Reviewerly/exHarmony huggingface dataset:
Early Detection of Dengue Hemorrhagic Fever (DHF) using Feed Forward Neural Network with Gravitational Search Algorithm Optimization

Abstract Health is an important part of life, it is also an indicator for progress of country development. But, there are still some health problems found in Indonesia, one of it is dengue infection. Data from the Department of Health states that in 2016 there were 77.96 per 100,000 pupolation (201,885 cases) with mortality rate is 0.79% (1,585 death cases). Clinical symptoms of dengue infection is so resemble with some other fevers, then the right diagnosis is very needed. Feed Forward Neural Network (FFNN) can be used to do an early detection to Dengue Hemorrhagic Fever (DHF) sufferer with training process to the existing data. In this research, we experiment the Gravitational Search Algorithm (GSA) as the Optimation Method to find the optimal weights of the FFNN. We use the medical record data of DHF sufferers who are getting treatment at Tugurejo District Hospital, start from January 2016 to Desember 2016. The accuracy of this research is 0.73188.
Path Planning with Autonomous Obstacle Avoidance Using Reinforcement Learning for Six-axis Arms

In this paper, a strategy of path planning for autonomous obstacle avoidance using reinforcement learning for six-axis arms is proposed. This strategy gives priority to planning the obstacle avoidance path for the terminal of the mechanical arm, and then uses the calculated terminal path to plan the poses of the mechanical arm. For the points on the terminal path that the mechanical arm cannot avoid obstacles within the limit of the safe distance, this strategy will record these points as new obstacles and plan a new obstacle avoidance path for the terminal of mechanical arm. The above process is accelerated by the assisted learning strategies and looped until the correct path being calculated. The method proposed in this paper has been applied to a six-axis mechanical arm, and the simulation results show that this method can effectively plan an optimal path and poses for the mechanical arm.
Role of Intelligent Techniques in Natural Language Processing: An Empirical Study

Nowadays intelligent computing with natural language processing (NLP) is growing rapidly world wide. This paper describes the use of intelligent computing strategies in the field of NLP. Various intelligent techniques such as Artificial Neural Network, Support Vector Machine, Conditional Random Field, Hidden Markov Assumption, and Bayes’ Rule and with some Stochastic Process are used in sub fields of NLP such as Machine Translation, Sentiment Analysis, Information Retrieval, Information Extraction, Question Answering and Named Entity Recognition are used in to develop a reliable and efficient system with high accuracy. In this application area, both training and testing datasets with authorized corpus are required to test the proposed model. Most of the exact experimental values are recorded to handle the real-time situation for further study.
List Decodability of Linear Subcodes of Gabidulin Codes

In this letter, we confirm that, with overwhelming probability, random Fq-linear subcodes of Gabidulin codes can be list decoded with a decoding radius beyond half of the minimum distance and constant upper bound of the list size. Furthermore, our results reveal the existence of an Fq-linear subcode of a square Gabidulin code that is list decodable with radius approaching the Gilbert-Varshamov bound, which is shown to be the optimal list decoding radius. On the other hand, when we are considering a Gabidulin code with the ratio of the number of its rows and columns being €, we can also find an Fq-linear subcode that is list decodable up to the Singleton bound. Moreover, we investigate the list decodability of Fqm-linear subcodes of Gabidulin codes.
Using Multiple Perspective Projections to Guide Visual Attention in Glyph-based Data Visualisations in VR

An important goal of glyph based data visualization is to detect anomalies in data sets or to manage complex search tasks by guiding the users attention to regions of interest. Previous studies suggest that the user’s attention is tied to regions where different perspective projections meet. We describe a method for perspective distortion of glyphs within a virtual scene, while the rest of the scene is projected according to the common model of the computer graphics camera. The result is a multi-perspective image that directs the user’s attention. The perspective distortion is only applied if the user focuses on an irrelevant area of the data visualization. As soon as the gaze moves towards the relevant glyph, the perspective distortion is gradually removed. Therefore we evaluate eye tracking data in our prototypical implementation.
On the Bounds of Coefficients of Daubechies Orthonormal Wavelets

[1]. In the present work, we provide bounds for Daubechies orthonormal wavelet coefficients for function spaces $$\mathcal{A}_k^p:=\{f: \|(i \omega)^k\hat{f}(\omega)\|_p< \infty\}$$ , $$k\in{\mathbb N}\cup\{0\}$$ , $$p\in(1,\infty)$$ .
A Low Inertia Guided Auto-Encoder for Anomaly Detection in Networks

