Reviewerly/exHarmony huggingface dataset:
The Ferranti Mark I* Installation in Rome

This computer was purchased with funds from Azienda Rilievo Alienazione Residuati (ARAR), an Italian government entity created in October 1945 for “… the recovery, custody and disposition of material remnants of war, left behind by the Allies or abandoned by the Germans in Italy or otherwise acquired”. It was installed in the Consiglio Nazionale delle Ricerche (CNR) building in central Rome, the inauguration ceremony being attended by the Italian Head of State, President Giovanni Gronchi. The Ferranti Mark I* in Rome was called FINAC, indicating that it was acquired and run by the Instituto Nazionale per le Applicazioni Calcolo (INAC). FINAC had a long and useful life—so much so that when it came to seeking a replacement computer, INAC in partnership with Olivetti built CINAC, a powerful successor machine with a special console giving the look and feel of FINAC. This allowed existing programs to be run directly through a simulator. CINAC replaced FINAC in 1967.
The Mersenne Low Hamming Combination Search Problem Can Be Reduced to an ILP Problem

In 2017, Aggarwal, Joux, Prakash, and Santha proposed an innovative NTRU-like public-key cryptosystem that was believed to be quantum resistant, based on Mersenne prime numbers $$q = 2^N-1$$ . After a successful attack designed by Beunardeau, Connolly, Géraud, and Naccache, the authors revised the protocol which was accepted for Round 1 of the Post-Quantum Cryptography Standardization Process organized by NIST. The security of this protocol is based on the assumption that a so-called Mersenne Low Hamming Combination Search Problem (MLHCombSP) is hard to solve. In this work, we present a reduction of MLHCombSP to an instance of Integer Linear Programming (ILP). This opens new research directions that are necessary to be investigated in order to assess the concrete robustness of such cryptosystem. We propose different approaches to perform such reduction. Moreover, we uncover a new family of weak keys, for whose our reduction leads to an attack consisting in solving $$<N^3$$ ILP problems of dimension 3.
Incorporating Latent Space Correlation Coefficients to Collaborative Filtering

Collaborative Filtering (CF) is a popular approach to generate predicted rating of a target user on an item by aggregating neighbor users' ratings; these ratings are weighted by a correlation coefficient between two users. Thus, the user-user similarity computation is a significant step in CF to select proper neighborhood and exploit suitable correlation coefficients for prediction, and multiple weighting techniques have been proposed to enhance the performance. However, existing approaches compute the similarity directly based on users' rating vectors, which may lead the system to suffer from severe low-sparsity problem, and will also cause the system to be less interpretive because the rating only represents user's preference on a certain item but does not include extra feature information like attributes or genres. In this paper, we propose a method to compute the user' correlations in latent space by incorporating matrix factorization (MF) technique, and exploit the correlation coefficients in the prediction step of CF. We have evaluated the proposed approach with variant methods on MovieLens dataset to validate the effectiveness in CF.
A large field of view visible cameras image acquisition and processing technology in EAST device

A large field of view visible image acquisition and processing system was designed to address the discharge visual needs for estimating the state of plasma operation and the first wall conditions during the plasma discharge of the Experimental Advanced Superconducting Tokamak (EAST) device. The entire system, which consisted of software and hardware, was introduced in detail. Moreover, three sets of the visible camera diagnostic systems with the different field-of-view images acquired by three industrial personal computers (IPCs) were used for forming the large field of view through image stitching. Three IPCs, and data storage and image processing servers are connected by a reflective memory network, which can realize the collection and fast transmission of image data during discharge. In addition, an image stitching algorithm based on block matching scale-invariant feature transformation was adopted. To accelerate the processing of the algorithm, parallelization was achieved by using the Graphics Processing Unit (GPU). Through the mesa test and experimental results, the proposed system provides a feasible way of transmitting and stitching images quickly and fulfills the EAST experimental demands.
A Primer of Statistical Methods for Classification

Classification techniques are commonly used by scientists and businesses alike for decision-making. They involve assignment of objects (or information) to pre-defined groups (or classes) using certain known characteristics such as classifying emails as real or spam using information in subject field of email. Here we describe two soft and four hard classifiers popularly used by statisticians in practice. To demonstrate their applications, two simulated and three real-life datasets are used to develop classification criteria. The results of different classifiers are compared using misclassification rate and an uncertainty measure.
Quality Assurance Practices and Techniques Used by QA Professional in Continuous Delivery

