Reviewerly/exHarmony huggingface dataset:
Norm Discriminant Eigenspace Transform for Pattern Classification

Most of the supervised dimensionality reduction (DR) methods design interclass scatter as the separability between the class means, which may force to assume unimodal Gaussian likelihoods and their projection space trends toward the class means. This paper presents a novel DR approach, norm discriminant eigenspace transform (NDET), in which average norms (l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ) of classes have been utilized to characterize the interclass separability and the within-class distance characterizes the intraclass compactness. NDET is intended to accommodate data distributions that may be multimodal and non-Gaussian. We derive an upper bound for NDET, and a specific solution space to attain this bound. Existence of the specific solution is very unwonted, thereby we have considered the solution space of upper bound to achieve better reduction of dimensionality and discrimination of classes. Also, a nonlinear version of NDET (kernel NDET) is developed to model nonlinear relationships between the features. We show, experimentally (on synthetic data) that NDET effectively overcomes the limitations, which arise due to unimodal and data distribution assumptions of the traditional algorithms. Extensive empirical studies are made; and the proposed method is compared with closely related state-of-the-art schemes on UCI machine learning repository and face recognition data sets, to establish its novelty.
Feature-Related Searching Control Model for Curve Detection

In this paper, a novel method is proposed for curve detection in images using a feature-related searching control model. It is composed of three parts: 1) prediction; 2) searching; and 3) updating. First, curve related features are modeled to a three order array. Then, equations of the prediction, searching, and curve parameter updating are deduced. Third, an optimal model for curve parameter estimation during iterations is given. Based on the proposed model, a curve detection algorithm is designed. Experiments on thousands of images demonstrate the effectiveness and advantages of the proposed method. Comparison experiments with state-of-the-art methods show that the proposed method outperforms the existing methods on most indexes. Our method can describe the contents of original images more completely with fewer curves. The contour evaluation framework and the Berkeley segmentation dataset are used to evaluate the performances of different curve detection methods. The proposed method can also detect curves in the order relates to their importance, which has been validated in experiments.
Parallel Enumeration of Custom Instructions Based on Multidepth Graph Partitioning

Automatic generation of custom instructions is the key to the extensible processors. The generation of custom instructions includes two crucial steps: 1) custom instruction enumeration and 2) custom instruction selection. This letter focuses on the custom instruction enumeration step. Enumerating all custom instruction candidates satisfying architectural constraints from an application graph is a computationally difficult problem. Considering the high complexity of the custom instruction enumeration, we propose a simple but efficient parallel search method based on multidepth graph partitioning for enumerating custom instructions. Experimental results show that the proposed method can give near-linear speedup and outperforms the latest parallel methods.
Medical data clustering based on particle swarm optimisation and genetic algorithm

Medical data clustering is popular scientific approach for finding hidden patterns from large medical dataset. Medical experts utilised these patterns to make clinical diagnosis for likelihood of a disease. Clustering groups the data objects of dataset into different groups based on data similarity within group is higher than other groups. In this work, a hybrid PSO-GA algorithm is developed for medical data clustering based on 'particle swarm optimisation (PSO) and genetic algorithm (GA)'. Hybrid PSO-GA performance has examined against K-means, PSO and GA with respect to six popular medical datasets namely iris, thyroid, breast cancer, heart, diabetes and pima adopted from UCI machine learning repository over three criterion namely, i.e., sum of intra cluster distance, error rate and CPU running time. Tabular and graphical results of simulation confirm hybrid PSO-GA technique for medical data clustering is superior against K-means, PSO and GA.
Rapid Retrieval of Secured Data from the Sensor Cloud using a Relative Record Index and Energy Management of Sensors

A massive amount of data is produced by sensors. These data finds a place in the cloud through a base station. Occasionally, the data collection process is disrupted as a result of the energy level of the sensor network due to voids. Void sensors do not propagate messages intended for the destination. We have addressed the issue of voids in sensors with the dynamic void removal algorithm. Retrieval of data from cloud is performed by relative record index associated with a security mechanism. Authenticated customers are given a secret key to rapidly retrieve data from the cloud. Meanwhile sensor networks require a secure mutual authentication scheme in an anxious network environment; our etiquette can handle all problems thrown up by the former schemes. Therefore, our protocol is more suited to an open and higher-security Sensor Network environment despite greater computation cost and energy.
Global library marketing research

