Reviewerly/exHarmony huggingface dataset:
3D human pose estimation from a single image via exemplar augmentation

3D human pose estimation from a single image is a challenging problem due to occlusion, viewpoint variance, and the ill-posed nature of back projection. We follow a standard two-step pipeline which first detects 2D joint locations and uses them to infer 3D pose. For the first step, we use a recent deep learning-based detector. For the second step, we propose a novel exemplar-based algorithm to implicitly augment the exemplar set for 3D human pose estimation. The motivation of this algorithm is to well represent various poses in the real world with finite real exemplars. We achieve it by a strategy of synthesizing virtual candidate poses which ensures that the augmented exemplar set has much more variety. Moreover, we also present an effective approach to select the best exemplar from candidate set to well match the detected 2D pose. Experimental results show that our method achieves competitive performance on Human3.6M dataset.
A Conjoint Application of Data Mining Techniques for Analysis of Global Terrorist Attacks

Terrorism has become one of the most tedious problems to deal with and a prominent threat to mankind. To enhance counter-terrorism, several research works are developing efficient and precise systems, data mining is not an exception. Immense data is floating in our lives, though the scarce availability of authentic terrorist attack data in the public domain makes it complicated to fight terrorism. This manuscript focuses on data mining classification techniques and discusses the role of United Nations in counter-terrorism. It analyzes the performance of classifiers such as Lazy Tree, Multilayer Perceptron, Multiclass and Naïve Bayes classifiers for observing the trends for terrorist attacks around the world. The database for experiment purpose is created from different public and open access sources for years 1970–2015 comprising of 156,772 reported attacks causing massive losses of lives and property. This work enumerates the losses occurred, trends in attack frequency and places more prone to it, by considering the attack responsibilities taken as evaluation class.
Inference Rules for the Partial Floyd-Hoare Logic Based on Composition of Predicate Complement

Classical Floyd-Hoare logic is sound when total pre- and post-conditions are considered. In the case of partial conditions (predicates) the logic becomes unsound. This situation may be corrected by introducing additional constraints to the rules of the logic. But such constraints, in particular, for the sequence and while rules, are rather complicated. In this paper we propose new simpler rules formulated in a program algebra extended with the composition of predicate complement. The obtained logic is called the Complemented Partial Floyd-Hoare Logic (CPFHL). The predicate component of this logic is related to three-valued logic. We prove the soundness theorem for CPFHL and discuss further investigations of the problem. The obtained results can be useful for software verification.
Depth image super-resolution reconstruction based on a modified joint trilateral filter

Depth image super-resolution (SR) is a technique that uses signal processing technology to enhance the resolution of a low-resolution (LR) depth image. Generally, external database or high-resolution (HR) images are needed to acquire prior information for SR reconstruction. To overcome the limitations, a depth image SR method without reference to any external images is proposed. In this paper, a high-quality edge map is first constructed using a sparse coding method, which uses a dictionary learned from the original images at different scales. Then, the high-quality edge map is used to guide the interpolation for depth images by a modified joint trilateral filter. During the interpolation, some information of gradient and structural similarity (SSIM) are added to preserve the detailed information and suppress the noise. The proposed method can not only preserve the sharpness of image edge, but also avoid the dependence on database. Experimental results show that the proposed method is superior to some state-of-the-art depth image SR methods.
A distance-type-insensitive clustering approach

In this paper, we offer a method aiming to minimize the role of distance metric used in clustering. It is well known that distance metrics used in clustering algorithms heavily influence the end results and also make the algorithms sensitive to imbalanced attribute/feature scales. To solve these problems, a new clustering algorithm using a per-attribute/feature ranking operating mechanism is proposed in this paper. Ranking is a rarely used discrete, nonlinear operator by other clustering algorithms. However, it also has unique advantages over the dominantly used continuous operators. The proposed algorithm is based on the ranks of the data samples in terms of their spatial separation and is able to provide a more objective clustering result compared with the alternative approaches. Numerical examples on benchmark datasets prove the validity and effectiveness of the proposed concept and principles.
Wavelet-based convolutional neural networks for gender classification

We develop a gender classification method using convolutional neural networks. We train Alexnet Architecture using the luminance (Y) component of the facial image (YCbCr) for the SoF, groups, and face recognition technology datasets. The Y component is reduced to a size of 32 × 32 via discrete wavelet transform (DWT). The use of the Y plane and a low-resolution subband image of the DWT significantly reduce the amount of processed data. We are able to achieve better results than other machine learning, rule-based approaches and the traditional convolutional neural net structure that are trained with three-dimensional RGB images. We are able to maintain comparably high recognition accuracy, even with the reduction of the number of network layers. We have also compared our structure with the state-of-the-art methods and provided the recognition rates.
Modal Planning for Cooperative Non-Prehensile Manipulation by Mobile Robots

