Reviewerly/exHarmony huggingface dataset:
Modelling Scholastic Aptitude Test of State Islamic Colleges in Indonesia using Least Square Spline Estimator in Longitudinal Semiparametric Regression

Abstract Scholastic aptitude test is one of tests used to select student candidates who want to enroll to the state Islamic colleges (SIC) in Indonesia. This test is affected by some factors for instances percentage of student candidates who pass to enroll in that enrolment year ( t ), and place of these colleges (SIC) where the student candidates enroll ( X ). Association between t and scholastic aptitude test score ( Y ) have no certain pattern (as nonparametric component). One of estimator in nonparametric regression is least square spline that include knots which can accommodate locally properties and give good flexibility. Place of SIC is dummy variable, so we can use parametric regression approach. To investigate Y from year to year we can use longitudinal data. Therefore, in this research model Y affected by t (as nonparametric component) and X (as parametric component) use longitudinal semiparametric regression based on least square spline estimator. The results give an estimated order one semiparametric regression model of scholastic aptitude test scores with three knots of 70.64; 80.32, and 90.67, and minimum generalized cross validation value of 204.42. Also, the results show that average of Y in western Indonesia is greater than in eastern Indonesia with difference of 20.45.
Seismic Signal Synthesis by Generative Adversarial Network with Gated Convolutional Neural Network Structure

Detecting earthquake events from seismic time series signal is a challenging task. Recently, detection methods based on machine learning have been developed to improve the accuracy and efficiency. However, accuracy of those methods rely on sufficient amount of high-quality training data. In many situations, the high-quality data is difficulty to obtain. We address and resolve this issue by using a Generative Adversarial Network (GAN) model for seismic signal synthesis. GAN already shows its powerful capability in generating high quality synthetic samples in multiple domains. In this paper, we propose a GAN model with gated CNN which can excellently capture sequential structure of seismic time series. We demonstrate its effectiveness via earthquake classification performance. The results show the synthetic data generated by our model indeed can improve the classification performance over the one trained with only real samples.
Deep Adversarial Reinforcement Learning for Object Disentangling

Deep learning in combination with improved training techniques and high computational power has led to recent advances in the field of reinforcement learning (RL) and to successful robotic RL applications such as in-hand manipulation. However, most robotic RL relies on a well known initial state distribution. In real-world tasks, this information is however often not available. For example, when disentangling waste objects the actual position of the robot w.r.t. the objects may not match the positions the RL policy was trained for. To solve this problem, we present a novel adversarial reinforcement learning (ARL) framework. The ARL framework utilizes an adversary, which is trained to steer the original agent, the protagonist, to challenging states. We train the protagonist and the adversary jointly to allow them to adapt to the changing policy of their opponent. We show that our method can generalize from training to test scenarios by training an end-to-end system for robot control to solve a challenging object disentangling task. Experiments with a KUKA LBR+ 7-DOF robot arm show that our approach outperforms the baseline method in disentangling when starting from different initial states than provided during training.
Constant elasticity of substitution function based RANSAC for image stitching

Feature matching is very important in image stitching. RANSAC algorithm is a representative algorithm for feature matching. However, RANSAC still has many shortcomings such as a large number of iterations, a large computational complexity and cannot completely eliminate mismatches. To address above problem, in this paper, we propose a novel method termed constant elasticity of substitution function based RANSAC (CES-RANSAC) for image stitching. Specifically, CES-RANSAC improves the RANSAC algorithm by constructing a utility function, optimizing the boundary of the utility function, calculating Cobb-Douglas coefficients. It also introduces Lindahl equilibrium to derive the return value t to help eliminate mismatches. Experiments show that compared with the traditional RANSAC algorithm, CES-RANSAC has improved matching accuracy and increased computational efficiency, which further improves the efficiency of the image matching algorithm.
Computational Semantics: How to solve the suspense of supersense

Understanding human language is a difficult task, with varied fields of study which aim at explaining and researching the human language principles. Linguistics, Psychology and Computer Science all use domain specific tools to describe and model language. Natural Language Processing is the field which aims at using computational mechanisms to process naturally occurring human language. Modeling syntax gives language structure. Using general sense classes, or "supersenses" one can potentially enrich texts with semantic information. Given a sentence with syntactic information, and a closed set of semantic supersenses, can a supersense tagged sentence be derived? Furthermore, can one demarcate boundaries for multiword expressions? The aim of this research study is to create a multiword expression boundary and supersense labelled sentence by training with Word, part-of-speech (POS), multiword expression (MWE) and supersense tagged training data. The semantically tagged sentences can be used for many tasks such as question answering systems, information retrieval, discourse and sentiment analysis.
TT-TSDF: Memory-Efficient TSDF with Low-Rank Tensor Train Decomposition

