Reviewerly/exHarmony huggingface dataset:
MCMC Guided CNN Training and Segmentation for Pancreas Extraction

Efficient organ segmentation is the precondition of various quantitative analysis. Segmenting the pancreas from abdominal CT images is a challenging task because of its high anatomical variability in shape, size and location. What’s more, the pancreas only occupies a small portion in abdomen, and the organ border is very fuzzy. All these factors make the segmentation methods of other organs less suitable for pancreas. In this work, we propose a Markov Chain Monte Carlo (MCMC) guided convolutional neural network (CNN) approach, in order to handle such difficulties in morphological and photometric variabilities. Specifically, the proposed method mainly consists of three steps: First, registration is carried out to mitigate the body weight and location variability. Then, an MCMC scheme is designed to guide the adaptive selection of 3D patches, which are fed to the CNN for training. At the same time, the pancreas distribution is also learned for subsequent segmentation. Eventually, the same MCMC process guides the segmentation process with patch-wise predictions fused using a Bayesian voting scheme. This method is evaluated on the NIH pancreatic dataset including 82 abdominal contrast-enhanced CT volumes. We have achieved a competitive result of 78.13% Dice Similarity Coefficient value and 82.65% Recall value in testing data.
Acoustics of the banjo: measurements and sound synthesis

Measurements of vibrational response of an American 5-string banjo and of the sounds of played notes on the instrument are presented, and contrasted with corresponding results for a steel-string guitar. A synthesis model, fine-tuned using information from the measurements, has been used to investigate what acoustical features are necessary to produce recognisable banjo-like sound, and to explore the perceptual salience of a wide range of design modifications. Recognisable banjo sound seems to depend on the pattern of decay rates of “string modes”, the loudness magnitude and profile, and a transient contribution to each played note from the “body modes”. A formant-like feature, peaking around 500–800 Hz on the banjo tested, is found to play a key role. At higher frequencies the dynamic behaviour of the bridge produces additional formant-like features, reminiscent of the “bridge hill” of the violin, and these also produce clear perceptual effects.
Unicycle Mobile Robot Formation Control in Hardware in the Loop Environments

This work presents the development of a formation control algorithm for three unicycle-type robots, to solve the problem in the implementation of controllers oriented to collaborative functions and also subject to an excessive economic cost. This leads to the approximation of the simulation technique in environments Hardware in the loop (HIL), which allow clearly visualize with a real idea and a high percentage of approximation of the behavior of mobile robots unicycle type integrating different types of advanced controllers that will allow the execution of tasks of mobile robots unicycle type the same that are determined by the trajectories that control the position and thus raises the strategy of nonlinear control with a centralized and decentralized formation in the work area, acting as a command and control management system that will in turn be able to receive input signals, process the information and deliver control signals, which will later be displayed and analyzed to help verify the control theory.
An Empirical Measurement Study of Free Live Streaming Services

Live streaming is one of the most popular Internet activities. Nowadays, there has been an increase in free live streaming (FLS) services that provide unauthorized broadcasting of live events, attracting millions of viewers. These opportunistic providers often have modest network infrastructures, and monetize their services through advertising and data analytics, which raises concerns about the performance, quality of experience, and user privacy when using these services. In this paper, we measure and analyze the behaviour of 20 FLS sports sites on Android smartphones, focusing on packet-level, video player, and privacy aspects. In addition, we compare FLS services with two legitimate online sports networks. Our measurement results show that FLS sites suffer from scalability issues during highly-popular events, deliver lower QoE than legitimate providers, and often use obscure and/or suspicious tracking services. Caution is thus advised when using FLS services.
Polyhedral separation via difference of convex (DC) programming

Abstract We consider polyhedral separation of sets as a possible tool in supervised classification. In particular, we focus on the optimization model introduced by Astorino and Gaudioso (J Optim Theory Appl 112(2):265–293, 2002) and adopt its reformulation in difference of convex (DC) form. We tackle the problem by adapting the algorithm for DC programming known as DCA. We present the results of the implementation of DCA on a number of benchmark classification datasets.
Building a Flexible Mobile Robotics Teaching Toolkit by Extending MATLAB/Simulink with ROS and Gazebo

