Reviewerly/exHarmony huggingface dataset:
An Alternating Least Square Based Algorithm for Predicting Patient Survivability

Breast cancer is the most common cancer to females worldwide. Using machine learning technology to predict breast-cancer patients’ survivability has drawn a lot of research interest. However, it still faces many issues, such as missing-value imputation. As such, the main objective of this paper is to develop a novel imputation algorithm, inspired by the recommendation system. More precisely, features with missing values are regarded as items to be evaluated for recommendation. Consequently, a matrix factorisation algorithm (Alternating Least Square, ALS) is employed to replace missing values; accordingly, four different prediction strategies based on the ALS result are further discussed. The proposed ALS-based imputation algorithm is evaluated by using a large patient dataset from the Surveillance, Epidemiology, and End Results (SEER) program. Experimental results demonstrates a significant improvement on the survivability prediction, compared to existing methods.
Observation and Interaction

This paper connects three concepts in computer science, zero-knowledge proofs, causal reasoning, and bisimulation, to show that interaction is more powerful than observation. Observation is the use of input data plus, possibly, tractable computation, in such a way that the observer has no effect on the source of the data. Interaction is observation plus action that affects the source of the data. Observation lets the data “speak for itself” and is objective, whereas interaction is first-person and subjective. Zero-knowledge proofs are a strategy for building confidence in some fact while acquiring no additional information other than that the fact is likely to be true. They fall short of absolute certainty and they require interaction. This paper shows that absolutely certainty for such scenarios can be modeled by a bisimulation relation. Causal reasoning has also been shown to require subjective involvement. It is not possible by observation alone, and like zero-knowledge proofs, requires first-person involvement and interaction. This paper shows that bisimulation relations can reveal flaws in causal reasoning.
Effect of extreme rain and land covering change in Jatihandap on 20 March 2018 flash flood

Flash flood occurred on March 20, 2018, in Jatihandap, Bandung City, West Java due to the overflow of Cipamokolan River. This has caused considerable losses, especially for people living in the area. The flood occurred due to high rainfall intensity and inadequate land covering, especially in the upstream area (North Bandung Region) where the area is filled with housing, villas, and open land. Consequently, the water catchment area decreases. The objective of this study is to identify flood discharge that occurs due to high rainfall intensity and land covering. Flood discharge is calculated using a rational method. Based on BMKG data, rain intensity on March 20, 2018, is 45 mm/hour. The results of this study show that with this intensity, the Cipamokolan River flow brings water with sediment and debris waste. The discharge of the river is 207 m3/s. It equals to 2.2 times river basin discharge and causing water to overflow into the land around the river. The debris flow also damage the levees in Jatihandap then it caused flash floods.
An intelligent assistant for mediation analysis in visual analytics

Mediation analysis is commonly performed using regressions or Bayesian network analysis in statistics, psychology, and health science; however, it is not effectively supported in existing visualization tools. The lack of assistance poses great risks when people use visualizations to explore causal relationships and make data-driven decisions, as spurious correlations or seemingly conflicting visual patterns might occur. In this paper, we focused on the causal reasoning task over three variables and investigated how an interface could help users reason more efficiently. We developed an interface that facilitates two processes involved in causal reasoning: 1) detecting inconsistent trends, which guides users' attention to important visual evidence, and 2) interpreting visualizations, by providing assisting visual cues and allowing users to compare key visualizations side by side. Our preliminary study showed that the features are potentially beneficial. We discuss design implications and how the features could be generalized for more complex causal analysis.
Computational Intelligence for Detecting Pedestrian Movement Patterns

This article presents a system that uses computational intelligence to detect pedestrian movement patterns by applying image processing and pattern detection. The system is capable of processing in real time multiple image/video sources and it is based on a pipes and filters architecture that makes it easy to evaluate different computational intelligence techniques. The system counts with two main stages: the first stage extracts the relevant features of images and the second stage is responsible for the detection of patterns. The experimental analysis performed over more than 1450 problem instances covers the two main stages of the system. The system was evaluated using PETS09-S2L1 videos and the results were compared with part of the MOTChallenge benchmark results. Results suggest that the proposed system is competitive, yet simpler, than other similar software methods.
Classification of structured validation data using stateless and stateful features

