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Quantum computing is currently gaining significant attention, not only from the academic community but also from industry, due to its potential applications across several fields for addressing complex problems. For any practical problem which may be tackled using quantum computing, it is imperative to represent the data used onto a quantum computing system. Depending on the application, many different types of data and data structures occur, including regular numbers, higher-dimensional data structures, e.g., n-dimensional images, up to graphs. This report aims to provide an overview of existing methods for representing these data types on gate-based quantum computers., Comment: 16 pages, 2 figures

The Big Data explosion has necessitated the development of search algorithms that scale sub-linearly in time and memory. While compression algorithms and search algorithms do exist independently, few algorithms offer both, and those which do are domain-specific. We present panCAKES, a novel approach to compressive search, i.e., a way to perform $k$-NN and $\rho$-NN search on compressed data while only decompressing a small, relevant, portion of the data. panCAKES assumes the manifold hypothesis and leverages the low-dimensional structure of the data to compress and search it efficiently. panCAKES is generic over any distance function for which the distance between two points is proportional to the memory cost of storing an encoding of one in terms of the other. This property holds for many widely-used distance functions, e.g. string edit distances (Levenshtein, Needleman-Wunsch, etc.) and set dissimilarity measures (Jaccard, Dice, etc.). We benchmark panCAKES on a variety of datasets, including genomic, proteomic, and set data. We compare compression ratios to gzip, and search performance between the compressed and uncompressed versions of the same dataset. panCAKES achieves compression ratios close to those of gzip, while offering sub-linear time performance for $k$-NN and $\rho$-NN search. We conclude that panCAKES is an efficient, general-purpose algorithm for exact compressive search on large datasets that obey the manifold hypothesis. We provide an open-source implementation of panCAKES in the Rust programming language.

The report explores the challenges and strategies for preserving 3D digital data in cultural heritage. It discusses the issue of technological obsolescence, emphasising the need for ustainable storage solutions and ongoing data management strategies. Key topics include understanding technological obsolescence, the lifecycle of digital content, digital continuity, data management plans (DMP), FAIR principles, and the use of public repositories. The report also covers the importance of metadata in long-term digital preservation, including types of metadata and strategies for building valuable metadata. It examines the evolving standards and interoperability in 3D format preservation and the importance of managing metadata and paradata. The document provides a comprehensive overview of the challenges and solutions for preserving 3D cultural heritage data in the long term.

The paper concerns the extension of the Heritage Digital Twin Ontology introduced in previous work to describe the reactivity of digital twins used for cultural heritage documentation by including the semantic description of sensors and activators and all the process of interacting with the real world. After analysing previous work on the use of digital twins in cultural heritage, a summary description of the Heritage Digital Twin Ontology is provided, and the existing applications of digital twins to cultural heritage are overviewed, with references to reviews summarizing the large production of scientific contributions on the topic. Then a novel ontology, named Reactive Digital Twin Ontology is described, in which sensors, activators and the decision processes are also semantically described, turning the previous synchronic approach to cultural heritage documentation into a diachronic one. Some case studies exemplify this theory., Comment: Submitted to Sensors (Open Access Journal from MDPI). 17 pages, 3 figures

Semantic relations among entities are a widely accepted method for relation extraction. PromptORE (Prompt-based Open Relation Extraction) was designed to improve relation extraction with Large Language Models on generalistic documents. However, it is less effective when applied to historical documents, in languages other than English. In this study, we introduce an adaptation of PromptORE to extract relations from specialized documents, namely digital transcripts of trials from the Spanish Inquisition. Our approach involves fine-tuning transformer models with their pretraining objective on the data they will perform inference. We refer to this process as "biasing". Our Biased PromptORE addresses complex entity placements and genderism that occur in Spanish texts. We solve these issues by prompt engineering. We evaluate our method using Encoder-like models, corroborating our findings with experts' assessments. Additionally, we evaluate the performance using a binomial classification benchmark. Our results show a substantial improvement in accuracy -up to a 50% improvement with our Biased PromptORE models in comparison to the baseline models using standard PromptORE.

Healthcare data contains some of the most sensitive information about an individual, yet sharing this data with healthcare practitioners can significantly enhance patient care and support research efforts. However, current systems for sharing health data between patients and caregivers do not fully address the critical security requirements of privacy, confidentiality, and consent management. Furthermore, compliance with regulatory laws such as GDPR and HIPAA is often deficient, largely because patients typically are asked to provide general consent for healthcare entities to access their data. Recognizing the limitations of existing systems, we present S3PHER, a novel approach to sharing health data that provides patients with control over who accesses their data, what data is accessed, and when. Our system ensures end to end privacy by integrating a Proxy ReEncryption Scheme with a Searchable Encryption Scheme, utilizing Homomorphic Encryption to enable healthcare practitioners to privately search and access patients' documents. The practicality and benefits of S3PHER are further validated through end to end deployment and use case analyses, with tests on real datasets demonstrating promising execution times., Comment: 20 pages, 1 figure, 2 tables in the appendix

