Reviewerly/exHarmony huggingface dataset:
Dynamic Nonparametric Random Forest Using Covariance

As the representative ensemble machine learning method, the Random Forest (RF) algorithm has widely been used in diverse applications on behalf of the fast learning speed and the high classification accuracy. Research on RF can be classified into two categories: <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mo stretchy="false">(</mml:mo><mml:mn fontstyle="italic">1</mml:mn><mml:mo stretchy="false">)</mml:mo></mml:math> improving the classification accuracy and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mo stretchy="false">(</mml:mo><mml:mn fontstyle="italic">2</mml:mn><mml:mo stretchy="false">)</mml:mo></mml:math> decreasing the number of trees in a forest. However, most of papers related to the performance improvement of RF have focused on improving the classification accuracy. Only some papers have focused on reducing the number of trees in a forest. In this paper, we propose a new Covariance-Based Dynamic RF algorithm, called C-DRF. Compared to the previous works, while ensuring the good-enough classification accuracy, the proposed C-DRF algorithm reduces the number of trees. Specifically, by computing the covariance between the number of trees in a forest and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mrow><mml:mi>F</mml:mi></mml:mrow></mml:math>-measure at each iteration, the proposed algorithm determines whether to increase the number of trees composing a forest. To evaluate the performance of the proposed C-DRF algorithm, we compared the learning time, the test time, and the memory usage with the original RF algorithm under the different areas of datasets. Under the same or higher classification accuracy, it is shown that the proposed C-DRF algorithm improves the performance of the original RF algorithm by as much as 58.68% at learning time, 47.91% at test time, and 68.06% in memory usage on average. As a practical application area, we also show that the proposed C-DRF algorithm is more efficient than the state-of-the-art RF algorithms in Network Intrusion Detection (NID) area.
Research on the Method of Robot Arm Trajectory Planning Under Chaos Control

The robot’s arm trajectory can be used to improve the quality of the robot work. The current method utilizes the searching ability of the Alopex algorithm to perform a rough search of the robot’s path of action to achieve the trajectory planning of its arm, but the planning efficiency is low. To this end, this paper uses chaos control to plan the robot arm trajectory. Based on the description of the robot arm trajectory planning, the robotic arm trajectory planning is completed by using the obstacle to the intersection time, probability prediction, motion planning strategy and real-time obstacle avoidance. Experiments show that this method can carry on the efficient planning to the robot arm trajectory.
Privacy and Security Data-sharing Technologies for Tampering Protection and Detection

Digital transformation enables the development and testing of available applications for customer use, done in a smart way. It is also clear that the vast amounts of available data related to business cannot be easily handled in a day without the use of these mobile resources. Yet, privacy and security factors affect the release of more data for publishing or exchange. Open data is ruled out due to insufficient available data for research, as related to mobile devices, where there is a need for real data sets for testing purposes. The Internet of Things demands stronger security mechanisms to keep attacks in buildings and automobiles at bay. However, it must not be ignored that rules and regulations require considerable time to come into effect, whereas self-control, imposed by a user made aware of the risks, takes little or no time at all.
Arabic Handwriting Word Recognition Based on Scale Invariant Feature Transform and Support Vector Machine

Offline Arabic handwritten recognition lies in a major field of challenge due to the changing styles of writing from one individual to another. It is difficult to recognize the Arabic handwritten because of the same appearance of the different characters.  In this paper a proposed method for Offline Arabic handwritten recognition. The   proposed method for recognition hand-written Arabic word without segmentation to sub letters based on feature extraction scale invariant feature transform (SIFT) and   support vector machines (SVMs) to enhance the recognition accuracy. The proposed method  experimented using (AHDB) database. The experiment result  show  (99.08) recognition  rate.
Fast Underwater Image Enhancement for Improved Visual Perception

In this paper, we present a conditional generative adversarial network-based model for real-time underwater image enhancement. To supervise the adversarial training, we formulate an objective function that evaluates the perceptual image quality based on its global content, color, local texture, and style information. We also present EUVP, a large-scale dataset of a paired and unpaired collection of underwater images (of `poor' and `good' quality) that are captured using seven different cameras over various visibility conditions during oceanic explorations and human-robot collaborative experiments. In addition, we perform several qualitative and quantitative evaluations which suggest that the proposed model can learn to enhance underwater image quality from both paired and unpaired training. More importantly, the enhanced images provide improved performances of standard models for underwater object detection, human pose estimation, and saliency prediction. These results validate that it is suitable for real-time preprocessing in the autonomy pipeline by visually-guided underwater robots. The model and associated training pipelines are available at this https URL.
Why Couldn't You do that? Explaining Unsolvability of Classical Planning Problems in the Presence of Plan Advice.

