nils-herrmann/clean_scirepeval huggingface dataset:
Supply Chain Management : Literature Review and Some Issues

Supply chain Management has assumed a significant r ole in firm's performance and has attracted serious research attention over the last few years. A literature review reveals a considerab l spurt in research in theory and practice of SCM. Co mbining and informing on features of Supply Management and distribution Management. This integr ation has resulted in the concept of extended enterprise and the supply chain is now manifest a s the collaborative supply chain across intercompan y borders to maximize the value across the entire sup ply chain. A large number of research papers have been published in various journals in last two deca s. In this paper an attempt is made to review th e status of literature on Supply Chain Management. A literature classification scheme is proposed. A t otal of 588 articles from 13 refereed academic journals are classified into articles in five methodologies .e. Exploratory, Normative, Methodology, Literature Rev iew and Hypothesis testing. This literature review finds that exploratory type of research is mostly preferred it is expected that with the maturity of SCM the hypothesis testing method will pick up. The art icles are further categorized in fifteen categories on the basis of content analysis. Based on this review , some possible research issues are identified.
A comparison between concept maps, mind maps, conceptual diagrams, and visual metaphors as complementary tools for knowledge construction and sharing

8 February 2006  13 April 2006 Online publication date: 22 June 2006  In this article, Novak's concept mapping technique is compared to three other types of visualization formats, namely mind maps, conceptual diagrams, and visual metaphors. The application parameters and the respective advantages and disadvantages of each format for learning and knowledge sharing are reviewed and discussed. It is argued that the combination of these four visualization types can play to the strength of each one. The article then provides real-life examples from such a use in undergraduate and graduate university teaching. The results provide first indications that the different visualization formats can be used in complementary ways to enhance motivation, attention, understanding and recall. The implications for a complementary use of these visualization formats in class room and meeting contexts are discussed and a future research agenda in this domain is articulated. Information Visualization (2006) 5, 202--210. doi:10.1057/palgrave.ivs.9500131
Edward: A library for probabilistic modeling, inference, and criticism

Probabilistic modeling is a powerful approach for analyzing empirical information. We describe Edward, a library for probabilistic modeling. Edward’s design reflects an iterative process pioneered by George Box: build a model of a phenomenon, make inferences about the model given data, and criticize the model’s fit to the data. Edward supports a broad class of probabilistic models, efficient algorithms for inference, and many techniques for model criticism. The library builds on top of TensorFlow to support distributed training and hardware such as GPUs. Edward enables the development of complex probabilistic models and their algorithms at a massive scale.
Olfaction-Enhanced Multimedia: A Survey of Application Domains, Displays, and Research Challenges

Recently, the concept of olfaction-enhanced multimedia applications has gained traction as a step toward further enhancing user quality of experience. The next generation of rich media services will be immersive and multisensory, with olfaction playing a key role. This survey reviews current olfactory-related research from a number of perspectives. It introduces and explains relevant olfactory psychophysical terminology, knowledge of which is necessary for working with olfaction as a media component. In addition, it reviews and highlights the use of, and potential for, olfaction across a number of application domains, namely health, tourism, education, and training. A taxonomy of research and development of olfactory displays is provided in terms of display type, scent generation mechanism, application area, and strengths/weaknesses. State of the art research works involving olfaction are discussed and associated research challenges are proposed.
Bigtable: A Distributed Storage System for Structured Data

Bigtable is a distributed storage system for managing structured data that is designed to scale to a very large size: petabytes of data across thousands of commodity servers. Many projects at Google store data in Bigtable, including web indexing, Google Earth, and Google Finance. These applications place very different demands on Bigtable, both in terms of data size (from URLs to web pages to satellite imagery) and latency requirements (from backend bulk processing to real-time data serving). Despite these varied demands, Bigtable has successfully provided a flexible, high-performance solution for all of these Google products. In this article, we describe the simple data model provided by Bigtable, which gives clients dynamic control over data layout and format, and we describe the design and implementation of Bigtable.
Biclustering via sparse singular value decomposition.