In traditional software development life cycle like waterfall and V-model, there are staged gates, where at the end of each phase, there are strict quality checks being done. If the check passes, the team is allowed to go to the next phase. After the emergence of agile software development, continuous integration and continuous delivery are widely practiced in most of the teams. Quality assurance checks happen as soon as the developer checks in and the code is further built and tested in the build pipeline. In continuous delivery, quality checks are being done instantly. The responsibility of the software quality assurance (SQA) professional is to ensure that the right process is defined in the project and the process works fine. The SQA conducts planning, reviews, and audits to ensure this. In the traditional SDLC like waterfall and V-model, audits and reviews are conducted before the end of each phase. There are lots of reference standards for this. However, the way software quality assurance should be done is very different in continuous delivery mode of development. There is less literature and standards available about how an SQA professional should operate when team is using continuous integration and continuous delivery. This paper provides an overall framework of continuous delivery quality assurance model, and the implementation results in information and communication technology (ICT)–Internet of things (IOT) domain.
Binomial Characterization of Cryptographic Sequences

The generalized self-shrinking generator is a sequence generator that produces binary sequences with good cryptographic properties. On the other hand, the binomial sequences are a well-defined class of sequences that can be obtained considering infinite successions of binomial coefficients modulo 2. In this work, we see that the generalized sequences can be computed as a finite binary sum of binomial sequences. Moreover, the cryptographic parameters of the generalized sequences can be studied in terms of the binomial sequences.
Big Data-Driven Smart University Architecture and Strategy Development

The university's main objectives go around excellence in teaching, research activities and both university and community services. These pillars are supported by a number of important resources and functional systems such as complex student's life-cycle management system that must be considered in any strategy development process. This paper discusses the role of the big data in the smart university campus architecture and strategies development process, and how it could be utilized in various stages of strategy development. Additionally, it highlights the university's ICT architecture, the foundation of the knowledge management system that has been implemented, to help the university harness knowledge out of the big data that is generated by various parties and scattered systems around the university. Furthermore, the chapter highlights some of the successes on how, with help of a BI tool, the university was able to make critical and quick decisions by tapping into the big data of more than 35 years of student records; one of the pillars of the smart university strategy.
Book Recommender System Using Linked Data for Improving Serendipity

Recent years, to overcome the flood of information, recommender systems (RSs) are being used in many scenarios, such as online shopping stores, movie website and so on. However, many recommender algorithms focus on accuracy based on a user profile, which may lead to reducing user's satisfaction. As high-accuracy based RSs suggest similar items that the user may have known before. As a result,the recommendation leads to hurt user’s satisfaction. And there is a concept called serendipity which is a way to address this problem. This paper focuses on improving the serendipity of the RSs. We use two approaches, the variety of resources in Linked Data and author similarity. We extract author information from DBpedia dataset, which is one of the datasets in Linked Data. Then we calculate author similarity based on this information. This paper describes methods of book recommendations from the Book-Crossing dataset, reports recommendation results and discusses our recommender system according to our results.
General Parallel Execution Model for Large Matrix Workloads

Large scale statistical computing is crucial for extracting useful information from huge amount of data for both large companies and research scientists. The Solutions developed by high-performance communities have been more limited to clusters or high-end machines for decades. The cost of maintaining such dedicated clusters are prohibiting, people start to look at cloud computing where we can rent a cluster by time and pay-as-we-go. In a cloud setting, system features including fault tolerance and scalability become important. In this paper, we proposed a simple and universal parallel execution model for large matrix workloads. We implement the model in Hadoop MapReduce framework using map-only jobs. Because of the superiority of the model, experiments show that our Hadoop-based execution engine can reduce the execution time of matrix multiplication by half comparing with previous works.
The Implementation Strategies of Individualized Education in the Field of Basic Education

Basic education, as a foundation project to cultivate intellectuals and upgrade the national quality, plays an important role in the education of all countries.In China, "creating education suitable for students" is the development direction of basic education.Following the direction, individualized education shows great respect for individual differences of students, thus sticking to the principle of "student-oriented".This paper extracts the implementation strategies of individualized education from the perspective of learning methods, curriculum implementation and operation, teaching methods and curriculum system construction.
Secured Forensic Framework for Various Users in the Virtualized Environment of Cloud

