nils-herrmann/clean_scirepeval huggingface dataset:
Does Consumer Sentiment Predict Regional Consumption ?

the literature is the ability of consumer sentiment to forecast future consumption expenditures. Given that consumption expenditures directly correspond with economic growth, the issue is, then, whether consumer sentiment can predict economic growth. If consumer sentiment does predict economic growth, a further question is whether consumer sentiment captures the perceptions of individuals directly or whether it encompasses the forecasting information contained in other variables. The answer to this question is of interest, given the timeliness with which the sentiment indices are released, often ahead of other indicators.1 Carroll, Fuhrer, and Wilcox (1994) find that lagged values of the ICS significantly explain nearly 14 percent of growth in real personal consumption expenditures. However, after including other forecasting variables in their models, the incremental impact of lagged sentiment falls to 3 percent. Bram and Ludvigson (1998) extend the C onsumer sentiment is arguably the most cited indicator of current economic conditions, as it appears to be correlated with the strength of the economy. Following September 11, 2001, the two most common consumer sentiment indices— the University of Michigan’s Index of Consumer Sentiment (ICS) and the Conference Board’s Consumer Confidence Index (CCI)—fell an average of 20.9 percent through March 2003, reaching their lowest levels in nearly a decade. During the same period, real personal consumption expenditures grew by only 4.9 percent, compared with a 6.6 percent rate of growth over the two previous years when consumer sentiment was higher. In fact, there is little argument in the academic literature that contemporaneous consumer sentiment and national consumption expenditure growth are related, as illustrated in Figure 1. Quarterly data since 1970 reveal an average correlation of 0.43 between real personal consumption expenditures and both sentiment indices. What has been an important and controversial issue in This paper tests the ability of consumer sentiment to predict retail spending at the state level. The results here suggest that, although there is a significant relationship between consumer sentiment measures and retail sales growth in several states, consumer sentiment exhibits only modest predictive power for future changes in retail spending. Measures of consumer sentiment, however, contain additional explanatory power beyond the information available in other indicators. By restricting attention to fluctuations in retail sales that occur at the business cycle frequency, the authors uncover a significant relationship between consumer sentiment and retail sales growth in many additional states. In light of these results, the authors conclude that the practical value of sentiment indices to forecast consumer spending at the state level is, at best, limited.
Integrating SOM and fuzzy k-means clustering for customer classification in personalized recommendation system for non-text based transactional data

The world of e-commerce is reshaping marketing strategies based on the analysis of e-commerce data. Huge amounts of data are being collecting and can be analyzed for some discoveries that may be used as guidance for people sharing same interests but lacking experience. Indeed, recommendation systems are becoming an essential business strategy tool from just a novelty. Many large e-commerce web sites are already encapsulating recommendation systems to provide a customer friendly environment by helping customers in their decision-making process. A recommendation system learns from a customer behavior patterns and recommend the most valuable from available alternative choices. In this paper, we developed a two-stage algorithm using self-organizing map (SOM) and fuzzy k-means with an improved distance function to classify users into clusters. This will lead to have in the same cluster users who mostly share common interests. Results from the combination of SOM and fuzzy K-means revealed better accuracy in identifying user related classes or clusters. We validated our results using various datasets to check the accuracy of the employed clustering approach. The generated groups of users form the domain for transactional datasets to find most valuable products for customers.
Accurate Facial Parts Localization and Deep Learning for 3D Facial Expression Recognition

Meaningful facial parts can convey key cues for both facial action unit detection and expression prediction. Textured 3D face scan can provide both detailed 3D geometric shape and 2D texture appearance cues of the face which are beneficial for Facial Expression Recognition (FER). However, accurate facial parts extraction as well as their fusion are challenging tasks. In this paper, a novel system for 3D FER is designed based on accurate facial parts extraction and deep feature fusion of facial parts. Experiments are conducted on the BU-3DFE database, demonstrating the effectiveness of combing different facial parts, texture and depth cues and reporting the state-of-the-art results in comparison with all existing methods under the same setting.
The Karlsruhe Humanoid Head

The design and construction of truly humanoid robots that can perceive and interact with the environment depends significantly on their perception capabilities. In this paper we present the Karlsruhe Humanoid Head, which has been designed to be used both as part of our humanoid robots ARMAR-IIIa and ARMAR-IIIb and as a stand-alone robot head for studying various visual perception tasks in the context of object recognition and human-robot interaction. The head has seven degrees of freedom (DoF). The eyes have a common tilt and can pan independently. Each eye is equipped with two digital color cameras, one with a wide-angle lens for peripheral vision and one with a narrow-angle lens for foveal vision to allow simple visuo-motor behaviors. Among these are tracking and saccadic motions towards salient regions, as well as more complex visual tasks such as hand-eye coordination. We present the mechatronic design concept, the motor control system, the sensor system and the computational system. To demonstrate the capabilities of the head, we present accuracy test results, and the implementation of both open-loop and closed-loop control on the head.
Evaluation of requirements for Volt/Var control and optimization function in distribution management systems

