Distributed Real Time Database Systems in IoT

In present scenario, distributed and parallel systems in the form of grid, cloud and even cloud based Internet of things (IoT) are cater the needs of demand for computing capacity. Internet of Things (IoT) is a new come up to connect objects/things and therefore transmit information between a variety of entities of the corporeal world or to the control centers where interpret this information. By use of available resources are play very crucial role to ensure systems schedule. In distributed (Real time) database system, data allocation is one of the major problems. It affects the efficiency of the access to the requested data and thereby has large impact on the performance of the whole system. The data allocation involves data splitting, fragment replication, allocation choice to name a few issues. The distributed database system design putting all these factors together into consideration is complex and a Non-deterministic Polynomial (NP) hard. By applying Genetic Algorithm (GA), this work presents a virtual machine (VM) scheduling model to address the job allocation problem aiming to minimize the turnaround time. GA helps to attain a reasonable time for the query execution. The results of experiments have been examined to appraise the efficiency of our approach by comparing with best fit VM scheduling approach.
Enhancing Contextualized Learning via AR

An AR-enhanced contextualized learning was developed to facilitate EFL learning for junior high school students. A preliminary study was conducted to examine the effects of levels of prior knowledge (low vs. high) on participants' learning effectiveness and attitude toward the implemented AR-enhanced theme-based contextualized learning. The analysis suggested that prior knowledge affected participants' learning from the experimental AR-enhanced learning. With the advantage in prior knowledge, the high prior knowledge learners outperformed the low prior knowledge learners both in comprehension and application performance and possessed higher degrees of confidence, preferences, deep learning and learning strategy but with lower degrees of anxiety. It was suggested that suitable learning supports need to be considered to adapt to EFL beginners' levels of prior knowledge in order to facilitate their learning.