nils-herrmann/clean_scirepeval huggingface dataset:
CRM and customer-centric knowledge management: an empirical research

Current competitive challenges induced by globalization and advances in information technology have forced companies to focus on managing customer relationships, and in particular customer satisfaction, in order to efficiently maximize revenues. This paper reports exploratory research based on a mail survey addressed to the largest 1,000 Greek organizations. The objectives of the research were: to investigate the extent of the usage of customerand market-related knowledge management (KM) instruments and customer relationship management (CRM) systems by Greek organizations and their relationship with demographic and organizational variables; to investigate whether enterprises systematically carry out customer satisfaction and complaining behavior research; and to examine the impact of the type of the information system used and managers’ attitudes towards customer KM practices. In addition, a conceptual model of CRM development stages is proposed. The findings of the survey show that about half of the organizations of the sample do not adopt any CRM philosophy. The remaining organizations employ instruments to conduct customer satisfaction and other customer-related research. However, according to the proposed model, they are positioned in the first, the preliminary CRM development stage. The findings also suggest that managers hold positive attitudes towards CRM and that there is no significant relationship between the type of the transactional information system used and the extent to which customer satisfaction research is performed by the organizations. The paper concludes by discussing the survey findings and proposing future
Developing a Knowledge Management Strategy: Reflections from an Action Research Project

This paper presents the results of an action research project into the practice of formulating a knowledge management strategy for a middle-sized supply chain solution provider in Australia. The paper contrasts our practice of formulating the strategy with expectations and understandings of strategy and knowledge management strategy critical success factors identified from the literature. We have adopted an action research approach incorporating multiple iterative phases. With the cooperation of the company, this approach has enabled a change in organisational culture to one which now fosters a knowledge sharing environment.
The Role of Depression and Attachment Styles in Predicting Students’ Addiction to Cell Phones

The present study aimed at investigating the role of depression and attachment styles in predicting cell phone addiction.



In this descriptive correlational study, a sample including 100 students of Payame Noor University (PNU), Reyneh Center, Iran, in the academic year of 2013-2014 was selected using volunteer sampling. Participants were asked to complete the adult attachment inventory (AAI), Beck depression inventory-13 (BDI-13) and the cell phone overuse scale (COS).



Results of the stepwise multiple regression analysis showed that depression and avoidant attachment style were the best predictors of students' cell phone addiction (R(2) = 0.23).



The results of this study highlighted the predictive value of depression and avoidant attachment style concerning students' cell phone addiction.
Functions and Actors of Inland Ports : European and North American Dynamics

The emergence of inland ports took place in several regions around the world, notably where the growth of inland freight distribution required a massification of flows. Yet, there is no definitive consensus about how such inland facilities should be labeled, with terms such as dry ports being advocated. It is suggested in the paper that the term inland port is a more appropriate construct since it considers terminal activities as well as the crucial logistics activities taking place in co-location or in proximity of inland terminals. This perspective requires the investigation of how transport and supply chain functions and the various actors involved in their setting and operations are taking shape in inland ports. Case studies pertaining to European and North American inland ports are presented. Although inland ports are planned, set and operated by a wide variety of actors, ranging from public to private interests, transport and supply chain functions tend to label them as satellite terminals, load centers or transmodal centers.
Identifying Sarcasm in Twitter: A Closer Look

Sarcasm transforms the polarity of an apparently positive or negative utterance into its opposite. We report on a method for constructing a corpus of sarcastic Twitter messages in which determination of the sarcasm of each message has been made by its author. We use this reliable corpus to compare sarcastic utterances in Twitter to utterances that express positive or negative attitudes without sarcasm. We investigate the impact of lexical and pragmatic factors on machine learning effectiveness for identifying sarcastic utterances and we compare the performance of machine learning techniques and human judges on this task. Perhaps unsurprisingly, neither the human judges nor the machine learning techniques perform very well.
Oh that's what you meant!: reducing emoji misunderstanding

Emoji provide a way to express nonverbal conversational cues in computer-mediated communication. However, people need to share the same understanding of what each emoji symbolises, otherwise communication can breakdown. We surveyed 436 people about their use of emoji and ran an interactive study using a two-dimensional emotion space to investigate (1) the variation in people's interpretation of emoji and (2) their interpretation of corresponding Android and iOS emoji. Our results show variations between people's ratings within and across platforms. We outline our solution to reduce misunderstandings that arise from different interpretations of emoji.
DOCK: Detecting Objects by Transferring Common-Sense Knowledge