High-Performance Computing (HPC) systems excel in managing distributed workloads, and the growing interest in Artificial Intelligence (AI) has resulted in a surge in demand for faster methods of Machine Learning (ML) model training and inference. In the past, research on HPC I/O focused on optimizing the underlying storage system for modeling and simulation applications and checkpointing the results, causing writes to be the dominant I/O operation. These applications typically access large portions of the data written by simulations or experiments. ML workloads, in contrast, perform small I/O reads spread across a large number of random files. This shift of I/O access patterns poses several challenges to HPC storage systems. In this paper, we survey I/O in ML applications on HPC systems, and target literature within a 6-year time window from 2019 to 2024. We provide an overview of the common phases of ML, review available profilers and benchmarks, examine the I/O patterns encountered during ML training, explore I/O optimizations utilized in modern ML frameworks and proposed in recent literature, and lastly, present gaps requiring further R&D. We seek to summarize the common practices used in accessing data by ML applications and expose research gaps that could spawn further R&D.

In recent years, large-scale adoption of cloud storage solutions has revolutionized the way we think about digital data storage. However, the exponential increase in data volume, especially images, has raised environmental concerns regarding power and resource consumption, as well as the rising digital carbon footprint emissions. The aim of this research is to propose a methodology for cloud-based image storage by integrating image compression technology with SuperResolution Generative Adversarial Networks (SRGAN). Rather than storing images in their original format directly on the cloud, our approach involves initially reducing the image size through compression and downsizing techniques before storage. Upon request, these compressed images will be retrieved and processed by SRGAN to generate images. The efficacy of the proposed method is evaluated in terms of PSNR and SSIM metrics. Additionally, a mathematical analysis is given to calculate power consumption and carbon footprint assesment. The proposed data compression technique provides a significant solution to achieve a reasonable trade off between environmental sustainability and industrial efficiency., Comment: 5 pages, 5 figures

Recent advances in large language models have demonstrated remarkable effectiveness in information retrieval (IR) tasks. While many neural IR systems encode queries and documents into single-vector representations, multi-vector models elevate the retrieval quality by producing multi-vector representations and facilitating similarity searches at the granularity of individual tokens. However, these models significantly amplify memory and storage requirements for retrieval indices by an order of magnitude. This escalation in index size renders the scalability of multi-vector IR models progressively challenging due to their substantial memory demands. We introduce Embedding from Storage Pipelined Network (ESPN) where we offload the entire re-ranking embedding tables to SSDs and reduce the memory requirements by 5-16x. We design a software prefetcher with hit rates exceeding 90%, improving SSD based retrieval up to 6.4x, and demonstrate that we can maintain near memory levels of query latency even for large query batch sizes., Comment: 10 pages, 10 figures

We specify a file-oriented data format suitable for parallel, partition-independent disk I/O. Here, a partition refers to a disjoint and ordered distribution of the data elements between one or more processes. The format is designed such that the file contents are invariant under linear (i. e., unpermuted), parallel repartition of the data prior to writing. The file contents are indistinguishable from writing in serial. In the same vein, the file can be read on any number of processes that agree on any partition of the number of elements stored. In addition to the format specification we propose an optional convention to implement transparent per-element data compression. The compressed data and metadata is layered inside ordinary format elements. Overall, we pay special attention to both human and machine readability. If pure ASCII data is written, or compressed data is reencoded to ASCII, the entire file including its header and sectioning metadata remains entirely in ASCII. If binary data is written, the metadata stays easy on the human eye. We refer to this format as scda. Conceptually, it lies one layer below and is oblivious to the definition of variables, the binary representation of numbers, considerations of endianness, and self-describing headers, which may all be specified on top of scda. The main purpose of the format is to abstract any parallelism and provide sufficient structure as a foundation for a generic and flexible archival and checkpoint/restart. A documented reference implementation is available as part of the general-purpose libsc free software library., Comment: 17 pages, 7 figures and 2 tables

Performance metrics-driven context caching has a profound impact on throughput and response time in distributed context management systems for real-time context queries. This paper proposes a reinforcement learning based approach to adaptively cache context with the objective of minimizing the cost incurred by context management systems in responding to context queries. Our novel algorithms enable context queries and sub-queries to reuse and repurpose cached context in an efficient manner. This approach is distinctive to traditional data caching approaches by three main features. First, we make selective context cache admissions using no prior knowledge of the context, or the context query load. Secondly, we develop and incorporate innovative heuristic models to calculate expected performance of caching an item when making the decisions. Thirdly, our strategy defines a time-aware continuous cache action space. We present two reinforcement learning agents, a value function estimating actor-critic agent and a policy search agent using deep deterministic policy gradient method. The paper also proposes adaptive policies such as eviction and cache memory scaling to complement our objective. Our method is evaluated using a synthetically generated load of context sub-queries and a synthetic data set inspired from real world data and query samples. We further investigate optimal adaptive caching configurations under different settings. This paper presents, compares, and discusses our findings that the proposed selective caching methods reach short- and long-term cost- and performance-efficiency. The paper demonstrates that the proposed methods outperform other modes of context management such as redirector mode, and database mode, and cache all policy by up to 60% in cost efficiency., Comment: This is a pre-print version of the journal paper submitted to ACM Transactions in Internet of Things, which is currently under review