If we define a mode as a set of specific configurations that hold the same constraint, and if we investigate their transitions beforehand, we can efficiently probe the configuration space by using a manipulation planner. However, when multiple mobile robots together manipulate an object by using the non-prehensile method, the candidates for the modes and their transitions become enormous because of the numerous contacts among the object, the environment, and the robots. In some cases, the constraints on the object, which include a combination of robot contacts and environmental contacts, are incapable of guaranteeing the object’s stability. Furthermore, some transitions cannot appear because of geometrical and functional restrictions of the robots. Therefore, in this paper, we propose a method to narrow down the possible modes and transitions between modes by excluding the impossible modes and transitions from the viewpoint of statics, kinematics, and geometry. We first generated modes that described an object’s contact set from the robots and the environment while ignoring their exact configurations. Each multi-contact set exerted by the robots and the environment satisfied the condition necessary for the force closure on the object along with gravity. Second, we listed every possible transition between the modes by determining whether or not the given robot could actively change the contacts with geometrical feasibility. Finally, we performed two simulations to validate our method on specific manipulation tasks. Our method can be used in various cases of non-prehensile manipulations by using mobile robots. The mode transition graph generated by our method was used to efficiently sequence the manipulation actions before deciding the detailed configuration planning.
Multi-feature fusion method for medical image retrieval using wavelet and bag-of-features

Color, texture, and shape are the common features used for the retrieval systems. However, many medical images have a spot of color information. Therefore, the discriminative texture and shape features should be extracted to obtain a satisfied retrieval result. In order to increase the credibility of the retrieval process, many features can be combined to be used for medical image retrieval. Meanwhile, more features require more processing time, which will decrease the retrieval speed. In this paper, wavelet decomposition is adopted to generate different resolution images. Bag-of-feature, texture, and LBP feature are extracted from three different-level wavelet images. Finally, the similarity measure function is obtained by fusing these three types of features. Experimental results show that the proposed multi-feature fusion method can achieve a higher retrieval accuracy with an acceptable retrieval time.
The cluster correlation-network support vector machine for high-dimensional binary classification

Identifying homogeneous subsets of predictors in classification can be challenging in the presence of high-dimensional data with highly correlated variables. We propose a new method called cluster correlation-network support vector machine (CCNSVM) that simultaneously estimates clusters of predictors that are relevant for classification and coefficients of penalized SVM. The new CCN penalty is a function of the well-known Topological Overlap Matrix whose entries measure the strength of connectivity between predictors. CCNSVM implements an efficient algorithm that alternates between searching for predictors’ clusters and optimizing a penalized SVM loss function using Majorization–Minimization tricks and a coordinate descent algorithm. This combining of clustering and sparsity into a single procedure provides additional insights into the power of exploring dimension reduction structure in high-dimensional binary classification. Simulation studies are considered to compare the performance of our procedure to its competitors. A practical application of CCNSVM on DNA methylation data illustrates its good behaviour.
An EEG study on the brain representations in language learning

This paper presents an experimental study to understand the key differences in the neural representations when the subject is presented with speech signals of a known and an unknown language and to capture the evolution of neural responses in the brain for a language learning task. In this study, electroencephalography (EEG) signals were recorded while the human subjects listened to a given set of words from English (familiar language) and Japanese (unfamiliar language). The subjects also provided behavioural signals in the form of spoken audio for each input audio stimuli. In order to quantify the representation level differences for the auditory stimuli of two languages, we use a classification approach to discriminate the two languages from the EEG signal recorded during listening phase by designing an off-line classifier. These experiments reveal that the time-frequency features along with phase contain significant language discriminative information. The language discrimination is further confirmed with a second subsequent experiment involving Hindi (the native language of the subjects) and Japanese (unknown language). A detailed analysis is performed on the recorded EEG signals and the audio signals to further understand the language learning processes. A pronunciation rating technique on the spoken audio data confirms the improvement of pronunciation over the course of trials for the Japanese language. Using single trial analysis, we find that the EEG representations also attain a level of consistency indicating a pattern formation. The brain regions responsible for language discrimination and learning are identified based on EEG channel locations and are found to be predominantly in the frontal region.
Paleoclimatic implications of hydrogen and oxygen isotopic compositions of Cretaceous–Tertiary kaolins in the Douala Sub-Basin, Cameroon

