Reviewerly/exHarmony huggingface dataset:
kBrowse

The construction of k-nearest Neighbor Graph (kNNG) in several applications, such as a recommender system, similarity search, and data exploration is heavily based on the distance function which is usually unweighted and considered constant for all users. However, attributes are not all equally important and using different attribute weight gives different kNNGs. We present kBrowse, which allows users to explore, modify and understand kNNG computed from a weighted Manhattan distance function on loosely-defined weight space. It samples possible weight vectors, and computes their corresponding kNNGs. The system summarizes all the kNNGs into one graph by keeping all the edges with high edge certainty, a probabilistic measurement on how likely an edge is going to appear in the weight space. To make the weight space more defined, users can directly adjust the weight space or gives kNN examples. Sample weight vectors failing to satisfy the given conditions are then removed and the graph is summarized again. Finally, kBrowse also gives a user better understanding of kNN by showing which attribute is important in connecting nodes.
Privacy-Protected KNN Classification Algorithm Based on Negative Database

Negative database (NDB) is a new type of information representation method that protects private data by storing data in the raw data complement set. The KNN classification algorithm is a classic classification algorithm, and the Euclidean distance formula is one of the most commonly used distance calculation formulas in classification algorithms. However, the distance calculation method for the existing KNN classification algorithm based on negative database is the one-hot coded Hamming distance formula. For this encoding method, when data set have many attributes, the length of the binary string becomes extremely long after encoding, thereby it increases the computational cost and complexity of the classification algorithm. In this paper, we proposed a KNN classification algorithm based on the Euclidean distance formula on the negative database, which is used to complete the classification research under the premise of protecting data security. The experimental results show that the algorithm in this paper achieves high classification accuracy.
Eye-Movements During Information Extraction from Administrative Documents

A key aspect of digital mailroom processes is the extraction of relevant information from administrative documents. More often than not, the extraction process cannot be fully automated, and there is instead an important amount of manual intervention. In this work we study the human process of information extraction from invoice document images. We explore whether the gaze of human annotators during an manual information extraction process could be exploited towards reducing the manual effort and automating the process. To this end, we perform an eye-tracking experiment replicating real-life interfaces for information extraction. Through this pilot study we demonstrate that relevant areas in the document can be identified reliably through automatic fixation classification, and the obtained models generalize well to new subjects. Our findings indicate that it is in principle possible to integrate the human in the document image analysis loop, making use of the scanpath to automate the extraction process or verify extracted information.
Research on Transmission Lines Early Warning Technology Based on Deep Learning

Abstract At the present stage, high-voltage transmission lines are distributed in long distance chains, with large space span, complicated meteorological and geographical environment. The operating environment of the transmission lines is poor. Hence, manual patrol and maintenance are difficult. Various faults are very likely to affect the safe and stable operation of the power grid system. Therefore, this paper proposes a transmission lines early warning method based on Deep Learning. Through object detection technology of Deep Learning, intrusion objects in the monitoring screen are automatically identified, at same time, their positions and types are marked. The experimental results show that this method has high accuracy and is suitable for the current power grid monitoring system.
A Generalized Data Representation and Training-Performance Analysis for Deep Learning Based Communication Systems

Deep learning (DL) based autoencoder is a potential architecture to implement end-to-end communication systems. In this paper, we first give a brief introduction to the autoencoder-represented communication system. Then, we propose a novel generalized data representation (GDR) to improve the data rate of DL-based communication systems. Finally, simulation results show that the proposed GDR scheme has lower training complexity, comparable block error rate performance and higher channel capacity than the conventional one-hot vector scheme. Furthermore, we investigate the effect of signal-to-noise ratio (SNR) in DL-based communication systems and show that training at high SNR can produce a good training convergence performance for the autoencoder.
Crowd Density Estimation Based on the Improved Vibe Algorithm and a Noval Perspective Correction Method

Unusual crowding is often associated with unusual events. Estimating population density is a good way to control management, maintain crowd safety, and prevent riots or high-risk activities. Therefore, in the paper, an improved method is proposed for crowd density estimation. First, a dynamic controller is introduced to Vibe algorithm, helping error detection and ghost suppression for faster adaptation to scene changes. Second, take perspective correction into account for pixel counting and propose a novel modeling method based on pixel level. It is of great significance to enhance the accuracy of estimation. Next, hybrid features based on global and local texture information are proposed to provide robustness to noise. Finally, the improved method was verified using images selected from the UCSD database. Experimental results demonstrate the performance and potential of the method.
QSecret-Sharing Scheme (QSS)-Based Keyless: Titanium Secure (TitaniumSec) EMail Application

