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The article focuses on the substitution of paper diaries by electronic diaries, used by the employees. Papermills are gigantic and complex machines, incorporating state-of-the-art technology. As part of a larger project a papermill in Finland was used to provide support for factory floor workers in process industries. Paper diaries were substituted with electronic diaries on one production line employing 35 workers. Entries constitute dialogues within and between work shifts, and partially with other organizational levels. The e-diary design was a cooperative effort between workers in an oil refinery a superintendent and foreman from the papermill, and the research group. It was piloted in the oil refinery, and a refined prototype was used in the papermill. All 35 production line staff workers received about four hours training on the diary including its underlying Microsoft Windows and Lotus Notes applications. E-diary entries are a text version of talking-out-loud-with extended spatial and temporal scope. INSET: An Example of Extended Dialogue Reject Carrier Problems in.....

The article offers suggestions to use less paper in an organization. Topics discussed include planning a filing system to retrieve information quickly, scanning the documents provided by clients rather than photocopying, organizing files in computer, investments in good hardware such as scanner and high speed internet connection; and keeping back up of the data for safety and security.

Abstract: Data hiding technique can facilitate security and the safe transmission of important information in the digital domain, which generally requires a high embedding payload and good stego image quality. Recently, a steganographic framework known as wet paper coding has been utilized as an effective strategy in image hiding to achieve the requirements of high embedding payload, good quality and robust security. In this paper, besides employing this mechanism as a fundamental stage, we take advantage of two novel techniques, namely, an efficient n-indicator and a fuzzy edge detector. The first is to increase the robustness of the proposed system to guard against being detected or traced by the statistics methods while allowing the receiver without knowledge of secret data positions to retrieve the embedded information. The second is to improve the payload and enhance the quality of stego image. The experimental results show that our proposed scheme outperforms its ability to reduce the conflict among three steganography requirements. [Copyright &y& Elsevier]

In real applications, the label information on many data is inaccurate, or a completely reliable label needs to be obtained at a high cost. The previous supervised hashing algorithms consider only the label information in the mapping process from Euclidean space to Hamming space when learning hash codes. However, there is no doubt that these algorithms are suboptimal in maintaining the relationships between high-dimensional data spaces. To overcome this problem, this paper advances a new weakly supervised discrete discriminant hashing (WDDH) to ensure a more effective representation of data and better retrieval of information. First, we consider the nearest neighbour relationship between samples, and new neighbourhood graphs are constructed to describe the geometric relationship between samples. Second, the algorithm embeds the learning of the hash function into the model and optimises the hash codes by a one-step iterative updating algorithm. Finally, it is compared with the existing classical unsupervised hashing algorithm and supervised hashing algorithm on different databases. The results and discussion of the experiments clearly show that the proposed WDDH algorithm in this paper is more robust for data representation in learning low-quality label data, coarse-grained label data and noisy data. [ABSTRACT FROM AUTHOR]

Many applications in cognitive science and artificial intelligence utilize semantic similarity and relatedness to solve difficult tasks such as information retrieval, word sense disambiguation, and text classification. Previously, several approaches for evaluating concept similarity and relatedness based on WordNet or Wikipedia have been proposed. WordNet-based methods rely on highly precise knowledge but have limited lexical coverage. In contrast, Wikipedia-based models achieve more coverage but sacrifice knowledge quality. Therefore, in this paper, we focus on developing a comprehensive semantic similarity and relatedness method based on WordNet and Wikipedia. To improve the accuracy of existing measures, we combine various taxonomic and non-taxonomic features of WordNet, including gloss, lemmas, examples, sister-terms, derivations, holonyms/meronyms, and hypernyms/hyponyms, with Wikipedia gloss and hyperlinks, to describe concepts. We present a novel technique for extracting ' is-a ' and ' part-whole ' relationships between concepts using the Wikipedia link structure. The suggested technique identifies taxonomic and non-taxonomic relationships between concepts and offers dense vector representations of concepts. To fully exploit WordNet and Wikipedia's semantic attributes, the proposed method integrates their semantic knowledge at feature-level, combining semantic similarity and relatedness into a single comprehensive measure. The experimental results demonstrate the effectiveness of the proposed method over state-of-the-art measures on various gold standard benchmarks. [ABSTRACT FROM AUTHOR]

The article focuses on the challenges and complexities associated with citations in research papers. It highlights the importance of citations in validating information and guiding further research, but acknowledges the confusion and difficulties students face in understanding different citation styles and formats. It also discusses the role of citation generators in helping students create accurate citations and mentions several popular citation generator tools.

The article discusses the role that records and information management (RIM) professionals can play in shaping information governance (IG) and information technology (IT). It states that IG has been described by research company Gartner as an accountability framework that encourages desirable behavior in the creation, storage, and deletion of information. The capability of RIM professionals to troubleshoot IG gaps and manage information through its life cycle were also mentioned.

This paper presents the theory of a novel memcomputing paradigm based upon a memristive version of standard Cellular Nonlinear Networks. The insertion of a nonvolatile memristor in the circuit of each cell endows the dynamic array with the capability to store and retrieve data into and from the resistance switching memories, obviating the current need for extra memory blocks. Choosing the parameters of each cell circuit so that the memristors may undergo solely sharp transitions between two states, each processing element may be approximately described at any time as one of two first-order systems. Under this assumption, the classical Dynamic Route Map may be employed to synthesise and analyse the data storage and retrieval genes. A new system-theoretic methodology, called Second-Order Dynamic Route Map, is also introduced for the first time in this paper. This technique allows to study the operating principles of arrays with second-order processing elements, as is the case, in the proposed network, if the set up of cell circuit parameters induces analogue memristive dynamics. This paper shows how the novel tool may be adopted to investigate the operating mechanisms of a cellular array with second-order cells, which compute the element-wise logical OR between two binary images. [ABSTRACT FROM AUTHOR]

