nils-herrmann/clean_scirepeval huggingface dataset:
Lexical and syntactic ambiguity as a source of humor

The paper analyzes some forms of linguistic ambiguity in English in a specific register, i.e. newspaper headlines. In particular, the focus of the research is on examples of lexical and syntactic ambiguity that result in sources of voluntary or involuntary humor. The study is based on a corpus of 135 verbally ambiguous headlines found on web sites presenting humorous bits of information. The linguistic phenomena that contribute to create this kind of semantic confusion in headlines will be analyzed and divided into the three main categories of lexical, syntactic, and phonological ambiguity, and examples from the corpus will be discussed for each category. The main results of the study were that, firstly, contrary to the findings of previous research on jokes, syntactically ambiguous headlines were found in good percentage in the corpus and that this might point to di¤erences in genre. Secondly, two new configurations for the processing of the disjunctor/connector order were found. In the first of these configurations the disjunctor appears before the connector, instead of being placed after or coinciding with the ambiguous element, while in the second one two ambiguous elements are present, each of which functions both as a connector and
Underground Wireless Communication Using Magnetic Induction

Underground is a challenging environment for wireless communication since the propagation medium is no longer air but soil, rock and water. The well established wireless communication techniques using electromagnetic (EM) waves do not work well in this environment due to three problems: high path loss, dynamic channel condition and large antenna size. New techniques using magnetic induction (MI) can solve two of the three problems (dynamic channel condition and large antenna size), but may still cause even higher path loss. In this paper, a complete characterization of the underground MI communication channel is provided. Based on the channel model, the MI waveguide technique for communication is developed in order to reduce the MI path loss. The performance of the traditional EM wave systems, the current MI systems and our improved MI waveguide system are quantitatively compared. The results reveal that our MI waveguide system has much lower path loss than the other two cases for any channel conditions.
Neural Network for Graphs: A Contextual Constructive Approach

This paper presents a new approach for learning in structured domains (SDs) using a constructive neural network for graphs (NN4G). The new model allows the extension of the input domain for supervised neural networks to a general class of graphs including both acyclic/cyclic, directed/undirected labeled graphs. In particular, the model can realize adaptive contextual transductions, learning the mapping from graphs for both classification and regression tasks. In contrast to previous neural networks for structures that had a recursive dynamics, NN4G is based on a constructive feedforward architecture with state variables that uses neurons with no feedback connections. The neurons are applied to the input graphs by a general traversal process that relaxes the constraints of previous approaches derived by the causality assumption over hierarchical input data. Moreover, the incremental approach eliminates the need to introduce cyclic dependencies in the definition of the system state variables. In the traversal process, the NN4G units exploit (local) contextual information of the graphs vertices. In spite of the simplicity of the approach, we show that, through the compositionality of the contextual information developed by the learning, the model can deal with contextual information that is incrementally extended according to the graphs topology. The effectiveness and the generality of the new approach are investigated by analyzing its theoretical properties and providing experimental results.
Integrating web mining and neural network for personalized e-commerce automatic service

a Department of Mechatronics Engineering, National Changhua University of Education, No. 2, Shi-da Road, Changhua City 500, Taiwan, ROC b Department of Industrial Education and Technology, National Changhua University of Education, No. 2, Shi-da Road, Changhua City 500, Taiwan, ROC c College of Business Administration, National Changhua University of Education, No. 2, Shi-da Road, Changhua City 500, Taiwan, ROC
Doc2Sent2Vec: A Novel Two-Phase Approach for Learning Document Representation

Doc2Sent2Vec is an unsupervised approach to learn low-dimensional feature vector (or embedding) for a document. This embedding captures the semantics of the document and can be fed as input to machine learning algorithms to solve a myriad number of applications in the field of data mining and information retrieval. Some of these applications include document classification, retrieval, and ranking.
 The proposed approach is two-phased. In the first phase, the model learns a vector for each sentence in the document using a standard word-level language model. In the next phase, it learns the document representation from the sentence sequence using a novel sentence-level language model. Intuitively, the first phase captures the word-level coherence to learn sentence embeddings, while the second phase captures the sentence-level coherence to learn document embeddings. Compared to the state-of-the-art models that learn document vectors directly from the word sequences, we hypothesize that the proposed decoupled strategy of learning sentence embeddings followed by document embeddings helps the model learn accurate and rich document representations.
 We evaluate the learned document embeddings by considering two classification tasks: scientific article classification and Wikipedia page classification. Our model outperforms the current state-of-the-art models in the scientific article classification task by ?12.07% and the Wikipedia page classification task by ?6.93%, both in terms of F1 score. These results highlight the superior quality of document embeddings learned by the Doc2Sent2Vec approach.
FingOrbits: interaction with wearables using synchronized thumb movements