To meet the requirement from new visions within “smart grid” and to provide solutions for many challenges that DSOs (Distribution System Operators) are facing today, we need to develop advanced DMS (Distribution Management System) applications. A centralized Volt/Var Control (VVC) is one of the most desirable and important functions within Distribution Automation systems and DMSs. The overall Volt/Var control function is concerned with maintaining system voltage profile within a desired range and minimizing system losses by controlling the reactive power flow. The main objective of this paper is to review and evaluate the existing and recent techniques and algorithms for advanced VVC applications and identify the requirements for integrated Volt/Var control and optimization function in distribution management systems within the smart grid concepts.
An effective Web page recommender using binary data clustering

Through growth of the Web, the amount of data on the net is growing in an uncontrolled way, that makes it hard for the users to find the relevant and required information- an issue which is usually referred to as information overload. Recommender systems are among the appealing methods that can handle this problem effectively. Theses systems are either based on collaborative filtering and content based approaches, or rely on rating of items and the behavior of the users to generate customized recommendations. In this paper we propose an efficient Web page recommender by exploiting session data of users. To this end, we propose a novel clustering algorithm to partition the binary session data into a fixed number of clusters and utilize the partitioned sessions to make recommendations. The proposed binary clustering algorithm is scalable and employs a novel method to find the representative of a set of binary vectors to represent the center of clusters—that might be interesting in its own right. In addition, the proposed clustering algorithm is integrated with the $$k$$ k -means algorithm to achieve better clustering quality by combining its explorative power with fine-tuning power of the $$k$$ k -means algorithm. We have performed extensive experiments on a real dataset to demonstrate the advantages of proposed binary data clustering methods and Web page recommendation algorithm. In particular, the proposed recommender system is compared to previously published works in terms of minimum frequency and based on the number of recommended pages to show its superiority in terms of accuracy, coverage and F-measure.
RNA-Guided Human Genome Engineering via Cas 9 " ( 2013 ) , by Prashant Mali

In 2013, George Church and his colleagues at Harvard University  in Cambridge, Massachusetts published "RNA-Guided Human Genome Engineering via Cas 9," in which they detailed their use of RNA-guided Cas 9 to genetically modify genes  in human cells. Researchers use RNA-guided Cas 9 technology to modify the genetic information of organisms, DNA, by targeting specific sequences of DNA and subsequently replacing those targeted sequences with different DNA sequences. Church and his team used RNA-guided Cas 9 technology to edit the genetic information in human cells. Church and his colleagues also created a database that identified 190,000 unique guide RNAs for targeting almost half of the human genome  that codes for proteins. In "RNA-Guided Human Genome Engineering via Cas 9," the authors demonstrated that RNA-guided Cas 9 was a robust and simple tool for genetic engineering, which has enabled scientists to more easily manipulate genomes for the study of biological processes and genetic diseases.
Path finding for a NAO humanoid robot by fusing visual and proximity sensors

Humanoid robot path finding is one of the core-technologies in robot research domain. This paper presents an approach to finding a path for robot motion by fusing images taken by the NAO's camera and proximity information delivered by sonar sensors. The NAO robot takes an image around its surroundings, uses the fuzzy color extractor to segment its potential path colors, and selects a fitting line as path by the least squares method. Therefore, the NAO robot is able to perform the automatic navigation according to the selected path. As a result, the experiments are conducted to navigate the NAO robot to walk to a given destination and to grasp a box. In addition, the NAO robot uses its sonar sensors to detect a barrier and helps pick up the box with its hands.
Competition between honey bees and wild bees and the role of nesting resources in a nature reserve

The European honey bee exploits floral resources efficiently and may therefore compete with solitary wild bees. Hence, conservationists and bee keepers are debating about the consequences of beekeeping for the conservation of wild bees in nature reserves. We observed flower-visiting bees on flowers of Calluna vulgaris in sites differing in the distance to the next honey-bee hive and in sites with hives present and absent in the Lüneburger Heath, Germany. Additionally, we counted wild bee ground nests in sites that differ in their distance to the next hive and wild bee stem nests and stem-nesting bee species in sites with hives present and absent. We did not observe fewer honey bees or higher wild bee flower visits in sites with different distances to the next hive (up to 1,229 m). However, wild bees visited fewer flowers and honey bee visits increased in sites containing honey-bee hives and in sites containing honey-bee hives we found fewer stem-nesting bee species. The reproductive success, measured as number of nests, was not affected by distance to honey-bee hives or their presence but by availability and characteristics of nesting resources. Our results suggest that beekeeping in the Lüneburg Heath can affect the conservation of stem-nesting bee species richness but not the overall reproduction either of stem-nesting or of ground-nesting bees. Future experiments need control sites with larger distances than 500 m to hives. Until more information is available, conservation efforts should forgo to enhance honey bee stocking rates but enhance the availability of nesting resources.
Automatic data warehouse conceptual design

