Reviewerly/exHarmony huggingface dataset:
Neuro-fuzzy profile clustering in image enhancement

This paper proposes a technique for clustering features into profile groups to obtain optimum enhancement weights for reconstructing high resolution images. Neuro-fuzzy model, which combines the fuzzy reasoning behavior with the adaptive learning capability and connectionist structure of neural networks, is adopted to analyze and learn with gradient data and statistics and to generate gradient profile priors. In enhancement process, the optimum weights are appropriately chosen according to the gradient profile priors. From the experimental results, the proposed algorithm demonstrates quantitative performance improvement in classifying data and perceptual improvement in recovery of the high-resolution image.
Handwriting Character Recognition using Vector Quantization Technique

This paper seeks to explore Learning Vector Quantization (LVQ) processing stage to recognize The Buginese Lontara script from Makassar as well as explaining its accuracy. The testing results of LVQ obtained an accuracy degree of 66.66 %. The most optimal variant of network architecture in the recognition process is a variation of learning rate of 0.02, a maximum epoch of 5000 and a hidden layer of 90 neurons which was the result of recognition based on feature 8. Based on these variations, the obtained performance with a mean square error (MSE) of 0.0306 and the time required during the learning process was quite short, 6 minutes and 38 seconds. Based on the results of the testing, the LVQ method has not been able to provide good recognition results and still requires development to generate better recognition results.
Simulation of an Autonomous Vehicle Control System Based on Image Processing

Control of autonomous vehicle is a developing subject in the computer vision community. Control of autonomous vehicles has many applications in city cars, transportation trucks and specially in agronomy industry. We present preliminary results of a proposed control system simulation for an autonomous terrestrial vehicle. Our goal is to use an autonomous vehicle for inspection of plantations in agriculture. We constructed a prototype vehicle with a 3D printer and two motors. This prototype vehicle was used to obtain a dynamic model and, with this model, we developed a controller for navigating the vehicle. The considered visual features are colors of images, specifically, we are interested in detecting road-color. We have constructed a detector based on the color histogram to determine whether a pixel belongs to the road-color class or not. Depending on the number of pixels that belongs to this class, in a region of interest, the controller takes action through the DC-motor of the vehicle. For this simulation, we used a synthetic video database, where objects move toward or away from the camera. We have tested our proposal with video sequences using the model jointly with the controller and we have demonstrated that our proposal can avoid obstacles that move on a straight line or located in random positions on the road. We have compared our proposal with other method based on gradient. Our proposal can perform better than the method based on gradient in this specific task and in the considered database.
The Effect of Service Attributes in Korean Marine Transportation Services

Purpose: Marine transportation companies must improve service quality to secure competitive advantage and continuous relationships with customers....
Implementation of SIMD Instruction Set Extension for BLAKE2

Hashing algorithms, the corner stone in a cryptographer's armory, are used in innumerable real life situations to protect critical information. The hashing function transforms the base information into an incomprehensible form. Such algorithms are also used for data integrity verification using checksums and quick retrieval of objects from a database. Some of the popular hashing algorithms are MD5, SHA-1, 2, 3. A hardware implementation of BLAKE2 is elaborated on, using custom SIMD instructions. These custom SIMD instructions correspond to vital steps in the computation of a BLAKE2 digest. Along with a slew of common arithmetic instructions - they coalesce into a 32-bit processor capable of carrying out BLAKE2 computations.
Cardiovascular Risk Assessment Using Intuitionistic Fuzzy Logic System

For complex, ill-defined problems, where the uncertainty lies, fuzzy logic is the best suitable methodology to work with. We can achieve a certain level of accuracy and precision by accepting uncertainty. An Intuitionistic fuzzy logic represents imperfect knowledge that lies while dealing with real-life problems. For example, in decision making human opinion has two or more answers such as “may not be”, “might be” etc. In such a case human opinion is not firm or has some hesitancy in decision making. An intuitionistic fuzzy logic system can deal with this hesitancy and uncertainty that appears in the real world problems. This paper reviews on intuitionistic fuzzy set, its operations, intuitionistic fuzzy logic system and different applications with a case study in healthcare diagnosis.
SMART: SuperMaximal approximate repeats tool

