Reviewerly/exHarmony huggingface dataset:
NOD-CC: A Hybrid CBR-CNN Architecture for Novel Object Discovery

Deep Learning methods have shown a rapid increase in popularity due to their state-of-the-art performance on many machine learning tasks. However, these methods often rely on extremely large datasets to accurately train the underlying machine learning models. For supervised learning techniques, the human effort required to acquire, encode, and label a sufficiently large dataset may add such a high cost that deploying the algorithms is infeasible. Even if a sufficient workforce exists to create such a dataset, the human annotators may differ in the quality, consistency, and level of granularity of their labels. Any impact this has on the overall dataset quality will ultimately impact the potential performance of an algorithm trained on it. This paper partially addresses this issue by providing an approach, called NOD-CC, for discovering novel object types in images using a combination of Convolutional Neural Networks (CNNs) and Case-Based Reasoning (CBR). The CNN component labels instances of known object types while deferring to the CBR component to identify and label novel, or poorly understood, object types. Thus, our approach leverages the state-of-the-art performance of CNNs in situations where sufficient high-quality training data exists, while minimizing its limitations in data-poor situations. We empirically evaluate our approach on a popular computer vision dataset and show significant improvements to object classification performance when full knowledge of potential class labels is not known in advance.
Ensemble Method for Multi-view Text Clustering

Textual data frequently occurs as an unlabeled document collection, therefore it is useful to sort this collection into clusters of related documents. On the other hand, text has different aspects, which a single representation cannot capture. To this end, multi-view clustering present an efficient solution to integrate different representations called "views" by exploiting the complementary characteristics of these views. However, the existing methods consider only one representation mode for all views that is based on terms frequencies. Such representation leads to losing valuable information and fails to capture the semantic aspect of text. To overcome these issues, we propose a new method for multi-view text clustering that exploits different representations of text in order to improve the quality of clustering. The experimental results show that the proposed method outperforms other methods and enhances the clustering quality.
Detecting Arbitrarily Rotated Faces for Face Analysis

Current face detection concentrates on detecting tiny faces and severely occluded faces. Face analysis methods, however, require a good localization and would benefit greatly from some rotation information. We propose to predict a face direction vector (FDV), which provides the face size and orientation and can be learned by a common object detection architecture better than the traditional bounding box. It provides a more consistent definition of face location and size. Using the FDV is promising for all succeeding face analysis methods. As an example, we show that facial landmark detection can highly benefit from pre-aligned faces.
The Impact of Controller Type on Video Game User Experience in Virtual Reality

The recent resurgence of interest in and widespread availability of virtual reality (VR) technology have paved the way for the use of VR systems as a gaming console. With the incessant popularity of VR gaming, the question then arises as to which input method affords the most intuitive interactive experience and provides the most enjoyable gaming user experience (UX). In an attempt to address these questions, the current study examined the impact of controller type (native VR controller vs. traditional gamepad controller) on video game UX in VR while playing a strategy game and an FPS game. Using both the native VR controller and traditional gamepad controller in a counterbalanced order, participants played a strategy and FPS game in VR and provided video game UX satisfaction ratings. Results of the strategy game experiment indicated that the two controllers were comparable in terms of perceived controller naturalness, sense of presence, and video game UX satisfaction during the gameplay, indicating that using a more natural input device did not lead to a superior VR gaming UX. Results of the FPS game experiment indicated that the two controllers were comparable in terms of, sense of presence and video game UX satisfaction during the gameplay, indicating that using a more natural input device did not lead to a superior VR gaming UX. However, results indicated that perceived controller naturalness was rated higher in the Oculus controllers than in the Xbox controllers, exhibiting that the Oculus controllers felt more natural.
IoT Service Composition for Concurrent Timed Applications

Concurrent applications may share certain components which can be conducted once for all, while mandating the satisfaction of their spatial-temporal constraints. A mechanism is proposed in this paper to identify common components, and to integrate and optimize concurrent service requests, where a component corresponds to a snippet of IoT service compositions. Consequently, composing IoT services with respect to concurrent requests can be reduced to a constrained multi-objective optimization problem, which can be solved by heuristic algorithms. Experimental results demonstrate the efficiency of this technique in comparison with the state of art's techniques, especially when the number of IoT nodes and functionality-overlapping are relatively large.
Point Cloud Compression Incorporating Region of Interest Coding

We introduce Region-of-Interest (ROI) coding for point cloud attributes, using an input-weighted distortion measure where the weights are determined by the ROI. In terms of coding, we use the Region Adaptive Hierarchical Transform (RAHT), which relies on a set of weights. We use a measure-theoretic interpretation of RAHT to determine that the weights of the transform should be set to the weights of the distortion measure. The ROI is chosen as the 3D region of the face, which is detected from a set of 2D projections using the well-known Viola-Jones algorithm. Experimental results show subjectively meaningful improvements (7-8 dB PSNR) in a face ROI with subjectively insignificant degradations (under 1 dB PSNR) in the non-ROI.
Analytical Methods for Interpretable Ultradense Word Embeddings

