Reviewerly/exHarmony huggingface dataset:
Analysis of Hyperspectral Remote Sensing Images for Extraction Geological Rock Types Maps by Geospatial Techniques

Abstract Hyperspectral images involve a wealth of data, but interpreting them needs an understanding of exactly what properties of ground materials that it is trying to measure and how to relate to the measurements made by the hyperspectral sensor. The purpose of the study is to use hyperspectral remote sensing data for finding of geological locations and the creation of geological rock types map. In this research, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) image has been used and analyzed using geospatial techniques such as ENVI software. These techniques include: enhancement, de-correlation stretch, band ratio and classification. All these techniques have been performed to produce geological rock kinds map. Also, 3D surface for ASTER data can be displayed using ENVI software.
Adaptive Window Width Selection Algorithm for Gabor Transform Based on Improved Shannon Entropy

Non-stationary signal is a common signal in reality, but the existing single window discrete Gabor transform algorithm can not effectively display all signal components or the time-frequency accuracy is not high, so this paper proposes an improved algorithm based on Shannon entropy adaptive selection analysis window width. Firstly, the Gabor transformation coefficient is normalized, and Shannon entropy is obtained according to the transformation coefficient. Then, the value range of Shannon entropy is improved, and the time frequency aggregation degree is determined according to the value of Shannon entropy. Finally, the Shannon entropy of all window functions from the minimum window width to the maximum window width is calculated, and the window width corresponding to the maximum Shannon entropy is the optimal window width. This algorithm can not only select an appropriate window width to display all components when processing multi-component signals, but also improve the time-frequency accuracy. Even for multi-component signals with different frequency characteristics, it can choose a better analysis window and has a good anti-noise capability.
Privacy-Preserving Task Allocation Based on QoS in Crowd Sensing

Crowd sensing, an important research direction in wireless communication, realizes the allocation and collection of tasks through the smart devices carried by users. However, in the task allocation process, how to protect user privacy from being leaked and select high-quality users to guarantee the quality of task completion are two major challenges. Particularly, individual quality of service (QoS) affects the quality of the task completion. In this paper, we propose a differentially private task allocation scheme. During the process of task allocation, differential privacy is utilized for the location privacy protection. In addition, in order to reduce unnecessary privacy leakage, we simplify the task allocation process and select users with higher QoS to guarantee the QoS of task. Security analysis and experimental results state that our scheme provides differential privacy protection while ensuring QoS of tasks.
Partial Image Texture Translation Using Weakly-Supervised Semantic Segmentation

The field of Neural Style Transfer (NST) has led to interesting applications that enables to transform the reality as human beings perceive. Particularly, NST for material translation aims to change the material (texture) of an object to a different material from a desired image. In order to generate more realistic results, in this paper, we propose a partial texture style transfer method by combining NST with semantic segmentation. The original NST algorithm changes the style of an entire image including the style of background even though the texture is contained only in object regions. Therefore, we segment target objects using a weakly supervised segmentation method, and transfer the material of the style image to only material-based segmented areas. As a result, we achieved partial style transfer for only specific object regions, which enables us to change materials of objects in a given image as we like. Furthermore, we analyze the material translation capability of state-of-the-art image-to-image (I2I) translation algorithms, including the conventional NST method of Gatys, WCT, StarGAN, MUNIT, and DRIT++. The analysis of our experimental results suggests that the conventional NST produces more realistic results than other I2I translation methods. Moreover, there are certain materials that are easier to synthesize than others.
How to Segment Turkish Words for Neural Text Classification?

Neural text classifiers of agglutinative languages often suffer from large vocabulary sizes of training data and high out of vocabulary rates during the test time. The natural language processing community has developed and used numerous word segmentation procedures to alleviate these problems. However, their effect on the performance of neural classifiers of Turkish documents requires further investigation. In this empirical study, we carry out an extensive series of experiments to investigate the effect of the choice of word segmentation procedure on the performance of three different neural text classifiers on Turkish documents across multiple domains. Our experiments show that the choice of word segmentation procedure is another hyperparameter that needs tuning. This choice may depend on the domain and the neural architecture.
Query or Document Translation for Academic Search – What’s the Real Difference?

