Reviewerly/exHarmony huggingface dataset:
Skinscan: 3D Dermatologic Diagnosis and Documentation with Commodity Devices

Computational photography has become a valuable field of interest for dermatologists. Current telemedical approaches mainly use two-dimensional (2D) color imagery, which cannot always capture sufficient information for the physician. In clinical practice dedicated imaging systems can acquire valuable three-dimensional (3D) data to improve the patients outcome [1], [2]. The major drawback of clinical imaging systems is the price and the availability to the broad audience. We tackle this problem by providing an open-source software framework. The framework uses a photometric stereo-inspired gradient illumination technique, enabling detailed 3D measurements of fine skin structures, like fingerprints. Especially in vastly populated or underdeveloped areas, our system could show benefits for initial diagnosis and documenting the evolution of skin diseases.
Three - Dimensional Tunnel Passing Maneuvers of Mobile Robots

In this paper, we consider multiple point masses traversing in a 3-dimensional obstacle-ridden environment made up of cylindrical-shaped obstacles, which will act as tunnels. Each point mass will have different targets and avoiding each other and the cylindrical tunnels in its path. The motion of a robot is bounded within a workspace and its velocities are curtailed for safety reasons. Since the workspace is cluttered with 3-dimensional cylindrical shaped objects, we propose a collision-free path that can lead the mobile robot from the desired starting point to the desired location or target. A complete solution to this problem will use the Minimum Distance Technique (MDT) for the generation of the shortest, smoothest and safest path among all the collision-free paths between the initial position and the target. Here, the minimum distance between the center of the point mass, representing a mobile robot, and the surface wall of the cylinder is calculated; thus, only the avoidance of a point on the surface wall of the cylinder is considered. The simulations and derivations are verified by using Mathematica software.
Bimodal Key Data and Semantic Analysis

This research was aimed at combining individual natural language processing methodologies into a consolidated model that takes in data in both audio and text format. Models were selected and ranked by adherence to select criteria for use in speech recognition, keyword and topic mining, and semantic analysis. Upon selection of the models, they were implemented through libraries into a singular program, which had an input of a combinatorial text and audio dataset, and provided a report of the analysis resulting from data mining. The program was tested using data from TED talks, and provided a structured output of the generated statistics.
Construction of Hash Function Algorithm Based on Chen Chaotic System

In this paper, Chen chaotic system with relatively high complexity is used to construct hash function. Aiming at the low efficiency of hash function constructed by high-dimensional continuous chaotic mapping algorithm, an algorithm for grouping and mapping plaintext blocks is proposed. The experimental results show that compared with the existing mainstream chaotic hash algorithm, the avalanche characteristics and disordered distribution of this algorithm still remain, and the confusion and diffusion are more stable. At the same time, this algorithm is very suitable for running on parallel systems.
A Novel Skeleton-based Model with Spine for 3D Human Pose Estimation

Kinematic chain model is widely adopted in 3D human pose estimation tasks while it cannot accurately describe the curvature of the torso. This work proposes for the first time a novel model with spine curve to both express the movement of the limbs and the bending curvature of the torso. We parameterize the spine curve with the Bezier curve as it's controlled by anchor points. In this way, the estimation of the spine curve can be converted back to the estimation of points. The result shows that the anchor points of the spine curve can be estimated accurately by existing networks, similar to other joints. With the help of MOSH++ and SMPL model, the existing datasets can also be employed with our skeleton. We fit the SMPL models with the kinematic chain model and our model respectively, the fitting result shows that our model can provide richer information for the construction of human models.
Robust Spectral Clustering via the Ordering Metric

Spectral clustering is one of the most popular algorithms in unsupervised learning. However, it is difficult to construct an affinity graph that benefits spectral clustering, mainly due to the lack of a discriminative distance metric. In this article, motivated by the weakness of the traditional distance metric, we propose a novel metric named the ordering metric, in order to measure the class-consistency of two data points. Based on the proposed metric, a scalable Gaussian affinity graph is constructed. The ordering metric can distinguish classes more accurately than classical metrics, hence our proposed affinity graph based on it can simultaneously highlight intra-class connections and suppress inter-class connections. With these advantages, the spectral approach normalized cut can achieve a low-dimensional projection from the graph that contains clear and correct class information. Classical clustering approaches such as K-means can cluster these projection points perfectly, due to the significant separation of the spectral projection between classes. Numerical experiments on 2 synthetic 2D data sets and 5 real-world data sets show the outstanding clustering performance of our algorithm.
Fuzzy Similarity and Entropy (FSAE) Feature Selection Revisited by Using Intra-class Entropy and a Normalized Scaling Factor

