Reviewerly/exHarmony huggingface dataset:
An Automated Dermatological Images Segmentation Based on a New Hybrid Intelligent ACO-GA Algorithm and Diseases Identification Using TSVM Classifier

Dermatological diseases are the common and most extensive diseases worldwide. Diagnosis of dermatological diseases as well as skin cancer diseases is extremely difficult during its early to mid-stages by visual examination. Current researches propose efficient approach which can identify maximum 3- 9 types of skin diseases. It is necessary to develop an automated system which can identify more than 9 types of skin diseases. In this paper, we propose a new automated system for various kinds of dermatological diseases detection. In this automated system, a novel hybrid intelligent ACO-GA algorithm including ACO Algorithm, GA and tabu list is used for different kinds of skin lesion segmentation of dermatological images and Transductive Support Vector Machine (TSVM) is used for dermatological diseases identification. Ant Colony Optimization Algorithm and Genetic Algorithm work simultaneously to inquire most optimal cluster centers from the problem set. After image segmentation, pixels are divided into different classes. In testing phase, the testing samples are compared with training samples using TSVM classifier and features extraction which can give the accurate disease detection result. The result of the proposed system successfully finds out 24 different patterns of dermatological diseases with an accuracy rate of 95%.
Artifact Removal Using GAN Network for Limited-Angle CT Reconstruction

Computed tomography (CT) plays an increasingly important role in clinical diagnosis. However, in practical applications of CT, physical limitations on acquisition lead to some blind regions where data cannot be sampled. CT image reconstruction from limited-angle would enable a rapid scanning with a reduced x-ray dose delivered to the patient. As it is known, Generative Adversarial Networks (GAN) can keep the original information and details of the sample very well. In this paper, we propose an end-to-end Generative Adversarial Networks model used for removing artifacts from limited-angle CT reconstruction images. The proposed GAN is based on the conditional GAN with the joint loss function, which .can remove the artifacts while retaining the complete details and sharp edges. The intensity of reconstruction using the proposed method is very close to the full-view projective reconstructed image. Compared to several other classic methods, our GAN model shows better consequent, in terms of artifact reduction, feature preservation, and computational efficiency for limited-angle CT reconstruction.
Robust Emotion Navigation: Few-shot Visual Sentiment Analysis by Auxiliary Noisy Data

Few-shot visual sentiment analysis on social media is an important affective computing task. However, features acquired from few-shot samples are difficult, becasue the visual sentiment is a high-level integration task based on content and style. To address this issue, inspired by human learning processing, only a small number of multi-category emotions are learned from courses or specific occasions. In this paper, we propose a robust emotion navigation framework using auxiliary noisy data to re-focus on few-shot precise emotion knowledge. Firstly, we pre-trained the network on a large noisy data with cross-entropy loss, and the noise matrix can be estimated by predicted probability. Secondly, few-shot precise samples are applied as the prototype center to guide noisy data clustering. Here, the noise matrix is embedded into the loss function for re-weighting, which improves the noise robustness of the network. Finally, we relabel the noisy dataset with above joint training predictions and then re-train the network coarse-to-fine. We conduct experiments on three public sentiment datasets, including Sentibank, Twitter and Emotion6. The results demonstrate the effectiveness of the proposed method.
An Explanation of the Veridical Uniformity Universal

Abstract A semantic universal, which we here dub the Veridical Uniformity Universal, has recently been argued to hold of responsive verbs (those that take both declarative and interrogative complements). This paper offers a preliminary explanation of this universal: verbs satisfying it are easier to learn than those that do not. This claim is supported by a computational experiment using artificial neural networks, mirroring a recent proposal for explaining semantic universals of quantifiers. This preliminary study opens up many avenues for future work on explaining semantic universals more generally, which are discussed in the conclusion.
Detecting mammographically occult cancer in women with dense breasts using deep convolutional neural network and Radon Cumulative Distribution Transform

