Reviewerly/exHarmony huggingface dataset:
Discourse Level Factors for Sentence Deletion in Text Simplification

This paper presents a data-driven study focusing on analyzing and predicting sentence deletion — a prevalent but understudied phenomenon in document simplification — on a large English text simplification corpus. We inspect various document and discourse factors associated with sentence deletion, using a new manually annotated sentence alignment corpus we collected. We reveal that professional editors utilize different strategies to meet readability standards of elementary and middle schools. To predict whether a sentence will be deleted during simplification to a certain level, we harness automatically aligned data to train a classification model. Evaluated on our manually annotated data, our best models reached F1 scores of 65.2 and 59.7 for this task at the levels of elementary and middle school, respectively. We find that discourse level factors contribute to the challenging task of predicting sentence deletion for simplification.
CHARS

One of the most significative resource of a territory is represented by the Cultural Heritage (CH). CH may be seen as one of the scenarios where new technologies, for example adaptive systems able to promote services, can provide more interesting contributions and relevant information.
Machine learning design of a trapped-ion quantum spin simulator

Trapped ions have emerged as one of the highest quality platforms for the quantum simulation of interacting spin models of interest to various fields of physics. In such simulators, two effective spins can be made to interact with arbitrary strengths by coupling to the collective vibrational or phonon states of ions, controlled by precisely tuned laser beams. However, the task of determining laser control parameters required for a given spin–spin interaction graph is a type of inverse problem, which can be highly mathematically complex. In this paper, we adapt a modern machine learning technique developed for similar inverse problems to the task of finding the laser control parameters for a number of interaction graphs. We demonstrate that typical graphs, forming regular lattices of interest to physicists, can easily be produced for up to 50 ions using a single GPU workstation. The scaling of the machine learning method suggests that this can be expanded to hundreds of ions with moderate additional computational effort.
Automatic Segmentation of Human Placenta Images With U-Net

Placenta is closely related to the health of the fetus. Abnormal placental function will affect the normal development of the fetus, and in severe cases, even endanger the life of the fetus. Therefore, accurate and quantitative evaluation of placenta has important clinical significance. It is a common method to segment human placenta with semantic segmentation. However, manual segmentation relies too much on the professional knowledge and clinical experience of the staff, and it will also consume a lot of time. Therefore, based on u-net, we propose an automatic segmentation method of human placenta, which reduces manual intervention and greatly speeds up the segmentation, making large-scale segmentation possible. The human placenta data set we used was labeled by experts, which was obtained from prenatal examinations of 11 pregnant women, about 1,110 images. It was a comprehensive and clinically significant data set. By training the network with such data set, the robustness of the model will be better. After testing on the data set, the segmentation effect is basically consistent with the manual segmentation effect.
Strengthen user authentication on mobile devices by using user’s touch dynamics pattern

Abstract Mobile devices, particularly the touch screen mobile devices, are increasingly used to store and access private and sensitive data or services, and this has led to an increased demand for more secure and usable security services, one of which is user authentication. Currently, mobile device authentication services mainly use a knowledge-based method, e.g. a PIN-based authentication method, and, in some cases, a fingerprint-based authentication method is also supported. The knowledge-based method is vulnerable to impersonation attacks, while the fingerprint-based method can be unreliable sometimes. To overcome these limitations and to make the authentication service more secure and reliable for touch screen mobile device users, we have investigated the use of touch dynamics biometrics as a mobile device authentication solution by designing, implementing and evaluating a touch dynamics authentication method. This paper describes the design, implementation, and evaluation of this method, the acquisition of raw touch dynamics data, the use of the raw data to obtain touch dynamics features, and the training of the features to build an authentication model for user identity verification. The evaluation results show that by integrating the touch dynamics authentication method into the PIN-based authentication method, the protection levels against impersonation attacks is greatly enhanced. For example, if a PIN is compromised, the success rate of an impersonation attempt is drastically reduced from 100% (if only a 4-digit PIN is used) to 9.9% (if both the PIN and the touch dynamics are used).
Random Balance ensembles for multiclass imbalance learning

