nils-herrmann/clean_scirepeval huggingface dataset:
When Collections of Creatives Become Creative Collectives: A Field Study of Problem Solving at Work

T paper introduces a model of collective creativity that explains how the locus of creative problem solving shifts, at times, from the individual to the interactions of a collective. The model is grounded in observations, interviews, informal conversations, and archival data gathered in intensive field studies of work in professional service firms. The evidence suggests that although some creative solutions can be seen as the products of individual insight, others should be regarded as the products of a momentary collective process. Such collective creativity reflects a qualitative shift in the nature of the creative process, as the comprehension of a problematic situation and the generation of creative solutions draw from—and reframe—the past experiences of participants in ways that lead to new and valuable insights. This research investigates the origins of such moments, and builds a model of collective creativity that identifies the precipitating roles played by four types of social interaction: help seeking, help giving, reflective reframing, and reinforcing. Implications of this research include shifting the emphasis in research and management of creativity from identifying and managing creative individuals to understanding the social context and developing interactive approaches to creativity, and from a focus on relatively constant contextual variables to the alignment of fluctuating variables and their precipitation of momentary phenomena.
A 50-mA 1-nF Low-Voltage Low-Dropout Voltage Regulator for SoC Applications

In this paper, we present a low-voltage low-dropout voltage regulator (LDO) for a system-on-chip (SoC) application which, exploiting the multiplication of the Miller effect through the use of a current amplifier, is frequency compensated up to 1-nF capacitive load. The topology and the strategy adopted to design the LDO and the related compensation frequency network are described in detail. The LDO works with a supply voltage as low as 1.2 V and provides a maximum load current of 50 mA with a drop-out voltage of 200 mV: the total integrated compensation capacitance is about 40 pF. Measurement results as well as comparison with other SoC LDOs demonstrate the advantage of the proposed topology.
A convolutional neural network for robotic arm guidance using sEMG based frequency-features

Recently, robotics has been seen as a key solution to improve the quality of life of amputees. In order to create smarter robotic prosthetic devices to be used in an everyday context, one must be able to interface them seamlessly with the end-user in an inexpensive, yet reliable way. In this paper, we are looking at guiding a robotic device by detecting gestures through measurement of the electrical activity of muscles captured by surface electromyography (sEMG). Reliable sEMG-based gesture classifiers for end-users are challenging to design, as they must be extremely robust to signal drift, muscle fatigue and small electrode displacement without the need for constant recalibration. In spite of extensive research, sophisticated sEMG classifiers for prostheses guidance are not yet widely used, as systems often fail to solve these issues simultaneously. We propose to address these problems by employing Convolutional Neural Networks. Specifically as a first step, we demonstrate their viability to the problem of gesture recognition for a low-cost, low-sampling rate (200Hz) consumer-grade, 8-channel, dry electrodes sEMG device called Myo armband (Thalmic Labs) on able-bodied subjects. To this effect, we assessed the robustness of this machine learning oriented approach by classifying a combination of 7 hand/wrist gestures with an accuracy of ~97.9% in real-time, over a period of 6 consecutive days with no recalibration. In addition, we used the classifier (in conjunction with orientation data) to guide a 6DoF robotic arm, using the armband with the same speed and precision as with a joystick. We also show that the classifier is able to generalize to different users by testing it on 18 participants.
A Mask Based Segmentation Algorithm for Automatic Measurement of Cobb Angle from Scoliosis X-Ray Image

Scoliosis is a medical condition in which a person's spine is curved from side to side. Current methodology of diagnosis of scoliosis: The doctors analyze an X-ray image and determine the cobb angle and vertebral twist. These two parameters are critical in the treatment of scoliosis. Bottlenecks associated with current methodology are inherent errors associated with manual measurement of cobb angle and vertebral twist from X-rays by the concerned doctors and the treatment that is meted out to a particular case of 'cobb angle' and vertebral twist by different doctors may differ with varying results. Hence it becomes imperative to select the best treatment procedure for attaining the best results. Highlights of the new methodology proposed: An X-ray image is accepted as input, Cobb angle is measured by the computer which is programmed to do so, thus eliminating the errors associated with the doctors interpretation.
Compact Circularly Polarized Symmetric-Slit Microstrip Antennas