In this paper we apply the low-rank Tensor Train decomposition for compression and operations on 3D objects and scenes represented by volumetric distance functions. Our study shows that not only it allows for a very efficient compression of the high-resolution TSDF maps (up to three orders of magnitude of the original memory footprint at resolution of 512 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ), but also allows to perform TSDF-Fusion directly in the low-rank form. This can potentially enable much more efficient 3D mapping on low-power mobile and consumer robot platforms.
Text document clustering using statistical integrated graph based sentence sensitivity ranking algorithm

Abstract The proposed methodology employs a novel statistical integrated graph-based sentence sensitivity ranking algorithm for text document clustering. Clustering of documents is a task of grouping a document automatically into a list of meaningful clusters; in order for the documents inside a group to share the same topic. In this paper, first, a novel integrated graph-based methodology using the sentence sensitivity ranking is proposed to extract keyphrases from the documents. In the standard statistical approach, keyphrases are extracted on the basis of the sentence sensitivity ranking; and in the graph-based method, the candidate keyphrases are automatically created as graphs by applying the sentence sensitivity ranking. With the aid of the top listed keyphrases, the documents clustering are carried out by implementing the proposed sentence sensitivity ranking algorithm. The simulation results reveal that the proposed graph-based text document clustering using statistical integrated graph-based sentence sensitivity ranking algorithm obtained the best results for clustering the text documents.
Toward Data Augmentation and Interpretation in Sensor-Based Fine-Grained Hand Activity Recognition

Recognizing fine-grained hand activities has widely attracted the research community’s attention in recent years. However, rather than enriched sen-sor-based datasets of whole-body activities, there are limited data available for acceler-ator-based fine-grained hand activities. In this paper, we propose a purely convolution-based Generative Adversarial Networks (GAN) approach for data augmentation on accelerator-based temporal data of fine-grained hand activities. The approach consists of 2D-Convolution discriminator and 2D-Transposed-Convolution generator that are shown capable of learning the distribution of re-shaped sensor-based data and generating synthetic instances that well reserve the cross-axis co-relation. We evaluate the usability of synthetic data by expanding existing datasets and improving the state-of-the-art classifier’s test accuracy. The in-nature unreadable sensor-based data is interpreted by introducing visualization methods including axis-wise heatmap and model-oriented decision explanation. The experiments show that our approach can effectively improve the classifier’s test accuracy by GAN-based data augmentation while well preserving the authenticity of synthetic data.
Efficient Dialog Policy Learning With Hindsight, User Modeling, and Adaptation

Goal-oriented dialog systems aim to efficiently and accurately exchange information with people using natural language. A goal-oriented dialog policy is used for suggesting language actions for such dialog systems. Reinforcement learning (RL) has been used for computing dialog policies from the experience of language-based interaction. Learning efficiency is particularly important in dialog policy learning, due to the considerable cost of interacting with human users, and the potentially very poor user experience from low-quality conversations. In this article, we develop deep RL algorithms to improve the efficiency of dialog policy learning. Our contribution is threefold, aiming at the central goal of improving the efficiency of dialog policy learning. First, we present a novel “hindsight” approach to make use of unsuccessful dialog instances to provide the dialog learning agent with extra positive feedback. Second, we introduce user modeling, and enable the dialog agent to learn from simulated interaction experience. Third, we have developed a metalearning algorithm that enables the dialog agent to adaptively learn from simulated users and hindsight experience at the same time. The threefold contribution altogether, for the first time, enables our dialog agent outperforming a number of state-of-the-art dialog policy learning methods, as demonstrated via our experimental results.
Key Technologies of Large Power Grid Security and Stability Intelligent Sand Table Deduction

Existing power system simulation systems focus on calculations and lack effective analysis and display capabilities, which limits the application level of simulation calculation results. Aiming at the problem of power grid simulation data analysis and display combined with simulation calculation and artificial intelligence, this paper proposes the new connotation of large power grid safety and stability sand table deduction technology, and the corresponding data analysis model and agile analysis model. The work in this paper provides the necessary ideas and technical foundation for the establishment of a new grid simulation analysis environment combining human, artificial intelligence and simulation calculation.
Machine learning for predictive analytics