Purpose The purpose of this paper is to quantitatively and qualitatively analyze library marketing research output using select bibliometric indicators with the aim of identifying top-performing countries, subject subthemes, organizations, authors and journals in the area. Design/methodology/approach The present study has examined 520 publications in library marketing, as indexed in Scopus database during 2006–2017. The study has statistically assessed processed publications and citations data into tables in order to ascertain research growth rate trends, global publication output and share, citation impact and distribution of library marketing research by country of publication, international-level collaborating country share of publication, by broad subject areas and preferred media of research communication. The study also provides bibliometric profile of top research organizations and authors in terms of their publications and citation indicators. Findings The body of literature in library marketing research is still very small, highly scattered and has so far registered no growth during the last 12 years, i.e. 2006–2017. Library marketing research is yet to emerge as a popular research area in library and information science. The USA dominates The library marketing research in the world, whereas other top participating countries including India, China, Japan, Canada, etc., are distant cousins. Top research institutions in the area of library marketing across the world include City University of New York, Florida State University, University of Texas at Austin, USA, Loughborough University and University of Sheffield, UK. The major focus of library marketing research was on academic libraries, followed by public libraries and medical libraries. Of late, the use of social media has emerged as an alternative to traditional library marketing techniques. Research limitations/implications The scope of study describing the status of library marketing research is limited to the period 2006–2017. The results of the study should interest researchers in finding solutions to formulating future library strategies and programs for achieving sustainable growth in this area of study. Originality/value The present study in library marketing research should be of interest to researchers in evolving, and formulating theories/best practices/and policies/programs for outreach to current and potential users for promoting library products and services.
DCnet: a data centre network architecture that supports live VM migration

Cloud computing and storage have become an integral part of online Internet services. Over the past decade, enterprises have steadily moved their applications and storage to the cloud paradigm. Server virtualisation enables cloud providers to utilise computational resources efficiently by employing virtual machines (VMs) and containers. As more enterprise applications are hosted by VMs in the cloud, a significant amount of network communication occurs among servers within a data centre, increasing so-called ‘east-west communication’. In addition to scaling to accommodate more east-west traffic, future data centre networks must support efficient VM migration, which is used to optimise power consumption. This article proposes a new data centre network architecture called DCnet. DCnet changes addressing and routing at layers 2 and 3 completely to increase throughput and support live VM migration throughout an organisation, including across multiple data centres owned by an organisation that spans large geographic areas. DCnet retains compatibility with existing hardware by using the same frame format as standard protocols, specifically Ethernet and Internet protocol (IP). Furthermore, DCnet does not require any changes to applications and host operating systems or libraries. In addition to presenting the architecture, the article describes a test bed used to assess DCnet and reports experimental measurements.
Design and implementation aspects of a Surakarta program

In this paper, we present the techniques, such as bitboard, iterative-deepening aspiration search algorithm, endgame tablebase, used in our Surakarta program Fuchou. A bijective function similar to Schadd’s is developed to map a Surakarta board position to a unique address of the endgame tablebase. The function is one-to-one and onto and can be computed very quickly using some techniques compared to conventional methods. We have implemented it on our Surakarta program FuChou. Experimental results show that we have gotten advantages of speedup as well as accuracy on the endgame boards.
Augmenting Markov Decision Processes with Advising

This paper introduces Advice-MDPs, an expansion of Markov Decision Processes for generating policies that take into consideration advising on the desirability, undesirability, and prohibition of certain states and actions. AdviceMDPs enable the design of designing semi-autonomous systems (systems that require operator support for at least handling certain situations) that can efficiently handle unexpected complex environments. Operators, through advising, can augment the planning model for covering unexpected real-world irregularities. This advising can swiftly augment the degree of autonomy of the system, so it can work without subsequent human intervention.&#x0D; This paper details the Advice-MDP formalism, a fast AdviceMDP resolution algorithm, and its applicability for real-world tasks, via the design of a professional-class semi-autonomous robot system ready to be deployed in a wide range of unexpected environments and capable of efficiently integrating operator advising.
Efficient Algorithm for Multi-Bit Montgomery Inverse Using Refined Multiplicative Inverse Modular &lt;inline-formula&gt; &lt;tex-math notation="LaTeX"&gt;$2^K$ &lt;/tex-math&gt; &lt;/inline-formula&gt;