Context information is in demand more than ever with the rapid increase in the number of context-aware Internet of Things applications developed worldwide. Research in context and context-awareness is being conducted to broaden its applicability in light of many practical and technical challenges. One of the challenges is improving performance when responding to a large number of context queries. Context Management Platforms that infer and deliver context to applications measure this problem using Quality of Service (QoS) parameters. Although caching is a proven way to improve QoS, transiency of context and features such as variability and heterogeneity of context queries pose an additional real-time cost management problem. This paper presents a critical survey of the state-of-the-art in adaptive data caching with the objective of developing a body of knowledge in cost- and performance-efficient adaptive caching strategies. We comprehensively survey a large number of research publications and evaluate, compare, and contrast different techniques, policies, approaches, and schemes in adaptive caching. Our critical analysis is motivated by the focus on adaptively caching context as a core research problem. A formal definition for adaptive context caching is then proposed, followed by identified features and requirements of a well-designed, objective optimal adaptive context caching strategy., Comment: This is a preprint of an article submitted to the Journal of Systems of Software

Process management and process orchestration/execution are currently hot topics; prevalent trends such as automation and Industry 4.0 require solutions which allow domain-experts to easily model and execute processes in various domains, including manufacturing and health-care. These domains, in turn, rely on a tight integration between hardware and software, i.e. via the Internet of Things (IoT). While process execution is about actuation, i.e. actively triggering actions and awaiting their completion, accompanying IoT sensors monitor humans and the environment. These sensors produce large amounts of procedural, discrete, and continuous data streams, that hold the key to understanding the quality of process subjects (e.g. produced parts), outcome (e.g. quantity and quality), and error causes. Processes constantly evolve in conjunction with their IoT environment. This requires joint storage of data generated by processes, with data generated by the IoT sensors is therefore needed. In this paper, we present an extension of the process log standard format XES, namely SensorStream. SensorStream enables to connect IoT data to process events, as well as a set of semantic annotations to describe the scenario and environment during data collection. This allows to preserve the full context required for data-analysis, so that logs can be analyzed even when scenarios or hardware artifacts are rapidly changing. Through additional semantic annotations, we envision the XES extension log format to be a solid based for the creation of a (semi-)automatic analysis pipeline, which can support domain experts by automatically providing data visualization, or even process insights., Comment: 21 papges, 6 figures, 10 listings, not submitted to anywhere yet

The present paper concerns the design of the semantic infrastructure of the digital space for cultural heritage as envisaged by the European Commission in its recent documents. Due to the complexity of the cultural heritage data and of their intrinsic interrelationships, it is necessary to introduce a novel ontology, yet compliant with existing standards and interoperable with previous platforms used in this context, such as Europeana. The digital space organization must be tailored to the methods and the theory of cultural heritage, briefly summarized in the introduction. The new ontology is based on the Digital Twin concept, i.e. the digital counterpart of cultural heritage assets incorporating all the digital information pertaining to them. This creates a Knowledge Base on the cultural heritage digital space. The paper outlines the main features of the proposed Heritage Digital Twin ontology and provides some examples of application. Future work will include completing the ontology in all its details and testing it in other real cases and with the various sectors of the cultural heritage community., Comment: Submitted to Data - An Open Access Journal from MDPI. 29 pages, 9 figures

The work presents a solution for completely decentralized data management systems in geographically distributed environments with administratively unrelated or loosely related user groups and in conditions of partial or complete lack of trust between them. The solution is based on the integration of blockchain technology, smart contracts and provenance metadata driven data management. Architecture, operation principles and algorithms developed provides fault-tolerant, safe and reliable management of provenance metadata, control of operations with data files, as well as resource access management in collaborative distributed computing systems. The latter refer to distributed systems formed by combining into a single pool of computer resources of various organizations (institutions) to work together in the framework of some project., Comment: 7 pages, 1 figure

Decentralized storage is still rarely used in an academic and educational environment, although it offers better availability than conventional systems. It still happens that data is not available at a certain time due to heavy load or maintenance on university servers. A decentralized solution can help keep the data available and distribute the load among several peers. In our experiment, we created a cluster of containers in Docker to evaluate a private IPFS cluster for an academic data store focusing on availability, GET/PUT performance, and storage needs. As sample data, we used PDF files to analyze the data transport in our peer-to-peer network with Wireshark. We found that a bandwidth of at least 100 kbit/s is required for IPFS to function but recommend at least 1000 kbit/s for smooth operation. Also, the hard disk and memory size should be adapted to the data. Other limiting factors such as CPU power and delay in the internet connection did not affect the operation of the IPFS cluster., Comment: 2 pages, 2 figures, Proceedings of the ACM/IEEE Joint Conference on Digital Libraries (JCDL), poster paper