Random Balance strategy (RandBal) has been recently proposed for constructing classifier ensembles for imbalanced, two-class data sets. In RandBal, each base classifier is trained with a sample of the data with a random class prevalence, independent of the a priori distribution. Hence, for each sample, one of the classes will be undersampled while the other will be oversampled. RandBal can be applied on its own or can be combined with any other ensemble method. One particularly successful variant is RandBalBoost which integrates Random Balance and boosting. Encouraged by the success of RandBal, this work proposes two approaches which extend RandBal to multiclass imbalance problems. Multiclass imbalance implies that at least two classes have substantially different proportion of instances. In the first approach proposed here, termed Multiple Random Balance (MultiRandBal), we deal with all classes simultaneously. The training data for each base classifier are sampled with random class proportions. The second approach we propose decomposes the multiclass problem into two-class problems using one-vs-one or one-vs-all, and builds an ensemble of RandBal ensembles. We call the two versions of the second approach OVO-RandBal and OVA-RandBal, respectively. These two approaches were chosen because they are the most straightforward extensions of RandBal for multiple classes. Our main objective is to evaluate both approaches for multiclass imbalanced problems. To this end, an experiment was carried out with 52 multiclass data sets. The results suggest that both MultiRandBal, and OVO/OVA-RandBal are viable extensions of the original two-class RandBal. Collectively, they consistently outperform acclaimed state-of-the art methods for multiclass imbalanced problems.
Source Model Selection for Deep Learning in the Time Series Domain

Transfer Learning aims to transfer knowledge from a source task to a target task.We focus on a situation when there is a large number of available source models, and we are interested in choosing a single source model that can maximize the predictive performance in the target domain.Existing methods compute some form of ''similarity'' between the source task data and the target task data.They then select the most similar source task and use the model trained on it for transfer learning.Previous methods do not account for the fact that it is the model parameters that are transferred rather than the data.Therefore, the ''similarity'' of the source data does not directly influence transfer learning performance.In addition, we would like the possibility of confidently selecting a source model even when the data it was trained on is not available, for example, due to privacy or copyright constraints.We propose to use the truncated source models as encoders for the target data.We then select a source model based on how well it clusters the target data in the latent encoding space, which we calculate using the Mean Silhouette Coefficient.We prove that if the encodings achieve a Mean Silhouette Coefficient of 1, optimal classification can be achieved using just the final layer of the target network.We evaluate our method using the University of California, Riverside (UCR) time series archive and show that the proposed method achieves comparable results to previous work, without using the source data.
Panorama

Deep convolutional neural networks (CNNs) achieve state-of-the-art accuracy for many computer vision tasks. But using them for video monitoring applications incurs high computational cost and inference latency. Thus, recent works have studied how to improve system efficiency. But they largely focus on small "closed world" prediction vocabularies even though many applications in surveillance security, traffic analytics, etc. have an ever-growing set of target entities. We call this the "unbounded vocabulary" issue, and it is a key bottleneck for emerging video monitoring applications. We present the first data system for tacking this issue for video querying, Panorama. Our design philosophy is to build a unified and domain-agnostic system that lets application users generalize to unbounded vocabularies in an out-of-the-box manner without tedious manual re-training. To this end, we synthesize and innovate upon an array of techniques from the ML, vision, databases, and multimedia systems literature to devise a new system architecture. We also present techniques to ensure Panorama has high inference efficiency. Experiments with multiple real-world datasets show that Panorama can achieve between 2x to 20x higher efficiency than baseline approaches on in-vocabulary queries, while still yielding comparable accuracy and also generalizing well to unbounded vocabularies.
A Bibliometric Overview of the Field of Type-2 Fuzzy Sets and Systems [Discussion Forum]

Fuzzy Sets and Systems is an area of computational intelligence, pioneered by Lotfi Zadeh over 50 years ago in a seminal paper in Information and Control. Fuzzy Sets (FSs) deal with uncertainty in our knowledge of a particular situation. Research and applications in FSs have grown steadily over 50 years. More recently, we have seen a growth in Type-2 Fuzzy Set (T2 FS) related papers, where T2 FSs are utilized to handle uncertainty in realworld problems. In this paper, we have used bibliometric methods to obtain a broad overview of the area of T2 FSs. This method analyzes information on the bibliographic details of published journal papers, which includes title, authors, author address, journals and citations, extracted from the Science and Social Science Citation Indices in the Web of Science (WoS) database for the last 20 years (1997-2017). We have compared the growth of publications in the field of FSs, and its subset T2 FSs, identified highly cited papers in T2 FSs, highly cited authors, key institutions, and main countries with researchers involved in T2 FS related research.
Improved small blob detection in 3D images using jointly constrained deep learning and Hessian analysis