We have applied the Radon Cumulative Distribution Transform (RCDT) as an image transformation to highlight the subtle difference between left and right mammograms to detect mammographically occult (MO) cancer in women with dense breasts and negative screening mammograms. We developed deep convolutional neural networks (CNNs) as classifiers for estimating the probability of having MO cancer. We acquired screening mammograms of 333 women (97 unilateral MO cancer) with dense breasts and at least two consecutive mammograms and used the immediate prior mammograms, which radiologists interpreted as negative. We used fivefold cross validation to divide our dataset into a training and independent test sets with ratios of 0.8:0.2. We set aside 10% of the training set as a validation set. We applied RCDT on the left and right mammograms of each view. We applied inverse Radon transform to represent the resulting RCDT images in the image domain. We then fine-tuned a VGG16 network pretrained on ImageNet using the resulting images per each view. The CNNs achieved mean areas under the receiver operating characteristic (AUC) curve of 0.73 (standard error, SE = 0.024) and 0.73 (SE = 0.04) for the craniocaudal and mediolateral oblique views, respectively. We combined the scores from two CNNs by training a logistic regression classifier and it achieved a mean AUC of 0.81 (SE = 0.032). In conclusion, we showed that inverse Radon-transformed RCDT images contain information useful for detecting MO cancers and that deep CNNs could learn such information.
Comparison of FPGA and Microcontroller Implementations of an Innovative Method for Error Magnitude Evaluation in Reed–Solomon Codes

Reed–Solomon (RS) codes are one of the most used solutions for error correction logic in data communications. RS decoders are composed of several blocks: among them, many efforts have been made to optimize the error magnitude evaluation module. This paper aims to assess the performance of an innovative algorithm introduced in the literature by Lu et al. under different systems configurations and hardware platforms. Several configurations of the encoded message chosen between those typically used in different applications have been designed to be run on an FPGA (field programmable gate array) device and an MCU (microcontroller unit). The performances have been evaluated in terms of resource usage and output delay for the FPGA and in terms of code execution time for the MCU. As a benchmark in the analysis, the well-established Forney’s method is exploited: it has been implemented in the same configurations and on the same hardware platforms for a proper comparison. The results show that the theoretical finding are fully confirmed only in the MCU implementation, while on FPGA, the choice of one method with respect to the other depends on the optimization feature (i.e., time or area) that has been decided as a preference in the specific application.
Improved Detection of Kidney Stone in Ultrasound Images Using Segmentation Techniques

The medical images are often corrupted by various noises and blurriness. In particular, the noises presented in ultrasound images may lead to an inaccurate diagnosis of smaller kidney stones and affect its treatment. This paper proposes an improved technique for detection of kidney stone from the ultrasound images of kidney. The ultrasound kidney images are preprocessed to remove labels and change from RGB to Gray images. Further, image contrast is enhanced by adjusting the image intensity. To remove the noises, median filtering is used. The filtered image is taken as input for morphological segmentation process; initially applied dilation and then seed region growing algorithm is used to segment the renal calculi from ultrasound image of kidney. The region parameters are extracted from the segmented region. Finally, area of each renal calculi is calculated. The various performance evaluation GLCM features such as entropy, contrast, angular second moment and correlation are used to judge the quality of output images. The confusion matrix is also prepared to analyze the sensitivity, specificity, and accuracy of the final system. The overall accuracy of classification system is around 90%. The proposed technique may help medical professionals in easy detection of kidney stones and benefit the patients.
Bivariate Thiele-Like Rational Interpolation Continued Fractions with Parameters Based on Virtual Points

The interpolation of Thiele-type continued fractions is thought of as the traditional rational interpolation and plays a significant role in numerical analysis and image interpolation. Different to the classical method, a novel type of bivariate Thiele-like rational interpolation continued fractions with parameters is proposed to efficiently address the interpolation problem. Firstly, the multiplicity of the points is adjusted strategically. Secondly, bivariate Thiele-like rational interpolation continued fractions with parameters is developed. We also discuss the interpolant algorithm, theorem, and dual interpolation of the proposed interpolation method. Many interpolation functions can be gained through adjusting the parameter, which is flexible and convenient. We also demonstrate that the novel interpolation function can deal with the interpolation problems that inverse differences do not exist or that there are unattainable points appearing in classical Thiele-type continued fractions interpolation. Through the selection of proper parameters, the value of the interpolation function can be changed at any point in the interpolant region under unaltered interpolant data. Numerical examples are given to show that the developed methods achieve state-of-the-art performance.
Risk Assessment and Recommendation Strategy Based on COBIT 5 for Risk: Case Study SIKN JIKN Helpdesk Service