These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal. [ABSTRACT FROM AUTHOR]

Ranking is one of the central machine learning tasks arising from supervised learning and has many important applications in information retrieval. Many ranking approaches have been designed and extensively studied. In this paper we study two kernel ranking algorithms, the regularized least square ranking and its bias corrected version, which learn scoring functions from reproducing kernel Hilbert spaces (RKHSs). We say a kernel is perfect for ranking if an optimal scoring function lies in the associated RKHS. It is imperfect if an optimal scoring function can be approximated by the RKHS. The two regularized kernel ranking algorithms have been shown theoretically justified and empirically effective when the kernel is perfectly chosen. In practice, however, kernels are usually selected via cross validation process. It is more common than not that the kernel is imperfectly chosen. We develop a novel leave-two-out analysis technique to evaluate the generalization performance of the two regularized ranking algorithms in this situation. We show that they are still effective and can achieve the capacity independent optimal convergence rates. [ABSTRACT FROM AUTHOR]

K-nearest neighbors search (K-NNS) is a fundamental problem in many areas of machine learning and data mining. In an attempt to solve NNS problems by locality-sensitive hashing (LSH)-based algorithms and avoid the F1-trap, in this paper, we theoretically prove the necessity of maintaining close edges of each dimension, the independence of the partition location and the precision of retrieval. Based on the theories, a new method is proposed for constructing the hash function family. Then, a volume-balancing locality-sensitive hashing (VBLSH) algorithm is implemented. In this algorithm, the problem of constructing the hash function family is transformed into an optimization problem under a fixed number of hypercubes. By solving the optimization problem, different hyperplane intervals are set for each dimension to ensure that the hash functions can evenly segment the data space, where each segmented hypercube has a close volume and close side lengths. Finally, the analysis of the algorithm shows that the VBLSH algorithm has a high index coding efficiency and hash code calculation efficiency. The experimental results show that the VBLSH algorithm can simultaneously take into account the efficiency, precision and recall rate, and avoid the F1-trap; it shows excellent effectiveness on 4 datasets. [ABSTRACT FROM AUTHOR]

Reports on the development of an electronic pen by Anoto of Lund. Ability of pen to recognize words and send them over the internet via mobile phone; How the pen works; Uses in the electronic commerce field; Information storage; Outlook for the pen.

• Based on type-2 fuzzy theory, a distributionally robust optimization method for the sustainable development problems is developed. • The robust counterpart of original distributionally robust fuzzy sustainable development model is established. • A domain decomposition method is designed to find the robust optimal solution that can immunize against the distribution uncertainty. The existing literature on sustainable development problems mainly focuses on multicriteria decision analyses, and ignores the impact of uncertain information on optimal labor allocation decisions. It is necessary for sustainable development to implement suitable policies under uncertainty that integrate several competing aspects including economic, environmental, energy and social criteria. Based on type-2 fuzzy theory, this paper develops a distributionally robust optimization method for sustainable development problems. In our new model, the uncertain per capita gross domestic product (GDP), per capita electricity consumption and per capita greenhouse gas (GHG) emissions are characterized by parametric interval-valued (PIV) possibility distributions and their associated uncertainty distribution sets. Under two assumptions on the underlining decision-making environment, the robust counterpart of the original distributionally robust fuzzy sustainable development model is formally established. To solve the proposed robust sustainable development model, this paper discusses the computational issue concerning the infinitely many integral objective functions and credibilistic constraints, and turns the robust counterpart model into its computationally tractable equivalent deterministic submodels. Taking advantage of the structural characteristics of the equivalent submodels, a domain decomposition method is designed to find the robust optimal solution that can protect against distribution uncertainty. Finally, this paper applies the proposed optimization method for the key economic sectors of the United Arab Emirates (UAE) to provide quantitative justification in planning future labor and resource allocation. [ABSTRACT FROM AUTHOR]

Since Torra introduced the concept of hesitant fuzzy sets, it has seen wide applications in decision-making problems, cluster analysis, medical diagnosis, personnel appraisal, information retrieval and so on. And, nowadays, there arise many discussions related to hesitant fuzzy sets. In particular, recently, Hu extends the truth values set of hesitant fuzzy sets and hesitant fuzzy relations from the family of subsets of unit interval to the family of nonempty subsets of any linearly ordered set with an involutive negator and calls them hesitant sets and hesitant relations, respectively. This paper continues to consider this topic and mainly intends to give a systematical discussion to hesitant relations on their properties and potential applications in three-way decisions. First, this paper shows some novel properties of the operations on linearly ordered set with an involutive negator. Second, this paper investigates some vital properties of hesitant relations, especially for the compositions, projections, reflexivity, symmetry, *-transitivity and so on. Third, this paper proposes various types of ordered hesitant relations. Finally, this paper discusses some applications of hesitant relations in the constructions of decision evaluation functions in three-way decision spaces. [ABSTRACT FROM AUTHOR]