Sparse singular value decomposition (SSVD) is proposed as a new exploratory analysis tool for biclustering or identifying interpretable row-column associations within high-dimensional data matrices. SSVD seeks a low-rank, checkerboard structured matrix approximation to data matrices. The desired checkerboard structure is achieved by forcing both the left- and right-singular vectors to be sparse, that is, having many zero entries. By interpreting singular vectors as regression coefficient vectors for certain linear regressions, sparsity-inducing regularization penalties are imposed to the least squares regression to produce sparse singular vectors. An efficient iterative algorithm is proposed for computing the sparse singular vectors, along with some discussion of penalty parameter selection. A lung cancer microarray dataset and a food nutrition dataset are used to illustrate SSVD as a biclustering method. SSVD is also compared with some existing biclustering methods using simulated datasets.
Person-on-Person Violence Detection in Video Data

We address the problem of detecting human violence in video, such as fist fighting, kicking, hitting with objects, etc. To detect violence we rely on motion trajectory information and on orientation information of a person’s limbs. We define an Acceleration Measure Vector (AMV) composed of direction and magnitude of motion and we define jerk to be the temporal derivative of AMV. We present results from several data sequences involving multiple types of violent activities.
Efficient Character-level Document Classification by Combining Convolution and Recurrent Layers

Document classification tasks were primarily tackled at word level. Recent research that works with character-level inputs shows several benefits over word-level approaches such as natural incorporation of morphemes and better handling of rare words. We propose a neural network architecture that utilizes both convolution and recurrent layers to efficiently encode character inputs. We validate the proposed model on eight large scale document classification tasks and compare with character-level convolution-only models. It achieves comparable performances with much less parameters.
Urban Water Quality Prediction Based on Multi-Task Multi-View Learning

Urban water quality is of great importance to our daily lives. Prediction of urban water quality help control water pollution and protect human health. In this work, we forecast the water quality of a station over the next few hours, using a multitask multi-view learning method to fuse multiple datasets from different domains. In particular, our learning model comprises two alignments. The first alignment is the spaio-temporal view alignment, which combines local spatial and temporal information of each station. The second alignment is the prediction alignment among stations, which captures their spatial correlations and performs copredictions by incorporating these correlations. Extensive experiments on real-world datasets demonstrate the effectiveness of our approach.
Forecasting Civil Unrest Using Social Media and Protest Participation Theory

Activists have used social media during modern civil uprisings, and researchers have found that the generated content is predictive of off-line protest activity. However, questions remain regarding the drivers of this predictive power. In this paper, we begin by deriving predictor variables for individuals’ protest decisions from the literature on protest participation theory. We then test these variables on the case of Twitter and the 2011 Egyptian revolution. We find significant positive correlations between the volume of future-protest descriptions on Twitter and protest onsets. We do not find significant correlations between such onsets and the preceding volume of political expressions, individuals’ access to news, and connections with political activists. These results locate the predictive power of social media in its function as a protest advertisement and organization mechanism. We then build predictive models using future-protest descriptions and compare these models with baselines informed by daily event counts from the Global Database of Events, Location, and Tone (GDELT). Inspection of the significant variables in our GDELT models reveals that an increased military presence may be predictive of protest onsets in major cities. In sum, this paper highlights the ways in which online activism shapes off-line behavior during civil uprisings.
The Semantic Desktop-a Basis for Personal Knowledge Management

Knowledge Management software is software that integrates. Existing Data sources, process flows, application features from office appliances have to be brought together. There are different standards, consisting of data formats and communication protocols, that address this issue. The WWW and Semantic Web are designed to work on a worldwide scale and define those standards. We transfer the web standards to the desktop szenario, a vision we call Semantic Desktop – a Semantic Web enhanced desktop environment. Central is the idea of taking know-how from the Semantic Web to tackle personal information management. Existing desktop applications (email client, browser, office applications) are integrated, the semantic glue between them expressed using ontologies. We also present the www.gnowsis.org open source project by the DFKI that realizes parts of this vision. It is based on a Semantic Web Server running as desktop service. It was used in experiments and research projects and allows others to experiment. Knowledge management applications can be built on top of it, reducing the implementation cost.
Open University Learning Analytics dataset

Learning Analytics focuses on the collection and analysis of learners' data to improve their learning experience by providing informed guidance and to optimise learning materials. To support the research in this area we have developed a dataset, containing data from courses presented at the Open University (OU). What makes the dataset unique is the fact that it contains demographic data together with aggregated clickstream data of students' interactions in the Virtual Learning Environment (VLE). This enables the analysis of student behaviour, represented by their actions. The dataset contains the information about 22 courses, 32,593 students, their assessment results, and logs of their interactions with the VLE represented by daily summaries of student clicks (10,655,280 entries). The dataset is freely available at https://analyse.kmi.open.ac.uk/open_dataset under a CC-BY 4.0 license.
Supervised Topic Models for Microblog Classification

