Reviewerly/exHarmony huggingface dataset:
An Improved Pairwise Rotation Invariant Co-occurrence Local Binary Pattern Method for Texture Feature Extraction

An improved pairwise rotation invariant co-occurrence local binary pattern (PRICoLBP) algorithm blended with local texture information is proposed in regard to the problem of high feature dimensions to be calculated, poor rotation invariance and sensitivity to illumination change with the PRICoLBP algorithm for texture feature extraction. First, the binary sequence of image pixel points is maximized and minimized respectively, which yields the coordinates of two neighborhood pixel points, and the coordinates of co-occurrence point pairs are calculated from the coordinates of central pixel point and acquired coordinates of neighborhood pixel points. Second, texture information about each pixel point in the image is retrieved using the complete local binary pattern (CLBP) algorithm. With the same classifier, the proposed algorithm gets an increase of respectively 0.17, 0.24, 2.65, 2.39 and 2.04 percent in rate of identification over the PRICoLBP algorithm in the experiment of classifying Brodatz, Outex (TC10, TC12), Outex (TC14), CUReT and KTH_TIPS databases. Experiment results show that the proposed algorithm has better identification effectiveness when processing an image with rotation variant texture and diverse illumination conditions.
Missing Data Handling by Mean Imputation Method and Statistical Analysis of Classification Algorithm

The motive of data mining is to extract meaningful information from the large database. Because of the human errors, their high dimensionality, noisy data, and missing values, the process over dataset may degrade the performance. Therefore, the need for handling of those data in a proper way is important for improving the performance. There are many missing data handling methods available. Mean imputation is one of the methods for missing data in the dataset. This is the preprocessing operation performed before applying any machine learning algorithms. After applying mean imputation in a dataset, the decision is made either imputed mean value is good or bad. The rpart decision tree algorithm is applied on retailer dataset to handle more number of classes. From the experimental results, there is no significant difference among variables. The results of various GLM models with different were compared and analyzed to provide better performance.
Universal reconstruction of a string

Many properties of a string can be viewed as sets of dependencies between substrings of the string expressed in terms of substring equality. We design a linear-time algorithm which finds a solution to an arbitrary system of such constraints: a generic string satisfying a system of substring equations. This provides a general tool for reconstructing a string from different kinds of repetitions or symmetries present in the string, in particular, from runs or from maximal palindromes. The recursive structure of our algorithm in some aspects resembles the suffix array construction by Kärkkäinen et al. (2006) [23]. This is a full version of a paper presented at WADS 2015 [18].
Traffic Measurement Study on Video Streaming with the Amazon Echo Show

The Amazon Echo Show is one of the most widely used smart speakers with the ability to stream video. Due to its popularity, the traffic profiles of such devices are of interest to network operators and providers. This work presents a measurement study of the Amazon Echo Show in terms of network traffic and streaming behavior. More than 470\,hours of streaming data are collected and analyzed at network layer. Based on this, streaming quality is derived at application layer. The study quantifies the traffic and shows that streaming with the Amazon Echo Show is comparable to streaming with a native web browser, but in a more conservative way.
How to Calculate with Nondeterministic Functions

While simple equational reasoning is adequate for the calculation of many algorithms from their functional specifications, it is not up to the task of dealing with others, particularly those specified as optimisation problems. One approach is to replace functions by relations, and equational reasoning by reasoning about relational inclusion. But such a wholesale approach means one has to adopt a new and sometimes subtle language to argue about the properties of relational expressions. A more modest proposal is to generalise our powers of specification by allowing certain nondeterministic, or multi-valued functions, and to reason about refinement instead. Such functions will not appear in any final code. Refinement calculi have been studied extensively over the years and our aim in this article is just to explore the issues in a simple setting and to justify the axioms of refinement using the semantics suggested by Morris and Bunkenburg.
Comparable search efficiency for human and animal targets in the context of natural scenes

In a previous series of studies, we have shown that search for human targets in the context of natural scenes is more efficient than search for mechanical targets. Here we asked whether this search advantage extends to other categories of biological objects. We used videos of natural scenes to directly contrast search efficiency for animal and human targets among biological or nonbiological distractors. In visual search arrays consisting of two, four, six, or eight videos, observers searched for animal targets among machine distractors, and vice versa (Exp. 1). Another group searched for animal targets among human distractors, and vice versa (Exp. 2). We measured search slope as a proxy for search efficiency, and complemented the slope with eye movement measurements (fixation duration on the target, as well as the proportion of first fixations landing on the target). In both experiments, we observed no differences in search slopes or proportions of first fixations between any of the target–distractor category pairs. With respect to fixation durations, we found shorter on-target fixations only for animal targets as compared to machine targets (Exp. 1). In summary, we did not find that the search advantage for human targets over mechanical targets extends to other biological objects. We also found no search advantage for detecting humans as compared to other biological objects. Overall, our pattern of findings suggests that search efficiency in natural scenes, as elsewhere, depends crucially on the specific target–distractor categories.
Semi-supervised text classification with deep convolutional neural network using feature fusion approach