To improve the learning efficiency in robotic simulation environments with physical characteristics, this paper presents a toolkit for mobile robotics teaching via extending the MATLAB/Simulink using ROS and Gazebo simulator. The content discusses the proposed software architecture firstly, the access to virtual sensors and actuators has been integrated and the communication details are also hidden to allow the students to focus on programming and debugging their key algorithms. An example showing how to carry out the mobile robotic teaching using the proposed toolkit is also given. After properly creating the robot models under Gazebo, the code developed for the simulation process can be directly implemented in the real robot without modifications. It is proved by the teaching practice that the proposed toolkit can significantly speed up development of robotic solutions and boost the student active learning experience.
Active Learning for Knowledge Graph Schema Expansion

Both entity typing and relation extraction from text corpora are widely used to identify the semantic types of an entity and a relation in a knowledge graph (KG). Most existing approaches rely on a pre-defined set of entity types and relation types in a KG. They thus cannot map entity mentions (relation mentions) to unseen entity types (relation types). To fundamentally overcome the limitations, we should add new semantic types of entities and relations to a KG schema. However, schema expansion traditionally requires manual conceptualization through a user’s observation on the text corpus while assuming the existence of suitable target KG schemas. In this work, we propose an <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> ctive learning framework for <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</b> nowledge graph <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> chema <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</b> xpansion ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AKSE</b> ), which can generate a new semantic type for KG schemas, without depending on a set of target schemas and human users’ observation. Specifically, a granularity based active learning algorithm determines whether a KG schema requires new semantic types or not. We also introduce a KG schema attention-based neural method which assigns semantic types to the entities and relationships extracted. To the best of our knowledge, our work is the first study to expand a KG schema with active learning.
Mixed High-Order Non-Local Attention Network for Single Image Super-Resolution

Attention has been diffusely used in many tasks since it can guide network concentrating on the most important regions of an input pattern. Nevertheless, many advanced works focus on first-order attention design, e.g. channels and spatial attention, but ignore higher-order attention mechanisms. In this work, we propose the Mixed High-Order Attention (MHA) module to model the complex and high-order information in the attention mechanism, which captures the subtle texture and outputs the discriminative attention map. Besides, the region of the convolution is local, which can’t capture global context and long-range dependencies. Therefore, we propose a non-local block to obtain global attention features. We also propose the Mixed High-Order Non-local Attention Network (MHNAN) to improve the richness of attention. Extensive experiments are conducted to demonstrate the superiority of our MHNAN for super-resolution over several state-of-the-art models.
Photorealistic style transfer for video

This work proposes an efficient example-based photorealistic style transfer algorithm for video. Given a source unprocessed video and a reference image color graded by an artist, we describe an automatic algorithm to transfer the visual style of the reference onto the source footage. Our approach builds upon the color transfer methods based on the statistical properties of images. These methods are fast and of low-complexity, therefore suitable for real-time implementations. Our contribution is to adapt those methods to be used for unprocessed video from cinema cameras, and optionally to incorporate regions of interest previously selected by the user, affecting the final color transfer. The resulting videos are free from artifacts and provide an excellent approximation to the intended look, bringing savings in pre-production, shooting and post-production time. Results are free of spatio-temporal artifacts and the method outperforms diverse state-of-the-art methods according to observer preference experiments.
A Data Analytics Scheme for Security-aware Demand Response Management in Smart Grid System

The exponential growth in energy demand led to increased demand-response gaps and decreased service quality of modern ICT-based smart grid (SG) in industry 4.0. It necessitates an efficient Demand Response Management (DRM) system in order to tackle aforementioned challenges. However, several DRM solutions exist, but these solutions are not adequate in terms of peak loads reduction, consumer comfort, and data security issues. Motivated from above facts, in this paper, we propose a scheme ϵ-Sutra, which is a security-aware DRM scheme for the SG system based on blockchain technology and integrated with data analytics. Here, a DRM algorithm is proposed to reduce peak energy consumption along with an incentive mechanism to consumers. ϵ-Sutra is incorporated with Ethereum-based smart contract (ESC) to handle security issues and InterPlanetary File System (IPFS) for data storage cost issues. The efficacy of the ϵ-Sutra scheme is evaluated in contrast with existing solutions based on various evaluation metrics.
Dynamics of Online Toxicity in the Asia-Pacific Region