This paper makes two major contributions. First, we propose an algorithm to compute a multiplicative inverse modulo <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2^{k}$ </tex-math></inline-formula> without using integer division by refining the Arazi–Qi algorithm. We then generalize this algorithm into a scalable algorithm to compute an inverse modulo <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2^{kl}$ </tex-math></inline-formula> by iteratively exploiting a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -bit-based process when the inverse modulo <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2^{k}$ </tex-math></inline-formula> is given. Because of its scalability, this generalized algorithm can be cost-effectively implemented on hardware and applied to larger modulus operations by iteratively applying a processing unit. Furthermore, we highlight the possibility of the derivation of a binary representation of an inverse <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P^{-1}$ </tex-math></inline-formula> in terms of a representation of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P$ </tex-math></inline-formula> and demonstrate this operation for up to eight bits. Second, using this direct relationship, we propose a multi-bit Montgomery inverse algorithm that is at least three times faster than the original version. Finally, we derive various properties of this algorithm and compare it with previous algorithms. This fast calculation of a Montgomery inverse is important for public key cryptographic applications, such as elliptic curve cryptosystems, because the relative speed of calculating a modular inverse for modular multiplication is a critical parameter for deciding which coordinate systems to adopt.
Aligning Business Processes With the Services Layer Using a Semantic Approach

Organizations require their business processes goals and the underlying information technology (IT) to be in synchronization with each other, but the continual changes in business processes makes this difficult. To accomplish this synchronization, there needs to be an alignment between the business processes and the IT. Business processes are currently defined using such well-known notations as BPMN, and the IT is made available by different services. Hence, the alignment process can be defined as one between the organization’s BPMNs and the services provided by its IT. In practice, however, this process is a complex task which is carried out by hand and hence is error prone. The present communication analyzes the conditions, relations, and incompatibilities between BPMNs and the service descriptions. The incompatibilities are formalized mathematically in order to facilitate their identification and resolution. Then, an alignment process is defined taking into account these incompatibilities and their solutions. The wrapper code needed to resolve each incompatibility identified during the alignment process is generated automatically. Finally, a case study is presented to validate and illustrate the use of the proposed alignment process. The results provided by the semiautomatic alignment process were similar to those obtained manually by a group of experts.
Optimization of Hierarchical Regression Model with Application to Optimizing Multi-Response Regression K-ary Trees

THIS PAPER HAS BEEN RETRACTED The University of California, Merced campus, recently completed a formal investigation into allegations that content in this paper ("Optimization of Hierarchical Regression Model with Application to Optimizing Multi-Response Regression K-ary Trees," in Proceedings of the AAAI Conference on Artificial Intelligence, 2019, vol. 33, pp. 5133– 5142) was not properly attributed to the novel work of Professor Carreira—Perpiñán, reflected in a previously published paper, "Alternating Optimization of Decision Trees, with Application to Learning Sparse Oblique Trees,” published in Advances in Neural Information Processing Systems, 2018, (vol. 31, pp. 1211–1221). The UC Merced investigation was conducted in consultation with NSF, which funded the research reflected in the original Advances in Neural Information Processing Systems paper, and in accordance with UC Merced policies and procedures on research misconduct. The allegations were substantiated by a preponderance of the evidence. The investigation committee found, in pertinent part, that “key novel ideas in the AAAI paper were taken from the NeurIPS paper without appropriate credit." The initial submission of the AAAI paper, upon which the acceptance decision was made, did not include a reference to the NeurIPS paper, and thus represented a clear case of plagiarism. A citation to the NeurIPS paper was added in the final revision, after the reviewers had already decided to accept it, making it impossible for the AAAI peer-review process to consider the contributions of the AAAI article in light of the earlier work reported in the NeurIPS paper. Furthermore, the citation to the NeurIPS paper in this manuscript does not properly credit the original contributions of the NeurIPS paper and how those ideas were reflected in the work reported in this paper. In response to a formal request made by the University of California's Chancellor's office, and after careful review by the AAAI Publications Committee, AAAI has decided to retract this paper. One of the conditions of submission of a paper for publication in AAAI conference proceedings is that authors declare explicitly that their work is original and has not appeared in a publication elsewhere. Reuse of any data should be appropriately cited. As such this paper represents a severe abuse of the scientific publishing system. The scientific community takes a very strong view on this matter and apologies are offered to readers of this proceedings that this was not detected during the submission process.
In silico Prediction of Inhibitory Constant of Thrombin Inhibitors Using Machine Learning