We present a scalable approach for Detecting Objects by transferring Common-sense Knowledge (DOCK) from source to target categories. In our setting, the training data for the source categories have bounding box annotations, while those for the target categories only have image-level annotations. Current state-of-the-art approaches focus on image-level visual or semantic similarity to adapt a detector trained on the source categories to the new target categories. In contrast, our key idea is to (i) use similarity not at the image-level, but rather at the regionlevel, and (ii) leverage richer common-sense (based on attribute, spatial, etc.) to guide the algorithm towards learning the correct detections. We acquire such common-sense cues automatically from readily-available knowledge bases without any extra human effort. On the challenging MS COCO dataset, we find that common-sense knowledge can substantially improve detection performance over existing transfer-learning baselines.
Development of a robot balancing on a ball

This paper proposes a robot balanced on a ball. In contrast to an inverted pendulum with two wheels, such as the Segway Human Transporter, an inverted pendulum using a ball can traverse in any direction without changing its orientation, thereby enabling stable motion. Such robots can be used in place of the two-wheeled robots. The robot proposed in this paper is equipped with three omnidirectional wheels with stepping motors that drive the ball and two sets of rate gyroscopes and accelerometers as attitude sensors. The robot has a simple design; it is controlled with a 16-bit microcontroller and runs on Ni-MH batteries. It can not only stand still but also traverse on floor and pivot around its vertical axis. Inverted pendulum control is applied in two axes for attitude control, and commanded motions are converted into velocity commands for the three wheels. The mechanism, control method, and experimental results are described in this paper.
Using real time computer vision algorithms in automatic attendance management systems

This paper introduces a new approach in automatic attendance management systems, extended with computer vision algorithms. We propose using real time face detection algorithms integrated on an existing Learning Management System (LMS), which automatically detects and registers students attending on a lecture. The system represents a supplemental tool for instructors, combining algorithms used in machine learning with adaptive methods used to track facial changes during a longer period of time. This new system aims to be less time consuming than traditional methods, at the same time being nonintrusive and not interfere with the regular teaching process. The tool promises to offer accurate results and a more detailed reporting system which shows student activity and attendance in a classroom.
Learning to map sentences to logical form

One of the classical goals of research in artificial intelligence is to construct systems that automatically recover the meaning of natural language text. Machine learning methods hold significant potential for addressing many of the challenges involved with these systems. This thesis presents new techniques for learning to map sentences to logical form — lambda-calculus representations of their meanings. We first describe an approach to the context-independent learning problem, where sentences are analyzed in isolation. We describe a learning algorithm that takes as input a training set of sentences labeled with expressions in the lambda calculus. The algorithm induces a Combinatory Categorial Grammar (CCG) for the problem, along with a log-linear model that represents a distribution over syntactic and semantic analyses conditioned on the input sentence. Next, we present an extension that addresses challenges that arise when learning to analyze spontaneous, unedited natural language input, as is commonly seen in natural language interface applications. A key idea is to introduce non-standard CCG combinators that relax certain parts of the grammar — for example allowing flexible word order, or insertion of lexical items — with learned costs. We also present a new, online algorithm for inducing a weighted CCG. Finally, we describe how to extend this learning approach to the context-dependent analysis setting, where the meaning of a sentence can depend on the context in which it appears. The training examples are sequences of sentences annotated with lambdacalculus meaning representations. We develop an algorithm that maintains explicit, lambda-calculus representations of discourse entities and uses a context-dependent analysis pipeline to recover logical forms. The method uses a hidden-variable variant of the perception algorithm to learn a linear model used to select the best analysis. Experiments demonstrate that the learning techniques we develop induce accurate models for semantic analysis while requiring less data annotate effort than previous approaches.  Associate Professor
Investigating Information Systems with Positivist Case Study Research

This paper offers a rigorous step-by-step methodology for developing theories, and contains specific and detailed guidelines for IS researchers to follow in carrying out positivist case studies. The methodology is largely inspired by the work of Yin (2003), Eisenhardt (1989), Miles and Huberman (1994) and several others who are strong proponents of and have wide experience in this research approach. It also relies on previous key contributions to the positivist case study research method in IS (Benbasat et al., 1987; Lee, 1989; Dubé and Paré, 2003). We illustrate how this methodology can be applied in our field to help reveal new perspectives and empirical insights. In addition, the desired qualities associated with several of the proposed concepts and the techniques and tools included in the methodology are presented. We believe that the two detailed case studies presented in this paper represent highly rigorous, yet different applications of the positivist case study research method and, hence, we strongly encourage IS researchers to follow their respective approaches.
Functional and Effective Connectivity: A Review

