Reviewerly/exHarmony huggingface dataset:
Modeling location obfuscation for continuous query

One of the major problems of location-based services is ensuring the location privacy of a mobile user. However, it becomes more challenging in the case of a continuously moving user. Though many techniques for preserving location privacy for continuous query scenario have been studied in the last decade, a formal approach for quantification of privacy and justification for the correctness of the privacy guarantee remains mostly at infancy level. In this paper, we present a theoretical model for location obfuscation that is flexible enough to express several obfuscation mechanisms and allow to reason about their privacy guarantees. We illustrate the effectiveness of our theoretical model by analyzing a popular square grid-based obfuscation mechanism.
How Much Does Tokenization Affect Neural Machine Translation?

Tokenization or segmentation is a wide concept that covers simple processes such as separating punctuation from words, or more sophisticated processes such as applying morphological knowledge. Neural Machine Translation (NMT) requires a limited-size vocabulary for computational cost and enough examples to estimate word embeddings. Separating punctuation and splitting tokens into words or subwords has proven to be helpful to reduce vocabulary and increase the number of examples of each word, improving the translation quality. Tokenization is more challenging when dealing with languages with no separator between words. In order to assess the impact of the tokenization in the quality of the final translation on NMT, we experimented on five tokenizers over ten language pairs. We reached the conclusion that the tokenization significantly affects the final translation quality and that the best tokenizer differs for different language pairs.
Model-Based Pileup Events Correction via Kalman-Filter Tunnels

A Kalman-filter-based method is proposed to address challenges in nuclear signal processing, including pileup events and noise cancelation. The state-transition equation is established on the basis of suitable pulse shape models. By regarding the output signal of particle detectors as a measurable variable, the measurement equation is also introduced to correct dynamic processes of the state transition. A linear Kalman filter with n tunnels is constructed to separate pileup events and maintain the original waveforms of pulses. In addition, two terms are defined and addressed for separation operations, namely, Availability, which is used to prepare for upcoming events, and Clearance Mechanism, which is given to remove thumbnail information from the previous events and make occupied tunnels available to subsequent nuclear events. Simulation and field experiments demonstrate that the proposed method can handle critical situations of pulse pileup and minimize count loss and dead time in the X-ray energy spectrum while maintaining the original pulse shapes.
Tree-Search-Based Any-Time Time-Optimal Path-Constrained Trajectory Planning With Inadmissible Island Constraints

Time-optimal path-constrained trajectory planning is of significant importance for enhancing the work efficiency of robotic systems. In particular, when inadmissible island constraints are considered, existing approaches are typically offline. In order to solve the problem online, this paper proposes a heuristic tree-search-based any-time time-optimal trajectory planning algorithm to achieve incremental computation of feasible trajectories. In a limited planning period, the proposed algorithm iteratively generates feasible trajectories, as optimal as possible, until the period terminates. This any-time performance of the proposed algorithm ensures that feasible trajectories can be obtained in real time and even time-optimal trajectories can be obtained if the planning period is long enough for searching. Experimental results on active-casters-based omnidirectional wheeled mobile robots demonstrate the validity of the proposed algorithm.
Distributed Robust Bayesian Filtering for State Estimation

We study the problem of distributed filtering for state space models over networks, which aims to collaboratively estimate the states by a network of nodes. Most of existing works on this problem assume that both process and measurement noises are Gaussian and their covariances are known in advance. In some cases, this assumption breaks down and no longer holds. In this paper, we consider the case that both process and measurement noise covariances are unknown. A few related works have studied this problem. However, these works consider the situation of centralized processing, and they only derive smoothers which are not suitable for online processing in a network. Instead, this paper presents a novel robust Bayesian filter with unknown process and measurement noise covariances over sensor networks, which is distributed and online. A novel Bayesian model is formulated for a modified state space model. This Bayesian model is capable of dealing with outliers and heavy-tailed noises and improving the robustness of the filter to these non-Gaussian noises. Using this model, we first propose a novel centralized algorithm for the robust Bayesian filtering based on variational Bayesian methods. Then, we extend it to the distributed scenario based on the alternating direction method of multipliers (ADMM) technique. The proposed algorithm can simultaneously estimate the states and process/measurement noise covariances. Simulations on a target tracking problem are given to verify the effectiveness of the proposed model and algorithm. The results demonstrate that the proposed algorithm performs much better than the standard Kalman filter and as good as the corresponding centralized algorithm in the presence of outliers.
Verifiable Keyword-Based Semantic Similarity Search on Social Data Outsourcing