Advances in the field of machine learning are enabling the development of new predictive models in the field of cardiovascular medicine. In this chapter, we cover how predictive machine learning models are developed, their recent use-cases in cardiovascular medicine, and limitations for their expanded use in healthcare. Predictive modeling has long been used in medicine, but recently, machine learning methods offer the ability to learn patterns in large and heterogeneous health data for improved predictive modeling. In addition to describing the general methods by which machine learning models are developed, we share their recent applications in electrophysiology, interventional cardiology, heart failure, and preventive cardiovascular care. We conclude with a discussion about issues regarding data quality, generalizability, bias, and interpretability, and how these influence model development and clinical integration.
An Image Security Method Based on Low Dimensional Chaotic Environment and DNA Encoding

Transmission of digital images (or any other types of data) incurs a risk factor associated with it. Intruders always try to get access of the transmitted data. Therefore, it is necessary to protect the precious data before transmission, so that the threats, which are associated with the data transmission can be avoided and data communication becomes stress free. In this article, a new image encryption method is proposed which is based on chaos theory and DNA computing. Experiments show some promising results both visually and numerically. Various statistical analysis proves the power of the proposed image encryption method against various types of attacks. The method is simple to implement and strong enough to resist various types of attacks.
A Taxonomy of Interactive Online Machine Learning Strategies

In interactive machine learning, human users and learning algorithms work together in order to solve challenging learning problems, e.g. with limited or no annotated data or trust issues. As annotating data can be costly, it is important to minimize the amount of annotated data needed for training while still getting a high classification accuracy. This is done by attempting to select the most informative data instances for training, where the amount of instances is limited by a labelling budget. In an online learning setting, the decision of whether or not to select an instance for labelling has to be done on-the-fly, as the data arrives in a sequential order and is only valid for a limited time period. We present a taxonomy of interactive online machine learning strategies. An interactive learning strategy determines which instances to label in an unlabelled dataset. In the taxonomy we differentiate between interactive learning strategies when the computer controls the learning process (active learning) and those when human users control the learning process (machine teaching). We then make a distinction between what triggers the learning: active learning could be triggered by uncertainty, time, or randomly, whereas machine teaching could be triggered by errors, state changes, time, or factors related to the user. We also illustrate the taxonomy by implementing versions of the different strategies and performing experiments on a benchmark dataset as well as on a synthetically generated dataset. The results show that the choice of interactive learning strategy affects performance, especially in the beginning of the online learning process, when there is a limited amount of labelled data.
A Reliable Encryption Oriented Pseudo-chaotic Number Generator Using a Key Space Expandable Strategy

Pseudo-chaotic number generators (PCNGs), designed based on multiple chaotic maps, have a significant influence on the security of chaos-based cryptosystems. Since the majority of the chaotic maps are based on real numbers, most of the proposed PCNGs use floating-point notations. However, this data type, especially the double precision notation, has disadvantages of high computation cost and inefficient resource utilization. Also, due to the high sensitivity of the chaotic systems and the finite precision nature of software and hardware platforms, the truncation or round-off errors may cause a big difference in the generated pseudo-chaotic numbers, which may undermine the PCNG's security reliability. To overcome these drawbacks, a new secure PCNG scheme based on the reformulated skew tent maps over a 32-bit integer field is proposed in this paper. It not only reduces the resource utilization from the hardware perspective, but also ensures the PCNG's performance over various operation platforms. Furthermore, the proposed PCNG uses a parameter changeable strategy, which expands the key space of the cryptosystem that adopts the PCNG, and thus increases the immunity against the brute-force attack. The security and statistical test results have demonstrated that the proposed PCNG has reliable security properties with good randomness and it is suitable for cryptographic applications.
OptiCam: Optimal Camera Selection for Provisioning Camera - Network -as-a -Service

This work proposes an optimal camera selection scheme, OptiCam, for provisioning Camera-Network-as-a-Service (CNaaS) to multiple end-users while ensuring fair participation of the camera-network owners. Typically, in CNaaS, multiple cameras work together to form a virtual camera network and provide the services to the end-users. These cameras are procured and deployed by their respective owners, who receive rents for lending their cameras. In such a situation, the camera selection mechanism must be fair, so that, each of the owners receives an equal opportunity to participate in the CNaaS platform and earn the profit. On the other hand, multiple camera nodes may be available to serve an end-user application. Therefore, it is pertinent to select the suitable camera nodes, among the available ones, and serve the application while ensuring a longer lifetime of the network. The proposed scheme, OptiCam, is capable of selecting suitable camera nodes, optimally, while ensuring the fair participation of the owners, and increasing the network lifetime. To formulate the proposed scheme, we use a market-based auction model. The results of extensive simulations show that the average lifetime is improved in the case of OptiCam as compared to normal camera network (NCN) by 34.4%. Additionally, we observed that the total number of cameras activated per unit time increases by 8.63% in the case of NCN, while it decreases by 70.77%, in case of OptiCam.
SLPC: A VRNN-based approach for stochastic lidar prediction and completion in autonomous driving