Abstract Summary State-of-the-art repeat analysis tools rely on extending maximal repeated pairs to enumerate maximal k-mismatch repeats. These pairs can be quadratic in n, the length of the input sequence, and thus greedy heuristics are applied to speed up the extension. Here, we introduce supermaximal k-mismatch repeats, which are linear in n and capture all maximal k-mismatch repeats: every maximal k-mismatch repeat is a substring of some supermaximal k-mismatch repeat. We present SMART, a tool based on recent algorithmic advances implemented in C++ to compute supermaximal k-mismatch repeats directly, and show that these elements are statistically much more significant than the output of the state-of-the-art. Availability and implementation http://github.com/lorrainea/smart (GNU GPL v3.0). Supplementary information Supplementary data are available at Bioinformatics online.
Single shot 3D profilometry by polarization pattern projection

We demonstrate a uniaxial 3D profilometry system illuminating the sample with a linear polarization pattern and measuring a polarization camera. The linear polarization pattern is generated by a spatial light modulator and a quarter-wave plate in the optical system. The system can measure four different fringe patterns with a phase difference of 90 deg simultaneously in the polarization camera. Therefore, we can measure three-dimensional shapes in a single shot. We present the measurement principles of the system and show the results of a real-time 3D profilometry experiment.
Revisiting the Optimal Probability Estimator from Small Samples for Data Mining

Abstract Estimation of probabilities from empirical data samples has drawn close attention in the scientific community and has been identified as a crucial phase in many machine learning and knowledge discovery research projects and applications. In addition to trivial and straightforward estimation with relative frequency, more elaborated probability estimation methods from small samples were proposed and applied in practice (e.g., Laplace’s rule, the m -estimate). Piegat and Landowski (2012) proposed a novel probability estimation method from small samples Ep h √2 that is optimal according to the mean absolute error of the estimation result. In this paper we show that, even though the articulation of Piegat’s formula seems different, it is in fact a special case of the m -estimate, where p a =1 / 2 and m = √2 . In the context of an experimental framework, we present an in-depth analysis of several probability estimation methods with respect to their mean absolute errors and demonstrate their potential advantages and disadvantages. We extend the analysis from single instance samples to samples with a moderate number of instances. We define small samples for the purpose of estimating probabilities as samples containing either less than four successes or less than four failures and justify the definition by analysing probability estimation errors on various sample sizes.
Evaluation Framework for Future Privacy Protection Systems: A Dynamic Identity Ecosystem Approach

In this paper, we leverage previous work in the Identity Ecosystem, a Bayesian network mathematical representation of a person's identity, to create a framework to evaluate identity protection systems. Information dynamic is considered and a protection game is formed given that the owner and the attacker both gain some level of control over the status of other PII within the dynamic Identity Ecosystem. We present a policy iteration algorithm to solve the optimal policy for the game and discuss its convergence. Finally, an evaluation and comparison of identity protection strategies is provided given that an optimal policy is used against different protection policies. This study is aimed to understand the evolutionary process of identity theft and provide a framework for evaluating different identity protection strategies and future privacy protection system.
Multiple stream job performance optimization with source operator graph transformations

Summary Multiple distributed stream queries, which are executed on stream processing systems, need to be fine‐tuned to the compute cluster in order to harness the full potential of the hardware they run on. In this paper, we describe an automatic technique for conducting such stream query optimization in the presence of multiple stream jobs. During this autotuning process, we identify the structure of each program and conduct automatic program transformation to generate optimized unified streaming jobs. The operators on the unified secondary sample application are grouped into processing elements considering their performance characteristics and the stream graph topology structure to produce high performance stream query network. We implemented this multiple stream query optimization technique on a mechanism called Tahitica. We demonstrate our approach's ability for producing optimized stream query performance by comparing it to naive deployments using two real‐world stream processing applications in the domains of health care and search advertising. Our stream query optimization approach reported 7.1% throughput performance improvement compared to a naive deployment.
Symmetric kullback-leibler divergence of softmaxed distributions for anomaly scores

The design of an anomaly/outlier/novelty detection system, for a given application, fundamentally requires, on the one hand, the construction of a numerical score from available datasets, to rank data points as to their closeness to being normal, and on the other hand, the statement of a policy, based on the score, for declaring anomalies. The task of designing an anomaly score is challenging because the probability distribution of what may be considered normal cannot always be assumed a priori, especially in dynamic environments like monitoring a computing system. Hence, most anomaly scores in use presently are based on some form of a direct distance between the data points. The use of the Kullback-Leibler (KL) divergence, for probability distributions, along with a windowing scheme, is explored in this paper, for the design of anomaly scores. Distributions are built from the frequencies of a metric in a given time window. For context, KL is used to compare the distribution of the current window with that of a linear combination of several preceding windows. Ensemble-like scores may be built by using the method on independent metrics. Experiments are conducted to test the proposed method on a University of Victoria dataset and a synthetic dataset.
Decentralising online education using blockchain technology