Securing email, i.e. “electronic mail”, is a vast subject dealing with security against illegitimate usage. Standard methodology is to incorporate encryption techniques for securing email. This ultimately leads to securing the secret keys which are used for the secure communication. In order to facilitate secure communication, there arises a dependency on third-party devices as well as trusted third-party certifications. A third party is inappropriate to secure the keys storage and exchange from, whichever the case under consideration. The present work presents a novel keyless solution to secure email application. Keyless solution is based on QSecret-sharing scheme (QSS). The novelty of current work: (i) the integration of quasigroup in to secret-sharing scheme and (ii) introduces Titanium Secure (TitaniumSec) email application using indigenous QSS.
Exploration of Maximizing the Significance of Big Data in Cloud Computing

Currently information community has been bombarded by Big Data in Cloud computing to classify and coordinate into basic management process. On the other hand, the development of mobile computing is to extent "individual clouds", or open cloud resources in order to understanding the properties over the personal computing gadget like, advanced cells, tablets, workstations, shrewd TVs, and even joined frameworks inside automobiles. Big data, as it looks is enormous data used to designate the huge volume of data in unstructured and semi-structured format. Also, that is cloud computing comes in for the utilization the cloud as receptors for the majority of that information regardless of claim cloud or private cloud without particular amount in Petabytes and Exabyte of information. So it is increasingly using cloud deployments and therefore analytics needs to be surveyed with the aim at increasing value to address big data. In addition the entire consumer in Cloud computing, clients, servers, applications and other elements related to data centers are made available to IT and end users via the Internet. Organization needs to pay only as much for the computing infrastructure as they use. The way of billing type in cloud computing is similar to the electricity payment that we do on the basis of usage. It is a function of the allocation of resources on demand. The best booking ahead of time of assets is hard to be accomplished because of vulnerability of customer's future interest and supplier's asset costs. This paper renders the techniques behind maximizing Big Data in cloud computing. The issues, insights, analysis and management of Big Data, and advantages and learning outcome of Big Data in cloud computing, resource Provisioning Cost also have been studied.
Variations of the Itai-Rodeh Algorithm for Computing Anonymous Ring Size

We propose two adaptations of the probabilistic Itai-Rodeh algorithm for computing the size of an anonymous asynchronous ring. This Monte Carlo algorithm (inevitably) allows for wrong outcomes. Our adaptations reduce the chance that this happens. Furthermore, we propose a new algorithm that has a better message complexity.
EXPLORING AND CATEGORIZING DIVERSITY IN THE TECHNOLOGICAL AND PEDAGOGICAL APPROACHES OF VIRTUAL FIELDWORK EXPERIENCES

VFEs date to the dawn of humanity. Stories, carvings, and cave paintings took people to places they had not actually been. For millennia maps have offered models of places near and far. The Internet, virtual globes, immersive panoramas and innovations in 3D scanning and printing have made stunning changes to what can be reproduced at low cost and in high resolution. The authors have engaged in VFE development for more than a decade. This includes two currently NSF-funded projects: the Eastern Pacific Invertebrate Communities of the Cenozoic (EPICC) Project; and the Critical Zone Observatory Network. The discussion will highlight advances in media that are relevant to VFE creation and use; explore pedagogical advantages that can come from these advances; raise questions as to whether or not VFE use improves learner outcomes; and suggest characteristics of design that may be useful in the development of a VFE taxonomy. It will also reflect a research-informed bias that one the best things that VFEs do is catalyze actual fieldwork. Much of the structure of schooling is poorly aligned with what research indicates about how people learn. Does the use of virtual fieldwork more closely align with understandings of how people learn than traditional approaches? What makes one VFE more effective than another? There are several VFE characteristics, both pedagogical and technological, that vary as a product of design, use or both. Directedness: Does the user have autonomy in what and how to explore or is the VFE highly directed? Immersiveness: How deep a sense of place is given by the VFE? Resolution/Scale: At what scales can the site be investigated? Interdisciplinarity & Systems: Field sites are shaped by the interplay of rock, water, soil, air and life - with humans often having profound effects. Does the VFE invite exploration from a systems perspective? Technological adeptness: What technological skills and knowledge are needed for users and creators? Transferability: Can what is learned from exploring a VFE be transferred and applied to investigating other real and virtual sites? What important attributes are missing from this list? How do VFE components relate to what we understand about how people learn? In what ways can features, both individually and collectively, be optimized to enhance learning particular topics and skills?
Insights on landscape geochemistry and mineral exploration in the Fraser Range, Albany-Fraser Orogen, Western Australia

