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The article presents the position paper issued by the World Health Organization (WHO) regarding malaria vaccines that was reviewed by the WHO Malaria Policy Advisory Group (MPAG). Also cited are the method used by WHO in assessing the vaccines' efficacy, how malaria is transmitted as a vector-borne disease, and the number of deaths caused by malaria around the world.

The article focuses on the second licensed dengue vaccine, TAK-003, along with the position of the World Health Organization (WHO) for its use, and provides an update on the first licensed dengue vaccine, CYD-TDV. Topics discussed include epidemiology and transmission of dengue, classifications made by the WHO on dengue illness, and diagnosis, treatment and prevention.

Purpose: This paper aims to develop an algorithm to study the impact of dynamic resource disruption on project makespan and provide a suitable resource disruption ratio for various complex industrial and emergency projects. Design/methodology/approach: This paper addresses the RCPSP in dynamic environments, which assumes resources will be disrupted randomly, that is, the information about resource disruption is not known in advance. To this end, a reactive scheduling model is proposed for the case of random dynamic disruptions of resources. To solve the reactive scheduling model, a hybrid genetic algorithm with a variable neighborhood search is proposed. Findings: The results obtained on the PSLIB instances prove the performance advantage of the algorithm; through sensitivity analysis, it can be obtained, the project makespan increases exponentially as the number of disruptions increase. Furthermore, if more than 50% of the project's resources are randomly disrupted, the project makespan will be significantly impacted. Originality/value: The paper focuses on the impact of dynamic resource disruptions on project makespan. Few studies have considered stochastic, dynamic resource uncertainty. In addition, this research proposes a reasonable scheduling algorithm for the research problem, and the conclusions drawn from the research provide decision support for project managers. [ABSTRACT FROM AUTHOR]

Observing that the transition to the 988 National Suicide and Crisis Lifeline offers an opportunity to grow crisis response services along a continuum, the National Association of State Mental Health Program Directors (NASMHPD) and the Substance Abuse and Mental Health Services Administration (SAMHSA) last month released a series of 10 technical assistance collaborative papers providing important resources for stakeholders. [ABSTRACT FROM AUTHOR]

The article reports that the World Health Organization has issued a series of position papers on vaccines and combinations of vaccines against diseases that have an international public health impact. Topics mentioned include recommendations on the use of Bacille Calmette-Guérin vaccine for the prevention of leprosy, and the estimated number of people around the world to be infected with Mycobacterium tuberculosis.

The article focuses on some studies related to psychotheraphy published in the recent issue of the journal "Counseling and Psychotherapy Research (CPR)." One of the studies found that practitioners and clients experience a shared powerlessness when asylum rejections interrupts the therapeutic process abruptly.

The article offers information on the intersection of AI, research, and its impact on scientific literature. Topics include concerns about the ethical implications of AI-generated content, fraudulent research papers created by paper mills, and the increasing retractions of scientific literature. The author reflects on the potential consequences of the rise in spurious publications on research impact metrics and raises concerns about research integrity.

Reasons that the use of paper in law offices is important in providing and acquiring information. Comparison of using a paper dictionary over a digital dictionary; Production of legal documents to be used in litigation; Existence of the subtitles of information on paper.

The figure presents the pre-trained CTR model, the fine-tuned CTR model, and our proposed AutoFT framework from left to right. The pre-trained CTR model with random parameters starting point is optimized based on the data from source domains or based on the data from all domains. The fine-tuned CTR model is initialized with trained parameters in source scenarios and optimized with data from the new target domain. AutoFT involves three sets of parameters: the light-blue boxes contain parameters from the pre-trained CTR model, the dark-blue boxes contain parameters initialized by traditional fine-tuning strategy will be re-optimized during training and the red boxes contain the parameters of the policy networks. [Display omitted] • The proposed AutoFT automatically finds a route between the pre-trained network and the siamese fine-tuned network per instance in the target domain and is compatible with any deep CTR models. • The transfer policy for the embedding layer and the feature interaction layers can be trained synchronously with deep CTR models. • The result shows that the lower layers may represent more general features while higher layers need more fine-tuning to fit a specific target domain. In real business platforms, recommendation systems usually need to predict the CTR of multiple business. Since different scenarios may have common feature interactions, knowledge transferring based methods are often used by re-optimizing the pre-trained CTR model from source scenarios to a new target domain. In addition to knowledge transfer, it is noteworthy that generalizing target domain data outside of the CTR model accurately is also important when re-training all of the fine-tuned parameters. Generally, the pre-trained model trained on large source domains can represent the characteristics of different instances and capture typical feature interactions. It would be useful to directly reuse fine-tuned parameters from source domains to serve the target domain. However, different instances of the target domain may need different amounts of source information to fine-tune the model parameters, and these decisions of freezing or re-optimizing model parameters, which highly depend on the fine-tuned model and target instances, may require much manual effort. In this paper, we propose an end-to-end transfer learning framework with fine-tuned parameters for CTR prediction, called Automatic Fine-Tuning (AutoFT). The principal component of AutoFT is a set of learnable transfer policies that independently determine how the specific instance-based fine-tuning policies should be trained, which decide the routing in the embedding representations and the high-order feature representations layer by layer in deep CTR model. Extensive tests on two benchmarks and one real commercial recommender system deployed in Huawei's App Store show that AutoFT can greatly increase CTR prediction performance when compared to current transferring methodologies. [ABSTRACT FROM AUTHOR]