Supervised learning algorithms employ labeled training data for classification purposes while obtaining labeled data for large datasets is costly and time consuming. Semi-supervised learning algorithms, on the contrary, use a small set of labeled data and a large set of unlabeled data to improve predication performance and thus may be a good alternative to supervised learning algorithms for large text datasets. Although many semi-supervised learning algorithms have been proposed in the data science literature, most of these algorithms are not feasible for discrete and unstructured text data.
Capturing Semantic and Syntactic Information for Link Prediction in Knowledge Graphs

Link prediction has recently been a major focus of knowledge graphs (KGs). It aims at predicting missing links between entities to complement KGs. Most previous works only consider the triples, but the triples provide less information than the paths. Although some works consider the semantic information (i.e. similar entities get similar representations) of the paths using the Word2Vec models, they ignore the syntactic information (i.e. the order of entities and relations) of the paths. In this paper, we propose RW-LMLM, a novel approach for link prediction. RW-LMLM consists of a random walk algorithm for KG (RW) and a language model-based link prediction model (LMLM). The paths generated by RW are viewed as pseudo-sentences for LMLM training. RW-LMLM can capture the semantic and syntactic information in KGs by considering entities, relations, and order information of the paths. Experimental results show that our method outperforms several state-of-the-art models on benchmark datasets. Further analysis shows that our model is highly parameter efficient.
Better Performance in Human Action Recognition from Spatiotemporal Depth Information Features Classification

The recent revolution of sensor-based depth information opens attracting scope to work for human activity recognition. The activities due to human being can have a great interest in every domain of real life where human is always a major factor. Activity recognition is having a key importance due to its advantages in several domains like surveillance systems at the airport, patient monitoring system, and care of elderly people. The variation in spatial and temporal parameters can present any activity efficiently. In the natural color vision, it is not efficient to give complete information because it represents flatness for every portion of the images. The author proposes the objective of this work to recognize daily life human activities by spatiotemporal depth information. This work is carried out by three phases which comprise preprocessing, feature extraction, and action classification. Actions may be performed by a single person or more than one person at a time. For this purpose, the Kinect sensor is used in the data collection phase. The spatiotemporal depth features are computed for recognition by support vector machine classifier. The research work of this problem is experimented on Intel i5 processor with clock speed 3.1 GHz under the windows 8 environment and processing work is performed by commercial software MATLAB 2015b. There are nine classes of human actions in the database described by RGB-D human activity recognition and video database, Cornell activity datasets, and Berkeley multimodal human action database. The accuracy of nine actions is 90.38%. The research work carried out here proves that using the proposed work, the research community and organizations can get better performance that is tough to achieve through the normal video frames of human activities.
Research on Satellite Signal Vector Tracking Based on Prefilter under High Dynamic Conditions

Under high dynamic circumstances, due to the doppler effect, the user's high dynamic changes will lead to the significant doppler frequency shift of the satellite signal, especially in the carrier part, which makes the signal loss of lock easily. This paper discusses an application of the UKF carrier tracking loop to solve the contradiction between noise bandwidth and dynamic range in the traditional loop, which makes receivers in high dynamic environment able to obtain more accurate tracking results, and it can be a prefilter for constructing a cascade vector tracking loop, then, the ability to work of this vector tracking loop is verified under high dynamic environment where the traditional loop cannot work normally. Finally, as a new field of combination between the signal processing and the information processing, the vector tracking loop can provide an efficient platform for the ultra-tight integration and even broader information fusion methods.
Ensemble Clustering for Novelty Detection in Data Streams

