nils-herrmann/clean_scirepeval huggingface dataset:
CliNER 2.0: Accessible and Accurate Clinical Concept Extraction

Clinical notes often describe important aspects of a patient’s stay and are therefore critical to medical research. Clinical concept extraction (CCE) of named entities — such as problems, tests, and treatments — aids in forming an understanding of notes and provides a foundation for many downstream clinical decision-making tasks. Historically, this task has been posed as a standard named entity recognition (NER) sequence tagging problem, and solved with feature-based methods using handengineered domain knowledge. Recent advances, however, have demonstrated the efficacy of LSTM-based models for NER tasks, including CCE. This work presents CliNER 2.0, a simple-to-install, open-source tool for extracting concepts from clinical text. CliNER 2.0 uses a wordand characterlevel LSTM model, and achieves state-of-the-art performance. For ease of use, the tool also includes pre-trained models available for public use.
Information credibility on twitter

We analyze the information credibility of news propagated through Twitter, a popular microblogging service. Previous research has shown that most of the messages posted on Twitter are truthful, but the service is also used to spread misinformation and false rumors, often unintentionally.
 On this paper we focus on automatic methods for assessing the credibility of a given set of tweets. Specifically, we analyze microblog postings related to "trending" topics, and classify them as credible or not credible, based on features extracted from them. We use features from users' posting and re-posting ("re-tweeting") behavior, from the text of the posts, and from citations to external sources.
 We evaluate our methods using a significant number of human assessments about the credibility of items on a recent sample of Twitter postings. Our results shows that there are measurable differences in the way messages propagate, that can be used to classify them automatically as credible or not credible, with precision and recall in the range of 70% to 80%.
ITGovA: Proposition of an IT governance Approach

I To cope with issues related to optimization, rationalization, risk management, economic value of technology and information assets, the implementation of appropriate IT governance seems an important need in public and private organizations. It’s one of these concepts that suddenly emerged and became an important issue in the information technology area. Many organizations started with the implementation of IT governance to achieve a better alignment between business and IT, however, there is no method to apply the IT governance principles in companies. This paper proposes a new approach to implement IT governance based on five iterative phases. This approach is a critical business process that ensures that the business meets its strategic objectives and depending on IT resources for execution. Information technology, IT governance, lifecycle, strategic alignment,
Partition and compose: parallel complex event processing

Complex event processing uses patterns to detect composite events in streams of simple events. Typically, the events are logically partitioned by some key. For instance, the key can be the stock symbol in stock quotes, the author in tweets, the vehicle in transportation, or the patient in health-care. Composite event patterns often become meaningful only after partitioning. For instance, a pattern over stock quotes is typically meaningful over quotes for the same stock symbol. This paper proposes a pattern syntax and translation scheme organized around the notion of partitions. Besides making patterns meaningful, partitioning also benefits performance, since different keys can be processed in parallel. We have implemented partitioned parallel complex event processing as an extension to IBM's System S high-performance streaming platform. Our experiments with several benchmarks from finance and social media demonstrate processing speeds of up to 830,000 events per second, and substantial speedups for expensive patterns parallelized on multi-core machines as well as multi-machine clusters. Partitioning the event stream before detecting composite events makes event processing both more intuitive and parallel.
Coagulation and ablation patterns of high-intensity focused ultrasound on a tissue-mimicking phantom and cadaveric skin

High-intensity focused ultrasound (HIFU) can be applied noninvasively to create focused zones of tissue coagulation on various skin layers. We performed a comparative study of HIFU, evaluating patterns of focused tissue coagulation and ablation upon application thereof. A tissue-mimicking (TM) phantom was prepared with bovine serum albumin and polyacrylamide hydrogel to evaluate the geometric patterns of HIFU-induced thermal injury zones (TIZs) for five different HIFU devices. Additionally, for each device, we investigated histologic patterns of HIFU-induced coagulation and ablation in serial sections of cadaveric skin of the face and neck. All HIFU devices generated remarkable TIZs in the TM phantom, with different geometric values of coagulation for each device. Most of the TIZs seemed to be separated into two or more tiny parts. In cadaveric skin, characteristic patterns of HIFU-induced ablation and coagulation were noted along the mid to lower dermis at the focal penetration depth of 3 mm and along subcutaneous fat to the superficial musculoaponeurotic system or the platysma muscle of the neck at 4.5 mm. Additionally, remarkable pre-focal areas of tissue coagulation were observed in the upper and mid dermis at the focal penetration depth of 3 mm and mid to lower dermis at 4.5 mm. For five HIFU devices, we outlined various patterns of HIFU-induced TIZ formation along pre-focal, focal, and post-focal areas of TM phantom and cadaveric skin of the face and neck.
Learning Dependency-Based Compositional Semantics