• Proposed a Novel learn to rank model with explainable results for question routing. • Provided a systematic classification of and the introduction of 74 features that can be effective for the task of question routing. • Offered in-depth analysis on the impact of provided features and feature categories for question routing. • Adopted and reported on the Gini score to enhance interpretability of the results. • Compare the performance of the proposed model against strong neural methods for question routing. Community question answering (CQA) platforms are receiving increased attention and are becoming an indispensable source of information in different domains ranging from board games to physics. The success of these platforms dependent on how efficiently new questions are assigned to community experts, known ascalled question routing. In this paper, we address the problem of question routing by adopting a learning to rank approach over five CQA websites in the context of which we introduce 74 features and systematically classify them into content-based and social-based categories. Our extensive experiments on datasets from five real online question answering websites indicate that content-based features related to tags and topics as well as social features that are related to user characteristics and user temporality are effective for question routing. Our work shows the ability to improve performance compared to the state-of-the-art neural matchmaking methods that lack the interpretability offered by our work. The improvement can be as high as on average 2.47% and 1.10% in terms of common ranking metrics, Normalized Discounted Cumulative Gain (NDCG) and Mean Average Precision (MAP) respectively, compared to our best baselines. [ABSTRACT FROM AUTHOR]

The American Academy of Child and Adolescent Psychiatry's (AACAP's) Program Committee has issued a Call for Papers for submission of abstracts of original investigations for consideration in the 2024 Annual Meeting of AACAP, October 14‐19, in Seattle. AACAP is planning for an in‐person meeting in Seattle and as such, will require all presenters to be in‐person in Seattle for their presentation. The Call for Papers deadline is Feb. 15, 2024 (11:59 p.m. EST). For more information, visit https://aacap.confex.com/aacap/2024/cfp.cgi. [ABSTRACT FROM AUTHOR]

The article explores Clarivate's Institute for Scientific Information (ISI) proposed a new Collaborative CNCI (Collab-CNCI) indicator to analyze the credit authors of academic papers receive via citations. In its report, "Making It Count: Research Credit Management in a Collaborative World," ISI confirms that highly collaborative papers can distort summary results at a national and institutional level.

Transfer learning aims to improve the learning of the target domain with the help of knowledge from the source domain. Recently, learning using privileged information (LUPI) has been proposed to learn an accurate classifier with privileged information which is only obtained in the training stage. In this paper, we propose a new AdaBoost-based Transfer Learning with Privileged Information (AdaTLPI) method to solve the transfer learning problem with privileged information, in which AdaBoost is taken into account to combine the weak classifiers into a strong classifier. In the model, we utilize shared parameter to transfer knowledge from the source domain to the target domain. We then incorporate privileged information about the source and target domains into a unified model and AdaBoost is used to learn a strong classifier by combining the obtained weak classifiers. Finally, we present an effective optimization algorithm to solve the proposed objective model and present the boundary of training error of the proposed method. The experimental results manifest that the proposed method can outperform the previous methods. [ABSTRACT FROM AUTHOR]

The article offers information on the research "Your Paper has been Accepted, Rejected, or Whatever: Automatic Generation of Scientific Paper Reviews", by Eric Medvet and colleagues, published in the journal "Lecture Notes in Computer Science" which reveals robot-written review of papers.