Abstract Imaging biomarkers are being rapidly developed for early diagnosis and staging of disease. The development of these biomarkers requires advances in both image acquisition and analysis. Detecting and segmenting objects from images are often the first steps in quantitative measurement of these biomarkers. The challenges of detecting objects in images, particularly small objects known as blobs, include low image resolution, image noise and overlap between the blobs. The Difference of Gaussian (DoG) detector has been used to overcome these challenges in blob detection. However, the DoG detector is susceptible to over-detection and must be refined for robust, reproducible detection in a wide range of medical images. In this research, we propose a joint constraint blob detector from U-Net, a deep learning model, and Hessian analysis, to overcome these problems and identify true blobs from noisy medical images. We evaluate this approach, UH-DoG, using a public 2D fluorescent dataset for cell nucleus detection and a 3D kidney magnetic resonance imaging dataset for glomerulus detection. We then compare this approach to methods in the literature. While comparable to the other four comparing methods on recall, the UH-DoG outperforms them on both precision and F-score.
Advanced Cauchy Mutation for Differential Evolution in Numerical Optimization

Among many evolutionary algorithms, differential evolution (DE) has received much attention over the last two decades.DE is a simple yet powerful evolutionary algorithm that has been used successfully to optimize various real-world problems.Since it was introduced, many researchers have developed new methods for DE, and one of them makes use of a mutation based on the Cauchy distribution to increase the convergence speed of DE.The method monitors the results of each individual in the selection operator and performs the Cauchy mutation on consecutively failed individuals, which generates mutant vectors by perturbing the best individual with the Cauchy distribution.Therefore, the method can locate the consecutively failed individuals to new positions close to the best individual.Although this approach is interesting, it fails to take into account establishing a balance between exploration and exploitation.In this paper, we propose a sigmoid based parameter control that alters the failure threshold for performing the Cauchy mutation in a time-varying schedule, which can establish a good ratio between exploration and exploitation.Experiments and comparisons have been done with six conventional and six advanced DE variants on a set of 30 benchmark problems, which indicate that the DE variants assisted by the proposed algorithm are highly competitive, especially for multimodal functions.
Machine Learning for the Semantic Web: Lessons learnt and next research directions

Machine Learning methods have been introduced in the Semantic Web for solving problems such as link and type prediction, ontology enrichment and completion (both at terminological and assertional level). Whilst initially mainly focussing on symbol-based solutions, recently numeric-based approaches have received major attention, motivated by the need to scale on the very large Web of Data. In this paper, the most representative proposals, belonging to the aforementioned categories are surveyed, jointly with the analysis of their main peculiarities and drawbacks. Afterwards the main envisioned research directions for further developing Machine Learning solutions for the Semantic Web are presented.
In Search of a Fast and Efficient Serverless DAG Engine

Python-written data analytics applications can be modeled as and compiled into a directed acyclic graph (DAG) based workflow, where the nodes are fine-grained tasks and the edges are task dependencies.Such analytics workflow jobs are increasingly characterized by short, fine-grained tasks with large fan-outs. These characteristics make them well-suited for a new cloud computing model called serverless computing or Function-as-a-Service (FaaS), which has become prevalent in recent years. The auto-scaling property of serverless computing platforms accommodates short tasks and bursty workloads, while the pay-per-use billing model of serverless computing providers keeps the cost of short tasks low. In this paper, we thoroughly investigate the problem space of DAG scheduling in serverless computing. We identify and evaluate a set of techniques to make DAG schedulers serverless-aware. These techniques have been implemented in WUKONG , a serverless, DAG scheduler attuned to AWS Lambda. WUKONG provides decentralized scheduling through a combination of static and dynamic scheduling. We present the results of an empirical study in which WUKONG is applied to a range of microbenchmark and real-world DAG applications. Results demonstrate the efficacy of WUKONG in minimizing the performance overhead introduced by AWS Lambda — WUKONG achieves competitive performance compared to a serverful DAG scheduler, while improving the performance of real-world DAG jobs by as much as 4.1x at larger scale.
Fractional mesh-free linear diffusion method for image enhancement and segmentation for automatic tumor classification

Computer aided diagnostic (CAD) models have shown outstanding performance in identifying many kind of diseases. Tumor identification is one of the most useful application of CAD model. Benign and malignant are two categories of tumor cells. Both categories share some textural features due to which tumor classification becomes complex and difficult task. In the present manuscript, a novel CAD model is being presented for classifying tumor cells automatically. The proposed model has been divided into four modules (image enhancement, segmentation, feature extraction and classification). All the modules are equally important in tumor classification. The manuscript focuses on effect of first two modules namely image enhancement and segmentation on tumor classification. In the present work, a novel linear fractional mesh-free partial differential equation (FPDE) based image enhancement method has been proposed to improve quality of the images. The proposed enhancement model is able to preserve fine details in smooth areas also non-linearly increases high frequency information. A novel fractional mesh-free segmentation method has been proposed to extract tumor region. It has been found that the method is able segment tumor region more accurately. Thirteen textural features have been used for training and testing. Support vector machine (SVM) classifier has been used to classify extracted tumor region. Quantitative analysis of proposed image enhancement, segmentation and CAD model have been done with other popular models. Higher performance has been observed using proposed models.
Medical Image Segmentation by Partitioning Spatially Constrained Fuzzy Approximation Spaces

