Find research datasets worth reusing

BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 7. Results from SVM, ANN, SVMANN, and ANNSVM that used WLHT

From the results of ANNSVM_WL and ANNSVM_HT, we found that wavelet coefficients had a larger impact on classification than the Hough transformation data because the results from our proposed method applied to WL were more accurate than those of HT. The wavelet coefficients can capture the dominant characteristics from the graphs better than the Hough transformation. The one-dimensional image represented in the frequency domain had oscillations with different amplitudes depending on the graph types. For example, a dominant part of a pie chart should be in the low-frequency domain, because there is a large island of concatenated pixels in a onedimensional image, and it has only a few changes. Conversely, since the scatter plot contains many widely spread points, its dominant part should be located in the high-frequency domain. Performing the wavelet transformation, if a mother wavelet and a part of the wavelet function have a close match, the wavelet coefficient will be large. Assuming we use a suitable wavelet family with the example pie chart case, the wavelet coefficients in the low-frequency domain should be large as compared to other parts of the domain.

BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 9. Illustration of three different wavelets with three waves that have high amplitude values, as indicated by the dashed red circles

The mother wavelet of Coiflet 5 contained triple-high oscillation amplitude (i.e., Figure 9a). We considered that this mother wavelet was inappropriate for our data because overall our data possibly contained only a few matches with the mother wavelet of Coiflet 5. Moreover, the Symlet 10 (i.e., Figure 9b) and 20 (i.e., Figure 9c) also provided supportive results that were lower than others in ANNSVM_WLHT because their mother wavelets also had a similar shape as that of Coiflet 5. For similar reasons, the Haar wavelet was not proper because it is a step function.

BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 6. Results from ANNSVN that used WL and HT

To identify which features of data influentially impacted data separability, we conducted experiments for ANNSVM with WL and HT (i.e., Figure 6). The WL contained only wavelet coefficients, whereas HT included only results of the Hough transformation. We found that, again, results obtained via the linear kernel were not significant; however, using the RBF kernel, accuracy for WL was higher than that of HT, indicating that wavelet coefficients provide influential features that make data separable.

BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 3. Demonstrating the process of classification by applying the ANN, then the SVM

Essentially, if the number of nodes in the hidden layers increases, processing time increases, and the resultant ANN will suffer from over-fitting. Conversely, too small of a number of hidden layers will cause under-fitting for the ANN. In our setting, the number of hidden layers and the number of nodes in each hidden layer were fixed at five. Concerning the learning rate and momentum settings, these impact sensitive training performances are set to optimal values obtained via a grid search technique. The number of nodes in the output layer was three because there are three different class labels (i.e., 2Dchart, bar, and pie) in our datasets. We used the ANN here because our datasets have nonlinear separation, and the ANN is also highly applicable to nonlinear modeling. Thus the ANN with multiple hidden layers was an optimal candidate; however, since the ANN is a black box learning approach, it is difficult to interpret implicit relationships between inputs and outputs.

RAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 4. Processes of all experiments:

In this study, accuracy values of each dataset showed the performance of each method. These values represent are the proportion of the total number of predictions that were correctly classified. Initially, we classified training instances into three classes, with approximately 300 images per class. The graphs had been selectively gathered from the Web. We manually normalized the collected images by eliminating unused areas, such as unnecessary text. Moreover, we evaluated the experiments with 10 folds cross-validation because such an approach can mitigate the problem of over-fitting.

BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 5. Results from CNN and ANNSVM that used 1Dimg and 2Dimg

We compared the results of CNN_1Dimg, CNN_2Dimg, ANNSVM_1Dimg, and ANNSVM_2Dimg to confirm the validity of ANNSVM when applied to images. The 1Dimg represented the dataset of one-dimensional images, while 2Dimg represented the dataset of twodimensional images. Results are shown in Figure 5.

BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 2. Illustrating the core process of one-dimensional image construction by applying a DFT

First, we collect graph images as raw data, which contain different scales and sizes, and therefore need to be normalized. We clean the images by omitting irrelevant areas. For example, we omit unnecessary text that has nothing to do with our classification procedure. Moreover, to standardize the sizes and shapes of the images, we resize and reshape them to be 64 x 64 squares. Second, we examine each image pixel, each of which contains one color value. After each pixel is projected along the x- and y-axes, we count the number of projected pixels with a color value greater than zero to reduce image dimensionality. We, therefore, obtain two one-dimensional images from the x- and y-axes.