Document clustering is a well-known text-mining method that assists in the categorization and comprehension of textual data. Document clustering is vital in areas like information retrieval, knowledge management, and marketing, underscoring the need for a highly accurate clustering model. Current models in document clustering face significant hurdles, such as dealing with sparse, high-dimensional representations based on the bag-of-words (BOW) approach, which are not only computationally demanding on large datasets but also lack in capturing the semantic nuances of documents. Additionally, these models struggle with determining the ideal number of clusters and managing datasets with overlapping elements. To overcome these issues, this paper introduces a novel co-clustering strategy that merges community detection methods from social network analysis with advanced text analysis techniques. The proposed method transforms documents into a network structure, where each document is a node and connections (edges) are formed between documents that are most similar. Community detection algorithms are then employed to identify clusters within this network of documents. The study explores various document representation methods, including topic modelling and sentence embedding, to provide a rich contextual understanding of the documents. An extensive evaluation is carried out, examining different combinations of community detection algorithms, clustering methodologies, and document representation strategies, particularly focusing on their efficacy in handling overlapping and non-overlapping datasets. The findings demonstrate that the Element-Centric evaluation measure is effective in enabling community detection algorithms to autonomously ascertain the most suitable number of clusters, yielding promising results for both overlapping and non-overlapping datasets. The LCD model shows remarkable performance in addressing overlapping datasets. Furthermore, the research reveals that innovative document representation approaches significantly enhance the performance of the models. Additionally, the use of topic modelling in conjunction with co-clustering algorithms proves effective in clearly depicting the themes within the clusters. • A co-clustering approach has been introduced for document clustering. • A network construction method is presented to transform documents into a network. • Community detection algorithms are used to extract clusters of connected documents. • Topic modelling and sentence embedding used for feature representation [ABSTRACT FROM AUTHOR]

Data engineers invest significant effort in the early stages of data analysis, including identifying relevant datasets in large and complex data lakes. Data retrieval efficiency becomes an urgent concern as data volumes continue to increase with structures that vary in size and complexity. This paper presents a novel strategy for accelerating data discovery in data lakes. Our approach integrates self-supervised techniques and weighted similarity estimation for efficient dataset classification, facilitating faster search and retrieval. By extracting meta-feature characteristics, our approach improves data traceability in data lakes through clustering, resulting in significant improvements in modularity, showing an efficiency gain of 69.8%. Regarding dataset search through classification, it consistently achieves AUC-ROC scores exceeding 0.85, indicating strong performance in class differentiation. Finally, our method reduces the overall execution time more than twofold and shows promising applications for addressing real-world challenges in various data lake domains. [ABSTRACT FROM AUTHOR]

Text similarity measures have become increasingly pivotal in text-related research and applications, encompassing tasks such as information retrieval, text classification, document clustering. Unveiling the equality relationship between encrypted words is fundamental for ensuring privacy-preserving text similarity. Identity-based encryption with equality test (IBEET) emerges as a promising solution for computing similarity over encrypted data in cloud environment. However, prevalent IBEET schemes often lack control over the lifespan of trapdoors, potentially leading to misuse and unauthorized disclosure of users' privacy. In this paper, we introduce a novel primitive known as controllable forward secure identity-based encryption with equality test (CFS-IBEET), designed to support both permanent trapdoor and temporary trapdoor. We present a concrete CFS-IBEET scheme based on bilinear pairing and conduct a comprehensive security analysis against two types of adversaries in random oracle model. The comprehensive performance evaluation shows that our CFS-IBEET scheme offers significant advantages of high efficiency of encryption, decryption, trapdoor generation and test algorithms as well as the guarantee of controllable forward security, showcasing its applicability for collaborative filtering recommendation system in E-commerce age. • Propose a stronger IBEET for privacy-preserving text similarity. • Flexibly control the granularity of time-based authorization for forward security. • Formally prove its security against two adversaries in random oracle model. • Show its practical application in collaborative filtering recommendation system thanks to its significant high efficiency. [ABSTRACT FROM AUTHOR]

In this article, the author focuses on CyberAll project to encode, store and allow easy retrieval of all of a person's information for personal and professional use. Information is held in multiple formats to increase the likelihood for long-term retrievability. CyberAll archive includes books, CDs, correspondence, transactions, papers, photos and albums, and video; currently held in the Windows file system. The goal of the CyberAll project is to understand the problems of coping with the exponential increase in the amount of information that is becoming part of both personal and professional lives and encode everything and eliminate paper that is used for storage and transmission. There are a plethora of functions that can be invented to facilitate filing, labeling, and retrieval. The author has also used descriptive file names to aid retrieval. A name might include subject, organization, keywords and a date. It is forecasted that within the next decade personal computers will be capable of storing a terabyte of information on an individual machine. The author concludes that a system such as CyberAll could quite possibly be a killer application for personal computers.

The article offers information on a nation's economy is described in numbers, but academicians use words to explain those numbers. It discusses that Scholarly papers forecasting the state of the economy are more than equally matched with opinion pieces in the daily press that also attempt to forecast the state of the economy; and mentions that EconLit, the primary database covering the scholarly literature of economics, is produced by the American Economic Association (AEA).

Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication. [ABSTRACT FROM AUTHOR]

Mesa is a highly scalable analytic data warehousing system that stores critical measurement data related to Google's Internet advertising business. Mesa is designed to satisfy a complex and challenging set of user and systems requirements, including near real-time data ingestion and retrieval, as well as high availability, reliability, fault tolerance, and scalability for large data and query volumes. Specifically, Mesa handles petabytes of data, processes millions of row updates per second, and serves billions of queries that fetch trillions of rows per day. Mesa is geo-replicated across multiple datacenters and provides consistent and repeatable query answers at low latency, even when an entire datacenter fails. This paper presents the Mesa system and reports the performance and scale that it achieves. [ABSTRACT FROM AUTHOR]

The Library Marketing Project Team of the Andalusian Association of Librarians presents a guide for a social media plan for libraries. This guide is intended for public libraries in search of a tool to assist the librarians who may want to use social networks as a means of effective communication with the library users. The stages and most relevant aspects for a social media plan are listed throughout the paper. The most relevant platforms for data retrieval are also mentioned. The paper reviews platforms that may supply relevant information for the knowledge of users, and platforms that may have data about our influence on users via the social media. This allows for the adjustment and optimization of work plans over time. [ABSTRACT FROM AUTHOR]

