Reviewerly/exHarmony huggingface dataset:
Application of the geoelectrochemical extraction method to the Xiyi Pb-Zn deposit in southwestern China

This paper investigates Xiyi Pb-Zn deposit in Yunnan Province, China, which has little mineralized outcrop and rare supergene enrichment. The deposit is overlain by transported cover with a thickness of ~0.2–50 m. The geoelectrochemical extraction method (GEM) was tested over well-characterized areas of the deposit (sections A–B, C–D), and in an unexplored region (area E). GEM results are compared with soil geochemical methods (SGM) in areas of known mineralization and show that the geoelectrochemical anomalies (Pb, Zn, Ge, As, Au) exhibit a positive response to the deep concealed ore body, and that GEM can be more effective for finding the concealed Pb-Zn ore in the study area compared to SGM. A geoelectrochemical prospecting index was created for this deposit type and tested in an area without known mineralization (area E). The index shows that anomalies can be identified where the contrast value (CV) is >1 for Pb and Zn, and where there is the Y1 factor score (y1) >0 (for Pb-Zn-Ge-As-Mo-Bi). Correlation and factor analysis identify two comprehensive anomaly zones (CAZ) in area E with good prospecting potential, which can be used as the focus of the next exploration. The strong surface geoelectrochemical and soil geochemical anomalies above the fracture belt, with weak to moderate anomalies over thick transported cover above the fracture belt extension, suggest that dilatancy pumping is a dominant migration mechanism in the formation of anomalies in areas of shallow transported cover.
Absolutely maximally entangled states from phase states

We derive absolutely maximally entangled (AME) states from phase states for a multi-qudit system whose dynamics is governed by a two-qudit interaction Hamiltonian of Heisenberg type. AME states are characterized by being maximally entangled for all bipartitions of the multi-qudit system and present absolute multipartite entanglement. The key ingredient of this approach is the theory of phase states for finite-dimensional systems (qudits). We define further the unitary phase operators of [Formula: see text]-qudit systems and we give next the corresponding separable phase states. Using a qudit–qudit Hamiltonian acting as entangling operator on separable phase states, we generate entangled phase states. Finally, from the labeled entangled phase states, we derive the absolutely maximally entangled states.
Design of Chaotic Block Cipher Operation Mode for Intelligent Transportation Systems

Information security is a vital aspect of Intelligent Transportation Systems (ITS) involving public data collection. Road images captured for use as a basis of traffic manipulation in ITS should take all precautions for encrypting the wirelessly transferred image. This paper presents an Enhanced Cipher Block Chaining (ECBC) operation mode to ensure optimal cryptographic properties in a block cipher image encryption scheme. ECBC is based on the Cipher Block Chaining (CBC) operation mode and uses chaos based initialization vectors. Results show ECBC yields better information entropy and correlation coefficients than CBC.
Warping-Based Stereoscopic 3D Video Retargeting With Depth Remapping

Due to the recent availability of different stereoscopic display devices and online 3D media resources (e.g. 3D movies), there is a growing demand for stereoscopic video retargeting that can automatically resize a given stereoscopic video to fit the target display device. In this paper, we propose a warping-based approach that can simultaneously resize and remap the depth of a stereoscopic video to produce a better 3D viewing experience. Firstly, our method computes the significance map for each stereo video frame. It then performs volume warping using non-homogeneous scaling optimization to resize the stereoscopic video. A depth remapping constraint is used to remap the depth and a constraint is applied to preserve the significant content during warping process. Experimental results demonstrate the effectiveness of our method in preserving the significant content, ensuring motion consistency, and enhancing the depth perception of the retargeted video sequences within the comfort depth range.
A fully automated CT-based airway segmentation algorithm using deep learning and topological leakage detection and branch augmentation approaches

Quantitative CT-based characterization of bronchial morphology is widely used in chronic obstructive pulmonary disease (COPD) related research and clinical studies. There are no fully automated airway tree segmentation methods, which is critical for large multi-site COPD studies. A critical challenge is that airway segmentation failures, e.g., leakages or early truncation, in even a small fraction of cases warrants manual intervention for all cases. In this paper, we present a fullyautomated CT-based hybrid algorithm for human airway segmentation that combines both deep learning and conventional image processing approaches. A three-dimensional (3-D) U-Net is developed to compute a voxel-level likelihood map of airway lumen space from a chest CT image at total lung capacity (TLC). This likelihood map is fed into a conventional image processing cascade that iteratively augments airway branches and removes leakages using newly developed freezeand-grow and progressive threshold parameter relaxation approaches. The new method has been applied on fifteen TLC human chest CT scans from an ongoing COPD Study and its performance has been quantitatively compared with the results of a semi-automated industry-standard software involving manual review and correction. Experimental results show significant improvements in terms of branch level accuracy using the new method as compared to the unedited results from the industry-standard method, while matching with their manually edited results. In terms of segmentation volume leakage, the new method significantly reduced segmentation leakages as compared to both unedited and edited results of the industry-standard method.
Self-Supervised Learning for Specified Latent Representation