In this paper we present a topic model based approach for classifying micro-blog posts into a given topics of interests. The short nature of micro-blog posts make them challenging for directly learning a classification model. To overcome this limitation, we use content of the links embedded in these posts to improve the topic learning. The hypothesis is that since the link content is far richer than the content of the post itself, using link content along with the content of the post will help learning. However, how this link content can be used to construct features for classification remains a challenging issue. Furthermore, in previous methods, user based information is utilized in an ad-hoc manner that only work for certain type of classification, such as characterizing content of microblogs. In this paper, we propose supervised topic model, User-Labeled-LDA and its nonparametric variant that can avoid the ad-hoc feature construction task and model the topics in a discriminative way. Our experiments on a Twitter dataset shows that modeling user interests and link information helps in learning quality topics for sparse tweets as well as helps significantly in classification task. Our experiments further show that modeling this information in a principled way through topic models helps more than simply adding this information through features.
On the Future of Personal Assistants

The purpose of this panel is to explore issues that will arise in building future personal assistants (PAs), especially for family use. In this regard, we will consider implications of being an "assistant" and those of being "personal." The target timeframe is 3-10 years out, so that very near-term products will not be discussed. We will elaborate briefly on the kinds of communicative and inferential capabilities such PAs will need, and then examine their social and emotional capabilities. We will discuss pros and cons for their evolution and deployment. In this regard, we will discuss the kinds of support that could be provided by the HCI community in building personal assistant systems that are useful, delightful, functional, controllable, educational, ethical, and secure.
Improved supply chain management based on hybrid demand forecasts

E D P R Demand forecasts play a crucial role for supply chain management. The future demand for a certain product is the basis for the respective replenishment systems. Several forecasting techniques have been developed, each one with its particular advantages and disadvantages compared to other approaches. This motivates the development of hybrid systems combining different techniques and their respective strengths. In this paper, we present a hybrid intelligent system combining Autoregressive Integrated Moving Average (ARIMA) models and neural networks for demand forecasting. We show improvements in forecasting accuracy and propose a replenishment system for a Chilean supermarket, which leads simultaneously to fewer sales failures and lower inventory levels than the previous solution.
Measuring ISP topologies with rocketfuel

To date, realistic ISP topologies have not been accessible to the research community, leaving work that depends on topology on an uncertain footing. In this paper, we present new Internet mapping techniques that have enabled us to measure router-level ISP topologies. Our techniques reduce the number of required traces compared to a brute-force, all-to-all approach by three orders of magnitude without a significant loss in accuracy. They include the use of BGP routing tables to focus the measurements, the elimination of redundant measurements by exploiting properties of IP routing, better alias resolution, and the use of DNS to divide each map into POPs and backbone. We collect maps from ten diverse ISPs using our techniques, and find that our maps are substantially more complete than those of earlier Internet mapping efforts. We also report on properties of these maps, including the size of POPs, distribution of router outdegree, and the interdomain peering structure. As part of this work, we release our maps to the community.
Participatory noise pollution monitoring using mobile phones

Noise pollution is a major problem in cities around the world. The current methods to assess it neglect to represent the real exposure experienced by the citizens themselves, and therefore could lead to wrong conclusions and a biased representations. In this paper we present a novel approach to monitor noise pollution involving the general public. Using their mobile phones as noise sensors, we provide a low cost solution for the citizens to measure their personal exposure to noise in their everyday environment and participate in the creation of collective noise maps by sharing their geo-localized and annotated measurements with the community. Our prototype, called NoiseTube, can be found online .
Rumors at the Speed of Light? Modeling the Rate of Rumor Transmission During Crisis

Social media have become an established feature of the dynamic information space that emerges during crisis events. Both emergency responders and the public use these platforms to search for, disseminate, challenge, and make sense of information during crises. In these situations rumors also proliferate, but just how fast such information can spread is an open question. We address this gap, modeling the speed of information transmission to compare retransmission times across content and context features. We specifically contrast rumor-affirming messages with rumor-correcting messages on Twitter during a notable hostage crisis to reveal differences in transmission speed. Our work has important implications for the growing field of crisis informatics.
LMS vs XMSS : Comparion of two Hash-Based Signature Standards