Increased interest in paleoenvironmental studies is a result of climatic changes occurring at present and predicted for the future. Such studies could be done using the stable isotope compositions (δ2H and δ18O) of kaolins, which provide knowledge on the paleoenvironmental conditions prevailing during the time of kaolinisation. In this study, the stable isotopic compositions of clay-size fraction of kaolins occurring in Cretaceous and Tertiary Formations of the Douala Sub-Basin in Cameroon are presented, with the aim of reconstructing the paleoenvironmental conditions of the Sub-Basin. To achieve this, the clay-size fraction (< 2 μm fraction) of 8 kaolinite-rich samples were analysed for their δ2H and δ18O compositions, and results were reported as part per mil (‰) relative to the SMOW standard. The δ18O values of kaolins found in the Cretaceous–Tertiary Formations of the Douala Sub-Basin varied between +18.2 and +21.0‰, whereas the δ2H values varied between –69 and –53‰. Nine of the eleven samples plotted on the right of the supergene–hypogene line. Five of these nine samples plotted very close to the kaolinite line, which represents the composition of kaolinite in equilibrium with meteoric water at 20 °C; suggesting a supergene weathering origin of these kaolins. The determination of the temperature of kaolinisation yielded mean formation temperatures of 22 ± 2 °C and 27 ± 6 °C for Cretaceous and Tertiary kaolins, respectively. Excluding the two samples falling in the hypogene field, averages of kaolinisation temperatures were 20 and 25 °C during the Cretaceous and Tertiary periods, respectively. These temperatures are slightly below the present mean annual temperature in Douala (27 °C), thereby suggesting that the climate was becoming warmer from the Cretaceous to the Present. Therefore, Douala had a cooler and rainy climate during the Cretaceous, and the climate is gradually becoming hotter and more humid, favouring the refinement of existing kaolins and the kaolinisation of kaolin-forming minerals in the Sub-Basin.
Software Architecture for Automating Cognitive Science Eye-Tracking Data Analysis and Object Annotation

The advancement of wearable eye-tracking technology enables cognitive researchers to capture vast amounts of eye gaze information while participants are completing specific tasks without restrictions on their movement. However, while eye trackers can overlay a gaze indicator on the scene video, identifying the specific objects being looked at and analyzing the resulting dataset are accomplished mostly by manual annotation. This method is a cost-prohibitive and time-consuming approach that is prone to human error. Such analytic difficulty limits researchers' ability to data mine the information efficiently, ultimately restricting the number of scenarios that can feasibly be conducted within budget. Here, the first fully automated solution for eye-tracking data analysis is presented, which eliminates the need for manual annotation. The proposed software architecture, gaze to object classification (GoC), processes the gaze-overlaid video from commercially available wearable eye trackers, recognizes and classifies the specific object a user is focusing on and calculates the gaze duration time. GoC utilizes an image cross-correlation method to locate the gaze indicator and an image similarity measurement to support faster processing. The presented system has been successfully adopted by cognitive psychologists. GoC's exceptional performance in analyzing a case study spanning over 50 h of mobile eye-tracking is presented. The accuracy and a cost-analysis comparison between GoC and state-of-the-art manual annotation software are provided. GoC has game-changing potential for increasing the ecological validity of using eye-tracking technology in cognitive research.
Fractional-Order Retinex for Adaptive Contrast Enhancement of Under-Exposed Traffic Images

In this paper, a Fractional-order Retinex (FR) for the adaptive contrast enhancement of Under-Exposed Traffic Images (UETI) is proposed to be achieved by the fractional-order variational method. The disposable reconstructive results of the contrast enhancement of UETI play a significant role in traffic safety and are often taken as intermediate results for the traffic virtual reality and augmented reality of intelligent transportation systems. To this end, this paper proposes a state-ofthe-art application of a promising mathematical method, fractional calculus, to extend the classic integer-order Retinex to the fractional-order one, a FR, which leads to a fractional-order algebraic regularization term and contributes to better conditioning of the reconstruction problem. At first, the fractional-order isotropic equation related to a FR is implemented by the Fractional-order Steepest Descent Method (FSDM). Secondly, the corresponding restrictive fractional-order optimization is achieved. Finally, the capability of a FR to non-linearly preserve complex textural details as well as desired contrast enhancing is validated by experimental analysis, which is a major advantage superior to conventional contrast enhancement algorithms, especially for UETI rich in textural details. The paper gives a novel mathematical approach, fractional calculus, to the family of Retinex algorithms that differs from most of the previous approaches and as such, it represents an interesting theoretical contribution.
Deep metric learning via subtype fuzzy clustering