Cross-domain cold-start recommendation is an increasingly emerging issue for recommender systems. Existing works mainly focus on solving either cross-domain user recommendation or cold-start content recommendation. However, when a new domain evolves at its early stage, it has potential users similar to the source domain but with much fewer interactions. It is critical to learn a user's preference from the source domain and transfer it into the target domain, especially on the newly arriving contents with limited user feedback. To bridge this gap, we propose a self-trained Cross-dOmain User Preference LEarning (COUPLE) framework, targeting cold-start recommendation with various semantic tags, such as attributes of items or genres of videos. More specifically, we consider three levels of preferences, including user history, user content and user group to provide reliable recommendation. With user history represented by a domain-aware sequential model, a frequency encoder is applied to the underlying tags for user content preference learning. Then, a hierarchical memory tree with orthogonal node representation is proposed to further generalize user group preference across domains. The whole framework updates in a contrastive way with a First-In-First-Out (FIFO) queue to obtain more distinctive representations. Extensive experiments on two datasets demonstrate the efficiency of COUPLE in both user and content cold-start situations. By deploying an online A/B test for a week, we show that the Click-Through-Rate (CTR) of COUPLE is superior to other baselines used on Taobao APP. Now the method is serving online for the cross-domain cold micro-video recommendation.

We present a Spiking Neural Network (SNN) based Sparse Distributed Memory (SDM) implemented on the Nengo framework. We have based our work on previous work by Furber et al, 2004, implementing SDM using N-of-M codes. As an integral part of the SDM design, we have implemented Correlation Matrix Memory (CMM) using SNN on Nengo. Our SNN implementation uses Leaky Integrate and Fire (LIF) spiking neuron models on Nengo. Our objective is to understand how well SNN-based SDMs perform in comparison to conventional SDMs. Towards this, we have simulated both conventional and SNN-based SDM and CMM on Nengo. We observe that SNN-based models perform similarly as the conventional ones. In order to evaluate the performance of different SNNs, we repeated the experiment using Adaptive-LIF, Spiking Rectified Linear Unit, and Izhikevich models and obtained similar results. We conclude that it is indeed feasible to develop some types of associative memories using spiking neurons whose memory capacity and other features are similar to the performance without SNNs. Finally we have implemented an application where MNIST images, encoded with N-of-M codes, are associated with their labels and stored in the SNN-based SDM., Comment: 8 pages, 11 figures, accepted as poster in Bernstein Conference 2021

An increasing number of researchers support reproducibility by including pointers to and descriptions of datasets, software and methods in their publications. However, scientific articles may be ambiguous, incomplete and difficult to process by automated systems. In this paper we introduce RO-Crate, an open, community-driven, and lightweight approach to packaging research artefacts along with their metadata in a machine readable manner. RO-Crate is based on Schema$.$org annotations in JSON-LD, aiming to establish best practices to formally describe metadata in an accessible and practical way for their use in a wide variety of situations. An RO-Crate is a structured archive of all the items that contributed to a research outcome, including their identifiers, provenance, relations and annotations. As a general purpose packaging approach for data and their metadata, RO-Crate is used across multiple areas, including bioinformatics, digital humanities and regulatory sciences. By applying "just enough" Linked Data standards, RO-Crate simplifies the process of making research outputs FAIR while also enhancing research reproducibility. An RO-Crate for this article is available at https://w3id.org/ro/doi/10.5281/zenodo.5146227, Comment: 44 pages. Accepted for Data Science

Processing astronomical data often comes with huge challenges with regards to data management as well as data processing. MeerKAT telescope is one of the precursor telescopes of the World's largest observatory - Square Kilometre Array. So far, MeerKAT data was processed using the South African computing facility i.e. IDIA, and exploited to make ground-breaking discoveries. However, to process MeerKAT data on UK's IRIS computing facility requires new implementation of the MeerKAT pipeline. This paper focuses on how to transfer MeerKAT data from the South African site to UK's IRIS systems for processing. We discuss about our RapifXfer Data transfer framework for transferring the MeerKAT data from South Africa to the UK, and the MeerKAT job processing framework pertaining to the UK's IRIS resources., Comment: arXiv admin note: This article has been withdrawn due to an unresolvable internal author dispute

Sensitive signal detection and processing in classical world, especially in quantum regime, require nonreciprocal manipulation. In this paper we show how to achieve nonreciprocal quantum synchronization for two magnon modes in a two-sublattice antiferromagnet with strong isolation.The antiferromagnet is trapped in a cavity with two posts so that the two magnon modes not only couple to each other through a parametric-type interaction, but also interact with a same cavity respectively in a beam splitter-type and parametric-type ways. Under the condition of system's stability, we show that nonreciprocal quantum synchronization between two magnon modes is mainly dependent on resonance frequency of the cavity caused by direction of input currents. In addition,quantum synchronization is enhanced by the increase of interaction strength between two Bogoliubov modes and cavity mode. Moreover, numerical simulation results with parameters commonly used in current experiments show that the present scheme may be feasible., Comment: 8 pages, 5 figures. arXiv admin note: text overlap with arXiv:1408.2905 by other authors