Abstract A certificateless aggregate signature (CLAS) scheme employing a reduced signature size eliminates the complexity of certificate management in a traditional public key cryptosystem. A compact aggregate signature, where the size does not depend on the aggregate number, is desirable when the objective is to reduce storage cost and bandwidth. However, in conventional compact CLAS schemes, aggregation of signatures is restricted. The state information is meant to be used only once for security; it is generally used to restrict aggregation to signatures generated with the same state information. This paper proposes the first unrestricted and compact CLAS scheme where the signature size is constant and any combination of signatures can be aggregated. Aside from convenient storage and communication costs, our scheme is also equipped with a secure system against realistic adversaries. Moreover, we evaluate the performance of our CLAS scheme and demonstrate its effectiveness. Finally, this paper reveals that it is not possible to construct an unrestricted and compact CLAS scheme for a widely used structure with constant pairing computation. [ABSTRACT FROM AUTHOR]

Abstract A Top-k retrieval algorithm returns the k best answers of a query according to a given ranking. From a theoretical point of view, the solution of this query is straightforward if we do not take into consideration execution time. However, in many practical situations, execution time is a substantial issue. In such a case, the process of getting the top k in an optimal time becomes an interesting and non-trivial task. This paper presents an algorithm to retrieve the top-k associated to an arbitrary ranking function. The idea is to decompose the ranking function as a supremum of a certain set of functions where an efficient top-k retrieval procedure can be easily applied. Besides the theoretical presentation of the algorithm, the paper provides a set of experiments to validate the approach. [ABSTRACT FROM AUTHOR]

Memristors are being explored as a potential technology to replace CMOS for logic-in-memory systems that exploit the memristive non-volatility. Memristors are two-terminal, non-volatile device that exhibit a variable resistance that is dependent on the applied voltage history of the device, providing the capability to store and process information within the same structure. The ability of memristors to perform logic has been previously demonstrated, but previous analyses of memristor logic efficiency have not included the overhead CMOS circuitry that is required to control memristor logic operations. In this paper, the required overhead CMOS circuitry for implementing logic with memristors is evaluated for standard logic gates and a one-bit full adder to enable an analysis of the overall system efficiency. The results show that the number of CMOS devices in the overhead circuitry can be upwards of 50 times that of a conventional CMOS implementation, and that the power-delay product of the memristor logic with overhead circuitry is roughly one billion times greater than for conventional CMOS circuits. These results enable the conclusion that the overhead circuit requirements for stateful memristor logic threaten to negate any efficiency improvements that are achieved by the memristors themselves. [ABSTRACT FROM AUTHOR]

Information storage and transfer via current-induced domain wall (DW) motions exhibit significant density-speed-energy advantages, which inspires numerous emerging devices and circuits, such as racetrack memory (RM). However, the bi-directional propagation of DWs in the conventional tape-shaped nanowire will lead to data overflow issue, implicitly deteriorating storage density and operational performances. In this paper, we propose a non-volatile cache design based on spin-orbit torque-driven ring-shaped RM. The systematical investigations, covering from device modeling, to circuits design, to bit-cell layout design, and to system evaluation have been carried out. Thanks to the cells-overlapping design, the proposed RM L2 cache can achieve $48\times $ , $16\times $ , and $8\times $ improvements in term of capacity, compared with iso-area caches based on static random access memory (SRAM), spin transfer torque magnetic RAM (STT-MRAM), and tape-shaped RM, respectively. As proved by 4-core system experiment results, the proposed RM cache can improve 30.7% instructions per cycle (IPC) and save 58.2% energy compared with SRAM cache. [ABSTRACT FROM AUTHOR]

Because of the problems created by the explosion of papers in the mathematical sciences and the drawbacks that this places on research, it is suggested that a tree of all mathematical results and terminology be maintained in a multiterminal computer system. Users of the system can store in the computer an updated file of their current knowledge, and on selecting a paper to read, they can obtain from the computer the minimum subtree of theorems required to bring them from what they already know to the background knowledge which the paper assumes. Under certain conditions, means are also provided for the contribution of useful comments by the readers of a work and for interaction between commentators and with the author. This paper describes how the system can be organized and the role required of readers, writers, and commentators. [ABSTRACT FROM AUTHOR]

Sussenguth suggests in a paper (1963) that a file should be organized as a doubly-chained tree structure if it is necessary both to search and to update frequently. Such a structure provides a compromise between the fast search/slow update characteristics of binary searching and the slow search/fast update characteristics of serial searching. His method, however, contains the limiting restriction that all terminal nodes lie on the same level of the tree. This paper considers the effect of relaxing this restriction. First, trees which have the property that a priori the filial set of each node is well defined are studied. It is proved that coding the nodes within each filial set with respect to the number of terminal nodes reachable from each is necessary and sufficient to guarantee minimum average search time. Then the more general case (that is, where the entire structure of the tree is changeable) is treated. A procedure is developed for constructing a tree with a minimum average search time. A simple closed expression for this minimum average search time is obtained as a function of the number of terminal nodes. The storage capacity required to implement the doubly-chained tree structure on a digital computer is also determined. Finally, the total cost of the structure, using Sussenguth's cost criterion, is computed. It is shown that significant improvements in both the average search time and the total cost can be obtained by relaxing Sussenguth's restriction that all terminal nodes lie on the same level of the tree. [ABSTRACT FROM AUTHOR]

The article discusses seven ways to eliminate paper processes from the work flow of behavioral health providers. Topics covered include the financial and organizational challenges in complying with electronic health records (EHRs), the Medicaid EHR incentive, and the options to avoid paperwork inefficiencies such as scanning historical clinical data, migration to electronic billing, and using electronic signatures.