Suppose we want to build a system that answers a natural language question by representing its semantics as a logical forxm and computing the answer given a structured database of facts. The core part of such a system is the semantic parser that maps questions to logical forms. Semantic parsers are typically trained from examples of questions annotated with their target logical forms, but this type of annotation is expensive.Our goal is to instead learn a semantic parser from question–answer pairs, where the logical form is modeled as a latent variable. We develop a new semantic formalism, dependency-based compositional semantics (DCS) and define a log-linear distribution over DCS logical forms. The model parameters are estimated using a simple procedure that alternates between beam search and numerical optimization. On two standard semantic parsing benchmarks, we show that our system obtains comparable accuracies to even state-of-the-art systems that do require annotated logical forms.
Engineering for Predictive Modeling using Reinforcement Learning

Feature engineering is a crucial step in the process of predictive modeling. It involves the transformation of given feature space, typically using mathematical functions, with the objective of reducing the modeling error for a given target. However, there is no well-defined basis for performing effective feature engineering. It involves domain knowledge, intuition, and most of all, a lengthy process of trial and error. The human attention involved in overseeing this process significantly influences the cost of model generation. We present a new framework to automate feature engineering. It is based on performance driven exploration of a transformation graph, which systematically and compactly enumerates the space of given options. A highly efficient exploration strategy is derived through reinforcement learning on past examples.
Exploring Consumers ’ Attitudes and Behaviours toward Online Hotel Room Reservations

Marketers in Malaysia need to have solid understanding on consumers’ online attitude and buying behavior. This is mainly due to the shift and trend that consumers are moving fast into online purchasing. Malaysia is rank one of the ten countries in the world with the highest number of internet users. The travel market in the Asia Pacific region is fast growing and more consumers are reserving their hotel reservations via online travel agencies. Hence, the main purpose of this conceptual paper is to exp lore the factors that influence consumers’ attitude and purchase behaviour toward online hotel room reservations. Four factors which are informat ion, trust, price and convenience have been identified in this study. The focus of this study will be on consumers who reserve their hotel rooms via online travel agents. The research plans to adopt a survey method using stratified random sampling and the hypotheses will be tested using structural equation modelling.
Building energy-efficient multi-level cell STT-MRAM based cache through dynamic data-resistance encoding

With attractive advantages like high density and low leakage, Spin-Transfer Torque Magnetoresistive RAM (STT-MRAM) is a promising candidate to replace conventional SRAM technology to build large-size and low-power on-chip caches. Multi-level cell (MLC) STT-MRAM, with a higher density, further improves the on-chip cache capacity for chip multiprocessor (CMP) systems. However, the notorious high write energy impedes the adoption of MLC STT-MRAM. In this paper, we focus on minimizing the energy consumption during MLC STT-MRAM write operations. Based on the strong dependency of write energy on data values, a dynamic encoding technique is proposed to map the most frequently appearing data patterns to the most energy-efficient resistance states at runtime. Our experimental results show that, compared with the existing static data mapping scheme, our technique reduces write energy by 12.4% on average and up to 25.4% for a typical MLC STT-MRAM cache.
PSPACE-Completeness of Sliding-Block Puzzles and Other Problems through the Nondeterministic Constraint Logic Model of Computation

We present a nondeterministic model of computation based on reversing edge directions in weighted directed graphs with minimum in-flow constraints on vertices. Deciding whether this simple graph model can be manipulated in order to reverse the direction of a particular edge is shown to be PSPACEcomplete by a reduction from Quantified Boolean Formulas. We prove this result in a variety of special cases including planar graphs and highly restricted vertex configurations, some of which correspond to a kind of passive constraint logic. Our framework is inspired by (and indeed a generalization of) the “Generalized Rush Hour Logic” developed by Flake and Baum . We illustrate the importance of our model of computation by giving simple reductions to show that several motion-planning problems are PSPACE-hard. Our main result along these lines is that classic unrestricted sliding-block puzzles are PSPACE-hard, even if the pieces are restricted to be all dominoes (1 × 2 blocks) and the goal is simply to move a particular piece. No prior complexity results were known about these puzzles. This result can be seen as a strengthening of the existing result that the restricted Rush Hour puzzles are PSPACE-complete [4], of which we also give a simpler proof. We also greatly strengthen the conditions for the PSPACE-hardness of the Warehouseman’s Problem , a classic motion-planning problem. Finally, we strengthen the existing result that the pushing-blocks puzzle Sokoban is PSPACE-complete [2], by showing that it is PSPACE-complete even if no barriers are allowed.
Walklets: Multiscale Graph Embeddings for Interpretable Network Classification