Blockchain technology provides a decentralised peer-to-peer infrastructure, supporting openness, transparency, accountability, identity management and trust. As such, the Blockchain has the potential to revolutionise online education in a number of ways. Blockchain technology offers opportunities to thoroughly rethink how we find educational content and training services online, how we register and pay for them, as well as how we get accredited for what we have learned and how this accreditation affects our career trajectory. This paper explores the different aspects of online education that are affected by this new paradigm. In particular, we investigate the different scenarios where the use of Blockchain technology can make online education more open and decentralised, while placing learners in control of their learning process and its associated data. Additionally, we discuss various approaches to the Semantic Blockchain and the applications of these approaches on online education.
Early Prediction of Sepsis Using Random Forest Classification for Imbalanced Clinical Data

The early prediction of sepsis in intensive care units using clinical data is the objective of the PhysioNet/Computing in Cardiology Challenge 2019. In this paper, a machine learning approach is presented which uses an optimized Random Forest for prediction of a septic condition. After an initial data augmentation step, a customized learning process is performed for the trees to consider imbalance in the dataset. Finally, a feature reduction is implemented and the forest is trimmed to 50 trees for an optimal classification in terms of run time and accuracy. Using a 10-fold cross-validation on the complete training dataset, a mean utility score of 0.376 is achieved. In the final submission, a normalized observed utility score of 0.296 on the full test set is achieved. Our team name is The Septic Think Tank (final rank: 21).
Dynamic Assignment Model of Terminal Distribution Task Based on Improved Quantum Evolution Algorithm

Most logistics and express delivery enterprises will allocate distribution tasks according to fixed distribution service areas at the end of their distribution business. It is impossible to set up reasonable distribution resources for the dynamic distribution demand that changes frequently and is unevenly distributed, which causes the unbalanced workload of each terminal distribution node and further leads to the distribution scheduling management chaos and other problems. A distribution task assignment model considering distribution cost, resource utilization and workload ratio difference is established to solve the problem of terminal distribution task assignment. The stability of quantum group is adopted as the exit criterion to avoid the problem of early withdrawal and invalid iteration of the algorithm, and the mechanism of quantum variation and elimination is introduced to enhance the search ability of the algorithm for feasible solutions. The experimental results show the scheme presented by the algorithm can effectively alleviate the uneven distribution of distribution tasks and effectively reduce the overall distribution cost that compared with the distribution scheme according to distribution area.
Two-Phase Machine Learning Approach for Extractive Single Document Summarization

Summarization is the process of condensing the information with minimal loss of information and its importance has grown with the increased information availability. This paper explores two-phase machine learning approach for Single document summarization where in Phase I sentence selection is done using Bayesian Network and in Phase II coherence summary is generated using Affinity Propagation. Phase I model has explored variety of features as incidence features, scoring features including psycholinguistic features. The proposed model is built on the benchmark dataset DUC 2001 and tested on DUC 2002 dataset using Rouge scores. The proposed two-phase machine learning approach gives significantly improved results compared to many existing baseline models.
Memory-based Collaborative Filtering: Impacting of Common Items on the Quality of Recommendation

In this study, the impact of the common items between a pair of users on the accuracy of memory-based collaborative filtering (CF) is investigated.Although CF systems are a widely used recommender system, data sparsity remains an issue.As a result, the similarity weight between a pair of users with few ratings is almost a fake relationship.In this work, the similarity weight of the traditional similarity methods is determined using exponential functions with various thresholds.These thresholds are used to specify the size of the common items amongst the users.Exponential functions can devalue the similarity weight between a pair of users who has few common items and increase the similarity weight for users who have sufficient co-rated items.Therefore, the pair of users with sufficient co-rated items obtains a stronger relationship than those with few common items.Thus, the significance of this paper is to succinctly test the impacting of common items on the quality of recommendation that creates an understanding for the researchers by discussing the findings presented in this study.The MovieLens datasets are used as benchmark datasets to measure the effect of the ratio of common items on the accuracy.The result verifies the considerable impact exerted by the factor of common items.
An Application of Hybrid Forecasting Singular Spectrum Analysis – Extreme Learning Machine Method in Foreign Tourists Forecasting