Current latent representation methods using unsupervised learning have no semantic meaning; thus, it is difficult to directly express their physical task in the real world. To this end, this paper attempts to propose a specified latent representation with physical semantic meaning. First, a few labeled samples are used to generate the framework of the latent space, and these labeled samples are mapped to framework nodes in the latent space. Second, a self-learning method using structured unlabeled samples is proposed to shape the free space between the framework nodes in the latent space. The proposed specified latent representation therefore possesses the advantages provided by both supervised and unsupervised learning. The proposed method is verified by numerical simulations and real-world experiments.
A Control Architecture for Robot Swarms (AMEB)

A control architecture for heterogeneous robot swarms (AMEB) is proposed in this work. The architecture manages the processes that occur in the system and its main objectives are to allow the emergence and self-organization in the group. It is structured in three levels: an individual level, a collective level under the philosophy of emergent behavior, and a level for the management of learning and knowledge. The architecture includes a behavioral component that allows the inclusion of emotions in the members of the swarm. Finally, it described a method for verifying the occurrence of the emergence in the swarm, using fuzzy cognitive maps.
<i>hMod</i>: A software framework for assembling highly detailed heuristics algorithms

Summary Software design and component reuse for heuristic algorithms have gained in relevance; however, further innovation is needed. In this context, hMod is presented as a software framework suited for implementing heuristic algorithms, with a focus on intensive reuse of highly cohesive operator and data components within algorithmic structures, making it possible to dynamically (re)configure and manage such a structure. Rather than a fast‐prototyping tool, hMod supports heuristic implementation in the long term, whereby complexity can escalate from simple operators to major hyperheuristic architectures. In its core resides a novel object‐oriented representation of algorithms through a pattern‐like implementation, namely, algorithm assembling (AA). Additionally, it incorporates component integration features, such as dependency injection mechanisms. hMod has been mentioned in previous research, in which hyperheuristic methods were implemented and evaluated from an optimization perspective. In this work, a description of the framework is presented from the software design perspective, including the AA model, its architecture, and a detailed presentation of the main features of the framework. Previous hMod applications have demonstrated that it supports not only the software design requirements of heuristic algorithms but performance standards as well. Available sources of the framework can be found in http://gitlab.com/eurra/hmod .
Generating Tasks for Study of Struggling Search

Interactive information-seeking systems are designed to help users with their struggling during the searching for complex fact checking tasks, where a searcher may have clear information needs but experience difficulty in collecting required information. However, evaluation and comparison of such systems requires a large number of such tasks, which are difficult to collect or make up. To the best of our knowledge, there has not been a commonly used task set for evaluating struggling search of this kind. This paper proposes a convenient method to generate complex fact checking tasks. Each of the generated task has a clearly defined goal, which however takes an average searcher a significant amount of effort to reach. We conducted lab user studies to verify the feasibility of this method. The results confirmed its feasibility and efficiency.
Fragile neural networks: the importance of image standardization for deep learning in digital pathology

Recently in the field of digital pathology, there have been promising advances with regards to deep learning for pathological images. These methods are often considered “black boxes”, where tracing inputs to outputs and diagnosing errors is a difficult task. This is important as neural networks are fragile, and dataset variation, which in digital pathology is attributed to biological variance, can cause low accuracy. In deep learning, this is typically addressed by adding data to the training set. However, training is costly and time-consuming to create and may not address all variation seen in these images. Digitized histology carries a great deal of variation across many dimensions (color / stain variation, lighting intensity, presentation of a disease, etc.), and some of these “low-level” image variations may cause a deep network to break due to their fragility. In this work, we use a unique dataset – cases of serially-registered H and E tissue samples from oral cavity cancer (OCC) patients – to explore the errors of a classifier trained to identify and segment different tissue types. Registered serial sections allow us to eliminate variability due to biological structure and focus on image variability including staining and lighting, and try to identify sources of error that may cause deep learning to fail. We find that perceptually-insignificant changes in an image (minor lighting and color shifts) can result in extremely poor classification performance, even when the training process tries to prevent overfitting. This suggests that great care must be taken to augment and normalize datasets to prevent errors.
Categorization of spam images and identification of controversial images on mobile phones using machine learning and predictive learning