This paper presents a methodology to develop a dimensional data warehouse by integrating all three development approaches such as supply-driven, goal-driven and demand-driven. By having the combination of all three approaches, the final design will ensure that user requirements, company interest and existing source of data are included in the model. We proposed an automatic system using ontology as the knowledge domain. Starting from operational ER-D (Entity Relationship-Diagram), the selection of facts table, verification of terms and consistency checking will utilize domain ontology. The model will also be verified against user and company requirements. Any discrepancy in the final design requires designer and user intervention. The proposed methodology is supported by a prototype using a business data warehouse example.
Chinese Spelling Check Evaluation at SIGHAN Bake-off 2013

This paper introduces an overview of Chinese Spelling Check task at SIGHAN Bake-off 2013. We describe all aspects of the task for Chinese spelling check, consisting of task description, data preparation, performance metrics, and evaluation results. This bake-off contains two subtasks, i.e., error detection and error correction. We evaluate the systems that can automatically point out the spelling errors and provide the corresponding corrections in students’ essays, summarize the performance of all participants’ submitted results, and discuss some advanced issues. The hope is that through such evaluation campaigns, more advanced Chinese spelling check techniques will be emerged.
Critical research gaps and translational priorities for the successful prevention and treatment of breast cancer

Breast cancer remains a significant scientific, clinical and societal challenge. This gap analysis has reviewed and critically assessed enduring issues and new challenges emerging from recent research, and proposes strategies for translating solutions into practice. More than 100 internationally recognised specialist breast cancer scientists, clinicians and healthcare professionals collaborated to address nine thematic areas: genetics, epigenetics and epidemiology; molecular pathology and cell biology; hormonal influences and endocrine therapy; imaging, detection and screening; current/novel therapies and biomarkers; drug resistance; metastasis, angiogenesis, circulating tumour cells, cancer ‘stem’ cells; risk and prevention; living with and managing breast cancer and its treatment. The groups developed summary papers through an iterative process which, following further appraisal from experts and patients, were melded into this summary account. The 10 major gaps identified were: (1) understanding the functions and contextual interactions of genetic and epigenetic changes in normal breast development and during malignant transformation; (2) how to implement sustainable lifestyle changes (diet, exercise and weight) and chemopreventive strategies; (3) the need for tailored screening approaches including clinically actionable tests; (4) enhancing knowledge of molecular drivers behind breast cancer subtypes, progression and metastasis; (5) understanding the molecular mechanisms of tumour heterogeneity, dormancy, de novo or acquired resistance and how to target key nodes in these dynamic processes; (6) developing validated markers for chemosensitivity and radiosensitivity; (7) understanding the optimal duration, sequencing and rational combinations of treatment for improved personalised therapy; (8) validating multimodality imaging biomarkers for minimally invasive diagnosis and monitoring of responses in primary and metastatic disease; (9) developing interventions and support to improve the survivorship experience; (10) a continuing need for clinical material for translational research derived from normal breast, blood, primary, relapsed, metastatic and drug-resistant cancers with expert bioinformatics support to maximise its utility. The proposed infrastructural enablers include enhanced resources to support clinically relevant in vitro and in vivo tumour models; improved access to appropriate, fully annotated clinical samples; extended biomarker discovery, validation and standardisation; and facilitated cross-discipline working. With resources to conduct further high-quality targeted research focusing on the gaps identified, increased knowledge translating into improved clinical care should be achievable within five years.
Classification and Segmentation of Satellite Orthoimagery Using Convolutional Neural Networks

The availability of high-resolution remote sensing (HRRS) data has opened up the possibility for new interesting applications, such as per-pixel classification of individual objects in greater detail. This paper shows how a convolutional neural network (CNN) can be applied to multispectral orthoimagery and a digital surface model (DSM) of a small city for a full, fast and accurate per-pixel classification. The predicted low-level pixel classes are then used to improve the high-level segmentation. Various design choices of the CNN architecture are evaluated and analyzed. The investigated land area is fully manually labeled into five categories (vegetation, ground, roads, buildings and water), and the classification accuracy is compared to other per-pixel classification works on other land areas that have a similar choice of categories. The results of the full classification and segmentation on selected segments of the map show that CNNs are a viable tool for solving both the segmentation and object recognition task for remote sensing data.
Hypoglycemia Early Alarm Systems Based On Multivariable Models.