In the data-driven economy era, social data have tremendous business and potential values. Obtaining authentic social data is the first step in mining the business value of social data. In this paper, we consider an emerging social data outsourcing paradigm. Therein, different online social network (OSN) operators outsource their social data to a third-party social data provider (SDP), who resells them to data consumers who can be any individual or entities. However, a dishonest SDP may return untrusted query results to data consumers through various activities, such as adding fake data and deleting/modifying correct data. To deal with these dishonesties, we propose a basic scheme and an enhanced scheme to allow data consumers to verify the correctness and completeness of their received social data from the SDP. Data consumers in the basic scheme utilize the public APIs to collect the sampled social data and compare them with their received social data. This scheme is a probabilistic verification method as the data consumers only having a tiny proportion of the social data. To permit data consumers to verify the query results trustworthiness deterministically, we proposed an enhanced scheme, in which the OSN operator generates some cryptographic auxiliary information. The SDP can construct a verification object for the data consumer based on these information. Extensive experiments ran on a real Twitter dataset confirm that our schemes are effective and efficient.
Adversarial attacks against profile HMM website fingerprinting detection model

People are accustomed to using an anonymous network to protect their private information. The Profile HMM (Hidden Markov Model) Website Fingerprinting Detection algorithm can detect the website that the data stream accesses by pattern matching the captured data traffic. This makes the anonymous network lose its effect. In order to bypass the detection of this model, we propose a method based on genetic algorithm to generate adversarial samples. By migrating the problem of adversarial samples in deep learning, our approach is used for the broader machine learning detection model to do traffic confusion, and then achieves the purpose of bypassing the Profile HMM model detection. The key challenge is how to construct a suitable fitness function to generate an effective adversarial sample at minimal cost. The experimental results show that the success rate of our traffic confusion method is as high as 97%. At the same time, we only need to add less perturbation traffic than the traditional traffic confusion method.
Handwriting recognition by using deep learning to extract meaningful features

Recent improvements in deep learning techniques show that deep models can extract more meaningful data directly from raw signals than conventional parametrization techniques, making it possible to avoid specific feature extraction in the area of pattern recognition, especially for Computer Vision o r Speech tasks. In this work, we directly use raw text line images by feeding them to Convolutional Neural Networks and deep Multilayer Perceptrons for feature extraction in a Handwriting Recognition system. The proposed recognition system, based on Hidden Markov Models that are hybridized with Neural Networks, has been tested with the IAM Database, achieving a considerable improvement.
Secured Image Transmission in Medical Imaging Applications—A Survey

Radiology Information Systems (RIS) involves the transmission of medical images between the PACS server and the workstation over the network for analysis. The images during transmission are vulnerable due to the intruders, and it is essential to secure the images. Because processing the adulterated images is unethical and can mislead the radio diagnosis. The combination of image processing and the cryptographic techniques can resolve this problem to some extent. Over a period, the technology has improved a lot, and several images securing methods are discussed by various researchers. This review paper is an effort in reviewing the existing secured image transmission technologies, their pros, and cons and the scope of the future work.
QoS Prediction for Neighbor Selection via Deep Transfer Collaborative Filtering in Video Streaming P2P Networks

To expand the server capacity and reduce the bandwidth, P2P technologies are widely used in video streaming systems in recent years. Each client in the P2P streaming network should select a group of neighbors by evaluating the QoS of the other nodes. Unfortunately, the size of video streaming P2P network is usually very large, and evaluating the QoS of all the other nodes is resource-consuming. An attractive way is that we can predict the QoS of a node by taking advantage of the past usage experiences of a small number of the other clients who have evaluated this node. Therefore, collaborative filtering (CF) methods could be used for QoS evaluation to select neighbors. However, we might use different QoS properties for different video streaming policies. If a new video steaming policy needs to evaluate a new QoS property, but the historical experiences include very few evaluation data for this QoS property, CF methods would incur severe overfitting issues, and the clients then might get unsatisfied recommendation results. In this paper, we proposed a novel neural collaborative filtering method based on transfer learning, which can evaluate the QoS with few historical data by evaluating the other different QoS properties with rich historical data. We conduct our experiments on a large real-world dataset, the QoS values of which are obtained from 339 clients evaluating on the other 5825 clients. The comprehensive experimental studies show that our approach offers higher prediction accuracy than the traditional collaborative filtering approaches.
New Linear Codes over GF(3), GF(11), and GF(13)