International tourism is one indicator of measuring tourism development. Tourism development is important for the national economy since tourism could boost foreign exchange, create business opportunities, and provide employment opportunities. The prediction of foreign tourist numbers in the future obtained from forecasting is used as an input parameter for strategy and tourism programs planning. In this paper, the Hybrid Singular Spectrum Analysis – Extreme Learning Machine (SSA-ELM) is used to forecast the number of foreign tourists. Data used is the number of foreign tourists January 1980 - December 2017 taken from Badan Pusat Statistik (Statistics Indonesia). The result of this research concludes that Hybrid SSA-ELM performance is very good at forecasting the number of foreign tourists. It is shown by the MAPE value of 4.91 percent with eight observations out a sample.
Unsupervised deep learning for depth estimation with offset pixels

Offset Pixel Aperture (OPA) camera has been recently proposed to estimate disparity of a scene with a single shot. Disparity is obtained in the image by offsetting the pixels by a fixed distance. Previously, correspondence matching schemes have been used for disparity estimation with OPA. To improve disparity estimation we use a data-oriented approach. Specifically, we use unsupervised deep learning to estimate the disparity in OPA images. We propose a simple modification to the training strategy which solves the vanishing gradients problem with the very small baseline of the OPA camera. Training degenerates to poor disparity maps if the OPA images are used directly for left-right consistency check. By using images obtained from displaced cameras at training, accurate disparity maps are obtained. The performance of the OPA camera is significantly improved compared to previously proposed single-shot cameras and unsupervised disparity estimation methods. The approach provides 8 frames per second on a single Nvidia 1080 GPU with 1024×512 OPA images. Unlike conventional approaches, which are evaluated in controlled environments, our paper shows the utility of deep learning for disparity estimation with real life sensors and low quality images. By combining OPA with deep learning, we obtain a small depth sensor capable of providing accurate disparity at usable frame rates. Also the ideas in this work can be used in small-baseline stereo systems for short-range depth estimation and multi-baseline stereo to increase the depth range.
A Peg-in-hole Assembly Strategy Using Uncalibrated Visual Servoing

This paper presents a novel peg-in-hole assembly strategy using uncalibrated visual servoing. A three phases assembly strategy is proposed to increase the workspace and avoid repeated desired features sampling when multiple holes are used. Depth-independent interaction matrice for vanishing point and point are derived and update rule for estimating unknown parameters is designed. A multiple phases alignment strategy is proposed to solve the coupled position and attitude issue. An uncalibrated kinematic-based controller is presented and asymptotic convergence is proved. Experiments are conducted to verify the performance.
QR Code Watermarking for Digital Images

With the growing use of online digital media, it is becoming increasingly challenging to protect copyright and intellectual property. Data hiding techniques like digital watermarking can be used to embed data within a signal for purposes such as digital rights management. This paper investigates a watermarking technique for digital images using QR codes. The advantage of using QR codes for watermarking is that properties of the QR code structure include error correction and high data capacity. This paper proposes a QR code watermarking technique, and examines its robustness and security against common digital image attacks.
LF-TSP: Traveling salesman problem for HEVC-based light-field coding

We studied a coding scheme where light field (LF) images (dense multi-view images) are regarded as a sequence of temporal video frames and encoded with video codecs such as High Efficiency Video Coding (HEVC). An important issue with this scheme is how to determine the frame order of the LF images. We propose a method to find the optimum frame order through a formulation of the traveling salesman problem (TSP). Under the assumption that video codecs are more effective with temporally smooth videos, our method, named LF-TSP, defines frame-to-frame distances for each image pairs in an LF, and attempted to find the shortest route that visits all frames. Experiments showed that our method achieved an overall better rate-distortion performance than several previous methods.
Case Study: Knowledge Discovery Process using Computation Intelligence with Feature Selection Approach

Since today is the age of data which are presented using electronic documents, knowledge discovery process (KDP) for different types of data is become a popular topic in various application areas for developing automatic systems. Meanwhile, the capacity of computation intelligence (CI) for solving complex problem, for instance complex features, in KDP is also become a critical role in order to provide effective performance and efficient computation time. In this paper, we observed case study about new trend for KDP using CI for the area of text document classification (TDC). According to the experimental results from different cases, CI can enhance the performance of TDC by looking for optimal subset of feature according to the objective function of learning models.
Development of Intelligent Tag System

This paper introduces a new development approach of intelligent tag system. The designed system can be used to distribute video resource classification and content retrieval method. The system can comprehensively combine video audio, image and subtitles to get video information. On this basis, it can output more precise tags and a wider range of semantic tags through iterative algorithms. The results of the simulation demonstrate that the tags from the system which is supported by a real-time updated database are significant. By comparing with other existing solutions, the designed system in the paper has better performance from valid tag quantity, valid tag rate and Dispersed Contribution Coefficient.