Quantum computing poses challenges to public key signatures as we know them today. LMS and XMSS are two hash based signature schemes that have been proposed in the IETF as quantum secure. Both schemes are based on well-studied hash trees, but their similarities and differences have not yet been discussed. In this work, we attempt to compare the two standards. We compare their security assumptions and quantify their signature and public key sizes. We also address the computation overhead they introduce. Our goal is to provide a clear understanding of the schemes’ similarities and differences for implementers and protocol designers to be able to make a decision as to which standard to chose.
Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization

With exponential growth in the size of computer networks and developed applications, the significant increasing of the potential damage that can be caused by launching attacks is becoming obvious. Meanwhile, Intrusion Detection Systems (IDSs) and Intrusion Prevention Systems (IPSs) are one of the most important defense tools against the sophisticated and ever-growing network attacks. Due to the lack of adequate dataset, anomaly-based approaches in intrusion detection systems are suffering from accurate deployment, analysis and evaluation. There exist a number of such datasets such as DARPA98, KDD99, ISC2012, and ADFA13 that have been used by the researchers to evaluate the performance of their proposed intrusion detection and intrusion prevention approaches. Based on our study over eleven available datasets since 1998, many such datasets are out of date and unreliable to use. Some of these datasets suffer from lack of traffic diversity and volumes, some of them do not cover the variety of attacks, while others anonymized packet information and payload which cannot reflect the current trends, or they lack feature set and metadata. This paper produces a reliable dataset that contains benign and seven common attack network flows, which meets real world criteria and is publicly avaliable. Consequently, the paper evaluates the performance of a comprehensive set of network traffic features and machine learning algorithms to indicate the best set of features for detecting the certain attack categories.
Clustering for Mining in Large Spatial Databases

In the past few decades, clustering has been widely used in areas such as pattern recognition, data analysis, and image processing. Recently, clustering has been recognized as a primary data mining method for knowledge discovery in spatial databases, i.e. databases managing 2D or 3D points, polygons etc. or points in some d-dimensional feature space. The well-known clustering algorithms, however, have some drawbacks when applied to large spatial databases. First, they assume that all objects to be clustered reside in main memory. Second, these methods are too inefficient when applied to large databases. To overcome these limitations, new algorithms have been developed which are surveyed in this paper. These algorithms make use of efficient query processing techniques provided by spatial database systems.
Improving RNN with Attention and Embedding for Adverse Drug Reactions

Electronic Health Records (EHR) narratives are a rich source of information, embedding high-resolution information of value to secondary research use. However, because the EHRs are mostly in natural language free-text and highly ambiguity-ridden, many natural language processing algorithms have been devised around them to extract meaningful structured information about clinical entities. The performance of the algorithms however, largely varies depending on the training dataset as well as the effectiveness of the use of background knowledge to steer the learning process.
 In this paper we study the impact of initializing the training of a neural network natural language processing algorithm with pre-defined clinical word embeddings to improve feature extraction and relationship classification between entities. We add our embedding framework to a bi-directional long short-term memory (Bi-LSTM) neural network, and further study the effect of using attention weights in neural networks for sequence labelling tasks to extract knowledge of Adverse Drug Reactions (ADRs). We incorporate unsupervised word embeddings using Word2Vec and GloVe from widely available medical resources such as Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) II corpora, Unified Medical Language System (UMLS) as well as embed pharmaco lexicon from available EHRs. Our algorithm, implemented using two datasets, shows that our architecture outperforms baseline Bi-LSTM or Bi-LSTM networks using linear chain and Skip-Chain conditional random fields (CRF).
Sequence-to-Sequence Neural Net Models for Grapheme-to-Phoneme Conversion

Sequence-to-sequence translation methods based on generation with a side-conditioned language model have recently shown promising results in several tasks. In machine translation, models conditioned on source side words have been used to produce target-language text, and in image captioning, models conditioned images have been used to generate caption text. Past work with this approach has focused on large vocabulary tasks, and measured quality in terms of BLEU. In this paper, we explore the applicability of such models to the qualitatively different grapheme-to-phoneme task. Here, the input and output side vocabularies are small, plain n-gram models do well, and credit is only given when the output is exactly correct. We find that the simple side-conditioned generation approach is able to rival the state-of-the-art, and we are able to significantly advance the stat-of-the-art with bi-directional long short-term memory (LSTM) neural networks that use the same alignment information that is used in conventional approaches.