SummaryThe Albany-Fraser Orogen is an emerging recently opened mineral exploration province in Western Australia due to the recent discoveries of the Tropicana-Havana Au system in 2005, and the Nova-Bollinger Ni-Cu deposit in 2015. Simons Hill is located less than 10 km to the SW of the Nova–Bollinger system in the Albany-Fraser Orogen in Western Australia.This study analysed the nature and distribution of the regolith, and its stratigraphy, weathering and depositional history that have led to a variety of residual and transported regolith types. It aims to expand the understanding of the landscape geochemistry associated with Symons Hill in order to provide insights into mineral exploration techniques in this region.Symons Hill is located on a palaeo-topographical high that did not experience the transgression–regression cycles that affected the Albany–Fraser Region for the last 60My. Hence the Simons Hill stratigraphy lacks the marine geochemical influence which is widespread in the Norseman area. The landscape at Symons Hill evolved in a lacustrine/swamp environment. Poor drainage resulted in an extensive, thin (<30m) and homogeneous fine–grained transported cover that displays the same geochemical footprint as the underlying unweathered basement rocks. This cover includes the thicker palaeochannel sequences at the south of the tenement. Most of the transported cover is <5 m thick. Since this cover is the result of local recycling of weathered outcrops, it is an appropriate sampling medium to assess the geochemical composition of the rock at depth. Even if metal geochemical anomalies were displaced laterally, the source of the anomaly at depth is localised, and is most often located within a few 100s of metres from its source. This is a key element to keep in mind when undertaking mineral exploration in the Albany-Fraser Orogen.
Koder - A multi-register corpus for investigating register variation in contemporary German

This paper introduces the design decisions in building the Koder corpus, a multi-register-corpus of contemporary German. The purpose of this corpus is to serve as a basis for the investigation into the use of German across registers. In order to construct a representative corpus, the essential considerations are: the type and number of registers to include, the number of texts in each register and minimal text length. The paper describes which aspects were central in determining these issues as well the corpus composition and the necessary text processing.
Hotspot Mitigations for the Masses

In an IaaS cloud, the dynamic VM scheduler observes and mitigates resource hotspots to maintain performance in an oversubscribed environment. Most systems are focused on schedulers that fit very large infrastructures, which lead to workload-dependent optimisations, thereby limiting their portability. However, while the number of massive public clouds is very small, there is a countless number of private clouds running very different workloads. In that context, we consider that it is essential to look for schedulers that overcome the workload diversity observed in private clouds to benefit as many use cases as possible.
Electronic Resource Management and Digitisation

Electronic resource management (ERM) is the system and technique used by library professionals to track the selection, acquisition, access, maintenance, usage, evaluation of a library's electronic resources. The innovation of ERM conducted in the University Library, University of Calcutta has been discussed. It provides innovative insights into how technology transfer has operated in the University Library for digitising the documents. E-resources are a vital reference source for different types of library users. The main focus is on how ERM is done through the digitisation of resources in the digital library of the University of Calcutta. Digital libraries have been set up in all the campuses by the University Library System so that users can download e-resources from any campus. The University Library maintains an online catalogue for information search and retrieval. Theses have been digitised, and full texts are available. Through the innovation of the Digital Library System of the University, it would enhance the University's education and research value.
Feature-Based Phase Correlation in Image Registration

Image registration involves challenges to overcome the problems caused by the variations in spatial resolution, pixel spacing, orientation, and radiometric condition in image acquisitions for aligning conjugate points in the scenes in identical pixel positions. This paper proposes a feature-based phase correlation approach for registering multi-sensor images. Experiments on registering FORMOSAT-2 PAN and QuickBird MS1 images were conducted to show the performance of the proposed approach.
Rendering Untampered E-Votes Using Blockchain Technology

The Electronic voting is when a voter casts a ballot through a digital system rather on paper. In the present voting system, the Electronic Voting Machine comprises of two units, control unit and balloting unit. The control unit is with a surveying officer and the balloting Unit is set inside the voting compartment. When the voter makes the choice on the balloting unit by pressing the blue key, the Surveying Officer will press the Close Button. Be that as it may, in the present voting system there is significant probability of vote fixing. Votes can be altered effectively. This can be resolved by blockchain. Blockchain refers to a span of general-purpose technologies to exchange information and transact digital boon in distributed networks. This paper describes using the blockchain technology in an electorate system for Indian election. A threat in the current election system is the characteristics of security and transparency. In current system some problems occur with an administration that has complete authorization over the system and its database, it is affirmable to tamper the database with hefty opportunities. The planned system is basically intended for our nation based on biometric validation. Blockchain technology appears when they make their choice. This is incorporated inside Electronic Voting Machine. The subtleties are put away in independent block. On the off chance that there is a discretionary extortion it very well may be effectively recognized utilizing the blocks of data. This guarantees greater security, unique finger mark of voter is utilized as the fundamental validation asset. To decrease the swindling wellsprings of database control, blockchain can be received in the dispersion of database. This examination considers the voting consequence in blockchain algorithm from each place of election and recorded. A country with less voting rate will fight to develop. It is a potentiality method to the lack of involvement in voting amongst the young media-savvy population. This dispense an incorruptible election amid the democratic people. Blockchain technology is distributed and verified publicly such that degenerate is beyond the realm of imagination.
Website Log Statistical Testing and Analysis

