Reviewerly/exHarmony huggingface dataset:
Before Machine Learning: handling seismic data with Python and segyio

Summary We have truly entered the era of machine learning, and new and exciting models and techniques are developed and designed every day. One of the challenges of ML, however, is the rich variety of formats and data around. It is very difficult to build machine learning models if it is difficult to get data. SEG-Y has been an industry standard for over 40 years now, and data in SEG-Y files are quite valuable. Leveraging this data is a key component in many machine learning projects, and the format still represents a challenge. This is a hands-on workshop describing and demonstrating practical use of the free Python library segyio (https://github.com/equinor/segyio) for reading and writing SEG-Y. It has become quite popular in the open geoscience community, and is designed from scratch to be a suitable building block for new applications. The workshop will focus on dialogue and discussion, and address some common use cases for machine learning and implementing SEG-Y support in new machine learning and geoscience projects.
Knowledge Sharing System to Improve Teacher Performances in SMA Negeri 1 Ciwaru

Abstract This study is based on a view that today’s competitive high school institutions are able to exploit existing knowledge in every human resources to achieve excellences and competitiveness at the optimal level. Thus, this study aims to analyze the influence of knowledge sharing system that implemented in order to develop knowledge sharing culture among teachers to improve teacher performances in SMA Negeri 1 Ciwaru. The method that used in this study is the SECI model developed by Nonaka and combined to knowledge integration. While software quality testing techniques are tested by adapting the four characteristics of software quality of the ISO 9126 model of functionality, reliability, usability, and efficiency. The results of this studyshows the knowledge sharing system applied has excellent quality testing with 89.46%. So it could be the right solution to improve the performance of SMA Negeri 1 Ciwaru teachers to obtain tacit knowledge and explicit knowledge.
Hand Recovery for Geometry-Based Models Using EPD-Based Particle Swarm Optimisation

This chapter includes the results of the PSO $$+$$ EPD algorithm when estimating hand postures from a completely random initial posture. In hand recovery, when tracking is lost, or in the first frame, once hand posture estimation starts the hand posture should be found from a random initial guess (when using generative methods). In case of a correct hand posture estimation, each frame utilises the previous frame, which significantly reduces the difficulty for the optimisation algorithm.
Facility Location Problem in Differential Privacy Model Revisited

In this paper we study the facility location problem in the model of differential privacy (DP) with uniform facility cost. Specifically, we first show that under the hierarchically well-separated tree (HST) metrics and the super-set output setting that was introduced in Gupta et. al., there is an $\epsilon$-DP algorithm that achieves an $O(\frac{1}{\epsilon})$(expected multiplicative) approximation ratio; this implies an $O(\frac{\log n}{\epsilon})$ approximation ratio for the general metric case, where $n$ is the size of the input metric. These bounds improve the best-known results given by Gupta et. al. In particular, our approximation ratio for HST-metrics is independent of $n$, and the ratio for general metrics is independent of the aspect ratio of the input metric. On the negative side, we show that the approximation ratio of any $\epsilon$-DP algorithm is lower bounded by $\Omega(\frac{1}{\sqrt{\epsilon}})$, even for instances on HST metrics with uniform facility cost, under the super-set output setting. The lower bound shows that the dependence of the approximation ratio for HST metrics on $\epsilon$ can not be removed or greatly improved. Our novel methods and techniques for both the upper and lower bound may find additional applications.
Shape Based Speckle Removal for Ultrasound Image Segmentation

We propose a shape-based solution for speckle removal from ultrasound images. The method is operable in both low contrast and high contrast imaging scenarios. The approach introduces shape information alongside structural information in the speckle removing filter. By iteratively minimizing a shape fidelity penalty to reduce speckle, the proposed filter facilitates superior segmentation of blood vessels. The effectiveness of the proposed method is established through experimentation on the ultrasound images of human blood vessels. The results show at more than 7% improvement in PSNR values compared to other state-of-the-art approaches.
Deep Learning Using PySpark

Deep learning has been in the limelight for quite a few years and is making leaps and bounds in terms of solving various business challenges. From image language translation to self-driving cars, deep learning has become an important component in the larger scheme of things. There is no denying the fact that lots of companies today are betting heavily on deep learning, as a majority of their applications run using deep learning in the back end. For example, Google’s Gmail, YouTube, Search, Maps, and Assistance all use deep learning in some form or other. The reason is deep learning’s incredible ability to provide far better results, compared to some other machine learning algorithms.
MLlib: Machine Learning Library