In the case of One-class classification, the low variance directions tend to be more informative to build a model on target class. This paper introduces a low Inertia Autoencoder for anomaly detection. The proposed model emphasizes the low Inertia when training the network with gradient descent algorithm. The low Inertia Auto-encoder model is able to improve upon the classical Auto-encoder by incorporating a regularization term into the loss function, which is the Inertia of low dimensional data embedded in the Auto-encoder hidden layer. Experimental results, on KDDCup99 network traffic connections and Aegean WiFi Intrusion Dataset, have demonstrated clearly that our method has provided significantly better detection performance than the classical Auto-encoder. In fact, low inertia Auto-encoder allows better separation between reconstruction error distributions of normal data and anomalies.
Convergence Investigation of the Newton Method Used for Hyperbolic Equations Systems Solving

The coordinates determination of the radio emission source by the multi position passive radio monitoring systems is based on the time difference of arrival method. The main measured information to solve this problem is the delay time of signal at the receiving stations. The coordinates determination of the observed object is based on the solution of hyperbolic equations at the central receiving point. The system of nonlinear hyperbolic equations is constructed for the receiving stations, where one of them is the central receiving station, which expresses the structure of distances and the geometry of the mutual arrangement of stations and radio emission source. The most common method for solving such systems is the Newton method. Subject to certain requirements, the Newton method is also the most effective. However, its practical application is complicated by the difficulties connected with the poor conditionality of partial derivative matrices. It reduces the coordinate accuracy finding, the convergence rate, and may eventually create fundamental difficulties in solving problems. The article discusses the conditions of such situations and proposes some approaches to avoid these problems or reduce their negative impact. Consideration must also be given to the significant constraints on calculation time required to complete a computational procedure with a given accuracy, because these requirements are dictated by the real-time principle of the passive radio monitoring systems.
Improving Backbones Performance by Complex Architectures

Recently, Convolution Neural Networks (CNNs) have achieved great success in computer vision. To further boost the performance, the depth of the backbone network is continuously increased, which improves the capacity of feature learning but also brings the heavy burden in computation. To address the issues, this paper introduces a complex convolution method to systematically improve the performance of the backbone network. Our contributions are three-fold: 1) the complex architecture backbone network can improve the classification performance without increasing or even reducing the number of parameters; 2) for the detection task, the complex architecture backbone network can improve the ability of feature map extraction, at the same time our joint bounding box generation method using both real and imaginary parts of complex features can obviously improve the object detection ability. 3) the proposed method has a strong generalization ability for both detection and classification tasks. We have achieved significant performance improvements in both classification and detection tasks, which validate the effectiveness of our methods.
Study on 3D Pore Label of Porous Materials Based on CT Images

Abstract Microscopic pore structure is one of the important factors affecting macro porous materials physical properties, pore structure research for characterization of physical properties of porous materials is of great significance. In order to study the three-dimensional parameters of porous materials in each pore structure, such as pore size, volume, specific surface area, fractal and connectivity, it is necessary to label each pore structure in the three-dimensional structure. Two-dimensional CT image sequences in porous material as the research object, three-dimensional target marking algorithm is proposed. The sequence images using median filter algorithm to remove noise, through the automatic threshold segmentation algorithm, get the binary image. The boundary information of each pore is computed by Using the algorithm of two-dimensional multi-target label in each layer image. The 3D pore structure is labelled by the calculation of the projection relationship between the targets in the tomography image. The labelled result of pore structure is verified by single and whole reconstruction with marching cube algorithm. Results show that the porous three-dimensional pore structure was marked accurately, which lays a foundation for the calculation of 3D pore structure parameters.
The Analysis of Unbalanced Assignment Problems Using The Kotwal-Dhope Method To Develop A Massive Open Online Course

This study discusses the optimal solution of the unbalanced assignment of minimization case in order to develop massive open online course in the Islamic Higher Education by using a new method that is the Kotwal-Dhope Method. The method was formed with the help of the Hungarian method and the Matrix One's Assignment method was resolves the unbalanced assignment problem with a data size 8×4 which aims to minimize the total costs incurred by an Islamic Higher Education. The optimal solution with the Kotwal-Dhope Method begins by adding a dummy of one, carrying out the division operation of each column with the smallest element, after each row and column has a value of one, perform assignments in condition one so that each lecturer has their own job in develop massive open online course. Based on the results of this study it was found that, lecturer B was assigned to do stage 1 (Defining educational content), lecturer D was assigned to do stage 2 (Production and technical integration), lecturer E was assigned to do stage 3 (Communication), and lecturer H was assigned to do stage 4 (Course Animation and Overview). Then the assignments were: B → 1, D → 2, E → 3, H → 4. From the results of the assignment, the optimal solution for the minimum cost is 18 + 23 + 12 + 20 = 73 unit costs.
Nucleus Segmentation from Microscopic Bone Marrow Image