Explicit construction of linear codes with best possible parameters is one of the major and challenging problems in coding theory. Cyclic codes and their various generalizations, such as quasi-twisted (QT) codes, are known to contain many codes with best known parameters. Despite the fact that these classes of codes have been extensively searched, we have been able to refine existing search algorithms to discover many new linear codes over the alphabets $\mathbb{F}_{3}$, $\mathbb{F}_{11}$, and $\mathbb{F}_{13}$ with better parameters. A total of 38 new linear codes over the three alphabets are presented.
Fractional-order difference curvature-driven fractional anisotropic diffusion equation for image super-resolution

Image super-resolution methods-based existing edge indicating operators — namely Gauss curvature, mean curvature and gradient — cannot effectively identify the edges, ramps and flat regions and suffer from the loss of fine textures. To address these issues, this paper presents a fractional anisotropic diffusion equation based on a new edge indicator, named fractional-order difference curvature, which can characterize the intensity variations in images. We introduce the frequency-domain definition for fractional-order derivative by the Fourier transform, which is easy to implement numerically. The new edge indicator is better than the existing edge indicating operators in distinguishing between ramps and edges and can better handle the fine textures. Comparative results for natural images validate that the proposed method can yield a visually pleasing result and better values of MSSIM and PSNR.
Feature Selection With Ensemble Learning Based on Improved Dempster-Shafer Evidence Fusion

Feature selection or attribute reduction is an important data preprocessing technique for dimensionality reduction in machine learning and data mining. In this paper, a novel feature selection ensemble learning algorithm is proposed based on Tsallis entropy and Dempster–Shafer evidence theory (TSDS). First, an improved correlation criterion is used to obtain the relevant feature based on Tsallis entropy. A forward sequential approximate Markov blanket is then defined to eliminate the redundant feature. An ensemble learning is employed to achieve approximately optimal global feature selection, which can acquire the feature subsets from different perspectives. Finally, by fusing all the feature subsets, the improved evidence theory approach is utilized to gain the final feature subset. To verify the effectiveness of TSDS, nine datasets from two different domains are used in the experimental analysis. The experimental results demonstrate that the proposed algorithm can select feature subset more effectively and enhance the classification performance significantly.
Data Imputation Methods for Missing Values in the Context of Clustering

Missing data is a common problem for data clustering quality. Most real-life datasets have missing data, which in turn has some effect on clustering tasks. This chapter investigates the appropriate data treatment methods for varying missing data scarcity distributions including gamma, Gaussian, and beta distributions. The analyzed data imputation methods include mean, hot-deck, regression, k-nearest neighbor, expectation maximization, and multiple imputation. To reveal the proper methods to deal with missing data, data mining tasks such as clustering is utilized for evaluation. With the experimental studies, this chapter identifies the correlation between missing data imputation methods and missing data distributions for clustering tasks. The results of the experiments indicated that expectation maximization and k-nearest neighbor methods provide best results for varying missing data scarcity distributions.
Accurate Smoothing for Continuous-Discrete Nonlinear Systems With Non-Gaussian Noise

In this letter, an accurate Gaussian sum-smoothing approach is derived for the continuous-discrete systems, where the dynamics can be modeled with nonlinear Itô-type stochastic differential equations and the measurements are obtained at discrete sampling times with non-Gaussian noise. The proposed smoothing method is derived by applying a bank of parallel accurate continuous-discrete extended-cubature Kalman filters used in the classical Gaussian state estimation to approximate the nonGaussian estimation densities as a finite number of weighted sums of Gaussian densities. The performances of the proposed method are compared with the recently presented filters based on the maximum correntropy criterion in a simulated application and the numerical results show that the new approach is more accurate and robust than others.
Recurrent Neural Networks With Finite Memory Length

The working of recurrent neural networks has not been well understood to date.The construction of such network models, hence, largely relies on heuristics and intuition.This paper formalizes the notion of ''memory length'' for recurrent networks and consequently discovers a generic family of recurrent networks having maximal memory lengths.Stacking such networks into multiple layers is shown to result in powerful models, including the gated convolutional networks.We show that the structure of such networks potentially enables a more principled design approach in practice and entails no gradient vanishing or exploding during back-propagation.We also present a new example in this family, termed attentive activation recurrent unit (AARU).Experimentally we demonstrate that the performance of this network family, particularly AARU, is superior to the LSTM and GRU networks.
Local fusion networks with chained residual pooling for video action recognition