To reliably identify problems impacting the service quality and system dependability of mobile communication networks, the monitored data needs to be validated. This paper proposes and evaluates analysis methods, features and learning methods for the automatic validation of such data, with a special focus on failure data of mobile communication data. This data can be analyzed for discriminating failures caused by problems in the infrastructure (valid failures) from those caused by other circumstances like device imperfections (invalid failures), with the purpose of filtering the invalid failures, which effectively increases both dependability and value of the underlying data. To represent the complex structural and temporal properties of the mobile communication data, two complementary feature representations are proposed and compared, followed by a discussion of classification methods which are suitable for these feature spaces and for an interpretation of their results to support manual auditing. Their classification performances on these feature spaces are evaluated and compared to competitive approaches. In the evaluation a classification performances of up to 97% AUC–ROC is achieved. This renders our approach a good alternative to using manual matching rules, which require costly expert-knowledge and are much more time-consuming to define and maintain — while also highlighting the relevance of combining feature spaces of different problem perspectives. Additionally it is shown that using non-proprietary data analysis can enable feature representations nearly as expressive as those created by using proprietary analysis methods, which allows a broader application of the proposed methods, due to the lower processing requirements.
Big Data Challenges from a Human Factors Perspective

Big data, in the form of ever increasing amounts of healthcare data, promises to revolutionize and transform healthcare. Large amounts of personal health data, fitness data, genomic data and epidemiological data are being generated at an unprecedented rate and this trend will only continue. While advances are being made in the automated collection and analysis of such data, using machine learning, data mining and artificial intelligence techniques, the issue of the human factor in all these developments still remains central to the question of whether such large and complex collections of data are useful and effective in actually improving healthcare decision making and processes. The impact of big data as well as personalized medicine, will ultimately depends on human factors related to effective access, use and application of large data repositories. This chapter will explore some of the issues related to the human factors of big data. Human cognitive limitations and their implications for design of user interfaces and systems that involve big data are described, along with illustrative examples from a number of areas in health informatics. Finally challenges and future directions for the human factors of big data will be discussed.
Haze Transfer Between Images Based on Dark Channel Prior

This paper presents a method for transferring haze information between the source and target images. First, we applied the dark channel prior method to the input source and target images. The dark channel prior method was proposed for removing haze from an image. By applying the method thereof, an input image can be decomposed to a scene radiance image, global atmospheric color (haze color) vector, and transmission map. Subsequently, for transferring the haze information, we applied the color transfer method to the decomposed information of source and target images. Finally, we reconstructed a haze-transferred image from the decomposed images. Our results indicate that the proposed haze transfer method can reproduce fine haze-transferred appearance, compared with the input source and target images.
Is biometrics the answer to crypto-currency crime?

Crypto-currency – commonly defined as digital assets that use cryptography to secure transactions without the need for a central banking authority – is rising in popularity and being widely adopted across the globe. According to research by the University of Cambridge 1 , some 3 million people are estimated to be actively trading in crypto-currencies today, and many are already using crypto to pay for items such as hotels, games and even their rent.
Deinstitutionalization of the State and Violence in Sub-Saharan Africa: A Contribution to the Critique of the Neoinstitutionalist Analysis of Development

In recent years, a euphoric shift has taken place, typified by the narrative of “Africa rising,” which attempts to promote the idea of an economically emerging continent. In the Afro-optimist discourse, the idea is implicit that the neoliberal narrative has triumphed. However, critical voices argue that the discourse ignores the political environment under which African growth has been produced. This article critically reflects on the explanations of the neoinstitutionalist approach to development, the historical process of state formation and colonization in Africa. Following the proposal of postcolonial theory, sovereignty, tax, and violence are analyzed. Finally, the contradictory policies of the neoliberal experiment are demonstrated, which are leading to the deinstitutionalization of the State and to violence. JEL Classification: F54, F63, E02, N17
Association rules and regression linear model of the groundwater population by the evaluation of uranium

The uranium available more on groundwater samples of certain types on the total alkalinity were relatively the same. But, the content of the uranium was higher in the samples. The multiple linear regression for pH as a dependent variable showed that the pH negatively correlated to the uranium, but the uranium was not significant for the linear regression model. The data of groundwater population from the samples of 127 with 12 variables of measurement of the Energy Department of the United States of America resulted in those association rules and linear regression models. The data has five factors of Producing horizon namely Ogallala Formation (TPO), Dockum Formation (TRD), Quartermaster Group (POQ), Whitehorse and Cloud Chief Group (PGWC), El Reno Group and Blaine Formation (PGEB). The step-wise linear regression for each of the five producing horizon codes was fitted to the data. Then, the regression models for each variable of producing horizon were obtained if pH was the dependent variable. If the Uranium was a dependent variable, then the regression models obtained were four only, with the model for PGEB was not able to be made. When pH as a dependent variable, it was depended upon Boron, Total alkalinity, and Bicarbonate.
Quantitative Modeling of the Behaviour of an Autonomic Router

