nils-herrmann/clean_scirepeval huggingface dataset:
A gesture-free geometric approach for mid-air expression of design intent in 3D virtual pottery

The advent of depth cameras has enabled mid-air interactions for shape modeling with bare hands. Typically, these interactions employ a finite set of pre-defined hand gestures to allow users to specify modeling operations in virtual space. However, human interactions in real world shaping processes (such as pottery or sculpting) are complex, iterative, and continuous. In this paper, we show that the expression of user intent in shaping processes can be derived from the geometry of contact between the hand and the manipulated object. Specifically, we describe the design and evaluation of a geometric interaction technique for bare-hand mid-air virtual pottery. We model the shaping of a pot as a gradual and progressive convergence of the pot’s profile to the shape of the user’s hand represented as a point-cloud (PCL). Thus, a user does not need to learn, know, or remember any gestures to interact with our system. Our choice of pottery simplifies the geometric representation, allowing us to systematically study how users use their hands and fingers to express the intent of deformation during a shaping process. Our evaluations demonstrate that it is possible to enable users to express their intent for shape deformation without the need for a fixed set of gestures for clutching and deforming a shape.
Sexually responsive vascular tissue of the vulva.

To better understand the genital changes that occur during the female sexual response, using a gross anatomical and histological study of the vascular tissue of the vulva, supplemented with magnetic resonance imaging (MRI).



Seven cadaveric vulvectomy specimens were used; they were serially sectioned in coronal, sagittal, and axial planes, and stained with haematoxylin and eosin. Selected blocks were stained with elastic Masson's trichrome. Axial MR images were taken of two healthy women with intact sexual functioning using a gadolinium-based blood-pool contrast agent. A 1.5 T system was used for all MRI studies, with images taken at baseline and during sexual arousal while viewing an erotic videotape.



There are five vascular compartments of the female external genitalia, found in the clitoris, clitoral bulbs, labia minora, urethra, and vestibule/vagina. Of these five compartments, two distinct types of vascular tissue were identified, i.e. erectile and non-erectile/specialized genital. The erectile tissue compartments had the greatest change in blood volume during sexual arousal, as assessed by MRI.



The vulva contains a substantial amount of vascular tissue. These specialized tissues show a variable, but unified response to sexual arousal.
Security challenges of the internet of things

Internet of things (furthermore IoT) by definition refers to a networked interconnection of devices in everyday use that are often equipped with ubiquitous intelligence. It is based on processing of large quantities of data in order to provide useful information/service and enables an efficient regulatory policy in the area of IoT. One of the key challenges for the realization of the IoT includes security challenges, especially in the area of privacy and confidentiality among management of heterogeneities and limitations of network capacities. Those challenges will be based on information security management systems as well as on legal foundations. When considering the legal framework of security and privacy of the IoT, it has to be determined which model of regulation should be applied. Thereby, no traditional government regulation is actually appropriate for a global system such as IoT. The most important foundation consequently will be the regulatory foundations of European union (on the territory of EU) and, consequently building the model of self-regulation based partly on state and mostly on law international agreements which are to be considered as tools to govern the IoT. This paper gives an insight into the most important security challenges related to implementation of IoT and building appropriate regulatory framework. Adoption of the regulatory framework is crucial for the development of IoT services due to adequate legal security companies will get.
Social Media Use and Perceived Emotional Support Among US Young Adults