AbstractAbstractThe surge in popularity of instant messaging apps has greatly increased the volume of media exchanges, majorly consisting of images. This has resulted in cluttering of images, low storage, etc. In this paper, we are addressing these issues by proposing a system that combines various techniques of facial recognition, text extraction and Natural language processing and refines them through novel approaches to mark images as important or spam according to user's behaviour and preferences. Our system classifies the images on user's smartphone into separate categories and processes them further accordingly. Apart from the prominent categories based on the usability of the image, the system also recognizes duplicate and similar images which facilitates their easy deletion.Keywords: Facial recognitionImage classificationMachine leaningspam detectionComputer vision
Evaluating the impact of semantic technologies on bibliographic systems: A user-centred and comparative approach

Semantic and linked-data technologies are currently used by several cultural heritage institutions to make their content available through the Web. Although these technologies are heavily oriented towards data reuse and integration, one clear benefit highlighted by recent literature is the enhancement of human cultural consumption and user experience through the development of novel cultural end-user applications like Online Public Access Catalogues (OPACs). However, to the best of our knowledge, studies into the impact of these technologies on end-user applications are scarce. In order to address this lack, we report the results of two within-group user-centred studies of two online bibliographic systems in a realistic setting — using a widely deployed OPAC and its counterpart linked-data based system, datos.bne.es. The results of our first within-group study show that users of the system based on linked data required significantly less time and visited fewer pages to complete a typical search and retrieval activity. Additionally, the results of our user satisfaction tests also provided significantly better results for this new system. These results are consistent with the hypothesis that semantic technologies applied to library catalogues provide an enhancement that helps satisfy users' information needs.
Application of dynamic saliency maps to the video stream recognition systems with image quality assessment

In this paper we propose an original approach to optical video stream recognition system design, introducing new modules for image quality assessment, image quality correction and feedback. The main novelty of the proposed approach lays in combining image quality assessment results with the global dynamic object saliency maps which indicate the importance or the informative value of the corresponding image regions. The approach is applied to the identity documents video stream recognition system, where saliency maps are initially provided by document templates and are dynamically changing over time – for example, according to per-field stopping rules. Experiments demonstrated an increase of such essential recognition systems characteristics as accuracy, reliability and performance.
Design of a High-Throughput Match Search Unit for Lossless Compression Algorithms

This paper presents an attempt to combine recent research in fields of hardware- and software-based high-throughput universal lossless compression algorithms and their implementations, resulting into a case study focusing on one of the most critical parts of compression algorithms – a Match Search Unit (MSU) and its parallelization. The presented FPGA design combines ideas of the LZ4 algorithm (which is derived from the most common LZ77) with the state of the art hardware architectures for lossless compression also based on LZ77. This approach might lead to a smaller, better organized or more efficient building block for modern implementations of hardware driven lossless compression algorithms. The presented design focuses on optimization of the main problem of the LZ77 family, namely the construction of and searching in a compression dictionary. Particularly, we combine a Live Value Table (LVT) with multi-ported memory in order to improve the bandwidth of the dictionary and the Fibonacci hashing principle originating from LZ4 algorithm to decrease latency of the MSU and to achieve overall higher throughput rate. For the design synthesis an FPGA of the Xilinx Virtex-7 family was used.
Analytical Modeling of Chaotic Sampling of Regular Waveform for Random Number Generation

In this paper, an analytic approach is taken towards the analysis of a class of true random generators where an irregular square wave generated by a continuous-time chaotic oscillator and a comparator structure is used to sample a regular continuous-time waveform. Numerical and analytic equations for probability distribution have been derived for D flip-flop topology. Kernel density estimation method is utilized to describe bit distribution in the output. Then random bits are generated using analytical formulas and results from numerical simulations. Using the concepts of auto correlation and approximate entropy, the relationship between the regular and the chaotic waveform frequencies needed to generate uncorrelated bit stream have been investigated.
Students’ gesture of naive, routine, and shopisticated behavior oriented on mathematical problem solving