Word embeddings are useful for a wide variety of tasks, but they lack interpretability.By rotating word spaces, interpretable dimensions can be identified while preserving the information contained in the embeddings without any loss.In this work, we investigate three methods for making word spaces interpretable by rotation: Densifier (Rothe et al., 2016), linear SVMs and DensRay, a new method we propose.In contrast to Densifier, DensRay can be computed in closed form, is hyperparameterfree and thus more robust than Densifier.We evaluate the three methods on lexicon induction and set-based word analogy.In addition we provide qualitative insights as to how interpretable word spaces can be used for removing gender bias from embeddings.
360° Video Stitching for Dual Fisheye Cameras

Back-to-back dual fisheye camera configuration offers a cost-effective 360° video solution. However, the inherent parallax of this camera configuration and the unmatched scene along the seam between the two camera views present a great challenge to video stitching. To address the jittering and fragmented visual appearance issues, we present in this paper a robust method that preserves the geometric structure of the scene and enhances the stableness of the video along the temporal dimension. The proposed video stitching method entails a mesh deforming operation prior for the minimization of the geometric distortion and an adaptive seam carving operation that generates optimal spatial and temporal alignment. Experimental results show that our method can produce 360° videos without jitter and ghost artifact.
CUDA-JMI: Acceleration of feature selection on heterogeneous systems

Feature selection is a crucial step nowadays in machine learning and data analytics to remove irrelevant and redundant characteristics and thus to provide fast and reliable analyses. Many research works have focused on developing new methods that increase the global relevance of the subset of selected features while reducing the redundancy of information. However, those methods that select features with high relevance and low redundancy are extremely time-consuming when processing large datasets. In this work we present CUDA-JMI, a tool based on Joint Mutual Information that accelerates feature selection by exploiting the computational capabilities of modern heterogeneous systems that contain several CPU cores and GPU devices. The experimental evaluation has been carried out in three systems with different type and amount of CPUs and GPUs using five publicly available datasets from different fields. These results show that CUDA-JMI is significantly faster than its original sequential counterpart for all systems and input datasets. For instance, the runtime of CUDA-JMI is up to 52 times faster than an existing sequential JMI-based implementation in a machine with 24 CPU cores and two NVIDIA M60 boards (four GPUs). CUDA-JMI is publicly available to download from https://sourceforge.net/projects/cuda-jmi.
Development and Calibration of the TBI-QOL Ability to Participate in Social Roles and Activities and TBI-QOL Satisfaction With Social Roles and Activities Item Banks and Short Forms

To develop traumatic brain injury (TBI)-optimized versions of the Quality of Life in Neurological Disorders (Neuro-QoL) Ability to Participate in Social Roles and Activities and Satisfaction with Social Roles and Activities item banks, evaluate the psychometric properties of the item banks developed for adults with TBI, develop short form and computer adaptive test (CAT) versions, and report information to facilitate research and clinical applications.We used a mixed methods design to develop and evaluate Ability to Participate in Social Roles and Activities and Satisfaction with Social Roles and Activities items. Focus groups defined the constructs, cognitive interviews guided item revisions, and confirmatory factor analysis and item response theory methods helped calibrate item banks and evaluate differential item functioning related to demographic and injury characteristics.Five TBI Model Systems centers in the United States.Community-dwelling adults with TBI (N=556).None.Traumatic Brain Injury-Quality of Life (TBI-QOL) Ability to Participate in Social Roles and Activities and TBI-QOL Satisfaction with Social Roles and Activities item banks.Forty-five Ability to Participate in Social Roles and Activities and 41 Satisfaction with Social Roles and Activities items demonstrated good psychometric properties. Although some of the items are new, most were drawn from analogous banks in the Neuro-QoL measurement system. Consequently, the 2 TBI-QOL item banks were linked to the Neuro-QoL metric, and scores are comparable with the general population. All CAT and short forms correlated highly (>0.90) with the full item banks and demonstrate comparable construct coverage and measurement error.The TBI-QOL Ability to Participate in Social Roles and Activities and TBI-QOL Satisfaction with Social Roles and Activities item banks are TBI-optimized versions of the Neuro-QoL Ability to Participate in Social Roles and Activities and Satisfaction with Social Roles and Activities item banks and demonstrate excellent measurement properties in individuals with TBI. These measures, particularly in CAT or short form format, are suitable for efficient and precise measurement of social outcomes in clinical and research applications.
Estimating the impact of IT security incidents in digitized production environments