Conversational emotion recognition represents an important machine-learning problem with a wide variety of deployment possibilities. The key challenge in this area is how to properly capture the key conversational aspects that facilitate reliable emotion recognition, including utterance semantics, temporal order, informative contextual cues, speaker interactions as well as other relevant factors. In this paper, we present a novel Graph Neural Network approach for conversational emotion recognition at the utterance level. Our method addresses the outlined challenges and represents conversations in the form of graph structures that naturally encode temporal order, speaker dependencies, and even long-distance context. To efficiently capture the semantic content of the conversations, we leverage the zero-shot feature-extraction capabilities of pre-trained large-scale language models and then integrate two key contributions into the graph neural network to ensure competitive recognition results. The first is a novel context filter that establishes meaningful utterance dependencies for the graph construction procedure and removes low-relevance and uninformative utterances from being used as a source of contextual information for the recognition task. The second contribution is a feature-correction procedure that adjusts the information content in the generated feature representations through a gating mechanism to improve their discriminative power and reduce emotion-prediction errors. We conduct extensive experiments on four commonly used conversational datasets, i.e., IEMOCAP, MELD, Dailydialog, and EmoryNLP, to demonstrate the capabilities of the developed graph neural network with context filtering and error-correction capabilities. The results of the experiments point to highly promising performance, especially when compared to state-of-the-art competitors from the literature. [ABSTRACT FROM AUTHOR]

In the era of explosive data growth, data sources and volumes are rapidly increasing. A multi-source data refers to information from multi-sources. However, not every source of information is equally important; some sources are more important and some are essentially worthless. Therefore, it is very meaningful to study how to select the most valuable sources and to efficiently fuse information. Multi-source incomplete interval-valued data (MSIIV-data) is an important kind of multi-source data. This paper proposes a novel method to information fusion in MSIIV-data via conditional information entropy (CIE) and considers its application to attribute reduction based on mutual information entropy. First, the distance between two information values for each incomplete interval-valued data is defined, the neighborhood classes with a tunable parameter are obtained, and the neighborhood granularity structure is established. Then, a source selection method is given via CIE, which is used to fuse MSIIV-data into single-source incomplete interval-valued data (SSIIV-data). Based on the minimization of CIE, this method allows worthy and reliable information sources to be chosen. Moreover, an attribute reduction algorithm (denoted as MMQPSO) for the fused SSIIV-data is proposed by means of combining mutual information entropy and QPSO-algorithm. Finally, experiments are done to validate the effectiveness of the proposed algorithms. The results of experiment and statistical test on 12 datasets show that the proposed algorithms have certain feasibility and advancement than 6 other advanced algorithms. [ABSTRACT FROM AUTHOR]

The article focuses on the release of the 2022 Digitizing Hidden Collections Symposium proceedings by Council on Library and Information Resources (CLIR). Topics discussed include the celebration of the Digitizing Hidden Special Collections and Archives program funded by the Mellon Foundation on digital archives for liberatory uses, and various papers covering privacy protection, workflow implementation, and exhibit creation in digitization efforts.

Knowledge transfer across several streaming processes remain challenging problem not only because of different distributions of each stream but also because of rapidly changing and never-ending environments of data streams. Albeit growing research achievements in this area, most of existing works are developed for a single source domain which limits its resilience to exploit multi-source domains being beneficial to recover from concept drifts quickly and to avoid the negative transfer problem. An online domain adaptation technique under multi-source streaming processes, namely automatic online multi-source domain adaptation (AOMSDA), is proposed in this paper. The online domain adaptation strategy of AOMSDA is formulated under a coupled generative and discriminative approach of denoising autoencoder (DAE) where the central moment discrepancy (CMD)-based regularizer is integrated to handle the existence of multi-source domains thereby taking advantage of complementary information sources. The asynchronous concept drifts taking place at different time periods are addressed by a self-organizing structure and a node re-weighting strategy. Our numerical study demonstrates that AOMSDA is capable of outperforming its counterparts in 5 of 8 study cases while the ablation study depicts the advantage of each learning component. In addition, AOMSDA is general for any number of source streams. The source code of AOMSDA is shared publicly in https://github.com/Renchunzi-Xie/AOMSDA.git. [ABSTRACT FROM AUTHOR]