Explainable planning is widely accepted as a prerequisite for autonomous agents to successfully work with humans. While there has been a lot of research on generating explanations of solutions to planning problems, explaining the absence of solutions remains an open and under-studied problem, even though such situations can be the hardest to understand or debug. In this paper, we show that hierarchical abstractions can be used to efficiently generate reasons for unsolvability of planning problems. In contrast to related work on computing certificates of unsolvability, we show that these methods can generate compact, human-understandable reasons for unsolvability. Empirical analysis and user studies show the validity of our methods as well as their computational efficacy on a number of benchmark planning domains.
Balanced Map Generation Using Genetic Algorithms in the Siphon Board-Game

This paper describes an evolutionary system for the generation of balance maps for board games. The system is designed to work with the original game Siphon, but works as a proof of concept for the usage of such systems to create maps for other board games as well. Four heuristics and a constraint, developed in collaboration with the game designer, are used to evaluate the generated boards, by analyzing properties such as: symmetry, distribution of resources, and points of interest. We show how the system is able to create diverse maps that are able to display balanced qualities.
Temporal Landscapes: A Graphical Temporal Logic for Reasoning

We present an elementary introduction to a new logic for reasoning about behaviors that occur over time. This logic is based on temporal type theory. The syntax of the logic is similar to the usual first-order logic; what differs is the notion of truth value. Instead of reasoning about whether formulas are true or false, our logic reasons about temporal landscapes. A temporal landscape may be thought of as representing the set of durations over which a statement is true. To help understand the practical implications of this approach, we give a wide variety of examples where this logic is used to reason about autonomous systems.
Hybrid Elliptic Curve Cryptography for Secured Cloud Computing

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
ANALISIS SENTIMEN OPINI PUBLIK BAHASA INDONESIA TERHADAP WISATA TMII MENGGUNAKAN NAÏVE BAYES DAN PSO

Reviews are important for visitors who will travel because of the information found information about the destination. To get a more precise classification method, this experiment compared the Naive Bayes method and the Naive Bayes method with PSO. This experiment used 50 positive reviews and 50 negative reviews. Different accuracy was obtained from the two experiments. Accuracy obtained for the analysis of Indonesian public opinion sentiments toward tourism TMII uses Naive Bayes by 70% while the accuracy for Indonesian public public sentiment analysis of tourism TMII and PSO is 94.02% .
A Benchmark Proposal for Massive Scale Inference Systems

Many benchmarks have been proposed to measure the training/learning aspects of Artificial Intelligence systems. This is without doubt very important, because its methods are very computationally expensive, and, therefore, offering a wide variety of techniques to optimize the computational performance.The inference aspect of Artificial Intelligence systems is becoming increasingly important as the these system are starting to massive scale. However, there are no industry standards yet that measures the performance capabilities of massive scale AI deployments that must per-form very large number of complex inferences in parallel. In this work-in-progress paper we describe TPC-I, the industry's first benchmark to measure the performance characteristics of massive scale industry inference deployments. It models a representative use case, which enables hard- and software optimizations to directly benefit real customer scenarios.
Differential Evolution Variants in Robust Optimization Over Time

This paper presents an empirical comparison of four differential evolution variants to solve robust optimization over time problems. A set of test instances with different time window values are used to assess the performance of the algorithms analyzed, whose corresponding input parameters were fine-tuned by using an statistical-based tool to promote a fair comparison. Furthermore, the best differential evolution variant is compared against a state-of-the-art particle swarm optimization algorithm for robust optimization over time. The results obtained indicate that differential evolution is a viable option to deal with this particular type of dynamic search space.
Fusion of Local Binary Pattern and Local Phase Quantization features set for Gender Classification using Fingerprints

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Network Electronic Record Management Based on Linked Data

With the rapid development of e-government, e-commerce and self-media, a large number of electronic network records have been generated on the Internet. How to archive these resources has become a focus of attention in academ... | Find, read and cite all the research you need on Tech Science Press
Efficient Independence Testing for Quantum states