Owing to digitalization, manufacturing companies increasingly integrate IT services – such as control systems – into their production environments. This increases the flexibility of production and allows them to offer new data-based services (e.g., predictive maintenance). However, stepping up production-IT system connections also lead to an increased reliance on the availability of IT services as a means to value creation, both in internal production processes and at the customer interface. More interconnectivity also increases network complexity, and thus favors the rapid spread of cyber-attacks within the information network. The potential for damage is massive, as disruptions to IT services can harm the deliverability of both, connected IT services and production components. Despite existing studies on IT security, little has been written on ways to estimate the impact that availability incidents have on digitized production environments based on the IIoT – for example, smart factories. To help close this research gap, we provide an approach that enables users to simulate cyber-attacks and measure the impact of such attacks on value creation in digitized production environments. We compare the features of our model with our specific design objectives and competing artifacts, present our prototype and the results of a sensitivity analysis for selected model parameters, and illustrate the applicability of our model using the real-life case of a German manufacturing company. Our results indicate that the degree of interconnection in digitized production environments is the most important influencing factor when estimating the impact of an IT availability incident on value creation.
On the Visualization of Logic: A Diagrammatic Language Based on Spatial, Graphical and Symbolic Notations

Visual languages are studied in many different disciplines including Formal Logic. Several diagram methods have been proposed for the visual representation of the logical relations and in particular for First Order Predicate Logic (FOPL) formulas. Among these, logical symbolism, Euler diagrams, semantic networks, conceptual grids, conceptual spaces and so on. It is shown that these representations are formally equivalent and can be inter-translated algorithmically, but provide different and complementary visualizations such that the use of multiple representations may provide greater insight than any alone. We present a new visual language, V-Logic, which support different visual representation schemes: spatial, graphical and symbolic notations. It specifies rules for mapping FOPL formulas in special semantically equivalent diagrams, named V-diagrams. Logical inference based on the interpretation of a V-diagram is essentially a translation of the diagram into a logical formalism. Such a translation is natural and could be used to teach FOPL. We performed a preliminary study with 10 students. Results provided us with overall positive feedback about the effectiveness of our approach as well as further directions to explore.
Rewriting of plain SO tgds into nested tgds

Schema mappings express the relationships between sources in data interoperability scenarios and can be expressed in various formalisms. Source-to-target tuple-generating dependencies (s-t tgds) can be easily used for data transformation or query rewriting tasks. Second-order tgds (SO tgds) are more expressive as they can also represent the composition and inversion of s-t tgds. Yet, the expressive power of SO tgds comes with the problem of undecidability for some reasoning tasks. Nested tgds and plain SO tgds are mapping languages that are between s-t tgds and SO tgds in terms of expressivity, and their properties have been studied in the recent years. Nested tgds are less expressive than plain SO tgds, but the logical equivalence problem for nested tgds is decidable. However, a detailed characterization of plain SO tgds that have an equivalent nested tgd is missing. In this paper, we present an algorithmic solution for translating plain SO tgds into nested tgds. The algorithm computes one or more nested tgds, if a given plain SO tgd is rewritable. Furthermore, we are able to give a detailed characterization of those plain SO tgds for which an equivalent nested tgd exists, based on the structural properties of the source predicates and Skolem functions in the plain SO tgd. In the evaluation, we show that our algorithm covers a larger subset of plain SO tgds than previous approaches and that a rewriting can be computed efficiently although the algorithm has the exponential complexity.
MID Filter: An Orientation-Based Nonlinear Filter For Reducing Multiplicative Noise

In this study, an edge-preserving nonlinear filter is proposed to reduce multiplicative noise by using a filter structure based on mathematical morphology. This method is called the minimum index of dispersion (MID) filter. MID is an improved and extended version of MCV (minimum coefficient of variation) and MLV (mean least variance) filters. Different from these filters, this paper proposes an extra-layer for the value-and-criterion function in which orientation information is employed in addition to the intensity information. Furthermore, the selection function is re-modeled by performing low-pass filtering (mean filtering) to reduce multiplicative noise. MID outputs are benchmarked with the outputs of MCV and MLV filters in terms of structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), mean squared error (MSE), standard deviation, and contrast value metrics. Additionally, F Score, which is a hybrid metric that is the combination of all five of those metrics, is presented in order to evaluate all the filters. Experimental results and extensive benchmarking studies show that the proposed method achieves promising results better than conventional MCV and MLV filters in terms of robustness in both edge preservation and noise removal. Noise filter methods normally cannot give better results in noise removal and edge-preserving at the same time. However, this study proves a great contribution that MID filter produces better results in both noise cleaning and edge preservation.
Two-Layer Near-Lossless HDR Coding with Backward Compatibility to JPEG