We present FingOrbits, a wearable interaction technique using synchronized thumb movements. A thumb-mounted ring with an inertial measurement unit and a contact microphone are used to capture thumb movements when rubbing against the other fingers. Spectral information of the movements are extracted and fed into a classification backend that facilitates gesture discrimination. FingOrbits enables up to 12 different gestures through detecting three rates of movement against each of the four fingers. Through a user study with 10 participants (7 novices, 3 experts), we demonstrate that FingOrbits can distinguish up to 12 thumb gestures with an accuracy of 89% to 99% rendering the approach applicable for practical applications.
Day-ahead electricity price forecasting using the wavelet transform and ARIMA models

This paper proposes a novel technique to forecast day-ahead electricity prices based on the wavelet transform and ARIMA models. The historical and usually ill-behaved price series is decomposed using the wavelet transform in a set of better-behaved constitutive series. Then, the future values of these constitutive series are forecast using properly fitted ARIMA models. In turn, the ARIMA forecasts allow, through the inverse wavelet transform, reconstructing the future behavior of the price series and therefore to forecast prices. Results from the electricity market of mainland Spain in year 2002 are reported.
Machine Learning and Knowledge Discovery in Databases

The next decade will see a deep transformation of industrial applications by big data analytics, machine learning and the internet of things. Industrial applications have a number of unique features, setting them apart from other domains. Central for many industrial applications in the internet of things is time series data generated by often hundreds or thousands of sensors at a high rate, e.g. by a turbine or a smart grid. In a first wave of applications this data is centrally collected and analyzed in Map-Reduce or streaming systems for condition monitoring, root cause analysis, or predictive maintenance. The next step is to shift from centralized analysis to distributed in-field or in situ analytics, e.g in smart cities or smart grids. The final step will be a distributed, partially autonomous decision making and learning in massively distributed environments. In this talk I will give an overview on Siemens’ journey through this transformation, highlight early successes, products and prototypes and point out future challenges on the way towards machine intelligence. I will also discuss architectural challenges for such systems from a Big Data point of view. Bio.Michael May is Head of the Technology Field Business Analytics & Monitoring at Siemens Corporate Technology, Munich, and responsible for eleven research groups in Europe, US, and Asia. Michael is driving research at Siemens in data analytics, machine learning and big data architectures. In the last two years he was responsible for creating the Sinalytics platform for Big Data applications across Siemens’ business. Before joining Siemens in 2013, Michael was Head of the Knowledge Discovery Department at the Fraunhofer Institute for Intelligent Analysis and Information Systems in Bonn, Germany. In cooperation with industry he developed Big Data Analytics applications in sectors ranging from telecommunication, automotive, and retail to finance and advertising. Between 2002 and 2009 Michael coordinated two Europe-wide Data Mining Research Networks (KDNet, KDubiq). He was local chair of ICML 2005, ILP 2005 and program chair of the ECML PKDD Industrial Track 2015. Michael did his PhD on machine discovery of causal relationships at the Graduate Programme for Cognitive Science at the University of Hamburg. Machine Learning Challenges at Amazon
Situational maturity models as instrumental artifacts for organizational design

In order to identify and explore the strength and weaknesses of particular organizational designs, a wide range of maturity models have been developed by both, practitioners and academics over the past years. However, a systematization and generalization of the procedure on how to design maturity models as well as a synthesis of design science research with the rather behavioural field of organization theory is still lacking. Trying to combine the best of both fields, a first design proposition of a situational maturity model is presented in this paper. The proposed maturity model design is illustrated with the help of an instantiation for the healthcare domain.
Bringing Semantics to Web Services with OWL-S

Current industry standards for describing Web Services focus on ensuring interoperability across diverse platforms, but do not provide a good foundation for automating the use of Web Services. Representational techniques being developed for the Semantic Web can be used to augment these standards. The resulting Web Service specifications enable the development of software programs that can interpret descriptions of unfamiliar Web Services and then employ those services to satisfy user goals. OWL-S (“OWL for Services”) is a set of notations for expressing such specifications, based on the Semantic Web ontology language OWL. It consists of three interrelated parts: a profile ontology, used to describe what the service does; a process ontology and corresponding presentation syntax, used to describe how the service is used; and a grounding ontology, used to describe how to interact with the service. OWL-S can be used to automate a variety of service-related activities involving service discovery, interoperation, and composition. A large body of research on OWL-S has led to the creation of many open-source tools for developing, reasoning about, and dynamically utilizing Web Services.
Beck Depression Inventory Ii Pdf Scoring