Compact symmetric-slit square microstrip-patch antennas are proposed for circularly polarized (CP) radiation. A symmetric slit on a patch radiator can be used for circularly polarized radiation with a compact size. Various slit shapes embedded along the four diagonal directions on the patch radiators are realized for compact circularly polarized symmetric-slit microstrip antennas based on a single feed. Circularly polarized radiation is achieved by slightly varying the slit's circumference along one of the diagonal axes. The performance and sizes of the proposed antennas with various slit shapes are studied and compared. The proposed antennas were designed on a low-loss Rogers RO4003C substrate and on low-cost lossy FR4 substrate. The 3-dB axial ratio (AR) and 10-dB return-loss bandwidths of the antenna on the FR4 substrate were larger compared to those of the antenna on the RO4003C substrate. The gain of the antenna on the RO4003C substrate was higher compared to that of the antenna on the FR4 substrate with a fixed overall antenna size. The measured 10-dB return loss and 3-dB axial-ratio bandwidths were 3.85% and 1.5%, respectively, with a maximum gain of around 0.8 dBic, for the antenna on the FR4 substrate. For the antenna on the RO4003 substrate, the measured 10-dB return loss and 3-dB axial-ratio bandwidths were 1.5% and 0.5%, respectively, with a maximum gain of around 3.4 dBic.
Conceptualizing and Testing a Social Cognitive Model of the Digital Divide

T digital divide has loomed as a public policy issue for over a decade. Yet, a theoretical account for the effects of the digital divide is currently lacking. This study examines three levels of the digital divide. The digital access divide (the first-level digital divide) is the inequality of access to information technology (IT) in homes and schools. The digital capability divide (the second-level digital divide) is the inequality of the capability to exploit IT arising from the first-level digital divide and other contextual factors. The digital outcome divide (the third-level digital divide) is the inequality of outcomes (e.g., learning and productivity) of exploiting IT arising from the second-level digital divide and other contextual factors. Drawing on social cognitive theory and computer self-efficacy literature, we developed a model to show how the digital access divide affects the digital capability divide and the digital outcome divide among students. The digital access divide focuses on computer ownership and usage in homes and schools. The digital capability divide and the digital outcome divide focus on computer self-efficacy and learning outcomes, respectively. This model was tested using data collected from over 4,000 students in Singapore. The results generate insights into the relationships among the three levels of the digital divide and provide a theoretical account for the effects of the digital divide. While school computing environments help to increase computer self-efficacy for all students, these factors do not eliminate knowledge the gap between students with and without home computers. Implications for theory and practice are discussed.
Mobile Visual Search

Mobile phones have evolved into powerful image and video processing devices equipped with high-resolution cameras, color displays, and hardware-accelerated graphics. They are also increasingly equipped with a global positioning system and connected to broadband wireless networks. All this enables a new class of applications that use the camera phone to initiate search queries about objects in visual proximity to the user . Such applications can be used, e.g., for identifying products, comparison shopping, finding information about movies, compact disks (CDs), real estate, print media, or artworks.
Penile Dysmorphic Disorder: Development of a Screening Scale.

Penile dysmorphic disorder (PDD) is shorthand for men diagnosed with body dysmorphic disorder, in whom the size or shape of the penis is their main, if not their exclusive, preoccupation causing significant shame or handicap. There are no specific measures for identifying men with PDD compared to men who are anxious about the size of their penis but do not have PDD. Such a measure might be helpful for treatment planning, reducing unrealistic expectations, and measuring outcome after any psychological or physical intervention. Our aim was, therefore, to validate a specific measure, termed the Cosmetic Procedure Screening Scale for PDD (COPS-P). Eighty-one male participants were divided into three groups: a PDD group (n = 21), a small penis anxiety group (n = 37), and a control group (n = 23). All participants completed the COPS-P as well as standardized measures of depression, anxiety, social phobia, body image, quality of life, and erectile function. Penis size was also measured. The final COPS-P was based on nine items. The scale had good internal reliability and significant convergent validity with measures of related constructs. It discriminated between the PDD group, the small penis anxiety group, and the control group. This is the first study to develop a scale able to discriminate between those with PDD and men anxious about their size who did not have PDD. Clinicians and researchers may use the scale as part of an assessment for men presenting with anxiety about penis size and as an audit or outcome measure after any intervention for this population.
American Society of Clinical Oncology guideline recommendations for sentinel lymph node biopsy in early-stage breast cancer.

To develop a guideline for the use of sentinel node biopsy (SNB) in early stage breast cancer.



An American Society of Clinical Oncology (ASCO) Expert Panel conducted a systematic review of the literature available through February 2004 on the use of SNB in early-stage breast cancer. The panel developed a guideline for clinicians and patients regarding the appropriate use of a sentinel lymph node identification and sampling procedure from hereon referred to as SNB. The guideline was reviewed by selected experts in the field and the ASCO Health Services Committee and was approved by the ASCO Board of Directors.