Low emotional support is associated with poor health outcomes. Engagement with face-to-face social networks is one way of increasing emotional support. However, it is not yet known whether engagement with proliferating electronic social networks is similarly associated with increased emotional support. Thus, the purpose of this study was to assess associations between social media use and perceived emotional support in a large, nationally-representative sample. In October 2014, we collected data from 1796 U.S. adults ages 19–32. We assessed social media use using both total time spent and frequency of visits to each of the 11 most popular social media platforms. Our dependent variable was perceived emotional support as measured by the brief Patient-Reported Outcomes Measurement Information System (PROMIS) emotional support scale. A multivariable model including all sociodemographic covariates and accounting for survey weights demonstrated that, compared with the lowest quartile of time on social media, being in the highest quartile (spending two or more hours per day) was significantly associated with decreased odds of having higher perceived emotional support (AOR 0.62, 95 % CI 0.40, 0.94). However, compared with those in the lowest quartile, being in the highest quartile regarding frequency of social media use was not significantly associated with perceived emotional support (AOR 0.70, 95 % CI 0.45, 1.09). In conclusion, while the cross-sectional nature of these data hinder inference regarding directionality, it seems that heavy users of social media may actually feel less and not more emotional support.
A deep learning approach for the analysis of masses in mammograms with minimal user intervention

We present an integrated methodology for detecting, segmenting and classifying breast masses from mammograms with minimal user intervention. This is a long standing problem due to low signal-to-noise ratio in the visualisation of breast masses, combined with their large variability in terms of shape, size, appearance and location. We break the problem down into three stages: mass detection, mass segmentation, and mass classification. For the detection, we propose a cascade of deep learning methods to select hypotheses that are refined based on Bayesian optimisation. For the segmentation, we propose the use of deep structured output learning that is subsequently refined by a level set method. Finally, for the classification, we propose the use of a deep learning classifier, which is pre-trained with a regression to hand-crafted feature values and fine-tuned based on the annotations of the breast mass classification dataset. We test our proposed system on the publicly available INbreast dataset and compare the results with the current state-of-the-art methodologies. This evaluation shows that our system detects 90% of masses at 1 false positive per image, has a segmentation accuracy of around 0.85 (Dice index) on the correctly detected masses, and overall classifies masses as malignant or benign with sensitivity (Se) of 0.98 and specificity (Sp) of 0.7.
Student satisfaction with EFL speaking classes : Relating speaking self-efficacy and skills achievement

This study investigated the relationship between student satisfaction with speaking classes, speaking skills self-efficacy beliefs, and speaking skills achievement. To this end, one hundred Iranian EFL undergraduate students filled out two questionnaires; a research-made and pilot-tested questionnaire for student satisfaction with speaking classes, and a questionnaire for speaking skills self-efficacy beliefs, adapted from Rahimi and Abedini (2009), Gahungu (2007), Wang et al. (2013), and Saeidi and Ebrahimi Farshchi (2012). Participants' final scores in speaking skills were collected from their instructors and regarded as a measure of their speaking skills achievement. The results of Pearson correlation analyses showed that both student satisfaction with speaking classes and speaking skills self-efficacy beliefs had significant positive correlations with speaking skills achievement, with the latter being stronger. Moreover, the results of Pearson correlation analyses also indicated the existence of a significant positive correlation between student satisfaction with speaking classes and speaking skills self-efficacy beliefs. Multiple regression analyses showed that between the independent variables of the study, speaking self-efficacy beliefs was a significantly stronger predicator of Iranian EFL students' speaking skills achievement.
Design and Implementation of a Network Security Model for Cooperative Network

In this paper a design and implementation of a network security model was presented, using routers and firewall. Also this paper was conducted the network security weakness in router and firewall network devices, type of threats and responses to those threats, and the method to prevent the attacks and hackers to access the network. Also this paper provides a checklist to use in evaluating whether a network is adhering to best practices in network security and data confidentiality. The main aim of this research is to protect the network from vulnerabilities, threats, attacks, configuration weaknesses and security policy weaknesses. KeywordsNetwork security, network management, threats, security policy Received December 17, 2008;
Malware Classification with Deep Convolutional Neural Networks