Deep metric learning methods aim to transform data features from original scattered space to a discriminative subspace in an end-to-end way, and they have shown promising results on wide applications. Triplet loss functions are the most popular models to tackle deep metric learning problem as they simultaneously enhance separability between different classes and compactness of each class in the embedded subspace. Therefore, effective triplets selection is crucial to the classification performance. However, most of these methods only focus on mining hard negative pairs, which refer to the nearest sample pairs of different classes, while fail to take subtle cluster structure of each class into consideration. To take such information into the metric learning model, a novel scheme based on subtype fuzzy clustering is proposed in this paper. By defining a new clustering degree, we conduct fuzzy clustering for each class to mine classification-oriented subtype structure. The new clustering degree is inversely proportional to the pairwise distance, thus, we can choose the positive pairs of the highest clustering degrees directly based on the farthest distances within each class. This new sampling approach avoids off-line clustering step, for which the network weights update procedure has to be temporarily paused. In other words, our method builds positive pairs without explicit clustering degree computation or off-line clustering. Our method inputs the selected positive pairs and negative pairs into the standard triplet loss to achieve network feature learning. Experimental results show competitive metric learning performance on three benchmark datasets.
Pitch Delay Based Adaptive Steganography for AMR Speech Stream

Most existing speech steganography breaks the continuity of adjacent pitch delay, which obviously degrades their statistically undetectability. This paper presents a novel steganographic scheme for low bit-rate speech stream against pitch delay steganalysis. Three measures are adopted to enhance steganographic security. First, the short-term stability of pitch delay and the statistical distribution of adjacent subframe are considered for designing a distortion function. Second, syndrome-trellis codes (STCs) is utilized to minimize the overall embedding impact based on the defined distortion function. Third, the suboptimal pitch delay is searched to maintain speech quality. Experimental results demonstrate that our scheme achieves higher level of security, especially in the case of low embedding rate. When the relative embedding rate is 0.2 for 10.2 kbit/s AMR stream, the test error rate of our method rises by 12.44% compared with the existing algorithm.
KloudNet: Deep Learning for Sky Image Analysis and Irradiance Forecasting

We present a novel image-based approach for estimating irradiance fluctuations from sky images. Our goal is a very short-term prediction of the irradiance state around a photovoltaic power plant 5–10 min ahead of time, in order to adjust alternative energy sources and ensure a stable energy network. To this end, we propose a convolutional neural network with residual building blocks that learns to predict the future irradiance state from a small set of sky images. Our experiments on two large datasets demonstrate that the network abstracts upon local site-specific properties such as day- and month-dependent sun positions, as well as generic properties about moving, creating, dissolving clouds, or seasonal changes. Moreover, our approach significantly outperforms the established baseline and state-of-the-art methods.
Generalized Update Semantics

Abstract This paper explores the relationship between dynamic and truth conditional semantics for epistemic modals. It provides a generalization of a standard dynamic update semantics for modals. This new semantics derives a Kripke semantics for modals and a standard dynamic semantics for modals as special cases. The semantics allows for new characterizations of a variety of principles in modal logic, including the inconsistency of ‘p and might not p’. Finally, the semantics provides a construction procedure for transforming any truth conditional semantics for modals into a dynamic semantics for modals with similar properties.
Rashba control to minimize circuit cost of quantum Fourier algorithm in ballistic nanowires

The presented work provides a new prospective of quantum computer hardware development through ballistic nanowires with Rashba effect. We address Rashba effect as a possible mechanism to realize novel qubit gates in nanowires. We apply our results to the design of a new quantum circuit for Fourier algorithm, which shows an improved quantum cost in terms of the number of gates. The current system can be successfully implemented to increase the production of quantum computer experimentally thanks to the absence of impurities. The introduced circuit will enhance the scope of experimental and theoretical research for the realization of other quantum algorithms, such as phase estimation and Shor's algorithms.
Collaborative multimodal feature learning for RGB-D action recognition

The emergence of cost-effective depth sensors opens up a new dimension for RGB-D based human action recognition. In this paper, we propose a collaborative multimodal feature learning (CMFL) model for human action recognition from RGB-D sequences. Specifically, we propose a robust spatio-temporal pyramid feature (RSTPF) to capture dynamic local patterns around each human joint. The proposed CMFL model fuses multimodal data (skeleton, depth and RGB), and learns action classifiers using the fused features. The original low-level feature matrices are factorized to learn shared features and modality-specific features under a supervised fashion. The shared features describe the common structures among the three modalities while the modality-specific features capture intrinsic information of each modality. We formulate shared-specific features mining and action classifiers learning in a unified max-margin framework, and solve the formulation using an iterative optimization algorithm. Experimental results on four action datasets demonstrate the efficacy of the proposed method.