ROC analyses generated low area under the curve (.695, 95% confidence interval (.637.752)) and cutoff scores with poor sensitivity/specificity balance. BDI-II. Because the distribution of BDI-II scores was not normal, percentile ranks for raw scores were provided for the total sample and separately by gender. symptoms two scales were used: The Beck Depression Inventory-II (BDIII) smokers and non smokers, we found that the mean scores on the BDI-II (9.21 vs.
Cross-lingual Language Model Pretraining

Recent studies have demonstrated the efficiency of generative pretraining for English natural language understanding. In this work, we extend this approach to multiple languages and show the effectiveness of cross-lingual pretraining. We propose two methods to learn cross-lingual language models (XLMs): one unsupervised that only relies on monolingual data, and one supervised that leverages parallel data with a new cross-lingual language model objective. We obtain state-of-the-art results on cross-lingual classification, unsupervised and supervised machine translation. On XNLI, our approach pushes the state of the art by an absolute gain of 4.9% accuracy. On unsupervised machine translation, we obtain 34.3 BLEU on WMT’16 German-English, improving the previous state of the art by more than 9 BLEU. On supervised machine translation, we obtain a new state of the art of 38.5 BLEU on WMT’16 Romanian-English, outperforming the previous best approach by more than 4 BLEU. Our code and pretrained models will be made publicly available.
What is ERP?

Though enterprise resource planning (ERP) has gained some prominence in the information systems (IS) literature over the past few years and is a signi®cant phenomenon in practice, through (a) historical analysis, (b) meta-analysis of representative IS literature, and (c) a survey of academic experts, we reveal dissenting views on the phenomenon. Given this diversity of perspectives, it is unlikely that at this stage a broadly agreed de®nition of ERP can be achieved. We thus seek to increase awareness of the issues and stimulate further discussion, with the ultimate aim being to: (1) aid communication amongst researchers and between researchers and practitioners; (2) inform development of teaching materials on ERP and related concepts in university curricula and in commercial education and training; and (3) aid communication amongst clients, consultants and vendors. Increased transparency of the ERP-concept within IS may also bene®t other aligned ®elds of knowledge.
Empirical assessment of machine learning based software defect prediction techniques

The wide-variety of real-time software systems, including telecontrol/telepresence systems, robotic systems, and mission planning systems, can entail dynamic code synthesis based on runtime mission-specific requirements and operating conditions. This necessitates the need for dynamic dependability assessment to ensure that these systems perform as specified and not fail in catastrophic ways. One approach in achieving this is to dynamically assess the modules in the synthesized code using software defect prediction techniques. Statistical models; such as stepwise multi-linear regression models and multivariate models, and machine learning approaches, such as artificial neural networks, instance-based reasoning, Bayesian-belief networks, decision trees, and rule inductions, have been investigated for predicting software quality. However, there is still no consensus about the best predictor model for software defects. In this paper; we evaluate different predictor models on four different real-time software defect data sets. The results show that a combination of IR and instance-based learning along with the consistency-based subset evaluation technique provides a relatively better consistency in accuracy prediction compared to other models. The results also show that "size" and "complexity" metrics are not sufficient for accurately predicting real-time software defects.
Biometrics based attendance checking using Principal Component Analysis

In recent years, there have been very promising applications of biometric systems to improve access control systems and security of data recording. Of all the biometric systems available, fingerprint verification is the most dominant in commercial application due to its excellent performance and low cost. In this study, it is attempted to implement a fingerprint and face detection and recognition biometrics system for the professors' attendance management thus replacing the current manual system. Proposed system provides faculty face recognition using Viola-Jones Face Detection Method and Principal Component Analysis (PCA) integrated with fingerprint verification using Arduino. Test results improved attendance system accuracy and automate faculty attendance system.
Artificial Intelligence Techniques for Cancer Detection and Classification: Review Study