Personalized information retrieval has become more crucial and challenging in today's time, as every user is demanding information according to their own perspectives. To achieve personalization, this paper proposes a methodology with a focus on the selection of an effective composition corresponding to various supporting modules of a personalization methodology. Collaborative tagging can be quite helpful in constructing User Interest Profile (UIP) and Resource Illustration Profile (RIP). The proposed methodology also focuses on UIP augmentation using multiple strategies; and a novel approach has also been designed to handle outlier tags which caused ambiguity in collective RIP. Even a good UIP and RIP alone cannot create an efficient personalization methodology; they also require a suitable mapping with user's query requirement. Therefore, in the proposed methodology, the fuzzy satisfaction requirement-based novel mapping functions have been designed to measure query relevance score and user interest relevance score for a web resource. These scores have been further used to calculate the post-relevance score of a web resource after a suitable trade-off. Experiments using the del.icio.us dataset show that the proposed methodology has outperformed each and every baseline by a considerable margin. [ABSTRACT FROM AUTHOR]

Private Information Retrieval (PIR) allows a client to privately retrieve some data from a public database. There exist two types of PIR: (computational) Single-server PIR (SPIR) and (information-theoretic) multi-server PIR. In this paper, we focus on exploring SPIR. We first propose a simple and efficient additively-homomorphic encryption scheme of which privacy is based on the learning with binary errors assumption that is known as an interesting candidate for practical lattice-based cryptography. Then, according to our proposed homomorphic encryption scheme, we give a Verifiable (single/multi-bit) SPIR (VSPIR) scheme for the single-query case under the malicious server model. To the best of our knowledge, our proposal is the first practical non-interactive VSPIR scheme employing an efficient probabilistic proof that can discover the forged result with overwhelming probability. The corresponding communication complexity and computational complexity are comparable with those of some typical SPIR schemes. Moreover, we extend our single-query VSPIR scheme to construct a non-interactive multi-query solution. In particular, the corresponding communication complexity and computational complexity are the same as those of the single-query scheme. Finally, we provide detailed implementation results to confirm efficiency of our proposals. [ABSTRACT FROM AUTHOR]

• A new fuzzy squareness measure for evaluating how much a given shape is a fuzzy square is introduced. • Such a measure ranges through (0, 1], and reaches maximal value 1 iff for a fuzzy square. • The new fuzzy squareness measure is invariant w.r.t. similarity transformations. • The new measure is naturally defined and theoretically well-founded. • Effectiveness of the new measure is illustrated through classification tasks performed on three large well-known image datasets. In this paper, we define a new fuzzy squareness measure to quantify how much a given fuzzy shape matches a fuzzy square. The new fuzzy shape-based measure is naturally defined and theoretically well-founded, resulted in that its behavior can be understood and predicted in advance. It runs through the interval (0 , 1 and takes the maximum value equals 1 if and only if the shape measured is a fuzzy square. The new fuzzy squareness measure is also invariant to similarity transformations. Several various experiments to illustrate the behavior of the new measure, and to verify all the theoretically proven results are also shown. Effectiveness and usefulness of the new fuzzy squareness measure are demonstrated in the tasks of object classification performed on three large well-known modern image datasets such as MPEG-7 CE-1, Swedish Leaf, and Portuguese Leaves datasets. [ABSTRACT FROM AUTHOR]

A classic t -private PIR (private information retrieval) scheme allows a user to retrieve one out of n values from k communicating replicated databases while any t of the k databases cannot identify the value being retrieved. However, in reality, the user may be more interested in retrieving multiple values simultaneously. This paper is devoted to the PIR problem with m retrieved values and k databases against t colluding databases (t -private MPIR) in the context of information-theoretic security. The relationship m ⩽ w (k - t) is derived, where w is the largest average amount of information got by the user from each of the k databases. When w = 1 , via trace functions in finite fields, a t -private MPIR scheme retrieving m = k - t values is presented with the complexity O (log n). Our scheme provides a lower complexity than those in the literature (the best known complexity for general t and k is a fractional power function of n). [ABSTRACT FROM AUTHOR]

Source selection is the problem of identifying a subset of available data sources that best meet a user’s needs. In this paper we propose a user-driven approach to source selection that seeks to identify sources that are most fit for purpose. The approach employs a decision support methodology to take account of a user’s context, to allow end users to tune their preferences by specifying the relative importance between different criteria, looking to find a trade-off solution aligned with his/her preferences. The approach is extensible to incorporate diverse criteria, not drawn from a fixed set, and solutions can use a subset of the data from each selected source, rather than require that sources are used in their entirety or not at all. The paper describes and motivates the approach, presenting a methodology for modelling a user’s context, and its collection of optimisation algorithms for exploring the space of solutions, and compares and evaluates the resulting algorithms using multiple real world data sets. The experiments show how source selection results are produced that are attuned to each user’s preferences, both with respect to overall weighted utility and through faithful representation of a user’s preferences within a result, while scaling to potentially thousands of sources. [ABSTRACT FROM AUTHOR]