A continuous forensic system, made available on the Cloud Server, which is built and integrated with the architecture of the cloud will help in gathering expedient additional “evidences” against certain crimes which could be useful in framing the criminals in the court of Law in any country like, e.g., India, when cloud services are used to commit the crime. The evidences have to fit into the system which are complaint with the policies set by the Law regulatory authorities of India. Most of the software agents generate Log data and store them in an Insistent Database which is centralized by nature. Such centralized systems can be manipulated by certain malicious attackers who can even be the employees of the organizations or the organizations itself to prevent penalties for non-compliance to standard policies if such systems come to notice when a crime is being committed. The manipulation can be to such an extent that the evidences can be modified or even swapped with possibly false Logs to frame some innocent users. This research paper proposes a cloud-based framework termed as Forensics Braced Cloud for gathering of expedient evidences and keeping such crucial expedient evidence Logs in a confidential way on another server not under the control of Cloud service provider. The Logs kept on the Insistent Database are used to publish the evidences and can be later checked by the Law regulatory officer when needed to check the integrity of the evidence termed as evidence of past Logs. The results observed are currently specific to the Prototype environment.
Wave front sensing for metrology by using optical filter

An interferometric problem is the limited fringe density, which is due to the limited allowed slope difference of superimposed wave fronts. Thus, the angular dynamic range of measurable surfaces and objects under test is limited. In other words, all shapes that deviate from a plane surface or a sphere represent a measuring problem in interferometers, or require an individually adapted null optics, which might cost e.g. 10 k&isin; or more. In addition, ground surfaces cannot be measured in standard interferometers, except by using Speckle interferometry, which is limited in resolution. Freeform optics are very problematic. Even when polished, only tactile or confocal coordinate measurement might work. Several interferometers address the problem of the angular deviation to a sphere. For instance, lateral stitching on a curved surface, which is equivalent to the best-fit sphere, or longitudinal stitching is used. To use a tilted wave interferometer for asphere metrology is another option, which provides versatile measurement configurations. The approach discussed here is to use optical filters. The development of this technique is part of a project most recently started at the Technology Campus in Teisnach. The surface under test (SUT) is imaged onto an optical filter, which has a calibrated angular selectivity. Thus, the angles of the local wave front normal vectors are transferred into an intensity distribution. A set of angular measurements enables reduced uncertainty of the wave front measurement. Aspects as e.g. the working principle, boundary conditions and the identification of practical filters are discussed in the paper.
Steerable Features for Resilient Image Watermark

A long standing goal within information security in broadcast monitoring has been the ability of the image watermark operation to be resilient to passive alterations that do not make the image inoperative for recovering hidden data. This question, known as watermark robustness, requires a consistent process to keep image feature unchanged, while allowing attachment of additional data in the image. This study investigates the potential for using steerable wavelets to specify essential image features in watermark operation. Four types of wavelets with gradient property were examined and evaluated to this end. Here, we use wavelets design based on the Riesz transform to investigate ability of recovering hidden data in host images associated with scaling and rotation after data insertion. We evaluate the invisibility of hiding operation and resilience of the hidden data again image scaling and rotation, which show feasibility and efficiency of the method.
A Query Generation Technique for Measuring Comprehension of Statistical Graphics

In our information-driven society, there is increasing use of statistical graphics to convey information in a variety of settings, including industry, mass media, government operations, and health care. Current methods for assessing a reader’s ability to comprehend statistical graphics are custom-written, not widely accepted, usable only once, and/or reliant on subjective interpretations and inferences. We have developed a method for generating queries suitable for evaluating graph comprehension capability. Our method is based on the Sentence Verification Technique (SVT), an empirically validated framework for measuring an individual’s comprehension of prose material. Compared to ad hoc methods for testing graph comprehension, our technique is less subjective, requires less manual effort and subject matter expertise, and addresses the essential features of a given graph: values and relationships depicted, frames of reference, and style attributes. The SVT, and therefore our method, combat superficial comprehension by testing what the reader has encoded, as opposed to testing the reader’s ability at visual recall or ability to look up data without reaching real comprehension. We motivate and describe our query generation method and report on a pilot study using queries generated with it.
Web-GIS-Based Interface for a UBA Selected Village

The Unnat Bharat Abhiyan (UBA) mission is to enable people of higher educational institutions, work along with people of rural areas by identifying their development challenges and evolving solutions for accelerating sustainable growth. Planners and policy makers have to depend on spatial infrastructure and non-spatial data for effective planning and decision-making. The Web-GIS with integration of both spatial and non-spatial data, i.e. household data which helps in extending the information accessibility for rural mass and the administrators in planning development activities as well as monitoring different rural programs by the government. This aim of this work is to provide interactive geospatial database, which helps in accessing the household information including spatial data for Ramulapalli village of Kannuru gram panchayat selected under UBA program. Household survey data as non-spatial data, and google earth image as Spatial data integrated into Geospatial environment and provide through web interface. Web-based interface, containing information that is helpful planning in development activities has been developed using MapGuide open source platform.
Descriptive Seons: Measure of Brain Tissue Impairment

