nils-herrmann/clean_scirepeval huggingface dataset:
An Inertial-Measurement-Unit-Based Pen With a Trajectory Reconstruction Algorithm and Its Applications

This paper presents an inertial-measurement-unit-based pen (IMUPEN) and its associated trajectory reconstruction algorithm for motion trajectory reconstruction and handwritten digit recognition applications. The IMUPEN is composed of a triaxial accelerometer, two gyroscopes, a microcontroller, and an RF wireless transmission module. Users can hold the IMUPEN to write numerals or draw simple symbols at normal speed. During writing or drawing movements, the inertial signals generated for the movements are transmitted to a computer via the wireless module. A trajectory reconstruction algorithm composed of the procedures of data collection, signal preprocessing, and trajectory reconstruction has been developed for reconstructing the trajectories of movements. In order to minimize the cumulative errors caused by the intrinsic noise/drift of sensors, we have developed an orientation error compensation method and a multiaxis dynamic switch. The advantages of the IMUPEN include the following: 1) It is portable and can be used anywhere without any external reference device or writing ambit limitations, and 2) its trajectory reconstruction algorithm can reduce orientation and integral errors effectively and thus can reconstruct the trajectories of movements accurately. Our experimental results on motion trajectory reconstruction and handwritten digit recognition have successfully validated the effectiveness of the IMUPEN and its trajectory reconstruction algorithm.
Adversarial Learning for Image Forensics Deep Matching with Atrous Convolution

Constrained image splicing detection and localization (CISDL) is a newly proposed challenging task for image forensics, which investigates two input suspected images and identifies whether one image has suspected regions pasted from the other. In this paper, we propose a novel adversarial learning framework to train the deep matching network for CISDL. Our framework mainly consists of three building blocks: 1) the deep matching network based on atrous convolution (DMAC) aims to generate two high-quality candidate masks which indicate the suspected regions of the two input images, 2) the detection network is designed to rectify inconsistencies between the two corresponding candidate masks, 3) the discriminative network drives the DMAC network to produce masks that are hard to distinguish from ground-truth ones. In DMAC, atrous convolution is adopted to extract features with rich spatial information, the correlation layer based on the skip architecture is proposed to capture hierarchical features, and atrous spatial pyramid pooling is constructed to localize tampered regions at multiple scales. The detection network and the discriminative network act as the losses with auxiliary parameters to supervise the training of DMAC in an adversarial way. Extensive experiments, conducted on 21 generated testing sets and two public datasets, demonstrate the effectiveness of the proposed framework and the superior performance of DMAC.
Witches’ broom and normal crown clones from the same trees of Pinus sibirica: a comparative morphological study

Mutational witches’ broom clones show a growth redistribution compared with normal clones. The main factor affecting the variations in mutant clone morphology is the strength of the mutation. Mutational witches’ broom is a fragment of the tree crown with abnormally dense branching and slower shoot growth, compared with those of a normal crown. Thousands of dwarf ornamental cultivars widely used in landscape design have been developed from mutational witches’ broom. In this study, crown morphology was compared between grafted clones of witches’ broom and normal clones from the same trees. The results quantify variations in crown structure between the mutants and normal clones. The sample materials were 10 pairs of grafted witches’ broom and normal crown clones of Pinus sibirica. The mutant and normal clones were discrete sets. Many morphological traits were affected in the mutants. Compared with the normal-crown clones, the mutants showed male sterility, decreased apical dominance, reduced shoot and needle length, and increased branching and seed cone bearing. In terms of morphogenic changes induced by the mutation, the shoots of the witches’ broom clones were bicyclic, generated seed cones that were much shorter than those of normal clones, and had acquired the ability to form lateral buds. The extent of interclonal variation was significantly greater among witches’ broom clones than among normal clones. Compared with the morphological traits of normal clones, those of the mutants were shifted in the same direction but to different extents. Therefore, mutational witches’ broom is the expression of a mutation that can be weak, medium, or strong. These results will be useful for unraveling the genetic basis of witches’ broom in conifers and for breeding new dwarf cultivars.
Survey on Abstractive Text Summarization

This paper provides a review on some of the significant research work done on abstractive text summarization. The process of generating the summary from one or more text corpus, by keeping the key points in the corpus is called text summarization. The most prominent technique in text summarization is an abstractive and extractive method. The extractive summarization is purely based on the algorithm and it just copies the most relevant sentence/words from the input text corpus and creating the summary. An abstractive method generates new sentences/words that may/may not be in the input corpus. This paper focuses on the abstractive text summarization. This paper explains the overview of the various processes in abstractive text summarization. It includes data processing, word embedding, basic model architecture, training, and validation process and the paper narrates the current research in this field. It includes different types of architectures, attention mechanism, supervised and reinforcement learning, the pros and cons of different architecture. Systematic comparison of different text summarization models will provide the future direction of text summarization.
RETURNN as a Generic Flexible Neural Toolkit with Application to Translation and Speech Recognition