In recent years, many efficient evolutionary multitasking (EMT) algorithms have been proposed to solve multi-objective multi-task optimization problems. However, EMT algorithms often face negative transfer problems. In this paper, a novel multi-objective evolution strategy, called CT-EMT-MOES, is proposed based on a cultural transmission theory for solving multi-objective multitask optimization problems. First, two evolutionary operators inspired by cultural transmission theory are proposed. The elite-guided variation strategy can transfer the information from the current Pareto front to all individuals and guide the population to quickly converge. The horizontal cultural transmission strategy can efficiently transfer information from the source task to the target task. Second, to solve the negative transfer problem, an adaptive information transfer strategy is proposed to adaptively adjust the probability of an information transfer. Third, the proposed algorithm can gain a Pareto front with good convergence and diversity by utilizing a smaller population size and fewer computing resources. As a result, the proposed algorithm can effectively utilize the implicit similarity and complementarity between simultaneous optimized tasks to improve the overall convergence efficiency and reduce a negative transfer. Finally, comprehensive experimental results show that the proposed algorithm can achieve a better performance compared with previous state-of-the-art multi-objective EMT algorithms. [ABSTRACT FROM AUTHOR]

Annually, one in five childbearing persons in the United States experiences a mental health or substance use disorder before, during or after pregnancy, the American Psychiatric Association (APA) stated in an Aug. 1 news release. To ensure that psychiatrists and mental health clinicians are best positioned to respond to this need, the APA, with support from the CDC [Centers for Disease Control and Prevention] Foundation, has released a series of educational materials for addressing perinatal mental health in its Psychiatric Toolkit. The toolkit includes eight fact sheets for clinicians and patients, a white paper, and a four‐part webinar series covering this understudied and underserved area of mental health. These materials can be accessed at APA's Perinatal Mental Health website (https://www.psychiatry.org/psychiatrists/practice/professional‐interests/women‐s‐mental‐health/maternal‐mental‐health‐toolkit). The white paper calls for revisions to academic and continuing education curricula to improve competency and confidence within the behavioral care workforce for treating this population. It examines the care of vulnerable populations, raising such issues as cultural competency and lack of access. This "Mental Health Needs Assessment in the Management of Perinatal Psychiatric Disorders" is supported by the CDC and the U.S. Department of Health and Human Services (HHS) as part of a financial assistance award totaling $447,209 funded by the CDC/HHS. [ABSTRACT FROM AUTHOR]

The article discusses the use of intelligent Web services to support cross-organizational business transactions. The paper aims to create types of Web services known as intelligent Web services. Web service technology is being touted as the ideal solution to meet the requirements for the dynamic composition of enterprise information systems. Web services can be loosely defined as self-describing software components accessible through the Internet that support business processes. Offered by multiple providers, Web services are usually published in a central repository with a brokering mechanism. Web services can be blended to implement customized virtual supply chain or smaller-scale business processes confined to a single enterprise. There are fundamental shortcomings of Web services. Business process dynamics and non-functional properties of service-enabled processes are poorly addressed by existing service description languages and Web service flow languages. Current standards do not put forth a methodology to assist designers in building Web services. In order to overcome these shortcomings, the paper suggests leveraging Web services with techniques borrowed from other research domains, such as business object component technology and distributed artificial intelligence.

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.

Multivariate time series classification is a machine learning task with increasing importance due to the proliferation of information sources in different domains (economy, health, energy, crops, etc.). Univariate methods lack the ability to capture the relationships between the different variables that compose a multivariate time series and therefore cannot be directly extrapolated to multivariate environments. Despite the good performance and competitive results of the multivariate proposals published to date, they are hard to interpret due to their high complexity. In this paper, we propose a multivariate time series classification method based on an alternative representation of the time series, composed of a set of 41 descriptive time series features, in order to improve the interpretability of time series and results obtained. Our proposal uses traditional classifiers over the extracted features to look for relationships between the different variables that form a multivariate time series. We have selected four state-of-the-art algorithms as base classifiers to evaluate our method. We have tested our proposal on the complete University of East Anglia repository, obtaining highly interpretable results capable of explaining the relationships between the features that compose the time series and achieving performance results statistically indistinguishable from the best algorithms of the state-of-the-art. [ABSTRACT FROM AUTHOR]