Action recognition is an important yet challenging problem. We here present a novel method, multistage local fusion networks with residual connections, to boost the performance of video action recognition. In realistic videos, an action instance may have a long time span and some frames may suffer from deteriorated object appearance due to motion blur or video defocus. Our method enhances the per-frame representation by capturing information from neighboring frames. We propose a local fusion block which considers neighboring frames to capture appearance and local motion information for generating per-frame representation. Our local fusion is performed in a multistage manner allowing feature fusion from varying neighborhood sizes in the temporal dimension. We employ residual connections in the fusion blocks to enable effective gradient propagation through the whole network allowing effective end-to-end training. We achieve competitive results on two challenging and public available datasets, namely HMDB51 and UCF101, which shows the effectiveness of the proposed method.
Designing professional program instruction to align with students’ cognitive processing

In health professional programs, training for educators in instructional design is sparse and may be absent altogether. Implementing evidence-based, systematic instructional design can improve pharmacy curricula. This paper describes Gagné's instructional design model which is particularly effective because the instructional steps align with learners' cognitive processes. The model is applied to the teaching of liver pharmacotherapeutics in a pharmacy curriculum.Second-year pharmacy students completed 25 hours of liver pharmacotherapeutics instruction that was developed using Gagné's nine instructional events: gain attention, inform about objectives, stimulate recall of prior information, present information, provide guidance, elicit performance, provide feedback, assess performance, and enhance retention.Students completed individual and team-based assessments including SOAP notes, recorded case presentations, web-based quizzes, a pre- and post-knowledge assessment, and midterm and final exam multiple-choice test. Students demonstrated significant improvement in liver pharmacotherapeutics knowledge from the pre- to post-knowledge assessment. Student course instructor evaluations also reflected effectiveness of the format.Evidence-based instructional design strategies, such as Gagné's model, can enhance learning of challenging pharmacotherapeutics concepts using a systematic framework that aligns with cognitive processing.
Optimization based resource and cooling management for a high performance computing data center

This paper focuses on the problem of reducing energy consumption within high-performance computing data centers, especially for those with a large portion of "small size" jobs. Different from previous works, the efficiency of job scheduling and processing is made as the first priority. To reduce energy from servers while maintaining the processing efficiency of jobs, a new hysteresis computing resource-provisioning algorithm is proposed to adjust the total computing resource reactively. A dynamical thermal model is presented to reflect the relationship between the computational system and cooling system. The proposed model is used to formulate constrained optimal control problems to minimize the energy consumption of the cooling system. Then, a two-step solution is proposed. Firstly, a thermal-aware resource allocation optimizer is developed to decide where the resource should be increased or decreased. Secondly, an economic model predictive controller is designed to adjust the cooling temperature predictively along with the variation of the rack power. Performance of the proposed method is studied through simulations with real job trace. The results show that significant energy saving can be achieved with guaranteed service quality.
Review on Image Segmentation Techniques Incorporated with Machine Learning in the Scrutinization of Leukemic Microscopic Stained Blood Smear Images

This paper is a contemplated work of N-different methods that have been employed in the area of revealing and classifying leukocytes and leukoblast cells. Blood cell images obtained through digital microscopes are taken as input to the algorithms reviewed. In bringing out the nucleus and cytoplasm of White Blood Cells (WBCs), the images have been undergone by a variety of image segmentation techniques along with filtering, enhancement, edge detection, feature extraction, classification, and image recognition steps. Apart from image processing, the analysis and categorization of the leukemic images are handled using some other machine learning techniques of computer science discipline. Assessment of accuracy and correctness of the proposals were done by applying texture, color, contour, morphological, geometrical, and statistical features.
An Experience Replay Method Based on Tree Structure for Reinforcement Learning

Q-Learning, which is a well-known model-free reinforcement learning algorithm, a learning agent explores an environment to update a state-action function. In reinforcement learning, the agent does not require information about the environment in advance, so an interaction between the agent and the environment is for collecting the real experiences that is also an expensive and time-consuming process. Therefore, to reduce the burden of the interaction, sample efficiency becomes an important role in reinforcement learning. This study proposes an adaptive tree structure integrating with experience replay for Q-Learning, called ERTS-Q. In ERTS-Q method, Q-Learning is used for policy learning, a tree structure establishes a virtual model which perceives two different continuous states after each state transaction, and then the variations of the continuous state are calculated. After each state transition, all states with highly similar variation are aggregated into the same leaf nodes. Otherwise, new leaf nodes will be produced. For experience replay, the tree structure predicts the next state and reward based on the statistical information that is stored in tree nodes. The virtual experiences produced by the tree structure are used for achieving extra learning. Simulations of the mountain car and a maze environment are performed to verify the validity of the proposed modeling learning approach.