Over the past 20 years, neuroimaging has become a predominant technique in systems neuroscience. One might envisage that over the next 20 years the neuroimaging of distributed processing and connectivity will play a major role in disclosing the brain's functional architecture and operational principles. The inception of this journal has been foreshadowed by an ever-increasing number of publications on functional connectivity, causal modeling, connectomics, and multivariate analyses of distributed patterns of brain responses. I accepted the invitation to write this review with great pleasure and hope to celebrate and critique the achievements to date, while addressing the challenges ahead.
AutoDIAL: Automatic Domain Alignment Layers

Classifiers trained on given databases perform poorly when tested on data acquired in different settings. This is explained in domain adaptation through a shift among distributions of the source and target domains. Attempts to align them have traditionally resulted in works reducing the domain shift by introducing appropriate loss terms, measuring the discrepancies between source and target distributions, in the objective function. Here we take a different route, proposing to align the learned representations by embedding in any given network specific Domain Alignment Layers, designed to match the source and target feature distributions to a reference one. Opposite to previous works which define a priori in which layers adaptation should be performed, our method is able to automatically learn the degree of feature alignment required at different levels of the deep network. Thorough experiments on different public benchmarks, in the unsupervised setting, confirm the power of our approach.
LorSLIM: Low Rank Sparse Linear Methods for Top-N Recommendations

In this paper, we notice that sparse and low-rank structures arise in the context of many collaborative filtering applications where the underlying graphs have block-diagonal adjacency matrices. Therefore, we propose a novel Sparse and Low-Rank Linear Method (Lor SLIM) to capture such structures and apply this model to improve the accuracy of the Top-N recommendation. Precisely, a sparse and low-rank aggregation coefficient matrix W is learned from Lor SLIM by solving an l1-norm and nuclear norm regularized optimization problem. We also develop an efficient alternating augmented Lagrangian method (ADMM) to solve the optimization problem. A comprehensive set of experiments is conducted to evaluate the performance of Lor SLIM. The experimental results demonstrate the superior recommendation quality of the proposed algorithm in comparison with current state-of-the-art methods.
Semi-Supervised Events Clustering in News Retrieval

The presentation of news articles to meet research needs has traditionally been a document-centric process. Yet users often want to monitor developing news stories based on an event, rather than by examining an exhaustive list of retrieved documents. In this work, we illustrate a news retrieval system, eventNews, and an underlying algorithm which is event-centric. Through this system, news articles are clustered around a single news event or an event and its sub-events. The algorithm presented can leverage the creation of new Reuters stories and their compact labels as seed documents for the clustering process. The system is configured to generate top-level clusters for news events based on an editorially supplied topical label, known as a ‘slugline,’ and to generate sub-topic-focused clusters based on the algorithm. The system uses an agglomerative clustering algorithm to gather and structure documents into distinct result sets. Decisions on whether to merge related documents or clusters are made according to the similarity of evidence derived from two distinct sources, one, relying on a digital signature based on the unstructured text in the document, the other based on the presence of named entity tags that have been assigned to the document by a named entity tagger, in this case Thomson Reuters’ Calais engine.  Copying permitted for private and academic purposes. This volume is published and copyrighted by its editors. In: M. Martinez, U. Kruschwitz, G. Kazai, D. Corney, F. Hopfgartner, R. Campos and D. Albakour (eds.):
Genetic consequences of habitat fragmentation in plant populations: susceptible signals in plant traits and methodological approaches.

Conservation of genetic diversity, one of the three main forms of biodiversity, is a fundamental concern in conservation biology as it provides the raw material for evolutionary change and thus the potential to adapt to changing environments. By means of meta-analyses, we tested the generality of the hypotheses that habitat fragmentation affects genetic diversity of plant populations and that certain life history and ecological traits of plants can determine differential susceptibility to genetic erosion in fragmented habitats. Additionally, we assessed whether certain methodological approaches used by authors influence the ability to detect fragmentation effects on plant genetic diversity. We found overall large and negative effects of fragmentation on genetic diversity and outcrossing rates but no effects on inbreeding coefficients. Significant increases in inbreeding coefficient in fragmented habitats were only observed in studies analyzing progenies. The mating system and the rarity status of plants explained the highest proportion of variation in the effect sizes among species. The age of the fragment was also decisive in explaining variability among effect sizes: the larger the number of generations elapsed in fragmentation conditions, the larger the negative magnitude of effect sizes on heterozygosity. Our results also suggest that fragmentation is shifting mating patterns towards increased selfing. We conclude that current conservation efforts in fragmented habitats should be focused on common or recently rare species and mainly outcrossing species and outline important issues that need to be addressed in future research on this area.
Supervised and Unsupervised Discretization of Continuous Features

