nils-herrmann/clean_scirepeval huggingface dataset:
Towards a Self-service Data Analytics Framework

The need ?for Self-service data analytics is inevitable as it supports the business in making the right decisions. In ?this paper, we argue that self-service analytics frameworks should be based on a process-centric approach and ?visualized self-service components in order to meet current business demands. Further, we ?enunciate the need for mainly three components: Map component, Process Flow component ?and a Control Model component. Furthermore, we explain the architecture of a self-service ?analytics framework based on these components. Some parts of the proposed framework were deployed to ?different sites and are discussed in detail in this paper. The obtained results showed a clear ?enhancement of data warehouse operation spent from the IT departments&apos; side compared to ?the traditional BI architecture.
Touch Affordances

The workshop “Touch Affordances” addresses a concept relevant to human computer interactions based on touch. The main topic is the challenge of applying the notion of affordances to domains related to touch interactions (e.g. (multi)touch screens, RFID & NFC, ubiquitous interfaces). The goals of this workshop are to launch a community of researchers, designers, etc. interested in this topic, to create a common understanding of the field of touch affordances and to generate ideas for new research areas for intuitive touch interactions. The workshop will be highly interactive and will have a creative, generative character.
Mapping ICT use at home and telecommuting practices: A perspective from work/family border theory

This study draws on the work/family border theory to investigate the role of information communication technology (ICT) use at home in shaping the characteristics of work/family borders (i.e. flexibility and permeability) and consequently influencing individuals’ perceived work-family conflict, technostress, and level of telecommuting. Data were collected from a probability sample of 509 information workers in Hong Kong who were not selfemployed. The results showed that the more that people used ICT to do their work at home, the greater they perceived their work/family borders flexible and permeable. Interestingly, low flexibility and high permeability, rather than the use of ICT at home, had much stronger influences on increasing, in particular, family-to-work conflict. As expected, work-tofamily conflict was significantly and positively associated with technostress. Results also showed that the telecommuters tended to be older, had lower family incomes, used ICT frequently at home, and had a permeable boundary that allowed work to penetrate their home domain. The theoretical and practical implications are discussed.
Production of the antimalarial drug precursor artemisinic acid in engineered yeast

Malaria is a global health problem that threatens 300–500 million people and kills more than one million people annually. Disease control is hampered by the occurrence of multi-drug-resistant strains of the malaria parasite Plasmodium falciparum. Synthetic antimalarial drugs and malarial vaccines are currently being developed, but their efficacy against malaria awaits rigorous clinical testing. Artemisinin, a sesquiterpene lactone endoperoxide extracted from Artemisia annua L (family Asteraceae; commonly known as sweet wormwood), is highly effective against multi-drug-resistant Plasmodium spp., but is in short supply and unaffordable to most malaria sufferers. Although total synthesis of artemisinin is difficult and costly, the semi-synthesis of artemisinin or any derivative from microbially sourced artemisinic acid, its immediate precursor, could be a cost-effective, environmentally friendly, high-quality and reliable source of artemisinin. Here we report the engineering of Saccharomyces cerevisiae to produce high titres (up to 100 mg l-1) of artemisinic acid using an engineered mevalonate pathway, amorphadiene synthase, and a novel cytochrome P450 monooxygenase (CYP71AV1) from A. annua that performs a three-step oxidation of amorpha-4,11-diene to artemisinic acid. The synthesized artemisinic acid is transported out and retained on the outside of the engineered yeast, meaning that a simple and inexpensive purification process can be used to obtain the desired product. Although the engineered yeast is already capable of producing artemisinic acid at a significantly higher specific productivity than A. annua, yield optimization and industrial scale-up will be required to raise artemisinic acid production to a level high enough to reduce artemisinin combination therapies to significantly below their current prices.
FERAtt: Facial Expression Recognition with Attention Net