The adverse effect of online toxicity on social interactions is enormous. The proliferation of smart devices and mobile applications has further exacerbated these nefarious acts on various social media platforms. Toxic behavior can have a negative impact on a community or dissuade others from joining a community by perceiving it as a hostile or unfriendly environment. Despite the rich research on understanding toxicity on a global scale, there is currently a dearth of systematic research regarding toxicity within the user-generated content on online platforms. To further the understanding of this phenomenon, this paper proposes a precise definition and a case study in an applicable, real world, and large-scale scenario. Using blog posts related to Asia-Pacific political conflicts, we study the toxicity associated with the collected online discourse and pair this analysis with visualizations of the related topic models in order to better understand what type of discourse results in online toxicity. Our analysis shows the potential of the toxicity metric as a tool to understand online events by isolating toxic actors and identifying patterns.
Mobile learning and its effectiveness in mathematics

Abstract Mobile Learning is a development approach to distance learning. Extensive access to all mobile devices and unlimited learning opportunities for space and time, making mobile learning a critical role in the learning process. The purpose of this study is to determine how the characteristics of effective mobile learning based on the results of previous studies and to get an overview of the effectiveness of mobile learning in mathematics in senior high school. The method used in this study is an evaluative survey method with library research and field surveys. This study involved 75 eleventh grade students in one of the senior high schools in West Bandung District. The instruments used are questionnaires and interviews. Statistical analysis results show that the effectiveness of mobile learning in math learning is at a middle level. Based on the results of the survey can be concluded that the implementation of mobile learning on learning is an effective way of delivering mathematical content and able to impart self-reliance for students in enhancing the ability to understand the subject of his studies.
Some basic results on finite linear recurring sequence subgroups

An f-subgroup is a linear recurring sequence subgroup, a multiplicative subgroup of a field whose elements can be generated (without repetition) by a linear recurrence relation, where the relation has characteristic polynomial f. It is called non-standard if it can be generated in a non-cyclic way (that is, not in the order αi,αi+1,αi+2… for a zero α of f), and standard otherwise. We will show that a finite f-subgroup is necessarily generated by a subset of the zeros of f. We use this result to improve on a recent theorem of Brison and Nogueira. A old question by Brison and Nogueira asks if there exist automatically non-standard f-subgroups, f-subgroups that cannot be generated by a zero of f. We answer that question affirmatively by constructing infinitely many examples.
Data Storage Security Issues in Cloud Computing

Cloud computing gives on request administration on their information or data to its customers. Information storage(storing) is among one of the essential administration given by the cloud computing. Cloud specialist organization has the information of information proprietor on their server and client can get to their information from these servers. As information, proprietors and servers are various personalities, the worldview of information storage raises numerous security challenges. A free component is required to ensure that information is effectively facilitated to the cloud storage server. Right now, we will examine the various methods that are utilized for secure information storage on cloud.
A robust variance information fusion technique for real-time autonomous navigation systems

Abstract Real-time navigation systems rely on multi-object environment information for productive routing and assisted movements of vehicles. The multi-object environment includes infrastructure, neighbours, smart building, and traffic management information for providing assisted driving for the vehicles. The dynamic vehicle environment and sensor failures impact the navigation assistance for the driving users. A robust variance-based information fusion (VIF) technique is proposed in this paper for addressing this issue. The proposed technique makes use of swappable sensor information for fusion for providing reliable navigation assistance. This means the fusion process is performed based on the active sensor information in a decisive manner. For effective fusion with available information, this technique makes use of classification learning. Through this learning process, the error causing information due to sensor failures is identified and mitigated from the fusion. Multi-sensor information classification is performed for performing errorless assistance and decisions. This process is linear throughout the information sensing time intervals for reducing the errors in navigation assistance. The proposed technique's performance is verified using experiments, and the metrics processing time, error, accuracy, and swapping instances are verified.
Chi-Square Detection for PVD Steganography

Although the Pixel-Value Differencing (PVD) steganography can avoid being detected by the RS steganalysis, the histogram of the pixel-value differences poses an abnormal distribution. Based on this hiding characteristic, this paper proposes a PVD steganalysis based on chi-Square statistics. The degrees of freedom were adopted to be tested for obtaining various detection accuracies (ACs). Experimental results demonstrate the detection accuracies are all above 80%. When the degrees of freedom are set as 10 while the accuracy is the best (AC = 83%). It means that the proposed Chi-Square based method is an efficient detection for PVD steganography.
MATCH: Metadata-Aware Text Classification in A Large Hierarchy