Autonomic routers are the main component on which autonomic networking is founded. Our goal is to provide a first approach performance modeling method that can be usable by networking professionals that are not part of the Performance Evaluation community.
Agnostic attribute segmentation of dynamic scenes with limited spatio-temporal resolution

The presence of limited spatio-temporal resolution in dynamic scenes renders segmentation of foreground objects problematic, as it brings negative effects on candidate object missing or motion boundary overfilling caused by large displacements of corresponding points in consecutive frames. To alleviate these problems, our general framework introduces a novel agnostic attribute video object segmentation method that is suitable for segmenting foreground objects in dynamic scenes at low spatio-temporal resolution. We employ a fully connected network (FCN) to facilitate estimation of class-agnostic object proposals based on the semantic classification attributes. Instead of directly deriving a hard classification into objects, we propose a scheme by fusing different top ranked soft scores in the semantic space that allows the model to directly estimate probabilistic foreground hypotheses. A unified conditional random field model is proposed to incorporate the proposal information derived from the soft prediction scores and consequently build up an unary energy functional with additional location and appearance potentials. The pairwise energy functional imposes both spatial and temporal consistency constraints simultaneously on appearance, location and unary potentials. Our experiments on spatio-temporal subsampled video segmentation benchmarks demonstrate the effectiveness of the proposed method for robust segmentation of class-agnostic objects in dynamic scenes despite of abrupt motion and large displacements caused by limited spatio-temporal resolution.
Semantic-Based Indexing Approaches for Medical Document Clustering Using Cognitive Search

Biomedical literature is the primary repository of biomedical knowledge in which PubMed is the most absolute database for collecting, organizing and analyzing textual knowledge. The high dimensionality of the natural language text makes the text data quite noisy and sparse in the vector space. Hence, the data preprocessing and feature selection are important processes for the text processing issues. Ontologies select the meaningful terms semantically associated with the concepts from a document to reduce the dimensionality of the original text. In this chapter, semantic-based indexing approaches are proposed with cognitive search which makes use of domain ontology to extract relevant information from big and diverse data sets for users.
Security Risk Assessment of Multi-cloud System Adoption: Review and Open Research Issues

The cloud providers are many to specialize in all the business functions, which could be a convincing argument in favor of the Cloud. In fact, it is the problem that faced many users. In the light of the diversity of their own needs and the increasing specialization of cloud providers, many businesses may choose several service providers and use multiple clouds for achieving their business objectives. A multi-cloud strategy allows all cloud actors to adapt each cloud offer to each need. However, this system configuration does not go without a new measure of complexity regarding the security risk assessment. In this current research paper, we survey the top and major security risk assessment issues and concerns related to multi-cloud system.
Getting virtually personal

Have you watched the movie Her? Have you ever wondered or wished to have your own AI companion just like Samantha, who could understand you better than you know about yourself, and could tell you what you really are, whom your best partner may be, and which career path would be best for you? In this talk, I will present a computational framework for building responsible and empathetic Artificial Intelligent (AI) agents who can deeply understand their users as unique individuals and responsibly guide their behavior in both virtual and real world.
A Neuro-Fuzzy Approach to Assess the Soft Skills Profile of a PhD

In this paper a framework aimed at representing the soft skills profile of a job seeker by means of a 2-tuple fuzzy linguistic approach and a Neuro fuzzy controller is presented. The framework can be used in many contexts, in this work it is employed for designing a recommender system of candidates for recruiting agencies. The recommender system’s Neuro fuzzy controller simulates the decision of a Human Resource (HR) manager in evaluating the soft skills profile of a candidate and proposes only the best profiles w.r.t. a set of preferences. The framework has been developed in the context of the Find Your Doctor (FYD) start up and applied to the PhD recruiting task, but it is easily applicable to any recruiting activity.
Ontology-Based Information Retrieval: Development of a Semantic-Based Tool for the Media Industry

This paper describes the creation of an RDF ontology designed to support information retrieval needs of journalists and media professionals. The purpose of the ontology is to complete the automated extensions of query terms by using the relationships between the concepts and terms registered in the ontology. By using this ontology, end-users can identify additional concepts that are related to the selected topic, and incorporate new terms to the query that will be later launched against a full-text indexer based on SOLR. The ontology focuses on politics, and has been successfully tested with the collaboration of a large Spanish media company. The ontology contributes to a better recall of the search results.
Using an Ontological Model for Transfer Knowledge Between Universities