Depending on your requirements, there are multiple ways in which you can build machine learning models, using preexisting libraries, such as Python’s scikit-learn, R, and TensorFlow. However, what makes Spark’s Machine Learning library (MLlib) really useful is its ability to train models on scale and provide distributed training. This allows users to quickly build models on a huge dataset, in addition to preprocessing and preparing workflows with the Spark framework itself.
A BP Neural Network for Identifying Corporate Financial Fraud

The financial security is the lifeblood of a company. Effective identification of corporate financial fraud can protect the safety of funds for investors in some sense. This paper proposed a fraud identification model about corporate financial fraud problem based on principal component analysis (PCA) and BP neural network (BP NN). Compared with other methods, there was a significant improvement in the recognition rate of fraud on financial statements. The experimental results shown that our model is effective, which can accurately identify financial fraud and guarantee the 's financial security.
Near-optimal routing of noisy quantum states

Placement, routing, and scheduling are essential tasks for near-optimal performance of programs for noisy quantum processors. Reliable execution of an arbitrary quantum circuit on current devices requires routing methods that overcome connectivity limitations while meeting data locality requirements. However, current devices also express highly variable noise levels in both the quantum gates and quantum registers. This requires any routing algorithm to be adaptive to both the circuit and the operating conditions. We demonstrate near-optimal routing methods of noisy quantum states that minimize the overall error of data movement while also limiting the computational complexity of routing decisions. We evaluate our methods against the noise characteristics of a 20-qubit superconducting quantum processor.
Membangun Game Edutainment “Pengenalan Komputer” Menggunakan Shuffle Random (SR) Dan Finite State Machine (FSM) Untuk Anak Tunagrahita Ringan

Research to build an Edutainment Game "Introduction to Computers" for mild retarded children is a study designed for teachers to deliver material to special school students (SLB) with special needs such as children with visual impairment, hearing impairment, mental retardation, intellectual disability, autism and blindness. . In this game the player will be presented with a variety of learning menus about "Computer Devices" and a game menu consisting of a guessing picture quiz to train memory, then there are typing games and labyrinth games to develop motor movements and increase students' responses and interests. Shuffle and Finite State Machine randomization algorithms will be applied in this study, with the aim of arranging the image position to be randomized in the learning menu and typing games as well as randomizing the position of the main characters and enemies in the labyrinth game made in three levels. This is done to improve students' learning and curiosity. The technology applied in this research is intelligent agent (intelligent system) which has a game agent character that will accompany children to learn and play.
The Design and Realization of Dynamic Evaluation Strategy of Pieces in Military Chess Game System

In the system of military chess, The accuracy of the evaluation directly affects the function of the system. Because of the military chess game is incomplete information game with high complexity, the accuracy of guessing the enemy chess piece is not high in a limited time. Therefore, this paper uses history heuristic tables to guide the Monte Carlo algorithm to simulate enemy chess pieces, which greatly saves simulation time. And on the evaluation of our chess pieces, Q-learning algorithm based on the idea of exploration and income equalization was proposed to solve the problem of the evaluation based on the multiple kinds of military chess pieces and lack of timely evaluation, Improve the effectiveness of the evaluation. and constructs a complete game system based on dynamic estimation function with four factors by competing with static evaluation system of military chess game the winning rate of our approach reached 60%, And won the first prize in the 2018 National University Computer Game Competition. which verified the effectiveness of dynamic evaluation strategy.
Federated AI lets a team imagine together Federated Learning of GANs

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.
Constructing certificateless encryption with keyword search against outside and inside keyword guessing attacks

Searchable public key encryption is a useful cryptographic paradigm that enables an untrustworthy server to retrieve the encrypted data without revealing the contents of the data. It offers a promising solution to encrypted data retrieval in cryptographic cloud storage. Certificateless public key cryptography (CLPKC) is a novel cryptographic primitive that has many merits. It overcomes the key escrow problem in identity-based cryptography (IBC) and the cumbersome certificate problem in conventional public key cryptography (PKC). Motivated by the appealing features of CLPKC, several certificateless encryption with keyword search (CLEKS) schemes have been presented in the literature. But, our cryptanalysis demonstrates that the previously proposed CLEKS frameworks suffer from the security vulnerability caused by the keyword guessing attack. To remedy the security weakness in the previous frameworks and provide resistance against both inside and outside keyword guessing attacks, we propose a new CLEKS framework. Under the new framework, we design a concrete CLEKS scheme and formally prove its security in the random oracle model. Compared with previous two CLEKS schemes, the proposed scheme has better overall performance while offering stronger security guarantee as it withstands the existing known types of keyword guessing attacks.
A Path Planning Framework for a Flying Robot in Close Proximity of Humans