We compare query and document translation from and to English, French, German and Spanish for multilingual retrieval in an academic search portal: PubPsych. Both translation approaches improve the retrieval performance of the system with document translation providing better results. Performance inversely correlates with the amount of available original language documents. The more documents already available in a language, the fewer improvements can be observed. Retrieval performance with English as a source language does not improve with translation as most documents already contained English-language content in our text collection. The large-scale evaluation study is based on a corpus of more than 1M metadata documents and 50 real queries taken from the query log files of the portal.
AT-GAN

Recently, unsupervised image-to-image translation task have attracted great attention. In this paper, we propose a novel method for this task based on a new attention module and a new attention transfer mechanism. The attention module guides discriminator to focus on discriminative regions between source and target domains by the auxiliary classifier. The attention transfer mechanism computes attention map in corresponding discriminator and transfer it to help the generator focus on the most discrepant regions. We also introduce a Layer Normalization method in last two layers of generator to achieve match of global statistics. Both qualitative and quantitative experimental results comparison against state-of-the-art methods on benchmark datasets show the superiority of AT-GAN.
A New Heuristic Based on a Parallel Implementation of Firefly Algorithm

In this chapter, a new optimization algorithm is introduced, inspired by the Firefly Algorithm, which is based on the behavior of fireflies, especially on their flashing characteristics. This new algorithm runs C parallel executions of the algorithm that, after a determined number of iterations, are paused and their results are compared so that, using different restarting techniques, new positions for the fireflies in each thread are determined to continue the executions. This parallel implementation allows us to have more diversity in the experiments, allowing the Firefly Algorithm to obtain better results. For testing the restarting techniques mentioned before, we used 6 multimodal benchmark functions: Step, Sphere, Sum Square, Trid 10, Zakharov, and Rosenbrock functions. For this chapter, different tests were performed to determine the value of the parameters needed to run the algorithm. These values were used to run the final experiments. The results obtained with the implementation of our algorithm with the different restarting techniques and the parameters established are compared to the results published by other authors so that we were able to prove the efficiency of this new algorithm. We obtained significantly better results in some of the test functions, proving that our algorithm is a breakthrough in nature-inspired algorithms.
Lambek Grammars as Second-Order Abstract Categorial Grammars

We demonstrate that for all practical purposes, Lambek Grammars (LG) are strongly equivalent to Context-Free Grammars (CFG) and hence to second-order Abstract Categorial Grammars (ACG). To be precise, for any Lambek Grammar LG there exists a second-order ACG with a second-order lexicon such that: the set of LG derivations (with a bound on the ‘nesting’ of introduction rules) is the abstract language of the ACG, and the set of yields of those derivations is its object language. Furthermore, the LG lexicon is represented in the abstract ACG signature with no duplications. The fixed, and small, bound on the nesting of introduction rules seems adequate for natural languages. One may therefore say that ACGs are not merely just as expressive as LG, but strongly equivalent.The key is the algebraic description of Lambek Grammar derivations, and the avoidance of the Curry-Howard correspondence with lambda calculus.
A Novel Approach to Feature Selection Using PageRank algorithm for Web Page Classification

In this paper, a novel filter-based approach is proposed using the PageRank algorithm to select the optimal subset of features as well as to compute their weights for web page classification. To evaluate the proposed approach multiple experiments are performed using accuracy score as the main criterion on four different datasets, namely WebKB, Reuters-R8, Reuters-R52, and 20NewsGroups. By analyzing the obtained results, it is observed that the accuracy score of the classifier on WebKB, Reuters-R8, and Reuters-R52 datasets significantly improved from 91% up to 96% compared to the best result achieved by other feature selection methods like IG and Chi-2. Whereas, the accuracy score of the classifier on 20NewsGroups dataset didn't see any noticeable improvement and remained close to the most compared methods. Evaluating the performance of the proposed approach shows the superiority of it in obtaining higher accuracy scores when compared with the feature sets selected by other methods.
Improved SSD for Object Detection

We proposal an improved Single-shot multi-boxes algorithm for ameliorating the detection on small object detection. As we know, single shot multi-boxes for detection (SSD) is a state-of-the-art algorithm of object detection in 2016 by balancing the contradictions between speed and accuracy of detection. But it did not perform well on smaller objects. So how to promote the performance is a meaningful thing. This way, we introduce receptive field and deep large dimensional features for small detection, and achieves 1.2% improved on PASCAL VOC dataset. “The lower the dimension” does not equals to “the higher the abstraction”.
Re-identifying Pedestrians via Part Based Method