In data streams new classes can appear over time due to changes in the data statistical distribution. Consequently, models can become outdated, which requires the use of incremental learning algorithms capable of detecting and learning the changes over time. However, when a single classification model is used for novelty detection, there is a risk that its bias may not be suitable for new data distributions. A solution could be the combination of several models into an ensemble. Besides, because models can only be updated when labeled data arrives, we propose two unsupervised ensemble approaches: one combining clustering partitions using the same clustering technique; and other using different clustering techniques. We compare the performance of the proposed methods with well known novelty detection algorithms. The methods were tested on datasets commonly used in the novelty detection literature. The experimental results show that proposed ensembles have competitive performance for novelty detection in data streams.
Distributed Fusion Estimation for Multi-rate Multi-sensor Time-delayed Systems with Fading Measurements

This paper proposes a distributed fusion estimator for asynchronous multi-rate multi-sensor systems with time delays and fading measurements. The state is updated uniformly and different sensors uniformly sample measurements with different sampling rates, time delays and fading measurement rates. The independent random variables obeying certain probability distributions over different known intervals are employed to describe the fading measurement phenomena. First, a new state space model at measurement sampling points is constructed where the time-delayed system is transformed to a delay-free one with correlated noises in limited time intervals. Then, the real-time optimal local filters at measurement sampling points and at state update points are derived based on the new state space model. Further, estimation error cross-covariance matrices between any two local estimators are derived. Finally, a real-time distributed fusion estimator at state update points is obtained based on the optimal fusion criterion weighed by matrices in the linear unbiased minimum variance sense. A numerical example is given to illustrate the effectiveness of the proposed algorithms.
Blind watermark detection based on K‐S test in radio‐frequency signals

Radio-frequency (RF) watermarking is a new technology for physical layer authentication, whose carrier is not the traditional multimedia signals but the digital signals. In the available literature, the watermark detectors are based on the cooperative environment, where the parameters of the watermark signal are known to the receiver. In this Letter, a blind watermark detection scheme with Kolmogorov–Smirnov (K-S) test is presented. The simulation results show that the proposed scheme is effective.
Algorithm of Path Planning Based on Time Window for Multiple Mobile Robots in Warehousing System

Automation of goods transportation in warehousing system is essential to improve productivity and reduce costs for the modern logistic enterprises. Under this background, automatic warehousing is becoming a key to logistic systems due to its ability of scheduling and optimizing delivery tasks of robots and, consequently, increasing productivity and decreasing costs. This paper firstly describes briefly problems needed to be solved for working of multiple mobile robots in automatic warehousing systems. Then, we build a map model based on topological graph for the warehouse environment and choose a storage representation of adjacent matrix for it. This paper details the algorithm of task scheduling and path planning for multiple mobile robots in warehousing system. The presented algorithm applies the A* shortest-path method to solve path planning problem of single robot and, further, introduces time window approach to solve traffic jams and collisions of multiple robots. Computer simulation tests were conducted to compare the efficiency of the A* algorithm with other shortest-path methods of path planning for single robot. For logistics scenario of multiple robots, routing simulations were also carried out to verify performance of the purposed algorithm under different working conditions. Test results showed that the algorithm could generate collision-free and optimized paths for multiple mobile robots.
Main Factors Driving the Open Rate of Email Marketing Campaigns

Email Marketing is one of the most important traffic sources in Digital Marketing. It yields a high return on investment for the company and offers a cheap and fast way to reach existent or potential clients. Getting the recipients to open the email is the first step for a successful campaign. Thus, it is important to understand how marketers can improve the open rate of a marketing campaign. In this work, we analyze what are the main factors driving the open rate of financial email marketing campaigns. For that purpose, we develop a classification algorithm that can accurately predict if a campaign will be labeled as Successful or Failure. A campaign is classified as Successful if it has an open rate higher than the average, otherwise it is labeled as Failure. To achieve this, we have employed and evaluated three different classifiers. Our results showed that it is possible to predict the performance of a campaign with approximately 82% accuracy, by using the Random Forest algorithm and the redundant filter selection technique. With this model, marketers will have the chance to sooner correct potential problems in a campaign that could highly impact its revenue. Additionally, a text analysis of the subject line and preheader was performed to discover which keywords and keyword combinations trigger a higher open rate. The results obtained were then validated in a real setting through A/B testing.
A Machine Learning based Reduced-Reference Image Quality Assessment Method for Single-Image Super-Resolution

The existing image quality assessment methods can be classified into full-reference, no-reference, and reduced-reference methods. However, full-reference methods require ground-truth images which are unavailable for single-image super-resolution (SISR). No-reference methods do not require reference images, but establishing a model is more complicated. Low-resolution (LR) images are known for SISR, and partial information can be provided as reference. In this paper, we propose a machine learning based reduced-reference image assessment method for SISR. LR images are used as reference images. First, we extract the statistical features of LR images and super-resolution (SR) images in spatial domain and log-Gabor filter response respectively. Then, learn a two-stage regression model to measure the quality of SR images referring to LR images. Compared with the state-of-art methods, experimental results proved that the proposed method is more consistent with human visual perception.
Image Recall on Image-Text Intertwined Lifelogs