The literature review identified one published prospective randomized controlled trial in which SNB was compared with axillary lymph node dissection (ALND), four limited meta-analyses, and 69 published single-institution and multicenter trials in which the test performance of SNB was evaluated with respect to the results of ALND (completion axillary dissection). There are currently no data on the effect of SLN biopsy on long-term survival of patients with breast cancer. However, a review of the available evidence demonstrates that, when performed by experienced clinicians, SNB appears to be a safe and acceptably accurate method for identifying early-stage breast cancer without involvement of the axillary lymph nodes.



SNB is an appropriate initial alternative to routine staging ALND for patients with early-stage breast cancer with clinically negative axillary nodes.  ALND remains standard treatment for patients with axillary metastases identified on SNB. Appropriately identified patients with negative results of SNB, when done under the direction of an experienced surgeon, need not have completion ALND. Isolated cancer cells detected by pathologic examination of the SLN with use of specialized techniques are currently of unknown clinical significance. Although such specialized techniques are often used, they are not a required part of SLN evaluation for breast cancer at this time. Data suggest that SNB is associated with less morbidity than ALND, but the comparative effects of these two approaches on tumor recurrence or patient survival are unknown.
A neural network multi-task learning approach to biomedical named entity recognition

Named Entity Recognition (NER) is a key task in biomedical text mining. Accurate NER systems require task-specific, manually-annotated datasets, which are expensive to develop and thus limited in size. Since such datasets contain related but different information, an interesting question is whether it might be possible to use them together to improve NER performance. To investigate this, we develop supervised, multi-task, convolutional neural network models and apply them to a large number of varied existing biomedical named entity datasets. Additionally, we investigated the effect of dataset size on performance in both single- and multi-task settings. We present a single-task model for NER, a Multi-output multi-task model and a Dependent multi-task model. We apply the three models to 15 biomedical datasets containing multiple named entities including Anatomy, Chemical, Disease, Gene/Protein and Species. Each dataset represent a task. The results from the single-task model and the multi-task models are then compared for evidence of benefits from Multi-task Learning. With the Multi-output multi-task model we observed an average F-score improvement of 0.8% when compared to the single-task model from an average baseline of 78.4%. Although there was a significant drop in performance on one dataset, performance improves significantly for five datasets by up to 6.3%. For the Dependent multi-task model we observed an average improvement of 0.4% when compared to the single-task model. There were no significant drops in performance on any dataset, and performance improves significantly for six datasets by up to 1.1%. The dataset size experiments found that as dataset size decreased, the multi-output model’s performance increased compared to the single-task model’s. Using 50, 25 and 10% of the training data resulted in an average drop of approximately 3.4, 8 and 16.7% respectively for the single-task model but approximately 0.2, 3.0 and 9.8% for the multi-task model. Our results show that, on average, the multi-task models produced better NER results than the single-task models trained on a single NER dataset. We also found that Multi-task Learning is beneficial for small datasets. Across the various settings the improvements are significant, demonstrating the benefit of Multi-task Learning for this task.
A Compact Multiband Open-Ended Slot Antenna for Mobile Handsets

A novel compact multiband antenna formed by two printed open-ended slots, with the first one a T-shaped slot and the second one an E-shaped slot, cut at the edge of the ground plane of mobile handsets is presented. This antenna can generate five resonant modes to cover GSM900/DCS1800/ PCS1900/UMTS and 2.4-GHz-based WLAN bands, which can be controlled almost independently by the five respective monopole slots of different lengths. Furthermore, the proposed antenna has a simple planar structure and occupies a small area of only 10 × 42.5 mm2. It is also promising to bend the antenna into a E shape, inverted-T shape and meander line to reduce its volume occupied. Details of the antenna design and experimental results are presented and discussed.
Automatic Road Crack Detection and Characterization

A fully integrated system for the automatic detection and characterization of cracks in road flexible pavement surfaces, which does not require manually labeled samples, is proposed to minimize the human subjectivity resulting from traditional visual surveys. The first task addressed, i.e., crack detection, is based on a learning from samples paradigm, where a subset of the available image database is automatically selected and used for unsupervised training of the system. The system classifies nonoverlapping image blocks as either containing crack pixels or not. The second task deals with crack type characterization, for which another classification system is constructed, to characterize the detected cracks' connect components. Cracks are labeled according to the types defined in the Portuguese Distress Catalog, with each different crack present in a given image receiving the appropriate label. Moreover, a novel methodology for the assignment of crack severity levels is introduced, computing an estimate for the width of each detected crack. Experimental crack detection and characterization results are presented based on images captured during a visual road pavement surface survey over Portuguese roads, with promising results. This is shown by the quantitative evaluation methodology introduced for the evaluation of this type of system, including a comparison with human experts' manual labeling results.
Tiered Forensic Methodology Model for Digital Field Triage by Non-Digital Evidence Specialists