Cancer is the general name for a group of more than 100 diseases. Although cancer includes different types of diseases, they all start because abnormal cells grow out of control. Without treatment, cancer can cause serious health problems and even loss of life. Early detection of cancer may reduce mortality and morbidity. This paper presents a review of the detection methods for lung, breast, and brain cancers. These methods used for diagnosis include artificial intelligence techniques, such as support vector machine neural network, artificial neural network, fuzzy logic, and adaptive neuro-fuzzy inference system, with medical imaging like X-ray, ultrasound, magnetic resonance imaging, and computed tomography scan images. Imaging techniques are the most important approach for precise diagnosis of human cancer. We investigated all these techniques to identify a method that can provide superior accuracy and determine the best medical images for use in each type of cancer.
Intelligent Vehicle Applications Worldwide

one component of intelligent transportation systems, IV systems use sensing and intelligent algorithms to understand the vehicle’s immediate environment, either assisting the driver or fully controlling the vehicle. Following the success of information-oriented systems, IV systems will likely be the “next wave” for ITS, functioning at the control layer to enable the driver–vehicle “subsystem” to operate more effectively. This column provides a broad overview of applications and selected activities in this field. IV application areas
Sample-efficient Actor-Critic Reinforcement Learning with Supervised Data for Dialogue Management

Deep reinforcement learning (RL) methods have significant potential for dialogue policy optimisation. However, they suffer from a poor performance in the early stages of learning. This is especially problematic for on-line learning with real users. Two approaches are introduced to tackle this problem. Firstly, to speed up the learning process, two sampleefficient neural networks algorithms: trust region actor-critic with experience replay (TRACER) and episodic natural actorcritic with experience replay (eNACER) are presented. For TRACER, the trust region helps to control the learning step size and avoid catastrophic model changes. For eNACER, the natural gradient identifies the steepest ascent direction in policy space to speed up the convergence. Both models employ off-policy learning with experience replay to improve sampleefficiency. Secondly, to mitigate the cold start issue, a corpus of demonstration data is utilised to pre-train the models prior to on-line reinforcement learning. Combining these two approaches, we demonstrate a practical approach to learning deep RLbased dialogue policies and demonstrate their effectiveness in a task-oriented information seeking domain.
The Future of the CIO in a Digital Economy

When Procter & Gamble’s CEO Bob McDonald set a strategic goal announcing “We want to be the first company that digitizes from end to end” he turned to CIO and head of Global Business Services Filippo Passerini to lead the transformation. Many CIOs tell us their jobs are expanding and many CEOs tell us they would like their CIOs to do more. In addition to providing highquality and cost-effective IT services, today’s CIO often has other, and growing, responsibilities. These include helping with revenue generation, delivering shared services, optimizing enterprise business processes, improving the customer experience, overseeing business operations and digitizing the entire firm. We think these new responsibilities, and the pressures they place on CIOs, are a symptom of one of the biggest opportunities and challenges enterprises face today—the ever-increasing digitization of business as part of the move toward a more digital economy.3 Every interaction between a customer and a business, between a business and another business, between
Big data and quality: A literature review

Big Data refers to data volumes in the range of Exabyte (1018) and beyond. Such volumes exceed the capacity of current on-line storage and processing systems. With characteristics like volume, velocity and variety big data throws challenges to the traditional IT establishments. Computer assisted innovation, real time data analytics, customer-centric business intelligence, industry wide decision making and transparency are possible advantages, to mention few, of Big Data. There are many issues with Big Data that warrant quality assessment methods. The issues are pertaining to storage and transport, management, and processing. This paper throws light into the present state of quality issues related to Big Data. It provides valuable insights that can be used to leverage Big Data science activities.
Deepdocclassifier: Document classification with deep Convolutional Neural Network

This paper presents a deep Convolutional Neural Network (CNN) based approach for document image classification. One of the main requirement of deep CNN architecture is that they need huge number of samples for training. To overcome this problem we adopt a deep CNN which is trained using big image dataset containing millions of samples i.e., ImageNet. The proposed work outperforms both the traditional structure similarity methods and the CNN based approaches proposed earlier. The accuracy of the proposed approach with merely 20 images per class outperforms the state-of-the-art by achieving classification accuracy of 68.25%. The best results on Tobbacoo-3428 dataset show that our proposed method outperforms the state-of-the-art method by a significant margin and achieved a median accuracy of 77.6% with 100 samples per class used for training and validation.
A Tutorial on Multi-label Classification Techniques

Most classification problems associate a single class to each example or instance. However, there are many classification tasks where each instance can be associated with one or more classes. This group of problems represents an area known as multi-label classification. One typical example of multi-label classification problems is the classification of documents, where each document can be assigned to more than one class. This tutorial presents the most frequently used techniques to deal with these problems in a pedagogical manner, with examples illustrating the main techniques and proposing a taxonomy of multi-label techniques that highlights the similarities and differences between these techniques.