Multi-label text classification refers to the problem of assigning each given document its most relevant labels from a label set. Commonly, the metadata of the given documents and the hierarchy of the labels are available in real-world applications. However, most existing studies focus on only modeling the text information, with a few attempts to utilize either metadata or hierarchy signals, but not both of them. In this paper, we bridge the gap by formalizing the problem of metadata-aware text classification in a large label hierarchy (e.g., with tens of thousands of labels). To address this problem, we present the MATCH1 solution—an end-to-end framework that leverages both metadata and hierarchy information. To incorporate metadata, we pre-train the embeddings of text and metadata in the same space and also leverage the fully-connected attentions to capture the interrelations between them. To leverage the label hierarchy, we propose different ways to regularize the parameters and output probability of each child label by its parents. Extensive experiments on two massive text datasets with large-scale label hierarchies demonstrate the effectiveness of MATCH over the state-of-the-art deep learning baselines.
Evaluation Measures Based on Preference Graphs

The offline evaluation of search requires us to define a standard against which we measure the quality of results returned by a ranker. Frequently this standard is defined in absolute terms through relevance grades, but it can also be defined in relative terms through preferences. These preferences might be created through explicit preference judgments, derived from relevance grades, or inferred from clicks and other signals. Preferences from multiple sources might even be combined. In contrast to absolute grades, preferences avoid complex definitions of relevance, indicating only that a ranker should favor one result over another. Despite the simplicity and flexibility of preferences, widespread adoption has been limited by the lack of established evaluation measures. Recent work in this direction has taken two approaches: 1) measures based on weighted counts of agreements and disagreements between a set of preferences and an actual ranking generated by a ranker; and 2) measures that translate preferences into gain values for use with traditional measures, such as nDCG. Both approaches require methods for specifying weights or gains that have little or no theoretical foundation, and the values of these measures have no clear and meaningful interpretation. To address these problems, we propose an evaluation measure that computes the similarity between a directed multigraph of preferences and an actual ranking generated by a ranker. The measure computes an ordering for the vertices of the preference graph that maximizes its similarity to the actual ranking under a rank similarity measure. This maximum similarity becomes the value of the measure. Preference graphs are often acyclic, or nearly so, and to compute the measure we extend an approximate greedy algorithm that is known to produce good results for nearly acyclic graphs. For the rank similarity measure we employ Rank Biased Overlap (RBO) which was explicitly created to match the requirements of search and related applications. We validate the new measure over several collections of preferences explored in recent work.
Utility metric for unsupervised feature selection

Feature selection techniques are very useful approaches for dimensionality reduction in data analysis. They provide interpretable results by reducing the dimensions of the data to a subset of the original set of features. When the data lack annotations, unsupervised feature selectors are required for their analysis. Several algorithms for this aim exist in the literature, but despite their large applicability, they can be very inaccessible or cumbersome to use, mainly due to the need for tuning non-intuitive parameters and the high computational demands. In this work, a publicly available ready-to-use unsupervised feature selector is proposed, with comparable results to the state-of-the-art at a much lower computational cost. The suggested approach belongs to the methods known as spectral feature selectors. These methods generally consist of two stages: manifold learning and subset selection. In the first stage, the underlying structures in the high-dimensional data are extracted, while in the second stage a subset of the features is selected to replicate these structures. This paper suggests two contributions to this field, related to each of the stages involved. In the manifold learning stage, the effect of non-linearities in the data is explored, making use of a radial basis function (RBF) kernel, for which an alternative solution for the estimation of the kernel parameter is presented for cases with high-dimensional data. Additionally, the use of a backwards greedy approach based on the least-squares utility metric for the subset selection stage is proposed. The combination of these new ingredients results in the utility metric for unsupervised feature selection U2FS algorithm. The proposed U2FS algorithm succeeds in selecting the correct features in a simulation environment. In addition, the performance of the method on benchmark datasets is comparable to the state-of-the-art, while requiring less computational time. Moreover, unlike the state-of-the-art, U2FS does not require any tuning of parameters.