Many supervised machine learning algorithms require a discrete feature space. In this paper, we review previous work on continuous feature discretization, identify de ning characteristics of the methods, and conduct an empirical evaluation of several methods. We compare binning, an unsupervised discretization method, to entropy-based and purity-based methods, which are supervised algorithms. We found that the performance of the Naive-Bayes algorithm signi cantly improved when features were discretized using an entropy-based method. In fact, over the 16 tested datasets, the discretized version of Naive-Bayes slightly outperformed C4.5 on average. We also show that in some cases, the performance of the C4.5 induction algorithm signi cantly improved if features were discretized in advance; in our experiments, the performance never signi cantly degraded, an interesting phenomenon considering the fact that C4.5 is capable of locally discretizing features.
STUDY ON THE CELLULASE ENZYME PRODUCING ACTIVITY OF BACTERIA ISOLATED FROM MANURE WASTE AND DEGRADING SOIL

Cellulolytic bacteria were isolated from manure wastes (cow dung) and degrading soil (municipal solid waste). Nine bacterial strains were screened the cellulolytic activities. Six strains showed clear zone formation on Berg’s medium. CMC (carboxyl methyl cellulose) and cellulose were used as substrates for cellulase activities. Among six strains, cd3 and mw7 were observed in quantitative measurement determined by dinitrosalicylic acid (DNS) method. Maximum enzyme producing activity showed 1.702mg/ml and 1.677mg/ml from cd3 and mw7 for 1% CMC substrate. On the other hand, it was expressed 0.563mg/ml and 0.415mg/ml for 1% cellulose substrate respectively. It was also studied for cellulase enzyme producing activity optimizing with kinetic growth parameters such as different carbon source including various concentration of cellulose, incubation time, temperature, and pH. Starch substrate showed 0.909mg/ml and 0.851mg/ml in enzyme producing activity. The optimum substrate concentration of cellulose was 0.25% for cd3 but 1% for mw7 showing the amount of reducing sugar formation 0.628mg/ml and 0.669mg/ml. The optimum incubation parameters for cd3 were 84 hours, 40C and pH 6. Mw7 also had optimum parameters 60 hours, 40 C and pH6.
Best of Both Worlds: Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog Model

We present a novel training framework for neural sequence models, particularly for grounded dialog generation. The standard training paradigm for these models is maximum likelihood estimation (MLE), or minimizing the cross-entropy of the human responses. Across a variety of domains, a recurring problem with MLE trained generative neural dialog models (G) is that they tend to produce ‘safe’ and generic responses (‘I don’t know’, ‘I can’t tell’). In contrast, discriminative dialog models (D) that are trained to rank a list of candidate human responses outperform their generative counterparts; in terms of automatic metrics, diversity, and informativeness of the responses. However, D is not useful in practice since it can not be deployed to have real conversations with users. Our work aims to achieve the best of both worlds – the practical usefulness of G and the strong performance of D – via knowledge transfer from D to G. Our primary contribution is an end-to-end trainable generative visual dialog model, where G receives gradients from D as a perceptual (not adversarial) loss of the sequence sampled from G. We leverage the recently proposed Gumbel-Softmax (GS) approximation to the discrete distribution – specifically, a RNN augmented with a sequence of GS samplers, coupled with the straight-through gradient estimator to enable end-to-end differentiability. We also introduce a stronger encoder for visual dialog, and employ a self-attention mechanism for answer encoding along with a metric learning loss to aid D in better capturing semantic similarities in answer responses. Overall, our proposed model outperforms state-of-the-art on the VisDial dataset by a significant margin (2.67% on recall@10). The source code can be downloaded from https://github.com/jiasenlu/visDial.pytorch
Kernel Pooling for Convolutional Neural Networks

Convolutional Neural Networks (CNNs) with Bilinear Pooling, initially in their full form and later using compact representations, have yielded impressive performance gains on a wide range of visual tasks, including fine-grained visual categorization, visual question answering, face recognition, and description of texture and style. The key to their success lies in the spatially invariant modeling of pairwise (2nd order) feature interactions. In this work, we propose a general pooling framework that captures higher order interactions of features in the form of kernels. We demonstrate how to approximate kernels such as Gaussian RBF up to a given order using compact explicit feature maps in a parameter-free manner. Combined with CNNs, the composition of the kernel can be learned from data in an end-to-end fashion via error back-propagation. The proposed kernel pooling scheme is evaluated in terms of both kernel approximation error and visual recognition accuracy. Experimental evaluations demonstrate state-of-the-art performance on commonly used fine-grained recognition datasets.