We present Walklets, a novel approach for learning multiscale representations of vertices in a network. These representations clearly encode multiscale vertex relationships in a continuous vector space suitable for multi-label classification problems. Unlike previous work, the latent features generated using Walklets are analytically derivable, and human interpretable. Walklets uses the offsets between vertices observed in a random walk to learn a series of latent representations, each which captures successively larger relationships. This variety of dependency information allows the same representation strategy to model phenomenon which occur at different scales. We demonstrate Walklets’ latent representations on several multi-label network classification tasks for social networks such as BlogCatalog, Flickr, and YouTube. Our results show that Walklets outperforms new methods based on neural matrix factorization, and can scale to graphs with millions of vertices and edges.
Knowledge management, innovation and firm performance

To provide important empirical evidence to support the role of knowledge management within firms.  Data were collected using a mail survey sent to CEOs representing firms with 50 or more employees from a cross-section of industries. A total of 1,743 surveys were mailed out and 443 were returned and usable (27.8 percent response rate). The sample was checked for response and non-response bias. Hypotheses were tested using structural equation modelling.  This paper presents knowledge management as a coordinating mechanism. Empirical evidence supports the view that a firm with a knowledge management capability will use resources more efficiently and so will be more innovative and perform better.  The sample slightly over-represented larger firms. Data were also collected in New Zealand. As with most studies, it is important to replicate this study in different contexts.  Knowledge management is embraced in many organizations and requires a business case to justify expenditure on programs to implement knowledge management behaviours and practices or hardware and software solutions. This paper provides support for the importance of knowledge management to enhance innovation and performance.  This paper is one of the first to find empirical support for the role of knowledge management within firms. Further, the positioning of knowledge management as a coordinating mechanism is also an important contribution to our thinking on this topic.
Fuzzy Cognitive Maps Tool for Scenario Analysis and Pattern Classification

After 30 years of research, challenges and solutions, Fuzzy Cognitive Maps (FCMs) have become a suitable knowledgebased methodology for modeling and simulation. This technique is especially attractive when modeling systems that are characterized by ambiguity, complexity and non-trivial causality. FCMs are well-known due to the transparency achieved during modeling tasks. The literature reports successful studies related to the modeling of complex systems using FCMs. However, the situation is not the same when it comes to software implementations where domain experts can design FCM-based systems, run simulations or perform more advanced experiments. The existing implementations are not proficient in providing many options to adjust essential parameters during the modeling steps. The gap between the theoretical advances and the development of accurate, transparent and sound FCM-based systems advocates for the creation of more complete and flexible software products. Therefore, the goal of this paper is to introduce FCM Expert, a software tool for fuzzy cognitive modeling oriented to scenario analysis and pattern classification. The main features of FCM Expert rely on Machine Learning algorithms to compute the parameters defining the model, optimize the network topology and improve the system convergence without losing information. On the other hand, FCM Expert allows performing WHAT-IF simulations and studying the system behavior through a friendly, intuitive and easy-to-use graphical user interface.
A Social Network-Based Recommender System (SNRS)

Social influence plays an important role in product marketing. However, it has rarely been considered in traditional recommender systems. In this paper we present a new paradigm of recommender systems which can utilize information in social networks, including user preferences, an item's general acceptance, and influence from social friends. A probabilistic model is developed to make personalized recommendations from such information. We extract data from a real online social network, and our analysis of this large dataset reveals that friends have a tendency to select the same items and give similar ratings. Experimental results on this dataset show that our proposed system not only improves the prediction accuracy of recommender systems but also remedies the data sparsity and cold-start issues inherent in collaborative filtering. Furthermore, we propose to improve the performance of our system by applying semantic filtering of social networks, and validate its improvement via a class project experiment. In this experiment we demonstrate how relevant friends can be selected for inference based on the semantics of friend relationships and finer-grained user ratings. Such technologies can be deployed by most
Does Computer-Assisted Learning Improve Learning Outcomes ? Evidence from a Randomized Experiment in Migrant Schools in Beijing