Person re-identification (Re-id) has received more and more attention due to its wide applications and various approaches were proposed to solve the challenging task. In this paper, we adopt part-based model as the main method to perform person Re-id on the self-made dataset. In order to improve accuracy, transfer learning is applied in both a direct and indirect way by leveraging original public dataset Market-1501 and its transferred counterpart respectively. The transferred dataset is obtained by making the pedestrians in the original public dataset show similar styles to those of the self-made dataset while keeping their identities unchanged. Experimental results demonstrate the effectiveness of transfer learning and the superiority of applying transfer learning indirectly against applying transfer learning directly.
Benchmarking Inference Algorithms for Probabilistic Relational Models

In the absence of benchmark datasets for inference algorithms in probabilistic relational models, we propose an extendable benchmarking suite named ComPI that contains modules for automatic model generation, model translation, and inference benchmarking. The functionality of ComPI is demonstrated in a case study investigating both average runtimes and accuracy for multiple openly available algorithm implementations. Relatively frequent execution failures along with issues regarding, e.g., numerical representations of probabilities, show the need for more robust and efficient implementations for real-world applications.
A Novel Thematic Network Data Collector Based on Simulated Annealing Hybrid Genetic Algorithm

In the age of big data, the value of data has been paid more and more attention. How to get data efficiently and accurately is one of the fundamental study. Data collector is such a tool. Compared with the normal data collector, the Topic-focused network data collectors pay more attention to web pages related to pre-set topics. This paper designs and implements a network data collector and in order to improve the search efficiency, this paper uses annealing genetic algorithm by studying the hybrid genetic algorithm based on simulated annealing. The result indicated the hybrid algorithm can increase page coverage and crawling efficiency.
Mining Rare Patterns in Wind Tunnel by a Sequential Outlier Ensemble

Data-driven techniques have been extensively leveraged in modeling and control of wind tunnel systems. Under this situation, mining rare patterns (also referred to as outliers or anomalies) in wind tunnel databases is becoming increasingly critical, since these samples may imply interesting data patterns. Recently, some ensemble outlier detection methods have been developed to improve the performance of single detectors. However, these ensemble models are often parallel structure and designed for variance reduction. This paper proposes a sequential ensemble model, by which both variance and bias can be reduced simultaneously. Experimental results on real world wind tunnel databases have shown that our sequential ensemble model could outperform most existing parallel ensemble models and single models.
Personalized Arm Gesture Recognition Using the HMM-Based Signature Verification Engine

Gesture-based interfaces can significantly improve access to computer technologies for people with disabilities. However, the gestures of the disabled can be very unstable, which means that standard gesture recognition solutions may occur not applicable in this case. In this study, a personalized arm gesture recognition system is presented. It uses automatic handwritten signature verification (ASV) techniques which deal with the instability of human motion processes by default. The proposed gesture-based HCI framework involves the Microsoft Kinect sensor, the data acquisition module, and the formerly tested HMM offline signature verification engine. The evaluation process included intentional, non-intentional and randomly distorted gestures converted into images. The confusion matrices and receiver-operation characteristic (ROC) analysis were used to evaluate the accuracy of the system. The performed tests showed that the applied ASV software could effectively recognize unstable gestures, even when the evaluation set included both random and distorted gesture patterns.
A Fast Registration Method Based on Line Features

A fast registration method based on line features is proposed for optical remote sensing images with typical line features. Firstly, the line features are extracted by Line Segment Detection (LSD). LSD has attributes including rotation invariance, illumination changes insensitivity and noise resistant ability. Secondly, the initial fast image registration is implemented based on features consensus, to eliminate the influence of scaling, rotation and translation between images on the matching relationship of the lines. Then the line segments with same name are determined according to the proposed similarity criterion and the bidirectional matching relation. By utilizing the intersection points of these line pairs with same name as the matching control points, fine registration can be realized. Finally, the simulation results are analyzed, accuracy and computational efficiency of the proposed method are verified from subjective effect and objective evaluation indices.
Tecla Sound: Combining Single Switch and Speech Access

Using single switch scanning with a mobile Smartphone can often be frustrating and slow to use. Phone functions, such as answering a call, either have specific timeout imitations that are set by service providers or may not be accessible using non-standard access methods. Single switch scanning can take longer than these timeout settings allow, resulting in missed calls or becoming stuck in endless automated call option menu loops. The Tecla single switch-scanning system has been augmented with a simple speaker dependent, limited vocabulary, offline speech recognizer to offer users one solution to this dilemma. Users decide on and record applicative words for specific phone functions, such as “answer,” that can then be used instead of selecting that item from a scanning array. Given that speech production is often faster than selection from a scanning array, the user will be able to avoid the timeout limitation of their smartphone.
Rain Removal Method Based on Densely Connected Deep Residual Network