We propose an efficient two-layer near-lossless coding method using an extended histogram packing technique with backward compatibility to the legacy JPEG standard. The JPEG XT, which is the international standard to compress HDR images, adopts a two-layer coding method for backward compatibility to the legacy JPEG standard. However, there are two problems with this two-layer coding method. One is that it does not exhibit better near-lossless performance than other methods for HDR image compression with single-layer structure. The other problem is that the determining the appropriate values of the coding parameters may be required for each input image to achieve good compression performance of near-lossless compression with the two-layer coding method of the JPEG XT. To solve these problems, we focus on a histogram-packing technique that takes into account the histogram sparseness of HDR images. We used zero-skip quantization, which is an extension of the histogram-packing technique proposed for lossless coding, for implementing the proposed near-lossless coding method. The experimental results indicate that the proposed method exhibits not only a better near-lossless compression performance than that of the two-layer coding method of the JPEG XT, but also there are no issue regarding the combination of parameter values without losing backward compatibility to the JPEG standard.
Continental-scale stream sediment geochemical mapping in southern China: An insight into surface processes and tectonic framework

Continental-scale stream sediment geochemical data from the 76-element GEochemical Mapping (76-GEM) project covering southern China were analyzed in this contribution. 5244 composite stream sediment samples at the scale of ca. 1 sample per 500 km2 were used. The focus has been directly put on element associations of: (1) Light Rare Earth Elements (LREEs, i.e. La, Ce, Pr, Nd, Sm and Eu); (2) Heavy Rare Earth Elements (HREEs, i.e. Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu and Y); (3) Iron Group Elements (IGEs, Ti, V, Cr, Mn, total Fe2O3 (FeT), Co and Ni); (4) Platinum Group Elements (PGEs, i.e. Ru, Rh, Pd, Os, Ir and Pt) and (5) Mo, Sn, W and Bi. Given that geochemical data are typical of compositional (closed) data, the five associations were represented by the sum values of centered logratio (clr) transformed data of elements in the predefined groups. It presents the first-time the continental-scale patterns of representative element associations in southern China. Maps were compared to independent datasets of lithology, tectonics and topography to discover processes governing the distribution. The five element associations could reflect the processes of: (1) weathering of dominantly felsic intrusive rocks (LREEs and HREEs); (2) weathering of mafic/ultramafic rocks (IGEs and PGEs); (3) weathering of dominantly felsic intrusives and hosted mineralization (Mo-Sn-W-Bi) and (4) anthropogenic and possible geogenic additions (Mo-Sn-W-Bi and PGEs). A general good agreement was found between maps of element associations and maps of surface geology and tectonics. The clr patterns are interpretable geologically and respond reasonably well to the processes such as lithological control and secondary transportation. As indicated by the clr patterns, two prominent tectonic units with controversial boundary in southern China, i.e. the Yangtze and Cathaysia blocks can be geochemically demarcated by the spatial patterns of REEs, IGEs and PGEs at this scale. The distinct patterns across the Jiangshan-Shaoxing and Pingxiang-Chaling faults (or the Shi-Hang Belt) suggested a felsic end-member to the southeast and a mafic end-member to the northwest. The spatial patterns of IGEs, PGEs and Mo-Sn-W-Bi also reflected that lithology is in dominant control of the distribution rather than local mineralization at this scale. Future studies of this dataset regarding single elements and certain element associations of particular interests (e.g., Cd, Ni and REEs) are also needed, which may provide new insights into environmental issues, mineral resources and tectonic framework in southern China.
Development of the Method Local Navigation of Mobile Robot a Based on the Tags with QR Code and Wireless Sensor Network

This paper describes the improved method for locating a mobile platform. Base stations and a navigation system based on QR code tags is used along with an odometric positioning system using the tachometers. The principle of the program for controlling the movement of the robotic platform in the automated warehouse is described. The algorithm of determine the robot coordinates by the image of QR code tags is offered. Images come sequentially while moving during by means of a video camera, rigidly fixed at robot, is offered. The results of experiments for mobile robot, which confirm the effectiveness of the proposed approach, are presented also.
Comparison of Ensemble Simple Feedforward Neural Network and Deep Learning Neural Network on Phishing Detection

Phishing attack is one of wide spread cybercrimes due to the advancement of the Internet. There are many forms of phishing attack and the most common one is through email. The attacker tries to pretend by sending email from an official organization or body to deceive the user in giving in their credential user name and password. The username and password are then used for malicious purpose. Many methods have been used to detect these phishing attacks; however, the attack evolved too quickly to be solved by manual approach. Therefore, automated phishing detection through artificial intelligence approach would be more feasible. In this paper, a comparison study for phishing detection between two neural networks which are the feedforward neural network and the deep learning neural network is carried out. The result is empirically evaluated to determine which method performs better in phishing detection.