Predicting future 3D LiDAR pointclouds is a challenging task that is useful in many applications in autonomous driving such as trajectory prediction, pose forecasting and decision making. In this work, we propose a new LiDAR prediction framework that is based on generative models namely Variational Recurrent Neural Networks (VRNNs), titled Stochastic LiDAR Prediction and Completion (SLPC). Our algorithm is able to address the limitations of previous video prediction frameworks when dealing with sparse data by spatially inpainting the depth maps in the upcoming frames. Our contributions can thus be summarized as follows: we introduce the new task of predicting and completing depth maps from spatially sparse data, we present a sparse version of VRNNs and an effective self-supervised training method that does not require any labels. Experimental results illustrate the effectiveness of our framework in comparison to the state of the art methods in video prediction.
Algorithmic correspondence and canonicity for possibility semantics

Abstract The present paper develops a unified correspondence treatment of the Sahlqvist theory for possibility semantics, extending the results in the work by Yamamoto (2016, Journal of Logic and Computation, 27, 2411–2430) from Sahlqvist formulas to the strictly larger class of inductive formulas and from the full possibility frames to filter-descriptive possibility frames. Specifically, we define the possibility semantics version of the algorithm Ackermann lemma based algorithm (ALBA) and an adapted interpretation of the expanded modal language used in the algorithm. One notable feature of the adaptation of ALBA to possibility frames setting is that the so-called nominal variables, which are interpreted as complete join-irreducibles in the standard setting, are interpreted as regular open closures of ‘singletons’ in the present setting, which is a novelty of the present paper. We prove the soundness of the algorithm with respect to both (the dual algebras of) full possibility frames and (the dual algebras of) filter-descriptive possibility frames, use the algorithm to give an alternative proof to the one in the work by Holliday (2016, Possibility frames and forcing for modal logic. UC Berkeley Working Paper in Logic and the Methodology of Science. URL. http://escholarship.org/uc/item/9v11r0dq) that the inductive formulas are constructively canonical and show that the algorithm succeeds on inductive formulas. We make some comparisons among different semantic settings in the design of the algorithms and fit possibility semantics into this broader picture.
Model-based reinforcement learning with missing data

Model-based reinforcement learning is a powerful paradigm for learning tasks in robotics. However, real world learning tasks often involve complex patterns of missing data, and model-based reinforcement learning cannot handle missing data directly. To overcome this problem, in this paper, we focus on M-PGPE(GP) proposed by Mori et al. as a model-based reinforcement learning, and propose an extension of M-PGPE(GP) to handle missing data, which we call MM-PGPE(GP). The performance of the proposed MM-PGPE(GP) is assessed in two experiments with mountain car task. These experiments highlight the MM-PGPE(GP) produces higher average return and outperforms the conventional M-PGPE(GP) with simple linear interpolation.
An evaluation model for Cloud-based Data mining Systems with Hadoop

The traditional approach of mining is more expensive, slow, and is inefficient in case of big data. This calls the essence of cloud technology which has the capability of discovering knowledge from a huge database at a very high rate. The implementation of Hadoop technology makes the processing more efficient because of its underlying characteristic of parallelism and data locality. The aim is to have a system based on a review that resembles the most efficient cloud data mining technology. The system should have capabilities to mine big data and have greater application whilst address the problems of existing mining technologies. In doing so, the existing technologies described by some of the relevant works were taken to achieve the overall framework. Reviews of related works were performed for a better understanding of the existing technology on cloud data mining. Based on the references, some algorithms perform better in any given circumstance. The scalability, parallelism, and cost-effectiveness play a significant role in making the system more efficient. The data locality feature of Hadoop gives a maximum optimization in the mining process. Data mining is not a single task, and there is nothing like one algorithm fits all the tasks of mining procedures. The assumptions and given circumstance of data mining will define the accuracy of mining and overall performance. Data type and tasks are always changing which indicates the essence in dynamic algorithms and techniques of data mining.