The image captured by the camera in the rainy day contains a large amount of rain mark information. The existence of such a large amount of rain mark information will cause the background information of the image to be highly blurred, which will affect the visual effect of people’s observation or the processing effect of subsequent algorithms. The deep residual network (ResNet) solves the problem that the accuracy of the network reaches saturation and then degenerates sharply when the number of network layers increases. This method is now widely used in various fields of computer vision. The use of deep residual networks to remove rain marks in images is a hot topic of research. Existing deep learning methods usually require a large number of positive and negative samples to train the network.How to train the rain well with fewer samples is a problem that needs to be solved in the field.By improving the rensnet network and densenet network, we propose a rain removal method based on densely connected deep residual network images. First, use a guided filter to extract the detailed information of the image to generate a detailed image, and then train the detailed image through the constructed multi-scale deep residual network to extract Raindrop information.The extracted rainfall marker information is formed into a negative residual map. We can add the synthesized image with rain to the negative residual map to achieve the effect of rain removal.
Design Example of Planetary Exploration Mobile Robot

Mobile robot is the main tool for wide range exploration of extraterrestrial surfaces. At present, the successful applications of mobile robot are mainly to explore the surface of lunar and Mars.
Fast Texture Image Retrieval Using Learning Copula Model of Multiple DTCWT

In this work, we proposed a fast texture image retrieval method by using the learning Copula model of multiple Dual-tree complex wavelet transforms (DTCWTs). Compared with the discrete wavelet transform, DTCWT provides multiple-directions and multiple-scales decomposition to image and also has the fast calculation capability. In the proposed method Multiple DTCWTs is incorporated to get more texture features; compare to Gabor wavelet, DTCWT has less computational cost of decomposition. In DTCWT domains, we developed a Learning Copula Model (called LCMoMD) to describe the dependence between the subbands of multiple DTCWTs. For improving the retrieval performance, LCMoMD is first embedded in the linear space by utilizing matrix logarithm and Kernel Principal Component Analysis (KPCA) is used to calculate the features from the embedding Copula model in the linear space. Experiments demonstrate that our method has fast and robust performance of texture extraction compared to the state-of-the-art methods.
Improvement of Fuzzy Classification Systems Using Metaheuristic (PSO and ICA) with Dynamic Parameter Adaptation in Fuzzy Environment

In this research, fuzzy classification systems were improved by using some meta-heuristic methods with dynamic adaptation parameters. These methods were used for a set of data wherein membership functions and rules in fuzzy system are optimized by using meta-heuristic methods. In fuzzy classification method, being in different classes is assessed through membership functions which are in 0 to 1 interval. Each element belongs to the class where membership coefficient is higher. By assuming uncertainty in this research, a more realistic model for problem was made so that answers obtained from this model have more capability to be implemented. In this research, we improved fuzzy classification by using PSO1 and ICA2 methods with fuzzy parameters, which not only reduce classification error, but also provide a comparison between efficiency of fuzzy PSO and fuzzy ICA. In this research, data are not just for testing and these fuzzy algorithms were used for IRIS database (this database has 3 different classes for Iris flower and 150 data which each datum has four characteristics). We came to this conclusion that fuzzy classification systems can be improved by using fuzzy meta-heuristic methods and fuzzy ICA algorithm has more convergence velocity than fuzzy PSO algorithm.
The AI Domain Definition Language (AIDDL) for Integrated Systems

As individual sub-fields of AI become more developed, it becomes increasingly important to study their integration into complex systems. In this paper, we provide a first look at the AI Domain Definition Language (AIDDL) as an attempt to provide a common ground for modeling problems, data, solutions, and their integration across all branches of AI in a common language. We look at three examples of how automated planning can be integrated with learning and reasoning.
A Choice Function Analysis of Either in the Either/or Construction

In this paper, I propose an analysis that covers both the wide scope or reading of the either/or construction and the availability of Alternative Question and Yes/No Question readings, namely a hybrid of an ellipsis analysis and a choice function analysis of either. After presenting two sets of data, I introduce two hybrid analyses that combine an ellipsis analysis and a choice function analysis. The two differ from each other in terms of the item that introduces the choice function variable: in the first analysis, the disjunction particle or introduces the choice function variable while in the second analysis, either has that semantic role. It is demonstrated that the two analyses both account for the either/or construction data, whereas only the second hybrid analysis, in which either introduces the choice function variable, explains the Alternative Question and Yes/No Question data. Finally, I review another account proposed in previous research, namely the focus alternative semantics analysis, and point out its problems.