The XSD binary floating point datatypes are regularly used for precise numeric values in RDF. However, the use of these datatypes for knowledge representation can systematically impair the quality of data and, compared to the XSD decimal datatype, increases the probability of data processing producing false results. We argue why in most cases the XSD decimal datatype is better suited to represent numeric values in RDF. A survey of the actual usage of datatypes on the relevant subset of the December 2020 Web Data Commons dataset, containing 19453060341 literals from real web data, substantiates the practical relevancy of the described problem: 29 %-68 % of binary floating point values are distorted due to the datatype., Comment: 17 pages, 3 figures

Currently we produce 10 to power 21 digital bits of information annually on Earth. Assuming 20 percent annual growth rate, we estimate that 350 years from now, the number of bits produced will exceed the number of all atoms on Earth, or 10 to power 50. After 250 years, the power required to sustain this digital production will exceed 18.5 TW, or the total planetary power consumption today, and 500 years from now the digital content will account for more than half of the Earths mass, according to the mass energy information equivalence principle. Besides the existing global challenges such as climate, environment, population, food, health, energy and security, our estimates here point to another singularity event for our planet, called the Information Catastrophe., Comment: 4 pages

Byte-addressable persistent memory, such as Intel/Micron 3D XPoint, is an emerging technology that bridges the gap between volatile memory and persistent storage. Data in persistent memory survives crashes and restarts; however, it is challenging to ensure that this data is consistent after failures. Existing approaches incur significant performance costs to ensure crash consistency. This paper introduces Crafty, a new approach for ensuring consistency and atomicity on persistent memory operations using commodity hardware with existing hardware transactional memory (HTM) capabilities, while incurring low overhead. Crafty employs a novel technique called nondestructive undo logging that leverages commodity HTM to control persist ordering. Our evaluation shows that Crafty outperforms state-of-the-art prior work under low contention, and performs competitively under high contention., Comment: 32 pages, 24 figures. To appear in PLDI 2020

Semiconductor NAND Flash based memory technology dominates the electronic Non-Volatile storage media market. Though NAND Flash offers superior performance and reliability over conventional magnetic HDDs, yet it suffers from certain data-security vulnerabilities. Such vulnerabilities can expose sensitive information stored on the media to security risks. It is thus necessary to study in detail the fundamental reasons behind data-security vulnerabilities of NAND Flash for use in critical applications. In this paper, the problem of unreliable data-deletion/sanitization in commercial NAND Flash media is investigated along with the fundamental reasons leading to such vulnerabilities. Exhaustive software based data recovery experiments (multiple iterations) has been carried out on commercial NAND Flash storage media (8 GB and 16 GB) for different types of filesystems (NTFS and FAT) and OS specific delete/Erase instructions. 100 % data recovery is obtained for windows and linux based delete/Erase commands. Inverse effect of performance enhancement techniques like wear levelling, bad block management etc. is also observed with the help of software based recovery experiments., Comment: 8 pages, 12 figures

There is considerable amount of sensitive XML data stored in relational databases. It is a challenge to enforce node level fine-grained authorization policies for XML data stored in relational databases which typically support table and column level access control. Moreover, it is common to have conflicting authorization policies over the hierarchical nested structure of XML data. There are a couple of XML access control models for relational XML databases proposed in the literature. However, to our best knowledge, none of them discussed handling authorization conflicts with conditions in the domain of relational XML databases. Therefore, we believe that there is a need to define and incorporate effective fine-grained XML authorization models with conflict handling mechanisms in the presence of conditions into relational XML databases. We address this issue in this study., Comment: 14th International Conference on Information Technology - New Generations, Las Vegas, NV, April 10-12, 2017, Published by Springer, Cham; 6 pages, 5 figures, 2 tables

Data-intensive platforms such as Hadoop and Spark are routinely used to process massive amounts of data residing on distributed file systems like HDFS. Increasing memory sizes and new hardware technologies (e.g., NVRAM, SSDs) have recently led to the introduction of storage tiering in such settings. However, users are now burdened with the additional complexity of managing the multiple storage tiers and the data residing on them while trying to optimize their workloads. In this paper, we develop a general framework for automatically moving data across the available storage tiers in distributed file systems. Moreover, we employ machine learning for tracking and predicting file access patterns, which we use to decide when and which data to move up or down the storage tiers for increasing system performance. Our approach uses incremental learning to dynamically refine the models with new file accesses, allowing them to naturally adjust and adapt to workload changes over time. Our extensive evaluation using realistic workloads derived from Facebook and CMU traces compares our approach with several other policies and showcases significant benefits in terms of both workload performance and cluster efficiency., Comment: 16 pages, 17 figures, 4 tables