We compare the fast training and decoding speed of RETURNN of attention models for translation, due to fast CUDA LSTM kernels, and a fast pure TensorFlow beam search decoder. We show that a layer-wise pretraining scheme for recurrent attention models gives over 1% BLEU improvement absolute and it allows to train deeper recurrent encoder networks. Promising preliminary results on max. expected BLEU training are presented. We obtain state-of-the-art models trained on the WMT 2017 German↔English translation task. We also present end-to-end model results for speech recognition on the Switchboard task. The flexibility of RETURNN allows a fast research feedback loop to experiment with alternative architectures, and its generality allows to use it on a wide range of applications.
Improving C++ Software Quality with Static Code Analysis

Static code analysis automates time-consuming and error-prone manual code review processes. Unlike dynamic testing, which requires the software to be executed, static code analysis is performed directly on the source code, enabling quality checks to begin before the code is ready for integration and test. During analysis, Polyspace tools calculate complexity metrics and check the code for compliance with development standards, including MISRA C®, MISRA C++, and JSF++.
Service oriented architecture maturity models: A systematic literature review

In recent years, the maturity model research has gained wide acceptance in the area of information systems and many Service Oriented Architecture (SOA) maturity models have been proposed. However, there are limited empirical studies on in-depth analysis and validation of SOA Maturity Models (SOAMMs).  The objective is to present a comprehensive comparison of existing SOAMMs to identify the areas of improvement and the research opportunities.  A systematic literature review is conducted to explore the SOA adoption maturity studies.  A total of 20 unique SOAMMs are identified and analyzed in detail. A comparison framework is defined based on SOAMM design and usage support. The results provide guidance for SOA practitioners who are involved in selection, design, and implementation of SOAMMs.  Although all SOAMMs propose a measurement framework, only a few SOAMMs provide guidance for selecting and prioritizing improvement measures. The current state of research shows that a gap exists in both prescriptive and descriptive purpose of SOAMM usage and it indicates the need for further research.
State of the art in microRNA as diagnostic and therapeutic biomarkers in chronic lymphocytic leukemia.

Early diagnostic is one of the most important steps in cancer therapy which helps to design and choose a better therapeutic approach. The finding of biomarkers in various levels including genomics, transcriptomics, and proteomics levels could provide better treatment for various cancers such as chronic lymphocytic leukemia (CLL). The CLL is the one of main lymphoid malignancies which is specified by aggregation of mature B lymphocytes. Among different biomarkers (e.g., CD38, chromosomes abnormalities, ZAP-70, TP53, and microRNA [miRNA]), miRNAs have appeared as new diagnostic and therapeutic biomarkers in patients with the CLL disease. Multiple lines of evidence indicated that deregulation of miRNAs could be associated with pathological events which are present in the CLL. These molecules have an effect on a variety of targets such as Bcl2, c-fos, c-Myc, TP53, TCL1, and STAT3 which play critical roles in the CLL pathogenesis. It has been shown that expression of miRNAs could lead to the activation of B cells and B cell antigen receptor (BCR). Moreover, exosomes containing miRNAs are one of the other molecules which could contribute to BCR stimulation and progression of CLL cells. Hence, miRNAs and exosomes released from CLL cells could be used as potential diagnostic and therapeutic biomarkers for CLL. This critical review focuses on a very important aspect of CLL based on biomarker discovery covers the pros and cons of using miRNAs as important diagnostics and therapeutics biomarkers for this deadly disease.
Online Action Tube Detection via Resolving the Spatio-temporal Context Pattern

At present, spatio-temporal action detection in the video is still a challenging problem, considering the complexity of the background, the variety of the action or the change of the viewpoint in the unconstrained environment. Most of current approaches solve the problem via a two-step processing: first detecting actions at each frame; then linking them, which neglects the continuity of the action and operates in an offline and batch processing manner. In this paper, we attempt to build an online action detection model that introduces the spatio-temporal coherence existed among action regions when performing action category inference and position localization. Specifically, we seek to represent the spatio-temporal context pattern via establishing an encoder-decoder model based on the convolutional recurrent network. The model accepts a video snippet as input and encodes the dynamic information of the action in the forward pass. During the backward pass, it resolves such information at each time instant for action detection via fusing the current static or motion cue. Additionally, we propose an incremental action tube generation algorithm, which accomplishes action bounding-boxes association, action label determination and the temporal trimming in a single pass. Our model takes in the appearance, motion or fused signals as input and is tested on two prevailing datasets, UCF-Sports and UCF-101. The experiment results demonstrate the effectiveness of our method which achieves a performance superior or comparable to compared existing approaches.
Mindfully going omni-channel: An economic decision model for evaluating omni-channel strategies