BRAIN Journal-ANNSVM: A Novel Method for Graph-Type Classification by Utilization of Fourier Transformation, Wavelet Transformation, and Hough Transformation-Figure 1. Example displaying two scatter plots with different characteristics and patterns

In addition to this introductory section, the remainder of this paper is organized as follows. In Section 2, we present previous work related to our present study. In Section 3, we describe details regarding the methodology used in this study. In Section 4, we describe our experiments and results, then discuss our findings. Finally, we summarize the key content of our study in Section 5.

BRAIN Journal-Novel Detection Features for SSVEP Based BCI: Coefficient of Variation and Variation Speed-Figure 4: Visual stimuli

In order to test the developed features, the SSVEP datasets recorded in (Nakanishi et al. 2014) is used. Flickering boxes had been presented on 24-inch LCD monitor with a refresh rate of 75Hz. 32 visual stimuli had been generated with 8 different frequencies (8 Hz, 9 Hz, …, 15 Hz) and 4 different phases (0o , 90o , 180o , 270o ) as shown in Figure 4. Thirteen healthy adults had participated in the experiments. EEG data had been recorded by 16 electrodes (FPz, F3, F4, Fz, Cz, P1, P2, Pz, PO3, PO4, PO7, PO8, POz, O1, O2 and Oz). The sampling rate had been 512 Hz. The datasets are grouped into 4 groups. The 0-degree stimuli formed the 1st group, the 90- degree stimuli the 2nd group, the 180-degree stimuli the 3rd group and the 270-degree stimuli the 4th group. Thus, it is made possible to test the developed features in more datasets.

BRAIN Journal-Novel Detection Features for SSVEP Based BCI: Coefficient of Variation and Variation Speed-Figure 2: Overlapped EEG segments

In this study, the short time Fourier transformation (STFT) is used to examine the stability of the SSVEP. As the frequency resolution decreases with window size, overlapped EEG segments  (Figure 2) that give sufficient frequency resolution are used. Coefficient of variation and variation speed detection features are proposed by using frequency spectrum of the segments. In Equation 1, x(t) sequence defines overlapped EEG segments in time domain, and in Equation 2 the x(f) sequence defines these segments in frequency domain and m is number of segments.

BRAIN Journal-Novel Detection Features for SSVEP Based BCI: Coefficient of Variation and Variation Speed-Figure 1: Time vs frequency analysis of 10 Hz SSVEP response

The stability of the SSVEP signal was examined by using wavelet analysis (Wu and Yao 2008). Since there is a trade-off between time and frequency resolution in wavelet analysis, examining the stability of SSVEP with wavelet analysis is getting harder in systems where the visual stimulus frequencies are close to each other, as shown in Figure 1.

BRAIN Journal-Novel Detection Features for SSVEP Based BCI: Coefficient of Variation and Variation Speed-Figure 5. SSVEP detection accuracies by using PSD, CV and VS features

When the results in Figure 5 are analyzed, it is seen that CV and VS features provide detection results similar to PSD, which is a familiar feature. Considering that the chance level is 12.5% in these dataset, CV and VS can be used as discriminative features for SSVEP. Also on some subjects, like S3 on 1st and 4th datasets, and S11 on 1st, 2nd and 4th datasets, the proposed features have given clearly better detection results than PSD.

BRAIN Journal-Novel Detection Features for SSVEP Based BCI: Coefficient of Variation and Variation Speed-Figure 3:  (fi) sequences at different frequencies

In Equation 4, fnbk defines neighboring frequency and L defines number of neighboring. ► sequences are shown graphically in Figure 3.  The first developed parameter for the stability of SSVEP is the coefficient of variation (CV). The variation in  \( (fi)\) sequence that is obtained at EEG component with SSVEP response is expected to be less than the other sequences. . Equation 5 shows the calculation of CV. \( ( ) i   f \)  standard deviation of  (fi) sequence. ( ) i   f is the averaged value of  (fi) sequence.

opencc-by-4.0May 2018View details →

Character mentions in the German novel "Corpus Delicti" by Juli Zeh and annotations