The	  education	  of	  the	  poor	  and	  disadvantaged	  population	  has	  been	  a	  long-­‐standing challenge	  to	  the	  education	  system	  in	  both	  developed	  and	  developing	  countries. Although	  computer-­‐assisted	  learning	  (CAL)	  has	  been	  considered	  one	  alternative	  to improve	  learning	  outcomes	  in	  a	  cost-­‐effective	  way,	  the	  empirical	  evidence	  of	  its	  impacts on	  improving	  learning	  outcomes	  is	  mixed.	  This	  paper	  intends	  to	  explore	  the	  nature	  of the	  effects	  of	  CAL	  on	  student	  academic	  and	  non-­‐academic	  outcomes	  for	  underserved populations	  in	  a	  developing	  country.	  To	  meet	  this	  goal,	  we	  exploit	  a	  randomized	  field experiment	  of	  a	  CAL	  program	  involving	  over	  4000	  third-­‐grade	  students,	  mostly	  from poor	  migrant	  families,	  in	  43	  migrant	  schools	  in	  Beijing.	  The	  main	  intervention	  is	  a	  math CAL	  program	  that	  is	  held	  out	  of	  regular	  school	  hours.	  The	  program	  is	  tailored	  to	  the regular	  school	  math	  curriculum	  and	  is	  remedial	  in	  nature.	  Our	  results	  show	  that,	  the	  CAL program	  improved	  the	  student	  standardized	  math	  scores	  by	  0.14	  standard	  deviations and	  most	  of	  the	  program	  effect	  took	  place	  within	  two	  months	  after	  the	  start	  of	  the program.	  Low-­‐performing	  students	  and	  those	  with	  less-­‐educated	  parents	  benefited more	  from	  the	  program.	  Moreover,	  CAL	  also	  significantly	  increased	  the	  levels	  of	  self-­‐ efficacy	  of	  the	  students	  and	  their	  interest	  in	  learning.	  We	  observed	  at	  most	  a	  moderate program	  spillover	  in	  Chinese	  test	  scores.	  Our	  findings	  are	  robust	  to	  the	  Hawthorne effect	  and	  CAL	  program	  spillovers	  that	  might	  potentially	  bias	  the	  estimates	  of	  the program	  effects. 	  	  Education;	  Development;	  Computer	  Assisted	  Learning;	  Random Assignment;	  Test	  Scores;	  China;	  Migration JEL	  Codes:	  I20;	  I21;	  I28;	  O15
Imputing missing values using Inverse Distance Weighted Interpolation for time series data

Data mining is the process of analyzing and retrieving meaningful information from a database. Temporal data mining deals with time stamped data. In the real world, temporal data obtained may contain noisy, inconsistent data and in most cases the data may be missing; hence data preprocessing is one of the important steps that has to be carried out in data mining. Missing values may generate biased results and affect the accuracy of classification. In order to overcome this it is necessary to impute the missing values based on other information in the dataset. The work focuses on imputing missing values using Inverse Distance Weighted Interpolation method which best suits for data sampled at uneven intervals of time. This method assigns values to unknown points from a weighted sum of values of known points. Machine learning techniques applied to the imputed dataset will give better accuracy than that of the incomplete dataset.
Segmenting a deforming mesh into near-rigid components

Given a deforming mesh in an animation, we propose a new method to segment this mesh into several near-rigid sub-meshes. From this deforming mesh over all frames of an animation, we can analyze the degree of deformation between two nearby faces on the mesh. Then, our algorithm partitions the given deforming mesh into near-rigid components where the segmentation boundaries always pass at regions of large deformation. As a result, the mesh segmentation is invariant to all frames of the given animation and the motion of faces in each near-rigid-component can be represented by the same approximate affine transformation. To demonstrate the usefulness of the algorithm, we solve the restriction of deformation transfer for triangle meshes  which requires similar reference poses between source mesh and target mesh.
An end-to-end generative adversarial network for crowd counting under complicated scenes

Crowd counting and analyzing its distribution is a challenging video surveillance application. In this paper, a totally novel, end-to-end way to estimate the crowd number under complicated scenes is proposed. For the purpose, we apply the conditional adversarial networks to translate the input image to its density map. The conditional generative adversarial model is trained with input image and its corresponding density image. The proposed method avoid the design of complex CNN architecture to extract specific property features. Besides, no more data augmentation is needed in our method. Evaluated on the dataset of Shanghaitech which consists of two challenge parts, our methods shows convincing counting results with high quality estimated density images. Moreover, our experiments can been done in an efficient and labor saving way.
A Novel Deployment Scheme for Green Internet of Things

The Internet of Things (IoT) has been realized as one of the most promising networking paradigms that bridge the gap between the cyber and physical world. Developing green deployment schemes for IoT is a challenging issue since IoT achieves a larger scale and becomes more complex so that most of the current schemes for deploying wireless sensor networks (WSNs) cannot be transplanted directly in IoT. This paper addresses this challenging issue by proposing a deployment scheme to achieve green networked IoT. The contributions made in this paper include: 1) a hierarchical system framework for a general IoT deployment, 2) an optimization model on the basis of proposed system framework to realize green IoT, and 3) a minimal energy consumption algorithm for solving the presented optimization model. The numerical results on minimal energy consumption and network lifetime of the system indicate that the deployment scheme proposed in this paper is more flexible and energy efficient compared to typical WSN deployment scheme; thus is applicable to the green IoT deployment.