National Archive Information System and National Archive Information Network (SIKN JIKN) is a national priority program agenda of National Archives of the Republic of Indonesia (ANRI) as an archival institution. To support management and operation of SIKN JIKN, ANRI provides national helpdesk service. A helpdesk application is provided to help their duties, but it is not used optimally that caused the risks such as loss of data and requests or problems submitted by SIKN JIKN members as a user of the system. If the risk is not handled, it will cause losing of potential network node as well as the loss of ANRI. In this research paper, performed risk assessment of SIKN JIKN helpdesk activities was done by referring to COBIT 5 for Risk and COBIT 5 Enabling Process as guidelines, framework, and focusing in domains to support helpdesk activities which is DSS01 domain for operational procedures and APO12 for managing risks. The result of assessment may be used as a recommendation to improve helpdesk service to support SIKN JIKN implementation. It is also could minimize the risk impact to ensure the sustainability of helpdesk service of SIKN JIKN.
Multimodal 3D Object Detection from Simulated Pretraining

The need for simulated data in autonomous driving applications has become increasingly important, both for validation of pretrained models and for training new models. In order for these models to generalize to real-world applications, it is critical that the underlying dataset contains a variety of driving scenarios and that simulated sensor readings closely mimics real-world sensors. We present the Carla Automated Dataset Extraction Tool (CADET), a novel tool for generating training data from the CARLA simulator to be used in autonomous driving research. The tool is able to export high-quality, synchronized LIDAR and camera data with object annotations, and offers configuration to accurately reflect a real-life sensor array. Furthermore, we use this tool to generate a dataset consisting of 10 000 samples and use this dataset in order to train the 3D object detection network AVOD-FPN, with finetuning on the KITTI dataset in order to evaluate the potential for effective pretraining. We also present two novel LIDAR feature map configurations in Bird's Eye View for use with AVOD-FPN that can be easily modified. These configurations are tested on the KITTI and CADET datasets in order to evaluate their performance as well as the usability of the simulated dataset for pretraining. Although insufficient to fully replace the use of real world data, and generally not able to exceed the performance of systems fully trained on real data, our results indicate that simulated data can considerably reduce the amount of training on real data required to achieve satisfactory levels of accuracy.
A Coloured Petri Net Approach to Model and Analyze Safety-Critical Interactive Systems

To gain confidence in safety-critical interactive systems, formal modelling and analysis plays a vital role. The aim of this paper is to use Coloured Petri Nets to model and analyze safety-critical interactive systems. We present a technique to construct a single Coloured Petri Net model of the user interface, interaction and functionality of safety-critical interactive systems and then analyze the achieved Coloured Petri Net model using a state space analysis method. There are several reasons for using Coloured Petri Nets. Coloured Petri Nets provides a graphical representation and hierarchical structuring mechanism, and a state space verification technique, which allows querying the state space to investigate behaviours of a system. There are several tools that supports Coloured Petri Nets including the CPN Tool which helps in building CPN models and allows simulation and analysis using state spaces. The technique to model and analyze safety-critical interactive systems is illustrated using a simplified infusion pump example.
Exploring Semantics of Software Artifacts to Improve Requirements Traceability Recovery: A Hybrid Approach

Continuously maintaining software requirements traceability links is essential for managing and evolving software systems. Due to development pressure, traceability links are usually missing during the early development phase in practice, and thus many information retrieval-based approaches have been proposed to automatically recover the traceability links. However, such approaches typically calculate textual similarities among software artifacts without considering specific features of different software artifacts, leading to less accurate results. In this paper, we propose a hybrid approach to recover requirements traceability links, which combines machine learning and logical reasoning to explore features of use cases and code. On one hand, our approach engineers features of use cases and code by taking into account their semantics, based on which a classifier is trained by using supervised learning algorithms. On the other hand, we investigate and leverage the structural information of code to incrementally discover traceability links by defining a list of reasoning rules. We have carried out a series of experiments to compare our approach with state-of-the-art methods, the results of which show that our approach significantly outperforms others.
Sufficient dimension reduction for compositional data

Recent efforts to characterize the human microbiome and its relation to chronic diseases have led to a surge in statistical development for compositional data. We develop likelihood-based sufficient dimension reduction methods (SDR) to find linear combinations that contain all the information in the compositional data on an outcome variable, i.e., are sufficient for modeling and prediction of the outcome. We consider several models for the inverse regression of the compositional vector or transformations of it, as a function of outcome. They include normal, multinomial, and Poisson graphical models that allow for complex dependencies among observed counts. These methods yield efficient estimators of the reduction and can be applied to continuous or categorical outcomes. We incorporate variable selection into the estimation via penalties and address important invariance issues arising from the compositional nature of the data. We illustrate and compare our methods and some established methods for analyzing microbiome data in simulations and using data from the Human Microbiome Project. Displaying the data in the coordinate system of the SDR linear combinations allows visual inspection and facilitates comparisons across studies.
An Efficient Revocable Attribute-Based Signcryption Scheme With Outsourced Unsigncryption in Cloud Computing