This file contains all character mentions in the German novel "Corpus Delicti" by Juli Zeh. The annotation was conducted by members of the research group hermA (www.herma.uni-hamburg.de). The file includes the following columns: <ul> <li>id</li> <li>token: tokens of the character mention</li> <li>entity_nr: the (arbitrary) entity number. All mentions of one character share the same entity number.</li> <li>token_nr: position of the mention in the text in tokens</li> <li>sentence_nr: position of the mention in the text in sentences</li> <li>chapter: number of the chapter the mention occurs in (49 chapters in total)</li> <li>direct_speech: True if the mention occurs between quotation marks</li> <li>entity_name: a mapping of the entity number to the most frequent proper name used for the entity (if available)</li> <li>form: grammatical form of the mention, derived from an automatic part-of-speech tagging (NE = proper name; NP = noun phrase; PPER = personal pronoun; PPOSAT = possessive pronoun)</li> </ul>

opencc-by-4.0Dec 2017View details →

Leaf wound induced ultraweak photon emission is suppressed under anoxic stress: observations of Spathiphyllum under aerobic and anaerobic conditions using novel in vivo methodology

Dataset for paper: ABSTRACT:  Plants have evolved a variety of means to energetically sense and respond to abiotic and biotic environmental stress.  Two typical photochemical signaling responses involve the emission of volatile organic compounds and light.  The emission of certain leaf wound volatiles and light are mutually dependent upon oxygen which is subsequently required for the wound-induced lipoxygenase reactions that trigger the formation of fatty acids and hydroperoxides; ultimately leading to photon emission by chlorophyll molecules.  A low noise photomultiplier with sensitivity in the visible spectrum (300 – 720 nm) is used to continuously measure long duration ultraweak photon emission of dark-adapting whole Spathiphyllumleaves (in vivo).  Leaves were mechanically wounded after two hours of dark adaptation in aerobic and anaerobic conditions.  It was found that (1) nitrogen incubation did not affect the pre-wound basal photocounts; (2) wound induced leaf biophoton emission was significantly suppressed when under anoxic stress; and (3) the aerobic wound induced emission spectra observed was > 650 nm, implicating chlorophyll as the likely emitter. Limitations of the PMT photocathode’s radiant sensitivity, however, prevented accurate analysis from 700 – 720 nm. Further examination of leaf wounding profile photon counts revealed that the pre-wounding basal state (aerobic and anoxic), the anoxic wounding state, and the post-wounding aerobic state statistics all approximate a Poisson distribution.  It is additionally observed that aerobic wounding induces two distinct exponential decay events.  These observations contribute to the body of plant wound-induced luminescence research and provide a novel methodology to measure this phenomenon in vivo.

opencc-by-4.0Dec 2017View details →

Supplemental dataset for "Weather field reconstruction using aircraft surveillance data and a novel meteo-particle model"

This dataset contains the source data used for the experiments of the paper titled "Weather field reconstruction using aircraft surveillance data and a novel meteo-particle model".

Fig. 18. A in The new spider genus Palindroma, featuring a novel synapomorphy for the Zodariidae (Araneae)

Fig. 18. A. Amphiledorus sp., juvenile, right tibia I. B. Asceua sp., ♂, right tibia and metatarsus IV. C. Cryptothele doreyana Simon, 1890, ♀, left tibia and metatarsus II. D. Idem, detail. E. Cyrioctea marken Platnick & Jocqué, 1992, ♂, left tibia and metatarsus III. F. Holasteron aciculare Baehr, 2004, ♂, left tibia and metatarsus III. Scale bars: A, E = 50 µm, B = 20 µm, C–D, F = 100 mm. Arrow shows tibial process, asterisk indicates condyle.

Fig. 17 in The new spider genus Palindroma, featuring a novel synapomorphy for the Zodariidae (Araneae)

Fig. 17. Palindroma morogorom gen. et sp. nov. A. Left legs I and II, frontal view; arrows indicate tibial process. B. Left leg III, tibia and metatarsus, dorsal view. C. Detail of previous. D. Left leg II, tibia and metatarsus, dorsal view. E. Detail of previous. Scale bars: A–B = 0.5 mm, C–D = 100 mm, E = 50 mm.

Fig. 14 in The new spider genus Palindroma, featuring a novel synapomorphy for the Zodariidae (Araneae)

Fig. 14. Palindroma sinis gen. et sp. nov., holotype, ♂. A. Habitus, dorsal view. B. Idem, ventral view. C. Idem, lateral view. D. Palp, retrolateral view. E. Idem, ventral view. Scale bars: A–C = 2 mm, D–E = 0.5 mm.

Fig. 12 in The new spider genus Palindroma, featuring a novel synapomorphy for the Zodariidae (Araneae)

Fig. 12. Palindroma obmoimiombo gen. et sp. nov., holotype, ♂ (MRAC 241633). A. Palp, retrolateral view. B. Idem, dorsal view. C. Idem, prolateral view. D. Idem, ventral view. Scale bars = 0.5 mm.