We present a new end-to-end network architecture for facial expression recognition with an attention model. It focuses attention in the human face and uses a Gaussian space representation for expression recognition. We devise this architecture based on two fundamental complementary components: (1) facial image correction and attention and (2) facial expression representation and classification. The first component uses an encoder-decoder style network and a convolutional feature extractor that are pixel-wise multiplied to obtain a feature attention map. The second component is responsible for obtaining an embedded representation and classification of the facial expression. We propose a loss function that creates a Gaussian structure on the representation space. To demonstrate the proposed method, we create two larger and more comprehensive synthetic datasets using the traditional BU3DFE and CK+ facial datasets. We compared results with the PreActResNet18 baseline. Our experiments on these datasets have shown the superiority of our approach in recognizing facial expressions.
Expectations and First Experience with a Social Robot

This paper concerns interaction with social robots and focuses on the evaluation of a robot application that allows users to access interesting information from Wikipedia. The evaluation method compares the users' expectations with their experience with the robot, and takes into account their self-declared previous experience with robots. The results show that most participants had an overall positive experience, even though the averages indicate a slight negative tendency related to expectations of the robot's behavior and being understood by the robot. Interestingly, the most experienced users seem to be the most critical.
The Plastic Human Brain Cortex

One of the central questions in neuroscience is how particular tasks, or computations, are implemented by neural networks to generate behavior. The prevailing view has been that information processing in neural networks results primarily from the properties of synapses and the connectivity of neurons within the network, with the intrinsic excitability of single neurons playing a lesser role. As a consequence, the contribution of single neurons to computation in the brain has long been underestimated. Here we review recent work showing that neuronal dendrites exhibit a range of linear and nonlinear mechanisms that allow them to implement elementary computations. We discuss why these dendritic properties may be essential for the computations performed by the neuron and the network and provide theoretical and experimental examples to support this view. 503 A nn u. R ev . N eu ro sc i. 20 05 .2 8: 50 353 2. D ow nl oa de d fr om w w w .a nn ua lr ev ie w s. or g by M as sa ch us et ts I ns tit ut e of T ec hn ol og y (M IT ) on 0 6/ 26 /1 4. F or p er so na l u se o nl y. AR245-NE28-18 ARI 13 May 2005 14:15
Multi-Manifold Semi-Supervised Learning

We study semi-supervised learning when the data consists of multiple intersecting manifolds. We give a finite sample analysis to quantify the potential gain of using unlabeled data in this multi-manifold setting. We then propose a semi-supervised learning algorithm that separates different manifolds into decision sets, and performs supervised learning within each set. Our algorithm involves a novel application of Hellinger distance and size-constrained spectral clustering. Experiments demonstrate the benefit of our multimanifold semi-supervised learning approach.
Relative Analysis of Hierarchical Routing in Wireless Sensor Networks Using Cuckoo Search

Wireless sensor networks are those networks which are composed by the collection of very small devices mainly named as nodes. These nodes are integrated with small battery life which is very hard or impossible to replace or reinstate. For the sensing, gathering and processing capabilities, the usage of battery is must. Therefore, the battery life of Wireless Sensor Networks should be as large as possible in order to sense the information around it or in which the nodes are placed. The concept of hierarchical routing is mainly highlighted in this paper, in which the nodes work in a hierarchical manner by the formation of Cluster Head within a Cluster. These formed Cluster Heads then transfer the data or information in the form of packets from one cluster to another. In this work, the protocol used for the simulation is Low Energy adaptive Clustering Hierarchy which is one of the most efficient protocol. The nodes are of homogeneous in nature. The simulator used is MATLAB along with Cuckoo Search Algorithm. The Simulation results have been taken out showing the effectiveness of protocol with Cuckoo Search.  Wireless Sensor Network (WSN), Low Energy adaptive Clustering Hierarchy (LEACH), Cuckoo Search, Cluster Head (CH), Base Station (BS).
Cellular tagging as a neural network mechanism for behavioural tagging