Purpose The paper aims to provide a semiotic interpretation of the role played by entanglement in quantum-based models aimed to information retrieval and suggests possible improvements. Actual models are capable of retrieving documents relevant to a query composed of a keyword and its acceptation expressed by a given context. The paper also considers some analogies between this technique and quantum-based approaches in other disciplines to discuss the consequence of this quantum turn, as epistemology and philosophy of language are concerned.Design/methodology/approach We use quantum geometry to design a formal model for textual semiotics. In particular, the authors refer to Greimas’s work on semantics and information theory, to Eco’s writings on semantic memory and to Lotman’s work on a cybernetic notion of culture.Findings Quantum approaches imply a particular point of view on meaning. Meaning is not a real, positive quality of a given word. It is a net of relations constructed in the text, whose value is progressively determined during the reading process. Furthermore, reading is not a neutral operation: to read is to determine meaning. If it is said that, from a general semiotic point of view, meaning is stored in quantum semantic memories and is read/written by semantic machines, then the operation of “reading/writing” is analogous to the operation of measuring in quantum theory: in other terms, meaning is a value, and this implies an instance (not necessarily human) according to which values are valuable.Research limitations/implications The authors are not proposing a complete quantum semantics. At the present, quantum information retrieval can detect the presence of semantic relations. The authors suggest a way to characterize them, leaving open the problem on how to formalize the document as a vector in four-state semantic space.Practical implications A quantum turn shows deep semiotic implications on the approach to language, which shows an immanent semantic organization not reducible to syntax and morphology. This organization is probabilistic and indeterministic and explains to what extent text fixes the meaning of its lexical units.Social implications In the authors’ perspective, signification is not the exclusivity of a human subject. Criticizing Turing test, the great semiotic and cybernetic scholar Jurij Lotman wrote that if we identify “intelligent” and “human”, we raise the failings of an actual form of intelligence to the rank of an essential characteristic. On this line, meaning is considered as a feature of social, artificial and biological systems.Originality/value The adoption of quantum formalism seems in line with cybernetic framework, involving a probabilistic, non-cartesian point of view on meaning aimed to critically discuss the human–machine relation. Furthermore, Quantum theory (QT) implies a phenomenological point of view on the conditions of possibility of meaning. [ABSTRACT FROM AUTHOR]

Statistical matching aims at combining information available in distinct sample surveys referred to the same target population. The matching is usually based on a set of common variables shared by the available data sources. For matching purposes just a subset of all the common variables should be chosen, the so called matching variables. The paper presents a novel method for selecting the matching variables based on the analysis of the uncertainty characterizing the matching framework. The uncertainty is caused by unavailability of data for estimating parameters describing the association between variables not jointly observed in a single data source. The paper focuses on the case of categorical variables and presents a sequential procedure for identifying the most effective subset of common variables in reducing the overall uncertainty. [ABSTRACT FROM AUTHOR]

Question Answering (QA) systems based on Information Retrieval return precise answers to natural language questions, extracting relevant sentences from document collections. However, questions and sentences cannot be aligned terminologically, generating errors in the sentence retrieval. In order to augment the effectiveness in retrieving relevant sentences from documents, this paper proposes a hybrid Query Expansion (QE) approach, based on lexical resources and word embeddings, for QA systems. In detail, synonyms and hypernyms of relevant terms occurring in the question are first extracted from MultiWordNet and, then, contextualized to the document collection used in the QA system. Finally, the resulting set is ranked and filtered on the basis of wording and sense of the question, by employing a semantic similarity metric built on the top of a Word2Vec model. This latter is locally trained on an extended corpus pertaining the same topic of the documents used in the QA system. This QE approach is implemented into an existing QA system and experimentally evaluated, with respect to different possible configurations and selected baselines, for the Italian language and in the Cultural Heritage domain, assessing its effectiveness in retrieving sentences containing proper answers to questions belonging to four different categories. [ABSTRACT FROM AUTHOR]

Chameleon hash functions are collision resistant when only the hashing keys of the functions are known. In particular, without the knowledge of the secret information, the chameleon hash function is merely like a regular cryptographic hash function, where it is hard to find collisions. However anyone who has trapdoor keys can efficiently generate pre-images for the chameleon hash function. In some applications, such as redactable blockchains, unfortunately the existing properties do not suffice and we need more features. Actually, it is required that without knowing the trapdoor keys, nobody can compute collisions, even if he can see collisions for arbitrary hash functions. In 2017, Ateniese et al. introduced the notion of chameleon hash functions in the enhanced collision resistant model and proposed a construction in the standard model satifying the features. To date, efficient constructions of this kind of chameleon hash functions remain as an open research problem. In this paper, we answer this problem affirmatively by presenting efficient constructions of the chameleon hash function satisfying the enhanced collision resistance. The contributions of this work are twofold. First, we show the weakness of previous work. Then, we proceed with proposing new schemes with more efficiency. Technically, we present a new chameleon hash function in the basic model and based on simple assumptions. This chameleon hash function is well compatible with Groth-Sahai proof systems and the Cramer-Shoup encryption schemes, and can be used as a stepping stone to construct an efficient chameleon hash function in the enhanced collision resistant model. Moreover, we show our basic chameleon hash can be combined with optimal ZK-SNARKs of Groth and Maller that leads to shorter sizes for chameleon hash function in the enhanced collision resistant model. [ABSTRACT FROM AUTHOR]

• The sequential and tree-structured LSTM with attention is proposed. • Word-based features can enhance the relation extraction performance. • The proposed method is used for the relation extraction. • The relation extraction performance is demonstrated on public datasets. Semantic relation extraction is crucial to automatically constructing a knowledge graph (KG), and it supports a variety of downstream natural language processing (NLP) tasks such as query answering (QA), semantic search and textual entailment. In addition, the semantic relation extraction task is mainly responsible for identifying entity pairs from raw texts and extracting the semantic relations between the extracted entity pairs. Existing methods consider only lexical-level features and often ignore syntactic features, resulting in poor relation extraction performance. By analyzing the necessity of the syntactic dependency and the contributions of words in a sentence to relation extraction, this paper proposes an end-to-end method that uses bidirectional tree-structured long short-term memory (LSTM) to extract structural features based on the dependency tree of a sentence. To enhance the performance of the relation extraction, the bidirectional sequential LSTM with attention is used to identify word-based features including the positional information of entity pairs and the contribution of words. Then, structural features and word-based features are concatenated to optimize the relation extraction performance. Finally, the proposed method is used on the SemEval 2010 task 8 and the CoNLL04 datasets to validate its performance. The experimental results show that the proposed method achieves state-of-the-art results on the SemEval 2010 task 8 and the CoNLL04 datasets. [ABSTRACT FROM AUTHOR]