Social networks store a considerable amount of personal data, which are also a source of information for business. To comply with users' privacy rights, all social networks allow users to select the level of privacy they desire. However, what occurs if the privacy choices of a user are modified unilaterally by the social network? The privacy settings chosen by the user are stored by the social network, which acts as a privileged party , which could tamper with the user's choices at any time. This paper addresses this problem and proposes a decentralized approach to manage the privacy settings of a user. Any change in the privacy settings of a social network user is validated by a smart contract to ensure that it is compliant with users' expectations. The proposed solution has been implemented as an Ethereum-based decentralized application to validate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This study considers the problem of hybrid community detection in attributed networks based on the information of network topology and attributes with the aim to address the following two shortcomings of existing hybrid community detection methods. First, many of these methods are based on the assumption that network topology and attributes carry consistent information but ignore the intrinsic mismatch correlation between them. Second, network topology is typically treated as the dominant source of information, with attributes employed as the auxiliary source; the dominant effect of attributes is seldom explored or indeed considered. To address these limitations, this paper presents a novel Dual-channel Hybrid Community Detection (DHCD) method that considers the dominant effects of topology and attributes separately. The concept of transition relation between the topology and attribute clusters is introduced to explore the mismatch correlation between the two sources and learn the behavioral and content diversity of nodes. An extended overlapping community detection algorithm is introduced based on the two types of diversity. By utilizing network attributes, DHCD can simultaneously derive the community partitioning membership and corresponding semantic descriptions. The superiority of DHCD over state-of-the-art community detection methods is demonstrated on a set of synthetic and real-world networks. [ABSTRACT FROM AUTHOR]

This study considers the problem of hybrid community detection in attributed networks based on the information of network topology and attributes with the aim to address the following two shortcomings of existing hybrid community detection methods. First, many of these methods are based on the assumption that network topology and attributes carry consistent information but ignore the intrinsic mismatch correlation between them. Second, network topology is typically treated as the dominant source of information, with attributes employed as the auxiliary source; the dominant effect of attributes is seldom explored or indeed considered. To address these limitations, this paper presents a novel Dual-channel Hybrid Community Detection (DHCD) method that considers the dominant effects of topology and attributes separately. The concept of transition relation between the topology and attribute clusters is introduced to explore the mismatch correlation between the two sources and learn the behavioral and content diversity of nodes. An extended overlapping community detection algorithm is introduced based on the two types of diversity. By utilizing network attributes, DHCD can simultaneously derive the community partitioning membership and corresponding semantic descriptions. The superiority of DHCD over state-of-the-art community detection methods is demonstrated on a set of synthetic and real-world networks. [ABSTRACT FROM AUTHOR]

Multi-source data is a comprehensive data type that combines multiple sources of information or datasets. Compared to point-valued data, interval-valued data provides a more accurate representation of the uncertainty and variability associated with objects. In practical situations, data obtained from multiple sources may contain missing values for various reasons. Therefore, it is essential to develop multi-source information fusion technology in order to achieve information fusion or information extraction from multi-source incomplete data. This paper aims to explore the information fusion problem of multi-source incomplete interval-valued datasets. The primary contributions of this study involve utilizing the principle of statistical distribution and KL divergence to establish a metric for measuring the similarity between intervals. Firstly, this approach helps to reduce the problem of disregarding internal information within interval values, which can result in the loss of valuable information. Secondly, we establish an interval fuzzy similarity relation based on the mentioned concept of similarity among interval values. Moreover, we investigate the uncertainty measurement of incomplete interval-valued decision datasets and design an emerging information entropy fusion method. Finally, we comprehensively evaluate the effectiveness of the proposed method. Experimental results indicate that the proposed approach has advantage over the maximum, minimum, mean, and information entropy fusion method based on tolerance relationship. In addition, the distance metric used in this article can improve the fusion classification effect compared to several common interval-valued distance measures. [ABSTRACT FROM AUTHOR]

In D-S evidence theory, belief and commonality functions are well-defined and deeply researched. However, these concepts have not been extended to Random Permutation Set (RPS) because of the difficulties in giving a suitable definition of containment relationships of permutations. In this paper, we addressed this issue by defining both ordered and unordered containment relationships. Based on the defined containment relationships, the corresponding belief function and commonality functions for RPS are derived naturally. Besides, the plausibility function for RPS is also proposed following the definition in D-S evidence theory. Furthermore, the transformations between them and permutation mass functions are discussed and implemented by matrix converters. Thanks to the properties of commonality functions, the combination process in RPS, including left and right orthogonal sum, are both implemented by matrix operations. Moreover, based on matrix operations, the reverse transformation of combination, that is, the decomposition of fusion results given one information source is achieved for the first time. Numerical examples are listed to illustrate the calculation process. The proposed matrix operations provide a way to implement and accelerate information transformations, combinations and decompositions. [ABSTRACT FROM AUTHOR]