Hypoglycemia is a major challenge of artificial pancreas systems and a source of concern for potential users and parents of young children with Type 1 diabetes (T1D). Early alarms to warn the potential of hypoglycemia are essential and should provide enough time to take action to avoid hypoglycemia. Many alarm systems proposed in the literature are based on interpretation of recent trends in glucose values. In the present study, subject-specific recursive linear time series models are introduced as a better alternative to capture glucose variations and predict future blood glucose concentrations. These models are then used in hypoglycemia early alarm systems that notify patients to take action to prevent hypoglycemia before it happens. The models developed and the hypoglycemia alarm system are tested retrospectively using T1D subject data. A Savitzky-Golay filter and a Kalman filter are used to reduce noise in patient data. The hypoglycemia alarm algorithm is developed by using predictions of future glucose concentrations from recursive models. The modeling algorithm enables the dynamic adaptation of models to inter-/intra-subject variation and glycemic disturbances and provides satisfactory glucose concentration prediction with relatively small error. The alarm systems demonstrate good performance in prediction of hypoglycemia and ultimately in prevention of its occurrence.
Direct Yaw-Moment Control of an In-Wheel-Motored Electric Vehicle Based on Body Slip Angle Fuzzy Observer

A stabilizing observer-based control algorithm for an in-wheel-motored vehicle is proposed, which generates direct yaw moment to compensate for the state deviations. The control scheme is based on a fuzzy rule-based body slip angle (beta) observer. In the design strategy of the fuzzy observer, the vehicle dynamics is represented by Takagi-Sugeno-like fuzzy models. Initially, local equivalent vehicle models are built using the linear approximations of vehicle dynamics for low and high lateral acceleration operating regimes, respectively. The optimal beta observer is then designed for each local model using Kalman filter theory. Finally, local observers are combined to form the overall control system by using fuzzy rules. These fuzzy rules represent the qualitative relationships among the variables associated with the nonlinear and uncertain nature of vehicle dynamics, such as tire force saturation and the influence of road adherence. An adaptation mechanism for the fuzzy membership functions has been incorporated to improve the accuracy and performance of the system. The effectiveness of this design approach has been demonstrated in simulations and in a real-time experimental setting.
Research design: qualitative, quantitative and mixed methods approaches Research design: qualitative, quantitative and mixed methods approaches Creswell John W Sage 320 £29 0761924426 0761924426 [Formula: see text].

The second edition of Creswell's book has been significantly revised and updated. The author clearly sets out three approaches to research: quantitative, qualitative and mixed methods. As someone who has used mixed methods in my research, it is refreshing to read a textbook that addresses this. The differences between the approaches are clearly identified and a rationale for using each methodological stance provided.
Neurons as Monte Carlo Samplers: Bayesian Inference and Learning in Spiking Networks

We propose a spiking network model capable of performing both approximate inference and learning for any hidden Markov model. The lower layer sensory neurons detect noisy measurements of hidden world states. The higher layer neurons with recurrent connections infer a posterior distribution over world states from spike trains generated by sensory neurons. We show how such a neuronal network with synaptic plasticity can implement a form of Bayesian inference similar to Monte Carlo methods such as particle filtering. Each spike in the population of inference neurons represents a sample of a particular hidden world state. The spiking activity across the neural population approximates the posterior distribution of hidden state. The model provides a functional explanation for the Poissonlike noise commonly observed in cortical responses. Uncertainties in spike times provide the necessary variability for sampling during inference. Unlike previous models, the hidden world state is not observed by the sensory neurons, and the temporal dynamics of the hidden state is unknown. We demonstrate how such networks can sequentially learn hidden Markov models using a spike-timing dependent Hebbian learning rule and achieve power-law convergence rates.
Sentence Pair Scoring: Towards Unified Framework for Text Comprehension

We review the task of Sentence Pair Scoring, popular in the literature in various forms — viewed as Answer Sentence Selection, Semantic Text Scoring, Next Utterance Ranking, Recognizing Textual Entailment, Paraphrasing or e.g. a component of Memory Networks. We argue that all such tasks are similar from the model perspective and propose new baselines by comparing the performance of common IR metrics and popular convolutional, recurrent and attentionbased neural models across many Sentence Pair Scoring tasks and datasets. We discuss the problem of evaluating randomized models, propose a statistically grounded methodology, and attempt to improve comparisons by releasing new datasets that are much harder than some of the currently used well explored benchmarks. We introduce a unified open source software framework with easily pluggable models and tasks, which enables us to experiment with multi-task reusability of trained sentence models.