We present a path planning framework that takes into account the human's safety perception in the presence of a flying robot. The framework addresses two objectives: (i) estimation of the uncertain parameters of the proposed safety perception model based on test data collected using Virtual Reality (VR) testbed, and (ii) offline optimal control computation using the estimated safety perception model. Due to the unknown factors in the human tests data, it is not suitable to use standard regression techniques that minimize the mean squared error (MSE). We propose to use a Hidden Markov model (HMM) approach where human's attention is considered as a hidden state to infer whether the data samples are relevant to learn the safety perception model. The HMM approach improved log-likelihood over the standard least squares solution. For path planning, we use Bernstein polynomials for discretization, as the resulting path remains within the convex hull of the control points, providing guarantees for deconfliction with obstacles at low computational cost. An example of optimal trajectory generation using the learned human model is presented. The optimal trajectory generated using the proposed model results in reasonable safety distance from the human. In contrast, the paths generated using the standard regression model have undesirable shapes due to overfitting. The example demonstrates that the HMM approach has robustness to the unknown factors compared to the standard MSE model.
Perbandingan Algoritma ELM Dan Backpropagation Terhadap Prestasi Akademik Mahasiswa

Extreme Learning Machine and Backpropagation Algorithms are used in this study to find out which algorithm is most suitable for knowing student academic achievement. The data about students are explored to get a pattern so that the characteristics of new students can be known every year. The evaluation process of this study uses confusion matrix for the introduction of correctly recognized data and unknown data. Comparison of this algorithm uses student data at the beginning of the lecture as early detection of students who have problems with academics to be anticipated. The variables used are the value of the entrance examination for new students, the first grade IP value, Gender, and Working Status, while the output variable is the quality value as a classification of academic performance. The results of this study state that the Extreme Learning Machine algorithm has a 14.84% error rate lower than Backpropagation 28.20%. From the model testing stage, the most accurate result is the Extreme Learning Machine algorithm because it has the highest accuracy and the lowest error rate.
Low-Dimensional Bottleneck Features for On-Device Continuous Speech Recognition

Low power digital signal processors (DSPs) typically have a very limited amount of memory in which to cache data. In this paper we develop efficient bottleneck feature (BNF) extractors that can be run on a DSP, and retrain a baseline large-vocabulary continuous speech recognition (LVCSR) system to use these BNFs with only a minimal loss of accuracy. The small BNFs allow the DSP chip to cache more audio features while the main application processor is suspended, thereby reducing the overall battery usage. Our presented system is able to reduce the footprint of standard, fixed point DSP spectral features by a factor of 10 without any loss in word error rate (WER) and by a factor of 64 with only a 5.8% relative increase in WER.
CUNI–Malta system at SIGMORPHON 2019 Shared Task on Morphological Analysis and Lemmatization in context: Operation-based word formation

This paper presents the submission by the Charles University-University of Malta team to the SIGMORPHON 2019 Shared Task on Morphological Analysis and Lemmatization in context.We present a lemmatization model based on previous work on neural transducers (Makarov and Clematide, 2018b;Aharoni and Goldberg, 2016).The key difference is that our model transforms the whole word form in every step, instead of consuming it character by character.We propose a merging strategy inspired by Byte-Pair-Encoding that reduces the space of valid operations by merging frequent adjacent operations.The resulting operations not only encode the actions to be performed but the relative position in the word token and how characters need to be transformed.Our morphological tagger is a vanilla biLSTM tagger that operates over operation representations, encoding operations and words in a hierarchical manner.Even though relative performance according to metrics is below the baseline, experiments show that our models capture important associations between interpretable operation labels and fine-grained morpho-syntax labels.
Evaluation Method for Complex Electromagnetic Environment

To perform a complexity evaluation for an electromagnetic environment (EME), a new method based on the S-transform is proposed, which can simultaneously count the time occupancy, frequency occupancy, and energy occupancy in the time–frequency domain. The frequency coincidence, modulation similarity, and background noise intensity are selected as important evaluation indices, and their physical interpretations are analyzed and calculated. The Extreme Learning Machine (ELM) method is adopted to evaluate the environmental complexity. The proposed method (S-ELM) requires less training time and has a fast convergence rate. The simulation and experimental results confirm that the proposed method is accurate and efficient.
Main Metaheuristics Used for the Optimization of the Control of the Complex Systems