The article offers information on San Joaquin Valley in central California is home to our nation's largest agricultural region, and a disproportionate number of our residents live in poverty and how can we guarantee access for all of our students and children when their families and communities cannot provide the services they need. Topics include the group of dedicated library educators has decided to take on the challenge has presented this news paper headline in spring 2018.

An "infodemic" is defined as "an overabundance of information -- some accurate and some not -- occurring during an epidemic". This paper describes the characteristics of an infodemic, which combines an inordinately high volume of information (leading to problems relating to locating the information, storage capacity, ensuring quality, visibility and validity) and rapid output (making it hard to assess its value, manage the gatekeeping process, apply results, track its history, and leading to a waste of effort). This is bound up with the collateral growth of misinformation, disinformation and malinformation. Solutions to the problems posed by an infodemic will be sought in improved technology and changed social and regulatory frameworks. One solution could be a new trusted top-level domain for health information. The World Health Organization has so far made two unsuccessful attempts to create such a domain, but it is suggested this could be attempted again, in the light of the COVID-19 infodemic experience. The vital role of reliable information in public health should also be explicitly recognized in the Sustainable Development Goals, with explicit targets. All countries should develop knowledge preparedness plans for future emergencies. [ABSTRACT FROM AUTHOR]

In many real-life applications, the data collected from different information sources are always located in diverse sites and characterized by different types of attributes. In this paper, we call this type of data as multi-source hybrid data. Existed rough set models only work well with single source data or multi-source data with one type of attributes, which are not suitable for processing multi-source hybrid data. Furthermore, in dynamic data environment, multi-source hybrid data are typically evolving over time, including the addition of objects and attributes and the revision of missing values in each information source. It will result in the update of information granulation and rough approximation structures. To address these issues, we present an extended rough set model, named as multi-source composite rough sets (MCRS), by integrating different types of attributes and fusing multiple composite relations derived from different information sources. Two novel matrix operators are proposed to construct the matrix-based representation of rough approximations. Then, under the variations of objectives, attributes and attribute values in the multi-source hybrid information system, we present incremental mechanisms based on matrices for the maintenance of approximations by utilizing the previously accumulated matrices information and transmitting location information of part elements in each composite relation matrix. In addition, we develop the incremental algorithms for updating composite rough approximations. A series of comparative experiments show that the proposed algorithm outperforms the static algorithm and other extended incremental algorithms in terms of the efficiency of computation and transmission. [ABSTRACT FROM AUTHOR]

Presents several steps to prevent paper overload in the legislative office. Categorization of the papers; Decision on what papers to keep; Kind of papers which are unnecessary; Sources of information to retrieve needed papers.

The article focuses on Open Access that is well on the way from utopia to reality. Topics incldue exmaines the criticism of the paper-based system for the publication of scientific publications, which has been established for centuries and has been transferred to the electronic world, is not only being met with open ears by an increasing number of those involved. [ABSTRACT FROM AUTHOR]

Numerous quantitative graph measures have been defined and applied in various disciplines. Such measures may be differentiated according to whether they are information-theoretic or non-information-theoretic. In this paper, we examine an important property of Randić entropy, an information-theoretic measure, and examine some related graph measures based on random roots. In particular, we investigate the degeneracy of these structural graph measures and discuss numerical results. Finally, we draw some conclusions about the measures' applicability to deterministic and non-deterministic networks. [ABSTRACT FROM AUTHOR]

As one commonly used dimensionality reduction method, nonnegative matrix factorization (NMF), whose goal is to learn parts-based representations, has been widely studied and applied to various areas. However, classical NMF does not utilize any label information. In this paper, we propose a novel semi-supervised NMF algorithm named the hyperplane-based nonnegative matrix factorization (HNMF). This HNMF constructs a hyperplane for each cluster such that the labelled points are close to the corresponding hyperplane and far away from the other ones. Then, the discriminative abilities of representation vectors are greatly enhanced. Clustering experiments completed on five publicly available databases demonstrate the effectiveness of this proposed HNMF compared to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Information about the Appraisal of Electronic Records and Personal Papers symposium held at the Pyle Center on the University of Wisconsin-Madison campus on October 22-24, 2009 is presented. Topics include the challenges of appraisal conduction in the age of born-digital materials and the creation of appraisal plan in the workplace. The symposium featured several speakers including Richard Pearce-Moses, Joanne Kaczmarek, and Nan Kunde.