The article deals with construction of the ontological model to provide knowledge transfer between universities. It discusses a need to consider requirements of the professional standard for a specialist and labor market demand in process of educational programs design. Ontological and modular-competence approaches were used to simulate the mapping of competences. An ontological model of the professional competence standard was developed. The information model distributed knowledge database in the university consists of the ontology educational programs, the ontology of professional competencies demanded in the regional labor market and the ontology of scientific knowledge in the university. Classes taxonomy of the developed ontological model of professional competences is presented. The method of analyzing hierarchies was used to assess the conformity of learning outcomes with the functional requirements of labor market.
Distribution Properties of Binary Sequences Derived from Primitive Sequences Modulo Square-free Odd Integers

Recently, a class of nonlinear sequences, modular reductions of primitive sequences over integer residue rings, was proposed and has attracted much attention. In particular, modulo 2 reductions of primitive sequences over $$\mathbf {Z}/(2^{31}-1)$$ were used in the ZUC algorithm. In this paper, we study the distribution properties of modulo 2 reductions of primitive sequences over $$\mathbf {Z}/(M)$$ , where M is a square-free odd integer. Let $$\underline{a}$$ be a primitive sequence of order n over $$\mathbf {Z}/(M)$$ with period T and $$\left[ \underline{a}\right] _{\text {mod}\, 2}$$ the modulo 2 reduction of $$\underline{a}$$ . With the estimate of exponential sums over $$\mathbf {Z}/(M)$$ , the proportion $$f_{s}$$ of occurrences of s within a segment of $$\left[ \underline{a}\right] _{\text {mod}\, 2}$$ of length $$\mu T$$ is estimated, where $$s\in \left\{ 0,1\right\} $$ and $$0<\mu \le 1$$ . Based on this estimate, it is further shown that for given M and $$\mu $$ , $$f_{s}$$ tends to $$\frac{M+1-2s}{2M}$$ as $$n\rightarrow \infty $$ . This result implies that there exists a small imbalance between 0 and 1 in $$\left[ \underline{a}\right] _{\text {mod}\, 2}$$ , which should be taken into full consideration in the design of stream ciphers based on $$\left[ \underline{a}\right] _{\text {mod}\, 2}$$ .
The low-rank decomposition of correlation-enhanced superpixels for video segmentation

Low-rank decomposition (LRD) is an effective scheme to explore the affinity among superpixels in the image and video segmentation. However, the superpixel feature collected based on colour, shape, and texture may be rough, incompatible, and even conflicting if multiple features extracted in various manners are vectored and stacked straight together. It poses poor correlation, inconsistence on intra-category superpixels, and similarities on inter-category superpixels. This paper proposes a correlation-enhanced superpixel for video segmentation in the framework of LRD. Our algorithm mainly consists of two steps, feature analysis to establish the initial affinity among superpixels, followed by construction of a correlation-enhanced superpixel. This work is very helpful to perform LRD effectively and find the affinity accurately and quickly. Experiments conducted on datasets validate the proposed method. Comparisons with the state-of-the-art algorithms show higher speed and more precise in video segmentation.
Vector-Based Morphological Operations on Polygons Using Straight Skeletons for Digital Pathology

In this work we present an efficient implementation of vector-based mathematical morphology operators applied to simple polygons by performing wavefront propagation and computing polygon straight skeletons. In Digital Pathology (DP), the slide scanner generates important volume of images from tissues called Whole Slide Image (WSI). The main goal of the DP is to detect the biological stained structures in order to quantify the tissue pathology, such as lesions or cancerous regions. We propose the use of Adapted Straight Skeletons on polygons as an efficient technique in time and memory, to improve image segmentation and image analysis. Thanks to the use of polygons instead of bitmaps to store segmentation results, the performance of straight skeletons depends only on the polygon control points. These straight skeletons can be applied in order to perform fast morphological operations such as dilation, erosion, closing, opening, skeletonizing. When combined, these operations offer different interesting outcomes: (i) multiple disjoint-segmented shapes can be linked together to create a joint skeleton, (ii) the topological structure of segmentation can be extracted as a straight skeleton. Then, it can be used as features for structural and spatial tissue analysis.
Numerical range over finite fields: Restriction to subspaces

Let q be a prime power. For u=(u1,…,un),v=(v1,…,vn)∈Fq2n let 〈u,v〉:=∑i=1nuiqvi be the Hermitian form of Fq2n. For any n×n matrix M over Fq2 set Numk(M):={〈u,Mu〉|u∈Fq2n,〈u,u〉=k}. Num1(M) is the numerical range of q. In this paper we use Num1 to give a characterization of hermitian matrices, give some examples of 3×3 matrices M with Numk(M)=Fq2 for all k∈Fq and study Fq and Fq2 linear subspaces V⊂Mn,n(Fq2) such that either ♯(Num1(M))≤q for all M∈V or ♯(Num1(M))>q for all M∈V∖{0}.