Acute myeloid leukemia (AML) is a type of cancer that originates in the bone marrow and moves into the blood quickly. The disease progresses rapidly if not treated. Therefore identification of the disease at an early stage is very important. In the proposed work, different image segmentation techniques like K-medoids and watershed, region growing and active contour have been applied for the segmentation of the nucleus from the blast cells and the results are analyzed.
Operationalizing Declarative and Procedural Knowledge: A Benchmark on Logic Programming Petri Nets (LPPNs)

Modelling, specifying and reasoning about complex systems requires to process in an integrated fashion declarative and procedural aspects of the target domain. The paper reports on an experiment conducted with a propositional version of Logic Programming Petri Nets (LPPNs), a notation extending Petri Nets with logic programming constructs. Two semantics are presented: a denotational semantics that fully maps the notation to ASP via Event Calculus; and a hybrid operational semantics that process separately the causal mechanisms via Petri nets, and the constraints associated to objects and to events via Answer Set Programming (ASP). These two alternative specifications enable an empirical evaluation in terms of computational efficiency. Experimental results show that the hybrid semantics is more efficient w.r.t. sequences, whereas the two semantics follows the same behaviour w.r.t. branchings (although the denotational one performs better in absolute terms).
Neural Network Training with Safe Regularization in the Null Space of Batch Activations

We propose to formulate the training of neural networks with side optimization goals, such as obtaining structured weight matrices, as lexicographic optimization problem. The lexicographic order can be maintained during training by optimizing the side-optimization goal exclusively in the null space of batch activations. We call the resulting training method Safe Regularization, because the side optimization goal can be safely integrated into the training with limited influence on the main optimization goal. Moreover, this results in a higher robustness regarding the choice of regularization hyperparameters. We validate our training method with multiple real-world regression data sets with the side-optimization goal of obtaining sparse weight matrices.
Integration of Program Slicing with Cognitive Complexity for Defect Prediction

Researchers have identified several quality metrics to predict defects that rely on different types of information. However, these approaches lack metrics to estimate the effort of program understandability of system artifacts. Code that is understandable is often considered more maintainable. This paper briefly describes a dissertation which presents novel metrics to compute the cognitive complexity based on program slicing. These metrics help identify code that is more likely to have defects due to being challenging to comprehension. A thorough empirical investigation into how cognitive complexity correlates with and predicts defects is performed. Finally, this paper discusses potential direction for future research based upon the work conducted, as well as a reflection on the PhD study, providing advice for current students.
Achieving Probabilistic Atomicity With Well-Bounded Staleness and Low Read Latency in Distributed Datastores

Although it has been commercially successful to deploy weakly consistent but highly-responsive distributed datastores, the tension between developing complex applications and obtaining only weak consistency guarantees becomes more and more severe. The almost strong consistency tradeoff aims at achieving both strong consistency and low latency in the common case. In distributed storage systems, we investigate the generic notion of almost strong consistency in terms of designing fast read algorithms while guaranteeing Probabilistic Atomicity with well-Bounded staleness (PAB). This problem has been explored in the case where only one client can write the data. However, the more general case where multiple clients can write the data has not been studied. In this article, we study the fast read algorithm for PAB in the multi-writer case. We show the bound of data staleness and the probability of atomicity violation by decomposing inconsistent reads into the read inversion and the write inversion patterns. We implement the fast read algorithm and evaluate the consistency-latency tradeoffs based on the instrumentation of Cassandra and the YCSB benchmark framework. The theoretical analysis and the experimental evaluations show that our fast read algorithm guarantees PAB, even when faced with dynamic changes in the computing environment.
Research and Practice on the Influence of Internet Technology on Students’ Japanese Learning Mode

Abstract With the popularization and wide application of Internet technology, the influence of Internet is no longer limited to communication, information acquisition and sharing ways, but also affects individual thinking mode, information acquisition mode, communication mode and so as to even the learning mode. As a language teaching, Japanese students’ language output abilities and application abilities are the most important standards to measure the effectiveness of Japanese learning, and they are also the most concerned abilities and development goals of Japanese students and so as Japanese teaching. In the Internet social environment, due to the rapid changes of language learning environment and social environment, their learning mode of Japanese students should also be changed to adapt actively. In this paper, under the background of Internet society, the influence of Internet technology on students’ Japanese learning mode is studied and practiced. The purpose is to explore a new way of Japanese Teaching under the background of Internet society.
FLIE: Form Labeling for Information Extraction