We revisit the fuzzy similarity and entropy (FSAE) filter method for supervised feature selection and propose the C-FSAE filter method that only uses similarities of observations to their own class’s ideal vector (intra-class) and deploys a normalized scaling factor that accounts for the distance between ideal vectors normalized by the standard deviations of each feature in the classes. The same adjustments are implemented for the fuzzy entropy and similarity (FES) and the proposed version is termed as the C-FES. Three simple artificial example cases showcase that the C-FSAE and C-FES result in intuitive feature rankings and consistently rank the features according to their relevance to the classification problem. On most of the seven medical real-world data sets in this study the C-FSAE demonstrates competitive validation accuracies to ReliefF, Fisher score, Laplacian score and Symmetrical uncertainty and performs at least as good and often better than the FSAE. The test set accuracies indicate and confirm the competitive results of the C-FSAE, where it led for two data sets to the highest test set accuracy overall and for more than half of the data sets provided the highest test accuracy for at least one of the classifiers.
Graph Classification: Tradeoffs between Deep Neural Network Architecture and Graph Topology

Deep learning techniques such as convolutional neural networks (CNNs) have significantly impacted fields like computer vision and other Euclidean data domains. However, many domains have non-Euclidean data, and it is of interest to extend CNNs to leverage the data graph. There has been a surge of interest in the field of geometric deep learning that adapts CNNs to graph signals. As a result, researchers have developed several Graph Neural Network models to address graph classification. There is no clear winner among these models, as their performance depends on the data graph topology. In this paper, we explore the tradeoffs between the graph topology and the architecture of graph neural networks for graph classification on real and synthetic datasets. In particular, we look at 1) network metrics of the graph structures being classified and 2) neural network hyperparameters like the degree of polynomial filter and the number of convolutional layers. Our experimental results show that there is a tradeoff between the performance of graph CNNs (GCNNs) and the graph topology. Simple classifiers based on network metrics and signal statistics may perform better than GCNNs on real datasets and on synthetic datasets when only the graph structure is important.
Panic Behavior Detection using Unsupervised Machine Learning Techniques: A comparative study

Crowd-scenes analysis is a hot research topic in computer vision nowadays. Over past years, many cases of crowd disasters happened overall the world. Real-time and fast panic behavior detection is crucial to quickly alert the crowd managers so they take appropriate precautions. Machine learning, mainly, unsupervised learning, has made great progress in abnormalities detection. In another hand, the Optical Flow (OF) feature forms the core of numerous detection models as it has proven its capability to represent and interpret complex motion for a long time. In this paper, we review the recent existing works in the field. The empirical comparison of common unsupervised classifiers using OF is provided to investigate their performance for panic detection in terms of correct detection, false detection, and frame rate. The investigation on these performance metrics reveals that the Autoencoder has the best detection performance in most of the dataset scenarios, while K-mean and hierarchical clustering are the fastest.
Decommissioning Robot Retrieves Fuel Debris from High Altitude

Decommissioning Robot Competition is a robotics competition for decommissioning of reactor. The purpose of this study is to develop human resources which will be responsible for decommissioning technology in the future by giving them knowledge and interest in decommissioning work itself, as well as experience gained in the process. The task of the fourth competition is to go through a narrow pipe, to the pedestal section, and then to take a golf or tennis ball at the bottom of the hole in the pedestal section and a return. This study aims to build a robot that can perform the tasks in the 4th Decommissioning Robot Competition. The robot is composed of the main unit and sub-unit. The purpose of the main unit is to hang and lift the sub-unit safely and to pass the cables connected to the sub-unit safely. The purpose of the main unit is to hang and lift the subunit safely and to pass the cables connected to the sub-unit safely. An infinite track is used for the footrest, and a slide rail is attached to the top of the main unit to lift the sub-unit with a cable. The cable was bitten to a pulley attached to the slide rail, and the cable was collected by moving the pulley. The purpose of the sub-unit is to search for and hold the object to be recovered. We prepared a terminal to operate the sub-unit and a terminal to check the camera image. To hold the object, an arm is attached to the front of the sub-unit and the object is sandwiched between the arm and the front of the sub-unit. As a result, the robot was able to perform the tasks, but due to the lack of prior testing and coordination, it was not able to clear all the competition tasks.
Three-dimensional Data Outlier Detected by Angle Analysis