Gesture aims to see students understanding when such understanding is not visible in writing. This research is a qualitative research with descriptive method that aims to examine the gesture of three students whose orientations are different in solving the problem. Based on literature, there are three kind of behavior problem solving, there are naive, routine, and sophisticated. To reveal the gesture of the students of the three behaviors, six issues related to geometry were given to two junior high school students of equal ability. Data collection was done through mathematical problem solving test, recording of student presentation and interview between researcher and student after doing the problem. Based on the result of the research, sophisticated-gesture students tended to show a profound way of thinking. Routine-gesture students were more relaxed and occasionally showed doubt and unconfidence. Naive gesture students did not tended to behave that look thoughtful. These Gestures were shown consistently for each given question.
Classification of Brain Lesion using K- Nearest Neighbor technique and Texture Analysis

Texture is an important property for analyzing many types of images. It provides a rich source of information about the image. In this paper four statistical features (contrast, correlation, homogeneity and energy) are calculated from the gray level co-occurrence matrix in which the gray level of variation associated with gray of three samples from CT scan Images to patients with (hemorrhage, ischemic and cancer) in the brain, the region of interest has been obtained by classifying the images using the k-nearest neighbor (K-NN) five classes has been proposed for the classification purpose, the result shows two classes have infection region and the three other classes are healthy region. It has found that the cancer texture are highly correlated comparing with the stroke. The stroke texture is more homogenous than the cancer texture this make the distinguish between them more specific easy.
Adaptive fuzzy interpolative reasoning based on similarity measures of polygonal fuzzy sets and novel move and transformation techniques

In this paper, we propose a new adaptive fuzzy interpolative reasoning (AFIR) method based on similarity measures of polygonal fuzzy sets (PFSs) and novel move and transformation techniques for solving contradictory fuzzy interpolative reasoning (FIR) results that occur at FIR components. The proposed AFIR method is applied for dealing with the diarrheal disease prediction problem. The experimental results show that the proposed AFIR method obtains a higher consistency degree between the FIR results compared to the ones obtained by Chen and Adam's AFIR method (2017), Chen and Adam's AFIR method (2018), Chen and Barman's AFIR method (2019), Cheng et al.’s AFIR method (2016), Yang et al.’s AFIR method (2017) and Yang and Shen's AFIR method (2011).
Learning Receptive Field Size by Learning Filter Size

Covering various receptive fields within a layer is essential to effectively recognize the objects of various sizes and types at a specific layer for a convolutional neural network (CNN). In this work, we propose a novel adaptive learning method which learns the filter size (i.e. the kernel size of a convolutional filter) and distribution to learn the receptive field size. Directly optimizing with respect to the filter size is challenging because the filter size is discrete. To overcome this, we propose a masking technique, which enables the automatic allocation of resources over filters of different sizes and leads to efficient optimization. Through our proposed trainable formulation of the mask, the network self-organizes its structure through the standard backpropagation. The proposed adaptive CNN can be generalized to any single-path structures and multi-path structures as well. The effectiveness of our proposed approach is validated by several benchmark datasets compared with various previous structures on the image classification task for diverse network depths and widths. Furthermore, we demonstrate our adaptive CNN trained on a large-scale dataset can yield improved performance when applying to a transfer learning.
DWE-Med

Recent advances in unsupervised language processing methods have created an opportunity to exploit massive text corpora for developing high-quality vector space representation (also known as word embeddings) of words. Towards this direction, practitioners have developed and applied several data driven embedding models with quite good rate of success. However, a drawback of these models lies in their premise of static context; wherein, the meaning of a word is assumed to remain the same over the period of time. This is limiting because it is known that the semantic meaning of a concept evolves over time. While such semantic drifts are routinely observed in almost all the domains; their effect is acute in domain such as biomedicine, where the semantic meaning of a concept changes relatively fast. To address this, in this study, we aim to learn temporally aware vector representation of medical concepts from the timestamped text data, and in doing so provide a systematic approach to formalize the problem. More specifically, a dynamic word embedding based model that jointly learns the temporal characteristics of medical concepts and performs across time-alignment is proposed. Apart from capturing the evolutionary characteristics in an optimal manner, the model also factors in the implicit medical properties useful for a variety of bio-medical applications. Empirical studies conducted on two important bio-medical use cases validates the effectiveness of the proposed approach and suggests that the model not only learns quality embeddings but also facilitates intuitive trajectory visualizations.