Thrombin is the central protease of the vertebrate blood coagulation cascade, which is closely related to cardiovascular diseases. The inhibitory constant Ki is the most significant property of thrombin inhibitors.This study was carried out to predict Ki values of thrombin inhibitors based on a large data set by using machine learning methods. Taking advantage of finding non-intuitive regularities on high-dimensional datasets, machine learning can be used to build effective predictive models. A total of 6554 descriptors for each compound were collected and an efficient descriptor selection method was chosen to find the appropriate descriptors. Four different methods including multiple linear regression (MLR), K Nearest Neighbors (KNN), Gradient Boosting Regression Tree (GBRT) and Support Vector Machine (SVM) were implemented to build prediction models with these selected descriptors.The SVM model was the best one among these methods with R2=0.84, MSE=0.55 for the training set and R2=0.83, MSE=0.56 for the test set. Several validation methods such as yrandomization test and applicability domain evaluation, were adopted to assess the robustness and generalization ability of the model. The final model shows excellent stability and predictive ability and can be employed for rapid estimation of the inhibitory constant, which is full of help for designing novel thrombin inhibitors.
JOA: Joint Overlap Analysis of multiple genomic interval sets

Next-generation sequencing (NGS) technologies have produced large volumes of genomic data. One common operation on heterogeneous genomic data is genomic interval intersection. Most of the existing tools impose restrictions such as not allowing nested intervals or requiring intervals to be sorted when finding overlaps in two or more interval sets. We proposed segment tree (ST) and indexed segment tree forest (ISTF) based solutions for intersection of multiple genomic interval sets in parallel. We developed these methods as a tool, Joint Overlap Analysis (JOA), which takes n interval sets and finds overlapping intervals with no constraints on the given intervals. The proposed indexed segment tree forest is a novel composite data structure, which leverages on indexing and natural binning of a segment tree. We also presented construction and search algorithms for this novel data structure. We compared JOA ST and JOA ISTF with each other, and with other interval intersection tools for verification of its correctness and for showing that it attains comparable execution times. We implemented JOA in Java using the fork/join framework which speeds up parallel processing by taking advantage of all available processor cores. We compared JOA ST with JOA ISTF and showed that segment tree and indexed segment tree forest methods are comparable with each other in terms of execution time and memory usage. We also carried out execution time comparison analysis for JOA and other tools and demonstrated that JOA has comparable execution time and is able to further reduce its running time by using more processors per node. JOA can be run using its GUI or as a command line tool. JOA is available with source code at https://github.com/burcakotlu/JOA/ . A user manual is provided at https://joa.readthedocs.org
Memory safety in C by abstract interpretation

Pointer arithmetic is a core feature of the C programming language and C program analysis is impossible without an understanding of its effects. Many program analyses opt to be unsound in the presence of pointer arithmetic or preserve soundness at the cost of precision. However, the number of operations that can be performed on pointers is actually quite small. As was observed by Might et al., these few operations can be precisely modeled with a simplified Peano arithmetic. This paper presents an interpreter that uses a memory model based on this arithmetic. It desugars C programs to a simple imperative language using standard semantics-preserving techniques to simplify the interpretation. The result is a prototype analysis that reasons precisely about memory safety in full C programs without programmer annotations.
Digital Currencies and the Power Shift in the Economy

The emerging economies are no longer growing as rapidly as in the past. As the technological disruption accelerates, a fundamental change is underway. In an industrialized world where the market is as unpredictable as it gets; the right mix of technology could be a game changer for the financial institutions. It’s a power struggle of sorts. The regulatory bodies are struggling to deal with the dynamic shift in market structure due to the eruption of digital currencies. The impact of Ethereum is studied through an estimated model and a montecarlo simulation is applied on its existing dynamic structure. As the currencies are decentralized, the power centralizes and the opportunities that emerge are noteworthy. An impact of the financial technology on the new digitized economy is realized through a time series data and estimations are made for the future.
Voltage Induced by Superconducting Vortices in Open Nanostructured Microtubes (Phys. Status Solidi RRL 1/2019)

The cover image represents an array of open superconductor nanostructured microtubes in a magnetic field (shown with arrows), which is perpendicular to the tubes' axes, in a transport current that flows from one edge of a slit to the other. A nontrivial topology of the screening currents in an open tube leads to the dynamic patterns of superconducting vortices that depend on the curvature, as reported by Roman Rezaev and co-authors in article no. 1800251. A tube fabricated by the roll-up technology is demonstrated to produce less dissipation as compared to a planar membrane under the same magnetic field and at the same transport current. The induced voltage as a function of the magnetic field provides a unique information about vortex patterns, which helps understand topology- and geometry-driven effects on dissipation characteristics of nanostructured superconductor materials.