This article focuses on effective aids to assist computing professionals explore computing literature. Several efforts have been made to serve this community, including the Networked Computer Technical Reference Library, which grew out of efforts to collect technical reports from academic and research departments. The purpose of the paper is to provide illustrations of how the computing community can be assisted by richer support for exploratory search, related to the authors' work with the Computing and Information Technology Interactive Digital Educational Library (CITIDEL).

Given a temporal network that is composed of a stream of events that evolves continuously over time, like social posts on Reddit, how can we spot interesting events like anomalous posts? In general, it is critical to learn useful information from event time, attributes, and network structure, which means that the anomalous post can be detected by over-frequent posts, impolite comments, and incorrect choices of subreddits. In this paper, we propose a new learning objective, named Continuous-time Graph Directed Information Maximization (CGDIM), to learn informative node presentations for temporal networks. The proposed CGDIM is based on the popular mutual information maximization (InfoMax) method to learn node features that are shared across temporal neighborhoods. Specifically, to capture the time causal relations among edges with continuous time, we utilize directed information rather than mutual information as the measure. The designed learning objective is to maximize the directed information with the direction from the inputs of temporal neighborhoods to the node representations. By setting a source variable that only contains useful information on each node, we prove that directed information is more effective than mutual information for preserving information of the source variables. To show the effectiveness of the CGDIM, we train several popular backbone models for temporal network representation with the CGDIM and test the performance on temporal node/edge classification. Experiment results on four real-world temporal network datasets show that the proposed CGDIM can improve the Average-Precision scores of the backbone models by 3%-8%. [ABSTRACT FROM AUTHOR]

The article presents information on several research papers regarding computing surveys organized by the February 1995 issue of the journal "Communications of the ACM." While continuing to maintain Computing Survey reputation as the leading survey journal in information technology, we will also "keep up with the limes." For example, an online component of surveys to become operational in the summer or fall of 1995. One change will be broadening the scope of the surveys and tutorials to induce new areas. Changes in technology like the Internet have expanded what is important in the field and are also changing the journal publication and information delivery. The printed surveys will continue to have full-length tutorial and survey articles in the current tradition but will also include a range of shorter position papers, book reviews, and extended abstracts of electronically published articles. We will make increasing use of electronic publication over the next two or three years, the precise details of which will depend on feedback from readers.

The article discusses the Digital Commons @ EMU, online institutional repositories (IRs). Topics include providing opportunity for the library and the institution which impact on student and faculty research papers; containing collections from 244 disciplines such as social and behavioral sciences and business; and views of Julia Nims, scholarly communications librarian at EMU on place for sharing materials in university's history.

This paper focuses on the fact that the water-energy nexus remains an irrelevant issue on the energy policy agenda and on the priorities of the energy leaders in Spain. This is a striking fact given that this takes place in the most arid country in Europe, where almost two-thirds of electricity generation would have to be halted in the absence of an adequate water supply. We contend that part of the explanation may lie in the lack of official statistics and inconsistent sources of information on the water-energy nexus in Spain. To illustrate this point, we provide examples of the uneven data available for one of the most intensive freshwater users in the thermoelectric sector in Spain: nuclear power plants. Our research demonstrates the need for improved indicators as policy instruments in the water-energy nexus in Spain since it is impossible to improve what cannot be measured. [ABSTRACT FROM AUTHOR]