We provide simple and efficient testers of problems about quantum independence by collective measurements and independent measurements, respectively. We show that given mixed states $\rho,\sigma\in \mathcal{D}(\mathbb{C}^{d})$, $O(\frac{d^2}{\epsilon^2})$ copies are sufficient to test whether $\rho=\sigma$ using independent measurements. Together with the identity tester in collective measurements setting \cite{BOW17}, we prove the following. Given multipartite mixed state $\rho\in \mathcal{D}(\mathbb{C}^{d_1}\otimes \mathbb{C}^{d_2}\otimes\cdots\otimes\mathbb{C}^{d_m})$, $\tilde{O}(\frac{\Pi_{i=1}^m d_i}{\epsilon^2})$ copies are sufficient and necessary to test whether $\rho$ is independent, i.e., in the tensor product form, by collective measurements, and $O(\frac{\Pi_{i=1}^m d_i^2}{\epsilon^2})$ copies are sufficient by independent measurements. Given $\rho_1,\rho_2, \cdots,\rho_n\in \mathcal{D}(\mathbb{C}^{d})$ in the query model, and $O(\frac{d}{\epsilon^2})$ copies are sufficient and necessary to test whether $\rho_i$s are all identical by collective measurements, $O(\frac{d^2}{\epsilon^2})$ copies are sufficient by independent measurements. Given $\rho_1,\rho_2, \cdots,\rho_n\in \mathcal{D}(\mathbb{C}^{d_1}\otimes \mathbb{C}^{d_2}\otimes\cdots\otimes\mathbb{C}^{d_m})$, $\tilde{O}(\frac{\Pi_{i=1}^m d_i}{\epsilon^2})$ copies are sufficient and necessary to test whether $\rho_i$s are independent by collective measurements, and $O(\frac{\Pi_{i=1}^m d_i^2}{\epsilon^2})$ copies are sufficient by independent measurements. The identity testing and independence testing in $\mathcal{D}(\mathbb{C}^{d_1}\otimes \mathbb{C}^{d_2})$ can be accomplished with $O(\frac{d_1^2d_2^2}{\epsilon^2})$ copies using just local measurements, respectively. This technique is used to provide efficient testers of conditional independence for classical-quantum-quantum states.
Semantics Disentangling for Text-to-Image Generation

Synthesizing photo-realistic images from text descriptions is a challenging problem. Previous studies have shown remarkable progresses on visual quality of the generated images. In this paper, we consider semantics from the input text descriptions in helping render photo-realistic images. However, diverse linguistic expressions pose challenges in extracting consistent semantics even they depict the same thing. To this end, we propose a novel photo-realistic text-to-image generation model that implicitly disentangles semantics to both fulfill the high-level semantic consistency and low-level semantic diversity. To be specific, we design (1) a Siamese mechanism in the discriminator to learn consistent high-level semantics, and (2) a visual-semantic embedding strategy by semantic-conditioned batch normalization to find diverse low-level semantics. Extensive experiments and ablation studies on CUB and MS-COCO datasets demonstrate the superiority of the proposed method in comparison to state-of-the-art methods.
Resident Involvement in Tactical Medicine: 12 Years Later

Introduction: Interest in tactical medicine, the provision of medical support to law enforcement and military special operations teams, continues to grow. The majority of tactical physicians are emergency physicians with additional training and experience in tactical operations. A 2005 survey found that 18% of responding Emergency Medicine (EM) residencies offered their resident physicians structured exposure to tactical medicine at that time. Methods: This study sought to assess interval changes in tactical medicine exposure during EM residency and Emergency Medical Services (EMS) fellowship training. A secure online survey was distributed electronically to all 212 EM residency programs and 44 EMS fellowship programs in the United States. Results: Responses were received from 99 (46%) EM residency and 40 (91%) EMS fellowship programs. Results showed that 52 (53%) of the responding residencies offered physician trainees formal exposure to tactical medicine as part of their training (P &amp;lt; .0001 compared to 18% in 2005). In addition, 32 (72%) of the 40 responding EMS fellowships (newly established since the initial survey) offered this opportunity. Experiences ranged from observation to active participation during tactical training and call-outs. The EM residents and EMS fellows provide support to local, state, and federal law enforcement agencies. A small number of programs (six residencies and four fellowships) allowed a subset of qualified trainees to be armed during tactical operations. Conclusion: Overall, training opportunities in tactical medicine have grown significantly over the last decade from 18% to 53% of responding EM residencies. In addition, 72% of responding EMS fellowships incorporate tactical medicine in their training program. Petit NP, Stopyra JP, Padilla RA, Bozeman WP. Resident involvement in tactical medicine: 12 years later. Prehosp Disaster Med . 2019;34(2):217–219
Feature characterization for CPS software reuse