It is shown how to place some amount of matter into the cosmological singularity and to encode its state. A free and massless scalar field is considered as a prototype of matter. Two different but coherent approaches to this issue are presented. The expression for the scalar particles' spectral energy density, which is initially encoded at the singularity, is deduced. An informational aspect of the problem is discussed., Comment: 14 pages, 2 figures

We present algorithms for multi-modal route planning in road and public transit networks, as well as in combined networks. Therefore, we explore the nearest neighbor and shortest path problem and propose solutions based on Cover-Trees, ALT and CSA. Further, we illustrate the theory behind the algorithms, give a short overview of other techniques, present experimental results and compare the techniques with each other., Comment: Master's thesis, 80 pages, 24 figures

Traditional information retrieval treats named entity recognition as a pre-indexing corpus annotation task, allowing entity tags to be indexed and used during search. Named entity taggers themselves are typically trained on thousands or tens of thousands of examples labeled by humans. However, there is a long tail of named entities classes, and for these cases, labeled data may be impossible to find or justify financially. We propose exploring named entity recognition as a search task, where the named entity class of interest is a query, and entities of that class are the relevant "documents". What should that query look like? Can we even perform NER-style labeling with tens of labels? This study presents an exploration of CRF-based NER models with handcrafted features and of how we might transform them into search queries., Comment: 6 pages

Robots have potential to revolutionize the way we interact with the world around us. One of their largest potentials is in the domain of mobile health where they can be used to facilitate clinical interventions. However, to accomplish this, robots need to have access to our private data in order to learn from these data and improve their interaction capabilities. Furthermore, to enhance this learning process, the knowledge sharing among multiple robot units is the natural step forward. However, to date, there is no well-established framework which allows for such data sharing while preserving the privacy of the users (e.g., the hospital patients). To this end, we introduce RoboChain - the first learning framework for secure, decentralized and computationally efficient data and model sharing among multiple robot units installed at multiple sites (e.g., hospitals). RoboChain builds upon and combines the latest advances in open data access and blockchain technologies, as well as machine learning. We illustrate this framework using the example of a clinical intervention conducted in a private network of hospitals. Specifically, we lay down the system architecture that allows multiple robot units, conducting the interventions at different hospitals, to perform efficient learning without compromising the data privacy., Comment: 7 pages, 6 figures

Click-through rate prediction is an essential task in industrial applications, such as online advertising. Recently deep learning based models have been proposed, which follow a similar Embedding\&MLP paradigm. In these methods large scale sparse input features are first mapped into low dimensional embedding vectors, and then transformed into fixed-length vectors in a group-wise manner, finally concatenated together to fed into a multilayer perceptron (MLP) to learn the nonlinear relations among features. In this way, user features are compressed into a fixed-length representation vector, in regardless of what candidate ads are. The use of fixed-length vector will be a bottleneck, which brings difficulty for Embedding\&MLP methods to capture user's diverse interests effectively from rich historical behaviors. In this paper, we propose a novel model: Deep Interest Network (DIN) which tackles this challenge by designing a local activation unit to adaptively learn the representation of user interests from historical behaviors with respect to a certain ad. This representation vector varies over different ads, improving the expressive ability of model greatly. Besides, we develop two techniques: mini-batch aware regularization and data adaptive activation function which can help training industrial deep networks with hundreds of millions of parameters. Experiments on two public datasets as well as an Alibaba real production dataset with over 2 billion samples demonstrate the effectiveness of proposed approaches, which achieve superior performance compared with state-of-the-art methods. DIN now has been successfully deployed in the online display advertising system in Alibaba, serving the main traffic., Comment: Accepted by KDD 2018

As of today the programming language of the vast majority of the published source code is manually specified or programmatically assigned based on the sole file extension. In this paper we show that the source code programming language identification task can be fully automated using machine learning techniques. We first define the criteria that a production-level automatic programming language identification solution should meet. Our criteria include accuracy, programming language coverage, extensibility and performance. We then describe our approach: How training files are preprocessed for extracting features that mimic grammar productions, and then how these extracted grammar productions are used for the training and testing of our classifier. We achieve a 99 percent accuracy rate while classifying 29 of the most popular programming languages with a Maximum Entropy classifier., Comment: 13 pages, 4 tables, 4 examples

Much of work in semantic web relying on Wikipedia as the main source of knowledge often work on static snapshots of the dataset. The full history of Wikipedia revisions, while contains much more useful information, is still difficult to access due to its exceptional volume. To enable further research on this collection, we developed a tool, named Hedera, that efficiently extracts semantic information from Wikipedia revision history datasets. Hedera exploits Map-Reduce paradigm to achieve rapid extraction, it is able to handle one entire Wikipedia articles revision history within a day in a medium-scale cluster, and supports flexible data structures for various kinds of semantic web study., Comment: Pubished via CEUR-WS.org/Vol-1272