Disruptive digital technologies empower customers to define how they would like to interact with organizations. Consequently, organizations often struggle to implement an appropriate omni-channel strategy (OCS) that both meets customers’ interaction preferences and can be operated efficiently. Despite this strong practical need, research on omni-channel management predominantly adopts a descriptive perspective. There is little prescriptive knowledge to support organizations in assessing the business value of OCSs and comparing them accordingly. To address this research gap, we propose an economic decision model that helps select an appropriate OCS, considering online and offline channels, the opening and closing of channels, non-sequential customer journeys, and customers’ channel preferences. Drawing from investment theory and value-based management, the decision model recommends implementing the OCS with the highest contribution to an organization’s long-term firm value. We validate the decision model using real-world data on the omni-channel environment of a German financial service provider.
A novel 6-Gbps half-rate SST transmitter with impedance calibration and adjustable pre-emphasis

A novel half-rate source-series-terminated (SST) transmitter in 65nm bulk CMOS technology is presented in this paper. Compared to previous half-rate SST transmitters, the proposed one consists of four binary-weighted slices increasing proportionally as 1x, 2x, 4x and 8x and the range of pre-emphasis level is increased greatly by the clock-match block to adapt to different channel. The half-rate transmitter can adjust the pre-emphasis level from 1.2dB to 23dB. The transmitter output impedance is adjustable from 33ohms to 64ohms. A power consumption of 24mW is measured at a transmit rate of 6 GB/s which is power-efficient compared to previous half-rate SST transmitter.
Colours experienced in dreams.

Colours seen in dreams by six observers were recorded from memory and plotted on a CIE u, v, chromaticity diagram. Only about half the dreams recorded contained colour, and in those in which colour appeared the more saturated purples, blues and blue greens were absent. It is suggested that during achromatic dreams the areas of the visual cortex which seem to respond only to colour may be inoperative. The paucity of blue in dreams could be anatomically related to the small population of blue units in the colour areas of the cortex.
Hypergraph-Regularized Sparse NMF for Hyperspectral Unmixing

Hyperspectral image (HSI) unmixing has attracted increasing research interests in recent decades. The major difficulty of it lies in that the endmembers and the associated abundances need to be separated from highly mixed observation data with few a priori information. Recently, sparsity-constrained nonnegative matrix factorization (NMF) algorithms have been proved effective for hyperspectral unmixing (HU) since they can sufficiently utilize the sparsity property of HSIs. In order to improve the performance of NMF-based unmixing approaches, spectral and spatial constrains have been added into the unmixing model, but spectral-spatial joint structure is required to be more accurately estimated. To exploit the property that similar pixels within a small spatial neighborhood have higher possibility to share similar abundances, hypergraph structure is employed to capture the similarity relationship among the spatial nearby pixels. In the construction of a hypergraph, each pixel is taken as a vertex of the hypergraph, and each vertex with its k nearest spatial neighboring pixels form a hyperedge. Using the hypergraph, the pixels with similar abundances can be accurately found, which enables the unmixing algorithm to obtain promising results. Experiments on synthetic data and real HSIs are conducted to investigate the performance of the proposed algorithm. The superiority of the proposed algorithm is demonstrated by comparing it with some state-of-the-art methods.
Data mining of printed-circuit board defects

This paper discusses an industrial case study in which data mining has been applied to solve a quality engineering problem in electronics assembly. During the assembly process, solder balls occur underneath some components of printed circuit boards. The goal is to identify the cause of solder defects in a circuit board using a data mining approach. Statistical process control and design of experiment approaches did not provide conclusive results. The paper discusses features considered in the study, data collected, and the data mining solution approach to identify causes of quality faults in an industrial application.
The orbitofrontal–amygdala circuit and self-regulation of social–emotional behavior in autism