Due to budgetary constraints and the high level of training required, digital forensic analysts are in short supply in police forces the world over. This inevitably leads to a prolonged time taken between an investigator sending the digital evidence for analysis and receiving the analytical report back. In an attempt to expedite this procedure, various process models have been created to place the forensic analyst in the field conducting a triage of the digital evidence. By conducting triage in the field, an investigator is able to act upon pertinent information quicker, while waiting on the full report. The work presented as part of this paper focuses on the training of front-line personnel in the field triage process, without the need of a forensic analyst attending the scene. The premise has been successfully implemented within regular/non-digital forensics, i.e., crime scene investigation. In that field, front-line members have been trained in specific tasks to supplement the trained specialists. The concept of front-line members conducting triage of digital evidence in the field is achieved through the development of a new process model providing guidance to these members. To prove the model's viability, an implementation of this new process model is presented and evaluated. The results outlined demonstrate how a tiered response involving digital evidence specialists and nonspecialists can better deal with the increasing number of investigations involving digital
CONSTRUCTIVE NEURAL NETWORKS : A REVIEW

In conventional neural networks, we have to define the architecture prior to training but in constructive neural networks the network architecture is constructed during the training process. In this paper, we review constructive neural network algorithms that constructing feedforward architecture for regression problems. Cascade-Correlation algorithm (CCA) is a well-known and widely used constructive algorithm. Cascade 2 algorithm is a variant of CCA that is found to be more suitable for regression problems and is reviewed in this paper. We review our recently proposed two constructive algorithms that emphasize on architectural adaptation and functional adaptation during training. To achieve functional adaptation, the slope of the sigmoidal function is adapted during learning. The algorithm determines not only the optimum number of hidden layer nodes, as also the optimum value of the slope parameter of sigmoidal function. The role of adaptive sigmoidal activation function has been verified in constructive neural networks for better generalization performance and lesser training time.
Depth CNNs for RGB-D scene recognition: learning from scratch better than transferring from RGB-CNNs

Scene recognition with RGB images has been extensively studied and has reached very remarkable recognition levels, thanks to convolutional neural networks (CNN) and large scene datasets. In contrast, current RGB-D scene data is much more limited, so often leverages RGB large datasets, by transferring pretrained RGB CNN models and fine-tuning with the target RGB-D dataset. However, we show that this approach has the limitation of hardly reaching bottom layers, which is key to learn modality-specific features. In contrast, we focus on the bottom layers, and propose an alternative strategy to learn depth features combining local weakly supervised training from patches followed by global fine tuning with images. This strategy is capable of learning very discriminative depthspecific features with limited depth images, without resorting to Places-CNN. In addition we propose a modified CNN architecture to further match the complexity of the model and the amount of data available. For RGB-D scene recognition, depth and RGB features are combined by projecting them in a common space and further leaning a multilayer classifier, which is jointly optimized in an end-to-end network. Our framework achieves state-of-the-art accuracy on NYU2 and SUN RGB-D in both depth only and combined RGB-D data.
Removing rain and snow in a single image using guided filter

Dynamic weather conditions, such as rain and snow, degrade the performance of vision algorithms, which is served for video surveillance and analysis tasks. Many methods have been proposed to reduce the effects of raindrops and snow flakes that are regarded as noises in each frame of a certain video. However, the removing of rain-drop or snow-flake is quite challenging since each of them affects only on a very small region of an image, thus, leading us a rub to determine which region should be smoothed and which should not. In this paper, a guided filter, by which no pixel-based statistical information for detecting rain and snow are needed, is introduced in the removal stage. The results show that it may achieve better performance. In addition, the degradation-restoring algorithm is suitable for both single images and videos.
SVM-based Classifier System for Recognition of Cotton Leaf Diseases

The allocation and recognition of cotton leaf diseases are of the major importance as they have a cogent and momentous impact on quality and production of cotton. This paper presents a modus operandi for automatic classification of cotton leaf diseases through feature extraction of leaf symptoms from digital images. Otsu’s segmentation method is used for extracting color and shape features. Support vector machines (SVM) had been used to do classification on the extracted features. Three diseases have been diagnosed, namely Bacterial Blight, Myrothecium and Alternaria. The testing samples of the images are gathered from CICR Nagpur, cotton fields in Buldhana & Wardha district.
GRAS: An effective and efficient stemming algorithm for information retrieval

A novel graph-based language-independent stemming algorithm suitable for information retrieval is proposed in this article. The main features of the algorithm are retrieval effectiveness, generality, and computational efficiency. We test our approach on seven languages (using collections from the TREC, CLEF, and FIRE evaluation platforms) of varying morphological complexity. Significant performance improvement over plain word-based retrieval, three other language-independent morphological normalizers, as well as rule-based stemmers is demonstrated.