In Afghanistan, High school graduates, to continue higher education, need to pass the National University Entrance Exam (Kankor). Kankor is very important and requires further research as it is used as the only means to identify the participant's competence and skills which leads to a qualified higher education and manpower, employment opportunities, allocation of human capital in appropriate fields of study, and leading to a more specialized labor force in the long run. Having a better picture of Kankor in Afghanistan is essential before employing educational data mining techniques and other social and pedagogical approaches to support the students and the educational institutions. Hence, the author carried out a research on Kankor, in which he assessed and analyzed Kankor from different angles. This research paper provides positive contribution to the Ministry of Higher Education (MoHE), particularly, the Kankor committee, and other educational institutions - introducing the importance of data as an asset and a proper structure of recording and producing data effective for research studies, paving the way for in-depth descriptive and predictive analytics which improves the situation within the context of the existing structural models in Afghanistan. The author's research target is to study Kankor using descriptive analytics supported by Kankor Results Data (KRD) of 1.5 million records from 2003-2015, Detailed Kankor Results Data which is a subset of KRD from 2004-2006 of 120,000 records, High school performance and grades data from 2011-2013 of 6,000 records, data collected through conducting more than 2500 questionnaires among academicians as well as the author's personal observations, findings, and analyses as a student and faculty member at the University in the field of computer science., Comment: 15 pages, 5 figures

The dependence of the test function on the phenomenological parameters used in the "NAV" ("New Algol Variable") algorithm (Andronov, 2012Ap.....55..536A) is studied. Due to a presence of local minima, the method of minimization contains two steps: the "brute force" minimization at a grid in the 4D parameter space, and further iterations using the differential corrections. This method represents an effective approximation of the light curve using the special pattern (shape) separately for the primary and secondary minima. The application of the method to concrete stars is briefly reviewed., Comment: 7 pages, 5 figures, Advances in Astronomy and Space Physics (AASP 2016, V.6, No.2)

A comparative analysis of the special shapes (patterns, profiles) of the eclipses applied for the phenomenological modeling of the light curves of eclipsing binary stars is conducted. Families of functions are considered, generalizing local approximations (Andronov, 2010, 2012) and the functions theoretically unlimited in a width, based on a Gaussian (Mikulasek, 2015). For an analysis, the light curve of the star V0882 Car = 2MASS J11080308 - 6145589 of the classic Algol - subtype (\beta Persei) is used. By analyzing dozens of modified functions with additional parameters, it was chosen the 14 best ones according to the criterion of the least sum of squares of deviations. The best are the functions with an additional parameter, describing profiles, which are limited in phase., Comment: 15 pages, 3 figures

Imagine an online work environment where researchers have direct and immediate access to myriad data sources and tools and data management resources, useful throughout the research lifecycle. This is our vision for the next generation of the Dataverse Network: an Open Science Platform (OSP). For the first time, researchers would be able to seamlessly access and create primary and derived data from a variety of sources: prior research results, public data sets, harvested online data, physical instruments, private data collections, and even data from other standalone repositories. Researchers could recruit research participants and conduct research directly on the OSP, if desired, using readily available tools. Researchers could create private or shared workspaces to house data, access tools, and computation and could publish data directly on the platform or publish elsewhere with persistent, data citations on the OSP. This manuscript describes the details of an Open Science Platform and its construction. Having an Open Science Platform will especially impact the rate of new scientific discoveries and make scientific findings more credible and accountable., Comment: 32 pages, 8 figures

The proliferation of heterogeneous data sources of semantic knowledge base intensifies the need of an automatic instance matching technique. However, the efficiency of instance matching is often influenced by the weight of a property associated to instances. Automatic weight generation is a non-trivial, however an important task in instance matching technique. Therefore, identifying an appropriate metric for generating weight for a property automatically is nevertheless a formidable task. In this paper, we investigate an approach of generating weights automatically by considering hypotheses: (1) the weight of a property is directly proportional to the ratio of the number of its distinct values to the number of instances contain the property, and (2) the weight is also proportional to the ratio of the number of distinct values of a property to the number of instances in a training dataset. The basic intuition behind the use of our approach is the classical theory of information content that infrequent words are more informative than frequent ones. Our mathematical model derives a metric for generating property weights automatically, which is applied in instance matching system to produce re-conciliated instances efficiently. Our experiments and evaluations show the effectiveness of our proposed metric of automatic weight generation for properties in an instance matching technique., Comment: 17 pages, 5 figures, 3 tables, pp. 1-17, publication year 2015, journal publication, vol. 6 number 1

Bitmap indexes are routinely used to speed up simple aggregate queries in databases. Set operations such as intersections, unions and complements can be represented as logical operations (AND, OR, NOT). However, less is known about the application of bitmap indexes to more advanced queries. We want to extend the applicability of bitmap indexes. As a starting point, we consider symmetric Boolean queries (e.g., threshold functions). For example, we might consider stores as sets of products, and ask for products that are on sale in 2 to 10 stores. Such symmetric Boolean queries generalize intersection, union, and T-occurrence queries. It may not be immediately obvious to an engineer how to use bitmap indexes for symmetric Boolean queries. Yet, maybe surprisingly, we find that the best of our bitmap-based algorithms are competitive with the state-of-the-art algorithms for important special cases (e.g., MergeOpt, MergeSkip, DivideSkip, ScanCount). Moreover, unlike the competing algorithms, the result of our computation is again a bitmap which can be further processed within a bitmap index. We review algorithmic design issues such as the aggregation of many compressed bitmaps. We conclude with a discussion on other advanced queries that bitmap indexes might be able to support efficiently., Comment: This paper uses small fonts and colours and is only intended for electronic viewing