Behavioural tagging is the transformation of a short-term memory, induced by a weak experience, into a long-term memory (LTM) due to the temporal association with a novel experience. The mechanism by which neuronal ensembles, each carrying a memory engram of one of the experiences, interact to achieve behavioural tagging is unknown. Here we show that retrieval of a LTM formed by behavioural tagging of a weak experience depends on the degree of overlap with the neuronal ensemble corresponding to a novel experience. The numbers of neurons activated by weak training in a novel object recognition (NOR) task and by a novel context exploration (NCE) task, denoted as overlapping neurons, increases in the hippocampal CA1 when behavioural tagging is successfully achieved. Optical silencing of an NCE-related ensemble suppresses NOR-LTM retrieval. Thus, a population of cells recruited by NOR is tagged and then preferentially incorporated into the memory trace for NCE to achieve behavioural tagging.
Race/ethnic differences in exposure to traumatic events, development of post-traumatic stress disorder, and treatment-seeking for post-traumatic stress disorder in the United States.

To identify sources of race/ethnic differences related to post-traumatic stress disorder (PTSD), we compared trauma exposure, risk for PTSD among those exposed to trauma, and treatment-seeking among Whites, Blacks, Hispanics and Asians in the US general population.



Data from structured diagnostic interviews with 34 653 adult respondents to the 2004-2005 wave of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) were analysed.



The lifetime prevalence of PTSD was highest among Blacks (8.7%), intermediate among Hispanics and Whites (7.0% and 7.4%) and lowest among Asians (4.0%). Differences in risk for trauma varied by type of event. Whites were more likely than the other groups to have any trauma, to learn of a trauma to someone close, and to learn of an unexpected death, but Blacks and Hispanics had higher risk of child maltreatment, chiefly witnessing domestic violence, and Asians, Black men, and Hispanic women had higher risk of war-related events than Whites. Among those exposed to trauma, PTSD risk was slightly higher among Blacks [adjusted odds ratio (aOR) 1.22] and lower among Asians (aOR 0.67) compared with Whites, after adjustment for characteristics of trauma exposure. All minority groups were less likely to seek treatment for PTSD than Whites (aOR range: 0.39-0.61), and fewer than half of minorities with PTSD sought treatment (range: 32.7-42.0%).



When PTSD affects US race/ethnic minorities, it is usually untreated. Large disparities in treatment indicate a need for investment in accessible and culturally sensitive treatment options.
Pattern Discovery for Wide-Window Open Information Extraction in Biomedical Literature

Open information extraction is an important task in Biomedical domain. The goal of the OpenIE is to automatically extract structured information from unstructured text with no or little supervision. It aims to extract all the relation tuples from the corpus without requiring pre-specified relation types. The existing tools may extract ill-structured or incomplete information, or fail on the Biomedical literature due to the long and complicated sentences. In this paper, we propose a novel pattern-based information extraction method for the wide-window entities (WW-PIE). WW-PIE utilizes dependency parsing to break down the long sentences first and then utilizes frequent textual patterns to extract the high-quality information. The pattern hierarchical grouping organize and structure the extractions to be straightforward and precise. Consequently, comparing with the existing OpenIE tools, WW-PIE produces structured output that can be directly used for downstream applications. The proposed WW-PIE is also capable in extracting n-ary and nested relation structures, which is less studied in the existing methods. Extensive experiments on real-world biomedical corpus from PubMed abstracts demonstrate the power of WW-PIE at extracting precise and well-structured information.
Mammogram Classification Using Convolutional Neural Networks