Information extraction (IE) from forms remains an unsolved problem, with some exceptions, like bills. Forms are complex and the templates are often unstable, due to the injection of advertising, extra conditions, or document merging. Our scenario deals with insurance forms used by brokers in Switzerland. Here, each combination of insurer, insurance type and language results in a new document layout, leading to a few hundred document types. To help brokers extract data from policies, we developed a new labeling method, called FLIE (form labeling for information extraction). FLIE first assigns a document to a cluster, grouping by language, insurer, and insurance type. It then labels the layout. To produce training data, the user annotates a sample document by hand, adding attribute names, i.e. provides a mapping. FLIE applies machine learning to propagate the mapping and extracts information. Our results are based on 24 Swiss policies in German: UVG (mandatory accident insurance), KTG (sick pay insurance), and UVGZ (optional accident insurance). Our solution has an accuracy of around 84–89%. It is currently being extended to other policy types and languages.
The user as a data source: The advance of surveillance capitalism

The title of this chapter refers to the new role library users can have, especially in academic library contexts, when the library, through bibliometric procedures, communicates data on the researcher’s output, productivity, and quality to, for example, university research management units. To what extent are such data also relevant and demanded by the researchers themselves for networking and fund-raising purposes? The chapter also discusses to what extent commercial digital business models and the related concept of ‘surveillance capitalism’—coined by the Harvard Business School professor emerita, Shoshana Zuboff—makes sense in a present—or a future—digital library context.
IASR: An Item-Level Attentive Social Recommendation Model for Personalized Ranking

Most recommender systems provide recommendations by listing the most relevant items to a user. Such recommendation task can be viewed as a personalized ranking problem. Previous works have found it advantageous to improve recommendation performance by incorporating social information. However, most of them have two primary defects. First, in order to model interaction between users and items, existing works still resort to biased inner production, which has proved less expressive than neural architectures. Second, they do not delicately allocate weights of social neighbor influence based on the user feature or the item feature in a recommendation task. To address the issues, we propose an Item-level Attentive Social Recommendation model, IASR for short, in this paper. It employs an item-level attention mechanism to adaptively allocate social influences among trustees in the social network and gives more accurate predictions with a neural collaborative filtering framework. Extensive experiments on three real-world datasets are conducted to show our proposed IASR method out-performs the state-of-the-art baselines. Additionally, our method shows effectiveness in the cold-start scenario.
Unravelling SAT: Discussion on the Suitability and Implementation of Graph Convolutional Networks for Solving SAT

NP-complete class of problems have a wide range of applications in the real world. With the purpose of finding new ways to solve these problems, this study looks to use SAT as a representative of the NP-complete class. It looks to assimilate SAT with the intention of solving it by way of modeling its instances using graph neural networks, particularly a graph convolutional network. Modeling SAT formulae as graph representations is motivated by the fact that properties of the SAT equations can be aptly depicted by visualizing clauses, literals, and the corresponding relationships between them as a graph.
FamilyStories

Family members who are separated across time zones can easily miss out on feeling connected. We designed and studied the usage of an asynchronous storytelling system, called FamilyStories, to explore the use of audio-based sharing. FamilyStories allows family members to share activities and experiences over distance in different time zones using three different devices that contain different record, share, and playback audio stories over distance.
Bedazzled by Technology

This paper is about the socio-technical findings of a project that was carried out for an organisation in the educational sector. The main aim of the project was to explore the possibilities of developing a mobile Augmented Reality authoring tool that educators would use to create AR experiences in attempt to improve their teaching methods. A novel ‘solution’ was designed and prototyped, which initially seemed to have great potential, but as the project progressed it gradually became evident that although the solution was possible, it was not necessarily a good solution. The project ended up being a prime example of the nowadays common phenomenon of designing efficacious but ineffective solutions for ill-defined problems. This paper analyses the project in retrospective from a soft systems thinking point of view, reflecting on the problems and limitations of attempting to solve problems with ill-defined boundaries and requirements using hard systems thinking.
Generating Uncertainty Distributions for Seismic Signal Onset Times

ABSTRACT Signal arrival-time estimation plays a critical role in a variety of downstream seismic analyses, including location estimation and source characterization. Any arrival-time errors propagate through subsequent data-processing results. In this article, we detail a general framework for refining estimated seismic signal arrival times along with full estimation of their associated uncertainty. Using the standard short-term average/long-term average threshold algorithm to identify a search window, we demonstrate how to refine the pick estimate through two different approaches. In both cases, new waveform realizations are generated through bootstrap algorithms to produce full a posteriori estimates of uncertainty of onset arrival time of the seismic signal. The onset arrival uncertainty estimates provide additional data-derived information from the signal and have the potential to influence seismic analysis along several fronts.