Individuals with an autistic spectrum disorder are impaired not only in understanding others' mental states, but also in self-regulation of social-emotional behavior. Therefore, a model of the brain in autism must encompass not only those brain systems that subserve social-cognitive and emotional functioning, but also those that subserve the self-regulation of behavior in response to a changing social environment. We present evidence to support the hypothesis that developmental dysfunction of the orbitofrontal-amygdala circuit of the brain is a critical factor in the development of autism and that some of the characteristic deficits of persons with autism in socio-emotional cognition and behavioral self-regulation are related to early dysfunction of different components of this circuit. A secondary hypothesis posits that the degree of intellectual impairment present in individuals with autism is directly related to the integrity of the dorsolateral prefrontal-hippocampal circuit of the brain. Together, these hypotheses have the potential to help explain the neurodevelopmental basis of some of the primary manifestations of autism as well as the heterogeneity of outcomes.
Questioning reliability assessments of health information on social media

This narrative review examines assessments of the reliability of online health information retrieved through social media to ascertain whether health information accessed or disseminated through social media should be evaluated differently than other online health information. Several medical, library and information science, and interdisciplinary databases were searched using terms relating to social media, reliability, and health information. While social media's increasing role in health information consumption is recognized, studies are dominated by investigations of traditional (i.e., non-social media) sites. To more richly assess constructions of reliability when using social media for health information, future research must focus on health consumers' unique contexts, virtual relationships, and degrees of trust within their social networks.
Purposeful and timely nursing rounds: a best practice implementation project.

Purposeful and timely rounding is a best practice intervention to routinely meet patient care needs, ensure patient safety, decrease the occurrence of patient preventable events, and proactively address problems before they occur. The Institute for Healthcare Improvement (IHI) endorsed hourly rounding as the best way to reduce call lights and fall injuries, and increase both quality of care and patient satisfaction. Nurse knowledge regarding purposeful rounding and infrastructure supporting timeliness are essential components for consistency with this patient centred practice.



The project aimed to improve patient satisfaction and safety through implementation of purposeful and timely nursing rounds. Goals for patient satisfaction scores and fall volume were set. Specific objectives were to determine current compliance with evidence-based criteria related to rounding times and protocols, improve best practice knowledge among staff nurses, and increase compliance with these criteria.



For the objectives of this project the Joanna Briggs Institute's Practical Application of Clinical Evidence System and Getting Research into Practice audit tool were used. Direct observation of staff nurses on a medical surgical unit in the United States was employed to assess timeliness and utilization of a protocol when rounding. Interventions were developed in response to baseline audit results. A follow-up audit was conducted to determine compliance with the same criteria. For the project aims, pre- and post-intervention unit-level data related to nursing-sensitive elements of patient satisfaction and safety were compared.



Rounding frequency at specified intervals during awake and sleeping hours nearly doubled. Use of a rounding protocol increased substantially to 64% compliance from zero. Three elements of patient satisfaction had substantive rate increases but the hospital's goals were not reached. Nurse communication and pain management scores increased modestly (5% and 11%, respectively). Responsiveness of hospital staff increased moderately (15%) with a significant sub-element increase in toileting (41%). Patient falls decreased by 50%.



Nurses have the ability to improve patient satisfaction and patient safety outcomes by utilizing nursing round interventions which serve to improve patient communication and staff responsiveness. Having a supportive infrastructure and an organized approach, encompassing all levels of staff, to meet patient needs during their hospital stay was a key factor for success. Hard-wiring of new practices related to workflow takes time as staff embrace change and understand how best practice interventions significantly improve patient outcomes.
Query-based summarization using MDL principle

Query-based text summarization is aimed at extracting essential information that answers the query from original text. The answer is presented in a minimal, often predefined, number of words. In this paper we introduce a new unsupervised approach for query-based extractive summarization, based on the minimum description length (MDL) principle that employs Krimp compression algorithm (Vreeken et al., 2011). The key idea of our approach is to select frequent word sets related to a given query that compress document sentences better and therefore describe the document better. A summary is extracted by selecting sentences that best cover query-related frequent word sets. The approach is evaluated based on the DUC 2005 and DUC 2006 datasets which are specifically designed for query-based summarization (DUC, 2005 2006). It competes with the best results.
Lean on Wii: physical rehabilitation with virtual reality Wii peripherals.

In recent years, a growing number of occupational therapists have integrated video game technologies, such as the Nintendo Wii, into rehabilitation programs. 'Wiihabilitation', or the use of the Wii in rehabilitation, has been successful in increasing patients' motivation and encouraging full body movement. The non-rehabilitative focus of Wii applications, however, presents a number of problems: games are too difficult for patients, they mainly target upper-body gross motor functions, and they lack support for task customization, grading, and quantitative measurements. To overcome these problems, we have designed a low-cost, virtual-reality based system. Our system, Virtual Wiihab, records performance and behavioral measurements, allows for activity customization, and uses auditory, visual, and haptic elements to provide extrinsic feedback and motivation to patients.