Fine needle aspiration biopsies are a common method of testing for cancerous cells. As a surgical procedure, FNA biospies can be both invasive and costly. We explored the viability of analyzing mammograms (X-ray images of breasts) using an image recognition machine learning algorithm to assist in classification of benign and cancerous abnormalities as well as abnormality detection. Specifically, we analyzed the effectiveness of convolutional neural networks (CNN) in determining the existence of breast abnormalities in mammograms. If there is an abnormality, it also classifies it as either benign or malignant. In an effort to maximize our results working with a relatively small dataset, we employed a set of pre-processing techniques to strengthen our classifier, including image transformation and classification redistribution. We tested a variety of architectures to find the one that produced the best recall figures, which we deemed to be the most important measures of performance within the context of malignancy detection.
Robust Pedestrian Classification Based on Hierarchical Kernel Sparse Representation

Vision-based pedestrian detection has become an active topic in computer vision and autonomous vehicles. It aims at detecting pedestrians appearing ahead of the vehicle using a camera so that autonomous vehicles can assess the danger and take action. Due to varied illumination and appearance, complex background and occlusion pedestrian detection in outdoor environments is a difficult problem. In this paper, we propose a novel hierarchical feature extraction and weighted kernel sparse representation model for pedestrian classification. Initially, hierarchical feature extraction based on a CENTRIST descriptor is used to capture discriminative structures. A max pooling operation is used to enhance the invariance of varying appearance. Then, a kernel sparse representation model is proposed to fully exploit the discrimination information embedded in the hierarchical local features, and a Gaussian weight function as the measure to effectively handle the occlusion in pedestrian images. Extensive experiments are conducted on benchmark databases, including INRIA, Daimler, an artificially generated dataset and a real occluded dataset, demonstrating the more robust performance of the proposed method compared to state-of-the-art pedestrian classification methods.
A hierarchical architecture for behavior-based robots

Behavior-based systems (BBS) have been effective in a variety of applications, but due to their limited use of representation they have not been applied much to more complex problems, such as ones involving temporal sequences, or hierarchical task representations. This paper presents an approach to implementing these AI-level concepts into BBS, without compromising BBS' key properties. We describe a Hierarchical Abstract Behavior Architecture that allows for the representation and execution of complex, sequential, hierarchically structured tasks within a behavior-based framework. The architecture, obtained by introducing the notion of abstract behaviors into BBS, also enables reusability of behaviors across different tasks. The basis for task representation is the behavior network construct which encodes complex, hierarchical plan-like strategies. The approach is validated in experiments on a Pioneer 2DX mobile robot.
Evaluation of Elastic Lift for Neck Rejuvenation

The neck is not only the first anatomical area to show aging but also contributes to the persona of the individual. The understanding the aging process of the neck is essential for neck rejuvenation. Multiple neck rejuvenation techniques have been reported in the literature. In 1974, Skoog  described the anatomy of the superficial musculoaponeurotic system (SMAS) and its role in the aging of the neck. Recently, many patients have expressed interest in minimally invasive surgery with a low risk of complications and short recovery period. The use of thread for neck rejuvenation and the concept of the suture suspension neck lift have become widespread as a convenient and effective procedure; nevertheless, complications have also been reported such as recurrence, inadequate correction, and palpability of the sutures. In this study, we analyzed a new type of thread lift: elastic lift that uses elastic thread (Elasticum; Korpo SRL, Genova, Italy). We already use this new technique for the midface lift and can confirm its efficacy and safety in that context. The purpose of this study was to evaluate the outcomes and safety of the elastic lift technique for neck region lifting.
A design method of speech corpus for text-to-speech synthesis taking account of prosody

This paper proposes a method for designing a sentence set for utterances taking account of prosody. This method is based on a measure of coverage which incorporates two factors: (1) the distribution of voice fundamental frequency and phoneme duration predicted by the prosody generation module of a TTS; (2) perceptual damage to naturalness due to prosody modification. A set of 500 sentences with a predicted coverage of 82.6% was designed by this method, and used to collect a speech corpus. The obtained speech corpus yielded 88% of the predicted coverage. The data size was reduced to 49% in terms of number of sentences (89% in terms of number of phonemes) compared to a general-purpose corpus designed without taking prosody into account.
Depth Value Pre-Processing for Accurate Transfer Learning based RGB-D Object Recognition