We present a method to distinguish outliers from spherical cluster distributed three-dimensional data. The angle measurement method 3DOD transforms three-dimensional data to two-dimensional data, and then outliers can be detected by conventional two-dimensional data outlier algorithm.
Human Behavior Identification and Recognition System

the concept of human behavior recognition is an interesting yet daunting task to delve into. This paper presents the design and mathematical modeling of a human behavior recognition system image processing and pattern recognition. Human behavior recognition is a framework for understanding human behavior. Despite the many issues, such as privacy and ethical concerns, behavioral study still has the potential to revolutionize the way we interact with machines and each other.
AxomDB: A Distributed and Scalable In-memory Database System

Memory (RAM) sizes are increasing day by day with cheaper costs due to emerging technologies, and it will continue to increase with a more affordable price. Therefore, in-memory database (IMDB) system exists as suitable choice for databases due to the high demands for the high-speed distributed database system. Moreover, with growing demand for real-time analytics on big data, requirement of distributed in-memory database systems is growing. Although they may sound trouble-free, in-memory databases bring some issues, making it hard to gain widespread usage. We propose a novel and intrinsic in-memory database system called AxomDB. This paper presents an efficient distributed in-memory database with Online Analytical Processing (OLAP) and Online Transactional Processing (OLTP) query processing capability in simultaneous manner, thus eliminating the need for a data warehouse or batch processing of big-data for analytical purposes. Also, we demonstrate an IMDB system with minimal use of secondary storage devices to ensure data persistence.
Objective Object Segmentation Visual Quality Evaluation: Quality Measure and Pooling Method

Objective object segmentation visual quality evaluation is an emergent member of the visual quality assessment family. It aims to develop an objective measure instead of a subjective survey to evaluate the object segmentation quality in agreement with human visual perception. It is an important benchmark for assessing and comparing the performances of object segmentation methods in terms of visual quality. Despite its essential role, sufficient study compared with other visual quality evaluation studies is still lacking. In this article, we propose a novel full-reference objective measure that includes a two-level single object segmentation visual quality measure and a pooling method for multiple object segmentation overall visual quality. The single object segmentation visual quality measure combines a pixel-level sub-measure and a region-level sub-measure for evaluating the similarity of area, shape, and object completeness between the segmentation result and the ground truth in terms of human visual perception. For the proposed multiple object segmentation overall visual quality pooling method, the rank of each object’s segmentation quality as a novel factor is integrated into the weighted harmonic mean to evaluate the overall quality. To evaluate the performance of our proposed measure, we tested it on an object segmentation subjective visual quality assessment database. The experimental results demonstrate that our proposed two-level measure and pooling method with good robustness perform better in matching subjective assessments compared with other state-of-the-art objective measures.
New Intuitionistic Fuzzy Similarity Measure for Enhanced Decision Support System in Dementia Patients

Similarity measures have been proven over time for their usefulness in solving real-time problems. Researchers modify and develop new methods to suit different areas of application relative to specific data sets. The existing similarity measures on some application domains gave ineffective results. This study modified existing similarity measures and converted them into intuitionistic fuzzy similarity measure for word cognition detection in dementia patients. Similarity measures of bigram, Dice and Canberra were modified as Dice1, Dice2, Canberra1, Canberra2 and bigram which were extended to intuitionistic fuzzy similarity measures, and the text and pattern were classified into type-1 linguistic variable. Experiments were conducted on both existing and modified methods with stored data sets while evaluations were performed on generated results using average similarity measure, average processing time and root mean square error. Experimental results indicated that modified Dice2 gave the highest similarity value of 0.98, followed by Dice, modified Dice1, modified Canberra1, modified Canberra2, Canberra, modified bigram with the values of 0.93, 093, 0.90, 0.89, 0.84 and 0.82 respectively for 100 pairs of text and pattern matching of equal length of characters. Considering the processing time for experimental cases of 20, 50 and 100 sets of text and pattern matching of both equal and unequal length of strings, modified Dice2 computed the lowest processing time followed by modified Dice1, modified bigram, Canberra, modified Canberra1, Dice, and modified Canberra2. MD2 gave a more effective and efficient IFSM compared to existing IFSM for dementia patients. The results of root mean square error on both existing and modified methods indicated that modified Dice2 has the lowest value. Modified Dice2 classified the word entered by the user as against word generated randomly by the computer as type-1 linguistic variable of simple, moderate and high as suggested by the experts. Modified Dice2 gave the highest similarity value, lowest processing time, lowest root mean square error which could be used for word cognition detection and hence an enhanced IFSM for dementia patients.
A Machine Learning Approach to Track COVID-19 Pandemic using Sentiment Analysis