In this paper, we present an efficient revocable signcryption (CP-ABSC) with outsourced unsigncryption scheme based on ciphertext-policy attribute based encryption (CP-ABE) to secure the data sharing in cloud computing that require access control, data encryption, and authentication to ensure message integrity and confidentiality. It can be used to signcrypt a message based on the access rights specified by the message itself. A user can decrypt a ciphertext if and only if it possesses the attributes required by the access structure of the data. Thus CP-ABSC felxible encrypts data based on the access rights of the data specified by the data itself, which differs significantly from the traditional encryption where the user can decrypt is predetermined and must be known by the data source. In addition, the proposed scheme handles attributes revocation with high efficiency and demands low computation overhead on users' device during unsigncryption phase. Our scheme provides collusion attack resistance, message authentication, forgery prevention, and confidentiality. Furthermore, we compare the performance of our scheme with others in terms of the ciphertext, key size, computation cost and functionality.
Cross-modal recipe retrieval via parallel- and cross-attention networks learning

Cross-modal recipe retrieval refers to the problem of retrieving a food image from a list of image candidates given a textual recipe as the query, or the reverse side. However, existing cross-modal recipe retrieval approaches mostly focus on learning the representations of images and recipes independently and sewing them up by projecting them into a common space. Such methods overlook the interplay between images and recipes, resulting in the suboptimal retrieval performance. Toward this end, we study the problem of cross-modal recipe retrieval from the viewpoint of parallel- and cross-attention networks learning. Specifically, we first exploit a parallel-attention network to independently learn the attention weights of components in images and recipes. Thereafter, a cross-attention network is proposed to explicitly learn the interplay between images and recipes, which simultaneously considers word-guided image attention and image-guided word attention. Lastly, the learnt representations of images and recipes stemming from parallel- and cross-attention networks are elaborately connected and optimized using a pairwise ranking loss. By experimenting on two datasets, we demonstrate the effectiveness and rationality of our proposed solution on the scope of both overall performance comparison and micro-level analyses.
DeSIRe: Deep Signer-Invariant Representations for Sign Language Recognition

As a key technology to help bridging the gap between deaf and hearing people, sign language recognition (SLR) has become one of the most active research topics in the human-computer interaction field. Although several SLR methodologies have been proposed, the development of a real-world SLR system is still a very challenging task. One of the main challenges is related to the large intersigner variability that exists in the manual signing process of sign languages. To address this problem, we propose a novel end-to-end deep neural network that explicitly models highly discriminative signer-independent latent representations from the input data. The key idea of our model is to learn a distribution over latent representations, conditionally independent of signer identity. Accordingly, the learned latent representations will preserve as much information as possible about the signs, and discard signer-specific traits that are irrelevant for recognition. By imposing such regularization in the representation space, the result is a truly signer-independent model which is robust to different and new test signers. The experimental results demonstrate the effectiveness of the proposed model in several SLR databases.
Disagreement and (im)politeness in a Spanish family members’ WhatsApp group

The present paper explores disagreement and impoliteness in a WhatsApp interaction within a Spanish family that took place during the 2018 International Women’s Day. The conversation is linguistically examined using categories of disagreement strategies proposed by previous authors (Pomerantz 1984, Brown and Levinson 1987, Rees-Miller 2000, Locher 2004, Kreutel 2007, Malamed 2010, Shum and Lee 2013). Furthermore, multimodal analysis (Dresner and Herring 2010, 2013, Jewitt 2013, Bourlai and Herring 2014; Herring 2015) is used to consider not only participants’ linguistic strategies for expressing disagreement, but also the function of multimedia elements and emojis (Dresner and Herring 2010, 2013, Yus 2014, 2017, Sampietro 2016a, 2016b, Aull 2019). The analysis is followed by an interview to better understand the participants’ communicative intentions towards disagreements in relation to (im)politeness. A total of 427 instances of disagreement are identified, with the most common strategies being giving opposite opinions and emotional or personal reasons. This is to be expected since the group is divided from the very beginning into detractors and supporters of feminism, and they are also defending their opposite viewpoints by giving examples from their own life experience. Based on the participants’ opinions, the most significant result is the fact that, although disagreement may lead to face-threat, and thus impoliteness in other contexts (Langlotz and Locher 2012, Sifianou 2012, Shum and Lee 2013), in this WhatsApp interaction, the Spanish family members did not consider it to be impolite, and it is even evaluated in positive terms by some of the participants (Angouri and Locher 2012).