InPrzelaskowski, Artur@Przelaskowski, Artur Sobieszczuk, Ewa@Sobieszczuk, Ewa Domitrz, Izabela@Domitrz, Izabela this paper, new numerical interpretation of CT scans to aid stroke care is considered. Semantic models are used to explain the essence of the observed reality contained in complex structures and imaged features to have full cognition of the reality under investigation. Proposed concept and particular implementation of a cognitive seon is based on integrated, image-based descriptors of CT diagnostic imaging. Brain tissue impairment was numerically measured to characterize ischemic stroke severity, dynamics and extent. In consequence, emergent stroke decisions could be supported using the seon of the integrated descriptive components to predict stroke treatment output. Respective experiments with a database of 145 strokes and controls have confirmed the usefulness and significant efficiency of the specific seon while the correlation of the descriptive measurements to ground true of clinical assessments of stroke cases was satisfied. The most highlighted contribution is model-based interpretation of stroke problem with insightful data-driven parametrization.
Different Glasses to Look into the Three Cs: Component, Connector, Coordination

Component, connector, and coordination have been key concepts exploited in different communities to manage the complexity of concurrent and distributed system development. In this paper, we discuss three approaches within three different classes: composition in software architectures, coordination models, and programming abstractions for concurrency. These classes encompass different perspectives and solutions to face crucial challenges in developing concurrent and distributed systems. The approaches are discussed with respect to some characteristics of interest for the above classes: compositionality, incrementality, scalability, compositional reasoning, reusability, and evolution.
Functional polynomial multiple-index model

Functional polynomial multiple-index model which includes the functional linear model, functional quadratic model and functional single index model as the special cases is considered. In this paper we propose an iterative technique to estimate the link function and polynomial coefficients nonparametrically. Simulations show that the proposed methods have good prediction performances. The proposed model is also illustrated with tecator data.
Removing Irrelevant Features Using Feature Information Map for Unsupervised Learning

In this paper, an information contribution based graph-theoretic approach is used to remove the irrelevant features from the feature set for unsupervised learning. The graph-based mapping of information contribution and the process of selecting a subset of the features has been named Feature Information Map based Feature Selection for Unsupervised Learning (FIMFSUL). This approach is based on three main parameters - entropy of the features, inter-feature correlation and subset derivation using minimal vertex cover algorithm. Features are subsequently passed through three stages, wherein we decide the relevancy of each of the features. The efficacy of this approach has been proved by conducting experiments with publicly available data sets.
Comments, Future Prospects and Collection of Ideas

This chapter summarizes complementary remarks and collects several ideas for a possible improvement of the single-shot 3D movie camera. Section 10.1 is an addendum to the discussion of the state of the art in Chap. 3 . It deals with sensor principles using an approach related to the approach developed in this thesis. Section 10.2 deals with a current problem of the 3D movie camera and introduces options for a solution. Currently, these options are only first ideas.
A Framework for Ensemble Classification and Sensitivity Analysis in Privacy Preserving Data Mining

The perturbation mechanism for data streams is a challenging task. In the emerging world, data is erupting from various sources. The core applications need care on the data streams for further analysis and experimentation. As the micro data available with the core applications shall not be revealed to the public without taking any chance of breach, the perturbation challenges the analysis to get through the like results as of on the original datasets. In this paper, we have applied a concept of Perlin noise to distract the original data from the eyes of the analysts, however allowing them to perform their activities well. Applying security dynamically on such data is a challenging task. This paper deals about the concepts of generation of smooth noise and syntactic perturbation mechanism on the selective tuples as selective perturbation.
Algorithms for String Comparison in DNA Sequences

String comparison algorithms are the pathway to determine various characteristics of genomes, DNA or protein sequences. In this article, three different existing algorithms are described with some additional features, which provide better performance in terms of different performance measures. At first, a dynamic programming algorithm for small k-mer is described which is based on the existing hash table SSAHA method to search large DNA database that gives better run time for small k-mer. Then a genome alignment algorithm is described that will find out MUMs (Maximal Unique Match) where Burrows Wheeler Transform matrix and an additional data structure FM (Ferragina and Manzini) index are used instead of suffix tree. Finally, an improved version of existing Sibelia tool’s algorithm is proposed to find out synteny blocks from microbial genome, where existing algorithm determines only the loops greater than the size of bulges whereas the proposed algorithm determines loops of all sizes.