Object recognition is one of the important tasks in computer vision which has found enormous applications. Depth modality is proven to provide supplementary information to the common RGB modality for object recognition. In this paper, we propose methods to improve the recognition performance of an existing deep learning based RGB-D object recognition model, namely the FusionNet proposed by Eitel et al. First, we show that encoding the depth values as colorized surface normals is beneficial, when the model is initialized with weights learned from training on ImageNet data. Additionally, we show that the RGB stream of the FusionNet model can benefit from using deeper network architectures, namely the 16-layered VGGNet, in exchange for the 8-layered CaffeNet. In combination, these changes improves the recognition performance with 2.2% in comparison to the original FusionNet, when evaluating on the Washington RGB-D Object Dataset.
Multi-objective beamforming for energy-efficient SWIPT systems

In this paper, we study the resource allocation algorithm design for energy-efficient simultaneous wireless information and power transfer (SWIPT) systems. The considered system comprises a transmitter, an information receiver, and multiple energy harvesting receivers equipped with multiple antennas. We propose a multi-objective optimization framework to study the trade-off between the maximization of the energy efficiency of information transmission and the maximization of wireless power transfer efficiency. The proposed problem formulation takes into account the per antenna circuit power consumption of the transmitter and the imperfect channel state information of the energy harvesting receivers. The adopted non-convex multi-objective optimization problem is transformed into an equivalent rank-constrained semidefinite program (SDP) and optimally solved by SDP relaxation. Numerical results unveil an interesting trade-off between the considered conflicting system design objectives and reveal the benefits of multiple transmit antennas for improving system energy efficiency.
In vitro conservation of Bacopa monnieri (L.) using mineral oil

Bacopa monnieri (L.) Wettst., a traditional Indian medicinal plant with high commercial potential, is used as a potent nervine tonic. A slow growth protocol was developed for medium-term conservation using mineral oil (MO) overlay. Nodal segments of B. monnieri (two genotypes; IC249250, IC468878) were conserved using MO for 24 months. Single node explants were implanted on MS medium supplemented with 0.2 mg l−1 BA and were covered with MO. Subculture duration could be significantly enhanced from 6 to 24 months, on the above medium. Normal plants regenerated from conserved cultures were successfully established in soil. On the basis of 20 random amplified polymorphic DNA and 5 inter-simple sequence repeat primers analyses and bacoside A content using HPLC, no significant reproducible variation was observed between the controls and in vitro-conserved plants. The results demonstrate the feasibility of using MO for medium-term conservation of B. monnieri germplasm without any adverse genetical and biochemical effects.
Hyperspectral Image Super-Resolution via Non-Negative Structured Sparse Representation

Hyperspectral imaging has many applications from agriculture and astronomy to surveillance and mineralogy. However, it is often challenging to obtain high-resolution (HR) hyperspectral images using existing hyperspectral imaging techniques due to various hardware limitations. In this paper, we propose a new hyperspectral image super-resolution method from a low-resolution (LR) image and a HR reference image of the same scene. The estimation of the HR hyperspectral image is formulated as a joint estimation of the hyperspectral dictionary and the sparse codes based on the prior knowledge of the spatial-spectral sparsity of the hyperspectral image. The hyperspectral dictionary representing prototype reflectance spectra vectors of the scene is first learned from the input LR image. Specifically, an efficient non-negative dictionary learning algorithm using the block-coordinate descent optimization technique is proposed. Then, the sparse codes of the desired HR hyperspectral image with respect to learned hyperspectral basis are estimated from the pair of LR and HR reference images. To improve the accuracy of non-negative sparse coding, a clustering-based structured sparse coding method is proposed to exploit the spatial correlation among the learned sparse codes. The experimental results on both public datasets and real LR hypspectral images suggest that the proposed method substantially outperforms several existing HR hyperspectral image recovery techniques in the literature in terms of both objective quality metrics and computational efficiency.