Abstract In the field of complex social network hotspot analysis, mining the dynamic factors behind information propagation is a focus of current research. In this paper, user multidimensional attributes and evolutionary games are combined with the traditional susceptible-infected-recovered (SIR) epidemic model, which is used to quantify the impact of external and internal driving factors on group state transitions during hotspot propagation. First, the network structure attributes and historical attributes of social users are extracted, and user multidimensional attribute driven mechanism is constructed by multivariate linear regression. This is used to analyze the internal driving factors that affect users participating in hotspots. Second, taking into account multi-source information dissemination and the complexity of user interaction behaviors, the concept, perceived popularity, is defined, and the dynamic evolution strategy of user behaviors based on evolutionary game theory is proposed. This reveals that external driving factors affect the behaviors of social network users. Finally, considering the external and internal driving factors that affect hot topic information dissemination, we obtain the hotspot propagation model based on user multidimensional attributes and evolutionary games, combined with the traditional SIR epidemic model. Experiments show that the model can effectively reveal the impact of different driving factors on information dissemination, and depict the trend of information dissemination in social networks. [ABSTRACT FROM AUTHOR]

A number of recent papers and articles have dealt with views of Information systems (IS) managers about important issues facing them. These studies typically involve survey techniques that do not allow in-depth exploration of issues identified. A difficulty encountered in survey research is understanding why executives view these issues as important and exactly how to advance knowledge about management of these issues. The research reported in this article was intended to provide not only a view of some of the more important issues, but to place them into a research perspective and explore why issues are important. To accomplish this, detailed structured interviews with a select group of senior IS executives were conducted. The material in this article deals with views of this group about key issues facing them, how executives deal with issues and how they manage and develop IS resources relative to issues. The research was guided by two specific objectives. The first was to provide a composite, senior management view of several of the major IS management issues being considered by IS researchers or identified in the previous referenced surveys.

System R, an experimental database system, was constructed to demonstrate that the usability advantages of the relational data model can be realized in a system with the complete function and high performance required for everyday production use. This paper describes the three principal phases of the System R project and discusses some of the lessons learned from System R about the design of relational systems and database systems in general. [ABSTRACT FROM AUTHOR]

This paper studies the properties of a new class of file organizations (CRWR) where records relevant to every query are stored in consecutive storage locations but the organizations contain redundancy. Some theorems which provide tools for reducing redundancy in CRWR organizations have been also developed. Redundancies obtained by the application of these theorems are compared with that of query-inverted file organizations. Some CRWR organizations with minimum redundancy have also been developed for queries which specify sets of keys. [ABSTRACT FROM AUTHOR]

Presents information on the researchers, archives and sources on the history of England. Information on Thomas Babington Macaulay; Details on John Lingard; Information on the English State Paper Office.

Reviews two magazines published by teachers' unions in Great Britain. "The Teacher," from the National Union of Teachers; "Report," from the Association of Teachers and Lecturers.

Librarians support the publication process, but often are not published authors themselves. Any librarian can become a published author by following a simple process to partner with others to get published as a co-author. Described here is a process to find others interested in getting published and to partner with them to cooperatively write and publish. This work is a natural extension of the provision of lists of resources or bibliographies that many librarians in public services regularly supply. The librarian can start the partnering process and offer to provide extended support of a potential co-author's work by creating bibliographies, acquiring information sources, proposing writing the history of literature, introduction, conclusion, then editing the paper for style, and finding places to publish. This paper will document practical steps for the novice author to partner to become published. [ABSTRACT FROM AUTHOR]

Abstract: The cloud computing data center has numerous hosts as well as application requests. It needs to allocate resources dynamically according to the varied demands of users. It should not only provide QoS such as short response time and high throughput but also achieve efficient power consumption. This paper first puts forward a reconfiguration framework based on a request prediction, which anticipates the application request volume in advance. To determine the objective of relatively optimal configuration, it can work out the allocation scheme which can improve the resource utilization ratio as well as lower energy consumption. In addition, a concept of Utility Ratio Matrix (URM) is put forward to represent allocations of hosts and Virtual Machines (VMs), and a reconfiguration algorithm based on request prediction is also presented. The algorithm will predict the application requests so as to work out the allocation scheme in advance. The algorithm can separate the reconfiguration computing from the real allocation so that it can avoid a time delay between the reconfiguration result and the varied demands, and can also reduce the energy consumption in data center. The corresponding analysis and experimental results indicate the feasibility of the reconfiguration algorithm in this paper. [Copyright &y& Elsevier]

The article recommends items to hear, watch, wear and buy in March 2006. The album "The Little Willies," a compilation of Willie Nelson songs by Norah Jones and her friends should not be missed. The documentary "Paper Clips", Panoptx Vortex and Gale sun glasses, and an iPod home-stereo system are also recommended. For those looking for a vacation, Oceania Cruises is offering free airfare and two-for-one fares on some European voyages.