Many memory institutions hold large collections of hand-held media, which can comprise hundreds of terabytes of data spread over many thousands of data-carriers. Many of these carriers are at risk of significant physical degradation over time, depending on their composition. Unfortunately, handling them manually is enormously time consuming and so a full and frequent evaluation of their condition is extremely expensive. It is, therefore, important to develop scalable processes for stabilizing them onto backed-up online storage where they can be subject to highquality digital preservation management. This goes hand in hand with the need to establish efficient, standardized ways of recording metadata and to deal with defective data-carriers. This paper discusses processing approaches, workflows, technical set-up, software solutions and touches on staffing needs for the stabilization process. We have experimented with different disk copying robots, defined our metadata, and addressed storage issues to scale stabilization to the vast quantities of digital objects on hand-held data-carriers that need to be preserved. Working closely with the content curators, we have been able to build a robust data migration workflow and have stabilized over 16 terabytes of data in a scalable and economical manner., Comment: 11 pages, presented at iPres 2011. Also publishing in corresponding conference proceedings

Combining and summarizing meta-data from various kinds of data sources is one possible solution to the data fragmentation we are suffering from. Multiple projects have addressed this issue already. This paper presents a new approach named Memacs. It automatically generates a detailed linked diary of our digital artifacts scattered across local files of multiple formats as well as data silos of the internet. Being elegantly simple and open, Memacs uses already existing visualization features of GNU Emacs and Org-mode to provide a promising platform for life-logging, Quantified Self movement, and people looking for advanced Personal Information Management (PIM) in general., Comment: 6 pages, 3 figures, 21 references

This paper outlines certain scenarios from the fields of astrophysics and fluid dynamics simulations which require high performance data warehouses that support array data type. A common feature of all these use cases is that subsetting and preprocessing the data on the server side (as far as possible inside the database server process) is necessary to avoid the client-server overhead and to minimize IO utilization. Analyzing and summarizing the requirements of the various fields help software engineers to come up with a comprehensive design of an array extension to relational database systems that covers a wide range of scientific applications. We also present a working implementation of an array data type for Microsoft SQL Server 2008 to support large-scale scientific applications. We introduce the design of the array type, results from a performance evaluation, and discuss the lessons learned from this implementation. The library can be downloaded from our website at http://voservices.net/sqlarray/

Devising a weight assignment policy for assigning credits to multiple authors of a manuscript is a challenging task. In this paper, we present a scheme for assigning credits to multiple authors that we call a polynomial weight assignment scheme. We compare our scheme with other schemes proposed in the literature., Comment: 12 pages, 1 table, 1 figure

Assignment of weights to multiple authors of a paper is a challenging task due to its dependence on the conventions that may be different among different fields of research and research groups. In this paper, we describe a scheme for assignment of weights to multiple authors of a paper. In our scheme, weights are assigned in a linearly decreasing/increasing fashion depending upon the weight decrement/increment parameter. We call our scheme Arithmetic: Type-2 scheme as the weights follow an arithmetic series. We analyze the proposed weight assignment scheme and compare it with the existing schemes such as equal, arithmetic, geometric, and harmonic. We argue that the a positional weight assignment scheme, called arithmetic scheme, which we refer to Arithmetic: Type-1 in this paper, and the equal weight assignment scheme can be treated as special cases of the proposed Arithmetic: Type-2 scheme., Comment: 7 pages, 2 figures, 5 tables

The Lady Maisry ballads afford us a framework within which to segment a storyline into its major components. Segments and as a consequence nodal points are discussed for nine different variants of the Lady Maisry story of a (young) woman being burnt to death by her family, on account of her becoming pregnant by a foreign personage. We motivate the importance of nodal points in textual and literary analysis. We show too how the openings of the nine variants can be analyzed comparatively, and also the conclusions of the ballads., Comment: 27 pp., 13 figures. Submitted

File type identification and file type clustering may be difficult tasks that have an increasingly importance in the field of computer and network security. Classical methods of file type detection including considering file extensions and magic bytes can be easily spoofed. Content-based file type detection is a newer way that is taken into account recently. In this paper, a new content-based method for the purpose of file type detection and file type clustering is proposed that is based on the PCA and neural networks. The proposed method has a good accuracy and is fast enough., Comment: 6 Pages, 5 Figure, 2 Tables

In this article, we describe the XML storage system used in the WebContent project. We begin by advocating the use of an XML database in order to store WebContent documents, and we present two different ways of storing and querying these documents : the use of a centralized XML database and the use of a P2P XML database., Comment: Must be compiled with pdflatex