When optimizing many-objective problems (MaOP), the same strategy might behave differently when facing problems with different features. Therefore, obtaining problem features helps to obtain high-quality solutions. However, in practice, the problem features are unknown during the optimization process. In this case, learning to adjust strategies to match the problem features is a challenging work. In this paper, a learning-based algorithm is proposed, aimed to enhance the generalization ability. On the basis of a decomposition-based many-objective optimization framework, a learning automaton (LA) is included in the algorithm. The LA adjusts the evolutionary strategies of the algorithm to adapt to the problem characteristics, according to the feedback information during the optimizing procedure. An external archive is employed to store the Pareto non-dominant solutions. Based on the external archive, a reference vector adjustment strategy is designed to enhance the capability of solving problems with a degenerate or discrete Pareto front (PF). To validate the performance of the proposed algorithm, a comparison experiment is conducted on a novel authority test suite. Five state-of-the-art algorithms are selected as peer algorithms. The results of the experiment indicate that the proposed algorithm obtains satisfactory performance in determining the convergence and the approximation of the PF. [ABSTRACT FROM AUTHOR]

• A new multi-layer interaction preference based multi-objective optimization algorithm through decomposition (MLIP-MOEA/D) is proposed. • A fast way to get preference region is established, during the selection process, the preference weight vector is redefined using the angle-based method. • A novel multi-layer interaction strategy for searching the accurate solutions is proposed. During evolution, the preferred region is reduced gradually, making it easier for user to get the final preferred solution(s). • Besides ZDT problems with two objectives, the proposed algorithm shows its potentiality for solving many-objective problems, i.e., DTLZ problems with 3, 5, 8, 10 objectives. Many problems in real world have not only one objective to be met. In the majority of cases, a set of trade-off solutions which spread evenly along the entire Pareto optimal front are generated by multi-objective evolutionary algorithms (MOEAs). Taking the preference of decision maker (DM) into consideration, some specified solutions can be obtained, which is of great interest in practical applications. In this paper, a novel multi-layer interaction preference based multi-objective evolutionary algorithm through decomposition (denoted as MLIP-MOEA/D) is proposed. In MLIP-MOEA/D, a multi-layer interactive strategy is developed during evolution, in the first-layer interaction, the DM will provide a reference vector and an initial radius to determine a preference range, then all solutions in this range will be updated. The algorithm will stop if the DM is satisfied with the first output result, otherwise it will go on to the second-layer interaction. In this step, the most preferred solution generated from the first-layer interaction will be chosen as the new preference direction, and the weight vector is redefined by the angle-based method, and the range of preferred region is reduced gradually, until the closest solution that meet the DM's need is found. The algorithm is tested on a set of benchmark problems including DTLZ problems with more than three objectives, the experimental studies show that the proposed algorithm can effectively search the preferred solutions with the preference information and successfully deal with many-objective optimization problems. [ABSTRACT FROM AUTHOR]

This paper focuses on the problem of constructing aggregation functions which support the concept of partial agreement when only one or more sub-group(s) of decision makers and criteria, not necessarily all of them, is (are) involved to take the final action. The proposed method is started with computing all different α -combinations from an n -element set of inputs, according to the binomial coefficient, and then completed by combining their aggregations. This approach, so-called combination operator-based aggregation function, guarantees to consider various agreement scenarios at different consensus levels in contrary with the traditional composition-based construction methods that act as a full agreement. The attention is then given to the weighted case specially the conditions of weighting vectors for ensuring the well-defined property of the proposed method. As an application, the combination operator-based aggregation function is generalized over the Cartesian product of unit intervals [0,1] to deal with multi-polar information. The proposed aggregating methodology is then used to reach consensus in a group decision-making problem. [ABSTRACT FROM AUTHOR]

Collaborative filtering (CF) approaches are widely applied in recommender systems. Traditional CF approaches have high costs to train the models and cannot capture changes in user interests and item popularity. Most CF approaches assume that user interests remain unchanged throughout the whole process. However, user preferences are always evolving and the popularity of items is always changing. Additionally, in a sparse matrix, the amount of known rating data is very small. In this paper, we propose a method of online collaborative filtering with dynamic regularization (OCF-DR), that considers dynamic information and uses the neighborhood factor to track the dynamic change in online collaborative filtering (OCF). The results from experiments on the MovieLens100K, MovieLens1M, and HetRec2011 datasets show that the proposed methods are significant improvements over several baseline approaches. [ABSTRACT FROM AUTHOR]

A new rough neural network (RNN)-based model is proposed in this paper. The radial basis function network with dynamic decay adjustment (RBFNDDA) is applied to learn information directly from a data set and group it in terms of prototypes. Then, a neighborhood rough set-based procedure is applied to detect prototype outliers. This hybrid model is named rough RBFNDDA1. However, the removal of all outliers may cause information loss because some outliers may represent rare yet useful information in a classification task. As such, the parameters of a prototype outlier, i.e., its radius and weight, are exploited to gauge whether the information encoded by the prototype is meaningful. This hybrid model is named rough RBFNDDA2. The results from a benchmark experimental study show that rough RBFNDDA2 can retain meaningful prototype outliers and, at the same time, significantly reduce the number of prototypes from the original RBFNDDA model while maintaining classification accuracy. A real-world application in a power generation plant is used to evaluate and demonstrate the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Source retrieval for plagiarism detection retrieves documents that may be the sources of plagiarism for a suspicious document while minimizing the retrieval costs. Compared with traditional information retrieval, queries from the same suspicious document retain their contextual relations and are no longer isolated from each other. The correlation of queries leads to the aggregation of search results; the documents retrieved by different queries may be relevant or the same. However, previous studies have failed to devote sufficient attention to the aggregation of search results in source retrieval. In this paper, the task of source retrieval is formalized into a framework of learning to rank, and a Ranking Logistical Regression Model is utilized to implement the framework. Furthermore, addressing the aggregation of search results, we propose the Source Retrieval Model Focused on Aggregation for plagiarism detection. We evaluate various aspects of the proposed model on the PAN 2013 and the PAN 2014 Plagiarism Source Retrieval Corpus. With respect to established baselines, the experimental results indicate that the Source Retrieval Model Focused on Aggregation yields statistically significantly improved performance of source retrieval with equal or lower cost effectiveness. [ABSTRACT FROM AUTHOR]