In a variety of large data sets, the website log is an important data source. Analyzing the website log, one can get a web page views, viscosity, and related recommendations. The Hadoop ecosystem has the characteristics of open source, active community, covering all aspects of big data processing and a mature ecosystem. In this paper, Hadoop series framework is utilized to test and analyze the website access log. The experiment results demonstrate that the proposed technique can analyze and improve user behavior conveniently.
The factors of developing individual readiness to self-learning by means of media education at the stage of institutional training

The main peculiarity of the information age is the introduction of media technologies in various spheres of educational activity and human life, which means that the effectiveness of mastering everything new largely depends on self-learning individual readiness in the face of changing media realities. To form and develop self-learning individual readiness in order to continuously update the level of their knowledge and skills, both in their professional sphere and in ordinary life, is necessary to start in the process of their education at a higher educational establishment, taking into account external and internal factors of this development, and also their relationship. We have identified the shown factors at the stage of individual training at a university and developed a factor model of the media educational concept of developing self-learning individual readiness, demonstrating the main mechanism of the relationship between a person and the environment of their development. Based on this model, teachers can successfully influence on the process of developing self-learning individual readiness, both at the stage of formal education and at the interface of formal and non-formal forms of individual education, helping to improve their self-learning readiness after graduation from a university in constantly changing media conditions.
Non-symmetry and Anti-packing Pattern Representation Model in Visual Tracking

Based on the non-symmetry and anti-packing pattern representation model (NAM), we propose a new division model in object tracking, whose most important characteristic is asymmetry. For the target region, we divide it into homogeneous blocks of different sizes and irregular positions. Pixels in the same homogeneous block have similar values, so we can use mean value and area to represent the feature of this block. When tracking the object, the proposed algorithm updates the mean values of each block to adapt to the change of the object. The characteristics of the whole target consist of the feature information and the position information of each block. Compared with the traditional division models, the NAM has fewer divided blocks but more highlight target structure characteristics. The experimental results in this paper show that our proposed tracking algorithm based on the NAM is better than those based on the quadtree or the compression tracking (CT) in both accuracy and speed.
EDISON DENISOV AS A COMPOSER OF THE TAGANKA THEATRE: INTERRELATION OF “PURE” AND APPLIED ART

The article is devoted to the problem of interrelation of music and theatrical action. The problem is analysed by the example of creative cooperation of the composer Edison Denisov and the Taganka Theatre directed by Yuri Lyubimov. The paper summarizes information on the Taganka Theatre’s performances with Denisov’s music. Theatrical music is considered as a special sphere of the composer’s creative work. To illustrate the influence of theatrical music on Denisov’s “academic” music, his “Blue Notebook” on D. Kharms’s and A. Vvedensky’s poetry and the song “The Steamer Passes by the Marina” are examined.
A Pheromonal Artificial Bee Colony -pABC-Algorithm for Optimization Problems

Artificial Bee Colony (ABC) algorithm can be used for all kinds of optimization problems for which metaheuristic methods can be used, and it is observed to yield successful results in many applications for different kinds of optimization problems. Ant Colony Optimization (ACO) can successfully evaluate the correlations between solution parts through pheromone approach and ABC algorithm can spread across different regions of the solution space in searching process. In this study, a new hybrid method, developed to improve ABC's performance in terms of local search with pheromone method, is introduced. Proposed method is tested a set of benchmark problems.
Technical Perspective

When was the last time that you wrote code to implement a join algorithm? Chances are, it was during an undergraduate database class - if at all. The wide availability of database management systems in all their manifestations (admitting a wide definition, to encompass performing look-ups in a spreadsheet) mean that we do not have to (re)implement common operations over and over again. This brings many advantages. We benefit from time savings, both in development time, and also in execution time: we can expect that optimized professional code will outperform our ad-hoc efforts. Moreover, we expect such code to be robust, and less prone to crashing on unexpected inputs. It should produce results that can be relied on to be correct, and handle errors gracefully.