Image segmentation is an important prerequisite step for any automatic clinical analysis technique. It assists in visualization of human tissues, as accurate delineation of medical images requires involvement of expert practitioners, which is also time consuming. In this background, the rough-fuzzy clustering algorithm provides an effective approach for image segmentation. It handles uncertainties arising due to overlapping classes and incompleteness in class definition by partitioning the fuzzy approximation spaces. However, the existing rough-fuzzy clustering algorithms do not consider the spatial distribution of the image. They depend only on the distribution of pixels to determine their class labels. In this regard, this article introduces a new algorithm, termed as spatially constrained rough-fuzzy c-means (sRFCM) for medical image segmentation. The proposed sRFCM algorithm combines wisely the merits of rough-fuzzy clustering and local neighborhood information. In the proposed algorithm, the labels of local neighbors influence in the determination of the label of center pixel. The effect of local neighbors acts as a regularizer. Moreover, the proposed sRFCM algorithm partitions each cluster in possibilistic lower approximation or core region and probabilistic boundary region. The cluster centroid depends on the core and boundary regions, weight parameter, and neighborhood regularizer. A novel segmentation validity index, termed as neighborhood Silhouette, is proposed to find out the optimum values of regularizer and weight parameter, controlling the performance of the sRFCM. The efficacy of the proposed sRFCM algorithm, as well as several existing segmentation algorithms, is demonstrated on four brain MR volume databases and one HEp-2 cell image data.
Emotion Detection Framework for Twitter Data Using Supervised Classifiers

“The task of emotion detection usually involves the analysis of text. Humans show universal consistency in identifying emotions however shows an excellent deal of variation between individuals in their abilities.” We have detected the emotion for Twitter messages as they provide rich ensemble of human emotions. We have used machine learning algorithms namely Naive Bayes (NB) and k-nearest neighbor algorithm (KNN) to detect the emotion of Twitter message and then classify the Twitter messages into four emotional categories. We also made a comparative study of two supervised machine learning algorithms; the eager learning classifier (NB) performed well when compared with lazy learning classifier (KNN).
A semantic-based methodology for digital forensics analysis

Nowadays, more than ever, digital forensics activities are involved in any criminal, civil or military investigation and represent a fundamental tool to support cyber-security. Investigators use a variety of techniques and proprietary software forensics applications to examine the copy of digital devices, searching hidden, deleted, encrypted, or damaged files or folders. Any evidence found is carefully analysed and documented in a "finding report" in preparation for legal proceedings that involve discovery, depositions, or actual litigation. The aim is to discover and analyse patterns of fraudulent activities. In this work, a new methodology is proposed to support investigators during the analysis process, correlating evidence found through different forensics tools. The methodology was implemented through a system able to add semantic assertion to data generated by forensics tools during extraction processes. These assertions enable more effective access to relevant information and enhanced retrieval and reasoning capabilities.
Secret sharing with secure secret reconstruction

Threshold secret sharing is a fundamental building block in information security to provide secrecy and robustness services for various cryptographic protocols. According to the definition of (t, n) threshold secret sharing, the secret is divided into n shares, such that any t or more than t of these shares allow the secret to be reconstructed; but less than t shares reveal no information of the secret. In other words, this definition only considers protection of the secret from colluded insiders but not outsiders. In this paper, we propose an extended secret sharing scheme, called secret sharing with secure secret reconstruction, in which the secret can be protected in the reconstruction phase from both attacks of insiders and outsiders. In traditional secret sharing schemes, when more than t shares are presented in the secret reconstruction, outsiders only need to intercept t shares to recover the secret. But in our proposed basic scheme, outsiders need to intercept all the released shares to recover the secret. Obviously, requiring more shares in the reconstruction contributes to security enhancement for this process. The limitation of this basic scheme is that it cannot prevent outsiders from learning the secret if they intercept all the released shares. To address this issue, we further extend the basic scheme so that the reconstructed secret is only accessible to shareholders, but not to outsiders. To the best of our knowledge, our extended scheme is the first secret sharing scheme that satisfies this property with information theoretical security.