The notion of uncertainty measure is one of the most important topics in rough set theory and has been studied in different kinds of information tables. However, few studies have focused on the interval set information table, which is regarded as one of the generalized models of single-valued information tables. This paper aims at studying the uncertainty measurements for interval set information tables. Firstly, an interval δ -similarity relation is induced based on the similarity degree. The similarity relation induces the granules, which form a covering in interval set information tables. Secondly, four types of granularity measures are defined to measure the granularity of a covering. Thirdly, the concepts of accuracy and roughness in rough set theory are respectively extended to δ -accuracy and δ -roughness for interval set information tables. Furthermore, four new combinations ofuncertainty measures by considering proposed granularity measures and δ -accuracy and δ -roughness are defined and analyzed. Theoretical analyses and experimental results illustrate that the proposed measures are effective and accurate for interval set information tables. [ABSTRACT FROM AUTHOR]

Link prediction is an important task in complex network analysis and can be found in many real-world applications such as recommendation systems, information retrieval, and marketing analysis of social networks. This paper focuses on studying the evolution mechanism of real-world temporal networks. Specifically, given a set of temporal links during a fixed time window, how to predict the existence of links at any point in the future. To address this problem, we propose a novel semi-supervised learning framework, which integrates both survival analysis and game theory. First, we carefully define the ϵ-adjacent network sequence, and make use of time stamp on each link to generate the baseline network evolution sequence. Next, to capture the law of network evolution, we employ the Cox Proportional Hazard Model (Cox PHM) to study the relative hazard associated with each temporal link, so as to estimate the coefficients of covariates, which are defined as a set of neighborhood based proximity features. To narrow the area of inquiry, we further propose a game theory based two-way selection mechanism to predict the future network topology. We finally encapsulate these two new technologies in a robust Autonomy-Oriented-Computing (AOC) multi-agent system, and propose a paralleled algorithm to conduct the temporal link prediction task. Extensive experiments were applied to real-world temporal networks to demonstrate both effectiveness and scalability of the proposed approach. [ABSTRACT FROM AUTHOR]

• An optimized ranking algorithm for manycore systems. • Different shared-memory parallelization strategies are investigated. • A thorough evaluation of the proposed optimizations using a 72-core machine. Multimedia data, such as images and videos, has become very popular in people's daily life as a result of the widespread use of mobile devices. The ever-increasing amount of such data, along with the necessity for real-time retrieval, has lead to the development of new methods that can process them in a timely fashion with acceptable accuracy. In this paper, we study the performance of ReckNN, an unsupervised manifold learning algorithm based on the reciprocal neighbourhood and the authority of ranked lists. Most of the related work in this field do not fully investigate optimization strategies, an aspect that is becoming more important with the high availability of manycore machines. In order to address that issue, we fully investigate optimization opportunities in this article and make the following three main contributions. Firstly, we develop an efficient and scalable method for storing and accessing the distances between objects (e.g., video or image) based on dictionaries. Secondly, we employ memoization to speed up the computation of authority scores, leading to a significant performance gain even on single-core architectures. Lastly, we devise and implement several parallelization strategies and show that they are scalable on a 72-core Intel machine. The experimental results with MPEG-7, Corel5k and MediaEval benchmarks show that the optimized ReckNN delivers both efficiency and scalability, highlighting the importance of the proposed optimizations for manycore machines. [ABSTRACT FROM AUTHOR]

False or unverified information spreads just like accurate information on the web, thus possibly going viral and influencing the public opinion and its decisions. Fake news and rumours represent the most popular forms of false and unverified information, respectively, and should be detected as soon as possible for avoiding their dramatic effects. The interest in effective detection techniques has been therefore growing very fast in the last years. In this paper we survey the different approaches to automatic detection of fake news and rumours proposed in the recent literature. In particular, we focus on five main aspects. First, we report and discuss the various definitions of fake news and rumours that have been considered in the literature. Second, we highlight how the collection of relevant data for performing fake news and rumours detection is problematic and we present the various approaches, which have been adopted to gather these data, as well as the publicly available datasets. Third, we describe the features that have been considered in fake news and rumour detection approaches. Fourth, we provide a comprehensive analysis on the various techniques used to perform rumour and fake news detection. Finally, we identify and discuss future directions. [ABSTRACT FROM AUTHOR]

The number of component classifiers chosen for an ensemble greatly impacts the prediction ability. In this paper, we use a geometric framework for a priori determining the ensemble size, which is applicable to most of the existing batch and online ensemble classifiers. There are only a limited number of studies on the ensemble size examining majority voting (MV) and weighted MV (WMV). Almost all of them are designed for batch-mode, hardly addressing online environments. Big data dimensions and resource limitations, in terms of time and memory, make the determination of ensemble size crucial, especially for online environments. For the MV aggregation rule, our framework proves that the more strong components we add to the ensemble, the more accurate predictions we can achieve. For the WMV aggregation rule, our framework proves the existence of an ideal number of components, which is equal to the number of class labels, with the premise that components are completely independent of each other and strong enough. While giving the exact definition for a strong and independent classifier in the context of an ensemble is a challenging task, our proposed geometric framework provides a theoretical explanation of diversity and its impact on the accuracy of predictions. We conduct a series of experimental evaluations to show the practical value of our theorems and existing challenges. [ABSTRACT FROM AUTHOR]