People engage in lifelogging by taking photos with cameras and cellphones anytime anywhere and share the photos, intertwined with captions or descriptions, on social media platforms. The image-text intertwined data provides richer information for image recall. When images cannot keep the complete information, the textual information is a complement to describe the life experiences under the photos. This work proposes a multimodal retrieval model for image recall in image-text intertwined lifelogs. Our Attentive Image-Story model combines an Image model, which transfers visual information and textual information to a single representation space, and a Story model, which captures text-based contextual information, with an attention mechanism to reduce the semantic gap between visual and textual information. Experimental results show our model outperforms a state-of-the-art image-based retrieval model and the image/text hybrid system.
DeepTrackNet: Camera Based End to End Deep Learning Framework for Real Time Detection, Localization and Tracking for Autonomous Vehicles

Vehicle detection and tracking in real time remains as the most challenging tasks for the autonomous vehicles and mobile robots. The designed algorithms should have less latency and with high accuracy and performance. This paper proposed a unified end to end deep learning framework for real-time detection, localization and tracking for autonomous vehicles called DeepTrackNet. In the proposed DeepTrackNet architecture Mobilenet SSD is used for vehicle detection, localization and deep regression network is used for vehicle tracking. Based on the experimental analysis performed we infer that the proposed DeepTrackNet architecture has produced satisfactory results for real-time vehicle detection and tracking on an Nvidia Jetson embedded computing platform using monocular camera with a reasonable latency. Mobilenet SSD took 84 (ms) median detection time and deep regression tracker has the least overall average failure frames at 4.388 on the VTB TB-100 dataset ( http://cvlab.hanyang.ac.kr/tracker_benchmark/datasets.html ). The code along with detailed results are available in the GitHub repository ( https://github.com/dineshresearch/DeepTrackNet ).
Research on Machine Vision-based Reading Method for Pointer Meters

Because industrial wireless technology has been widely used today, using image information to read traditional pointer meters accurately has a wide range of practical significance. Although there are many image processing methods to achieve the automatic measurement of the meter, these methods have varying degrees of impairment in terms of reading speed, accuracy and robustness. This paper proposes a novel method on the calibration of the meter center, because the grayscale images has an obvious difference of gray value inside and outside the instrument dial, based on energy minimization principle, the center and radius information that leads the energy function down to its minimum can be iterated to strike. This method takes full advantage of the image gray level information of the instrument, and takes small disturbances by local area noise so that it gives accurate results of calibration.
Framework Code Samples: How Are They Maintained and Used by Developers?

Background: Modern software systems are commonly built on the top of frameworks. To accelerate the learning process of features provided by frameworks, code samples are made available to assist developers. However, we know little about how code samples are actually developed. Aims: In this paper, we aim to fill this gap by assessing the characteristics of framework code samples. We provide insights on how code samples are maintained and used by developers. Method: We analyze 233 code samples of Android and SpringBoot, and assess aspects related to their source code, evolution, popularity, and client usage. Results: We find that most code samples are small and simple, provide a working environment to the clients, and rely on automated build tools. They change frequently over time, for example, to adapt to new framework versions. We also detect that clients commonly fork the code samples, however, they rarely modify them. Conclusions: We provide a set of lessons learned and implications to creators and clients of code samples to improve maintenance and usage activities.
An enhanced symbiotic organisms search algorithm with perturbed global crossover operator for global optimization

Symbiotic organisms search (SOS) algorithm is a nature-inspired meta-heuristic algorithm, which has been successfully applied to solve a wide range of benchmarks and real-world optimization problems. In this paper, an extended version of SOS, namely symbiotic organisms search with perturbed global crossover operator (PGCSOS), is introduced to enhance the performance of the basic SOS. In parasitism phase, an organism can benefit from other organisms that are better than it, and a perturbed crossover scheme is incorporated into the parasitism phase, which aims at maintaining the trade-off between exploration and exploitation effectively, and preventing the current best solution from getting trapped into local optima. The performance of PGCSOS is assessed by solving global optimization functions with different characteristics and real-world problems. Compared to the SOS, modified SOS and other promising heuristic methods, numerical results reveal that the PGCSOS has better optimization performance.