Coronavirus disease or COVID-19 is one of the most frightening and infectious diseases of the twenty-first century. Since the outbreak of COVID-19 in Wuhan, China, numerous researches are conducted in this sector. At the preliminary stage, there was not sufficient numeric data for research but when we consider the text data such as trending topics of Social Media or patients sharing experiences about their symptoms, we get enough data to ace the navigation of the Coronavirus (SARS-CoV-2). Keeping aside the health complications related to COVID-19, there also has been huge public panic following the pandemic. Sentiment analysis helps to learn the emotions of a vast number of people about any particular topic. In this paper, we have used sentiment analysis methods to observe the public reaction to the COVID-19 pandemic and people&#x2019;s experience of the ongoing vaccination process. Machine Learning-based (ML-based) classification algorithms are implemented for text classification. Finally, the accuracy of the classification models is also calculated for further prediction.
Resource Efficient Top-&lt;i&gt;K&lt;/i&gt; Sorter on FPGA

The top-K sorting is a variant of sorting used heavily in applications such as database management systems. Recently, the use of field programmable gate arrays (FPGAs) to accelerate sorting operation has attracted the interest of researchers. However, existing hardware top-K sorting algorithms are either resource-intensive or of low throughput. In this paper, we present a resource-efficient top-K sorting architecture that is composed of L cascading sorting units, and each sorting unit is composed of P sorting cells. K=PL largest elements are produced when a variable length input sequence is processed. This architecture can operate at a high frequency while consuming fewer resources. The experimental results show that our architecture achieved a maximum 1.2x throughput-to-resource improvement compared to previous studies.
Influence of Parental Attention, Peer Environment, and Learning Motivation on the Learning Achievement of Office Administration Education Students, Faculty of Economics, Universitas Negeri Jakarta Class 2019/2020

The purpose of this study is to find out the influence of parental attention, peer environment, and learning motivation on student learning achievement Pendidikan Administrasi Pofficefaculty of economics Universitas Negeri Jakarta. The data collection method uses survey methods with instruments in the form of questionnaires. This study used a sample number of 119 respondents from office administration education students in the class of 2019 and 2020. To process and analyze the data of research results, the data analysis technique is using SPSS version 26. The results showed that variables in parental attention, peerenvironment, and learning motivation had a positive effect on learning achievement in students Pendidikan Administrasi PofficeUniversitas Negeri J akarta class 2019 and 2020.
Resource optimization for the quantum Internet

The quantum Internet enables networking based on the fundamentals of quantum mechanics. Here, we define methods and procedures of resource prioritization and resource balancing for the quantum Internet. The aim of the proposed solutions is to optimize the resource allocation mechanisms and to reduce the resource consumptions of the network entities.
Pool Block Withholding Attack with Rational Miners

We revisit pool block withholding (PBW) attack in this work, while taking miners’ rationality into consideration. It has been shown that a malicious mining pool in Bitcoin may attack other pools by sending some dummy miners who do not reveal true solutions but obtain reward portions from attacked pools. This result assumes that the infiltrating miners are always loyal and behave as the malicious pool desires; but why? In this work, we study rational infiltrating miners who behave by maximizing their own utilities. We characterize the infiltrating miners’ optimal strategies for two kinds of betraying behaviors depending on whether they return to the original pool or not. Our characterizations show that the infiltrating miners’ optimal strategy is simple and binary: they either betray or stay loyal all together, which depends on the total amount of them sent out by the malicious pool. Accordingly, we further compute the pool’s optimal attacking strategy given the miners’ strategic behaviors. Our experiments also show that by launching PBW attacks, the benefit to a pool is actually incremental.
A Document Image Quality Assessment Method Based on Feature Fusion

Document image quality assessment (DIQA) is an essential step in the development of optical character recognition (OCR) products. Due to the complex and diverse distortion types in the real captured document images, DIQA is still a challenging problem. In this paper, we propose a new DIQA model, which is based on the feature fusion in convolutional neural network (CNN). In our network, shallow network part is used to extract low-level local features of document images to represent local non-uniform distortions. And deep network part is used to learn global features to represent global uniform distortions in document images. In addition, a quality regression network is used to predict the document image quality score by using the fusion of the low-level and deep-level features. Experimental results demonstrate that our model outperforms the state-of-the-art methods on complex distortion datasets.
Implementing Algebraic Effects and Handlers in Non-functional Programming Languages

There are attempts trying to implement Algebraic Effects and Handlers in non-functional programming languages. However there are various methods to implement them depending on the languages, and they are not generalized well. We have summarized these methods, and proposed features which is compatible with implementing Algebraic Effects and Handlers based on the summary and our implementation in C&#x0023;.