Many organizations continue to have reusable systems because they are cheaper, and associated with less time to market. Moreover, the practitioners experience lower risk if they choose to continually improve the reusable system rather than building a new system from scratch. Many reusable cyber-physical system (CPS) exist which interact with multiple physical entities. Users today expect modern CPS to satisfy a wide range of constraints at runtime. This paper characterizes the reusable CPS software by identifying the functional behaviors of the CPS as features along with the hierarchical relationships among them. We also recover dependencies (mandatory, optional, or, alternative) and cross-tree constraints (require, exclude) among the features as well as identify possible valid feature-configurations. In the experimental analysis, we discuss features and their configurations for three existing CPS software. Our framework benefits the practitioners in all stages of abstraction such as design, development, and testing.
KEMAMPUAN BERPIKIR KRITIS SISWA KELAS IV SE-GUGUS 2 PURWASARI DALAM MEMBACA PEMAHAMAN MELALUI MODEL FIVES DAN MODEL GUIDED READING

Students' critical thinking ability is one of the components developed in 21st Century learning. However, this critical thinking ability is still very minimal and far from expectations, this is supported by the results of international surveys that show a low average value. Likewise, the results of the pretest of critical thinking skills of grade IV elementary school students in Cluster 2 Purwasari showed a low average value. This study aims to determine the improvement of students' critical thinking skills in reading comprehension learning through the use of the FIVES Model and the Guided Reading Model. The study used a quasi-experiment with Static Group Pretest-Postest Design. The results showed that the use of these two models could improve students' critical thinking skills. However, the use of Model FIVES experienced a significant increase compared to the Guided Reading Model. Thus, there is an increase in students' critical thinking skills through the FIVES Model compared to the Guided Reading Model.
Extended wavefunctions for the Variational Quantum Eigensolver

We extend the class of wavefunctions used within the framework of the Variational Quantum Eigensolver. Testing such extended variational space in simple cases, we achieve high quality wavefunction with reduced circuit depth.
A Learned Representation for Scalable Vector Graphics

Dramatic advances in generative models have resulted in near photographic quality for artificially rendered faces, animals and other objects in the natural world. In spite of such advances, a higher level understanding of vision and imagery does not arise from exhaustively modeling an object, but instead identifying higher-level attributes that best summarize the aspects of an object. In this work we attempt to model the drawing process of fonts by building sequential generative models of vector graphics. This model has the benefit of providing a scale-invariant representation for imagery whose latent representation may be systematically manipulated and exploited to perform style propagation. We demonstrate these results on a large dataset of fonts and highlight how such a model captures the statistical dependencies and richness of this dataset. We envision that our model can find use as a tool for graphic designers to facilitate font design.
Enhancing Decision Tree based Interpretation of Deep Neural Networks through L1-Orthogonal Regularization

One obstacle that so far prevents the introduction of machine learning models primarily in critical areas is the lack of explainability. In this work, a practicable approach of gaining explainability of deep artificial neural networks (NN) using an interpretable surrogate model based on decision trees is presented. Simply fitting a decision tree to a trained NN usually leads to unsatisfactory results in terms of accuracy and fidelity. Using L1-orthogonal regularization during training, however, preserves the accuracy of the NN, while it can be closely approximated by small decision trees. Tests with different data sets confirm that L1-orthogonal regularization yields models of lower complexity and at the same time higher fidelity compared to other regularizers.
Know Your Library Users: How Three Generations Use Digital Tech

To better serve their users, libraries and medical librarians need to be aware of the differences among the three main generations of library patrons: Baby Boomers, Gen Xers, and Millennials. It has been proven that each generation has its own information-seeking preferences, behaviors, and backgrounds. The key to serving each group most effectively is to become knowledgeable about its characteristics, how it approaches and adapts to new technologies, and the most effective ways to communicate with each group. Gaining insight into different generations has implications for library marketing and collection development.