Many optimization problems are usually NP-hard problems which prevent the implementation of exact solution methodologies. This is the reason why engineers prefer to use metaheuristics which are able to produce good solutions in a reasonable computation time. The metaheuristic approaches can be separated into two classes: the local search techniques and the global ones. Among the local search techniques, the taboo search and the simulated annealing are the most known. A possible acceleration of the convergence can be obtained by using tunneling algorithms. Concerning the global methods, the Genetic or Evolution Algorithms (GA), Ant Colony Optimization (ACO), and the Particle Swarm Optimization (PSO) are the most known.
A Handheld Mobile Augmented Reality Tool for On-Site Piping Assembly Inspection

In the context of Industry 4.0, Augmented Reality occupies an important role thanks to its unique capability to enhance the perception of the real world with virtual information. Taking advantage of this capability, the paper presents a handheld mobile Augmented Reality tool that supports manufacturing and production workers and engineers to easily check on-site the ongoing operations carried out in the manufacturing environment for the tubing and piping assembly. The tool runs on a modern tablet and performs an augmented reality visualization of the 3D models, as defined in the project plan, on the corresponding physical objects. In this manner the user can easily check the presence of assembly errors or detect design discrepancies. The level of acceptance of the proposed handheld mobile Augmented Reality tool has been assessed by means of a preliminary test carried out with representative users on a real case study. Results from the experiment are presented and discussed in the paper.
Structural Analysis of Offline Handwritten Mathematical Expressions

Structural analysisAggarwal, Ridhi helps in parsing the mathematicalHarit, Gaurav expressions. Various approaches for structuralTiwari, Anil Kumar analysis have been reported in literature, but they mainly deal with online and printed expressions. In this work, two-dimensional, stochastic context-free grammar is used for the structural analysis of offline handwritten mathematical expressions in a document image. The spatial relation between characters in an expression has been incorporated so that the structural variability in handwritten expressions can be tackled.
Enhancing Spatial Navigation in Robot-Assisted Surgery: An Application

Robot-assisted radical prostatectomy (RARP) has become a standardized practice in contemporary prostate cancer surgical procedures. Nowadays, the use of tailored surgical procedures in urologic surgery aims to maximize treatment efficacy while minimizing its impact on patient functions and health-related quality-of-life parameters. Augmented reality (AR) technology appears as a significant development in Image Guided Surgery (IGS) integrating surgical navigation with 3D virtual organ models registered on to the real patient’s anatomy. In particular, AR provides surgeons the ability to see through solid objects; as such, it has been exploited in different surgical specialties. In this paper, we present the development of a software system augmenting the spatial navigation of the surgical environment allowed by surgical robots. The application is able to visualize the 3D virtual model of the organ (prostate and kidneys) targeted by the surgical procedure, overlay it over its real counterpart, as captured by the endoscope camera, using of registration and tracking techniques in real time, and stream the augmentation to the surgeon.
Value of Information in Probabilistic Logic Programs

In medical decision making, we have to choose among several expensive diagnostic tests such that the certainty about a patients health is maximized while remaining within the bounds of resources like time and money.The expected increase in certainty in the patient's condition due to performing a test is called the value of information (VoI) for that test.In general, VoI relates to acquiring additional information to improve decision-making based on probabilistic reasoning in an uncertain system.This paper presents a framework for acquiring information based on VoI in uncertain systems modeled as Probabilistic Logic Programs (PLPs).Optimal decision-making in uncertain systems modeled as PLPs have already been studied before.But, acquiring additional information to further improve the results of making the optimal decision has remained open in this context.We model decision-making in an uncertain system with a PLP and a set of top-level queries, with a set of utility measures over the distributions of these queries.The PLP is annotated with a set of atoms labeled as "observable"; in the medical diagnosis example, the observable atoms will be results of diagnostic tests.Each observable atom has an associated cost.This setting of optimally selecting observations based on VoI is more general than that considered by any prior work.Given a limited budget, optimally choosing observable atoms based on VoI is intractable in general.We give a greedy algorithm for constructing a "conditional plan" of observations: a schedule where the selection of what atom to observe next depends on earlier observations.We show that, preempting the algorithm anytime before completion provides a usable result, the result improves over time, and, in the absence of a well-defined budget, converges to the optimal solution.