In this paper, we propose a deep learning framework for malware classification. There has been a huge increase in the volume of malware in recent years which poses a serious security threat to financial institutions, businesses and individuals. In order to combat the proliferation of malware, new strategies are essential to quickly identify and classify malware samples so that their behavior can be analyzed. Machine learning approaches are becoming popular for classifying malware, however, most of the existing machine learning methods for malware classification use shallow learning algorithms (e.g. SVM). Recently, Convolutional Neural Networks (CNN), a deep learning approach, have shown superior performance compared to traditional learning algorithms, especially in tasks such as image classification. Motivated by this success, we propose a CNN-based architecture to classify malware samples. We convert malware binaries to grayscale images and subsequently train a CNN for classification. Experiments on two challenging malware classification datasets, Malimg and Microsoft malware, demonstrate that our method achieves better than the state-of-the-art performance. The proposed method achieves 98.52% and 99.97% accuracy on the Malimg and Microsoft datasets respectively.
Developing a whole-arm exoskeleton robot with hand opening and closing mechanism for upper limb stroke rehabilitation

There is a lack of enough attention to the patients' hand posture. When robots implement assistive training, patients are often asked to grip a handle tightly, which may induce strong hand muscle contractions with the hand at an abnormal posture. If we ignore proper control of the muscle tension of subject's hand, the flexibility of hand/fingers may decrease and the robot training may potentially cause abnormal muscle tone. On the other hand, since the patient's fingers are already in an abnormal posture, properly aligning the joint and making the robot easy to attach is important. However, an existing multi-DOF hand exoskeleton systems may be difficult for the patients to put it on. the purpose of this paper was to design a patient-friendly mechanism drive by a single motor and attached to the whole-arm rehab robot for the hand and finger opening and closing functions. Using our 8+2 DOF whole-arm robot including this hand opening and closing mechanism, a novel integrated rehabilitation is performed including 1) strenuous stretching of the MCP-thumb joints and other spastic joints of the upper limb; 2) active assistive exercise is provided to improve voluntary neuromuscular control by using robot-computer games with a griping task; 3) outcome evaluations including the cross-coupling torques between the fingers/thumb and the other joints during hand opening/closing and other upper limb movements.
PixelLink: Detecting Scene Text via Instance Segmentation

Most state-of-the-art scene text detection algorithms are deep learning based methods that depend on bounding box regression and perform at least two kinds of predictions: text/nontext classification and location regression. Regression plays a key role in the acquisition of bounding boxes in these methods, but it is not indispensable because text/non-text prediction can also be considered as a kind of semantic segmentation that contains full location information in itself. However, text instances in scene images often lie very close to each other, making them very difficult to separate via semantic segmentation. Therefore, instance segmentation is needed to address this problem. In this paper, PixelLink, a novel scene text detection algorithm based on instance segmentation, is proposed. Text instances are first segmented out by linking pixels within the same instance together. Text bounding boxes are then extracted directly from the segmentation result without location regression. Experiments show that, compared with regression-based methods, PixelLink can achieve better or comparable performance on several benchmarks, while requiring many fewer training iterations and less training data.
Interactive Grounded Language Acquisition and Generalization in a 2D World

We build a virtual agent for learning language in a 2D maze-like world. The agent sees images of the surrounding environment, listens to a virtual teacher, and takes actions to receive rewards. It interactively learns the teacher’s language from scratch based on two language use cases: sentence-directed navigation and question answering. It learns simultaneously the visual representations of the world, the language, and the action control. By disentangling language grounding from other computational routines and sharing a concept detection function between language grounding and prediction, the agent reliably interpolates and extrapolates to interpret sentences that contain new word combinations or new words missing from training sentences. The new words are transferred from the answers of language prediction. Such a language ability is trained and evaluated on a population of over 1.6 million distinct sentences consisting of 119 object words, 8 color words, 9 spatial-relation words, and 50 grammatical words. The proposed model significantly outperforms five comparison methods for interpreting zero-shot sentences. In addition, we demonstrate human-interpretable intermediate outputs of the model in the appendix.
Cerebrospinal Fluid and Interstitial Fluid Motion via the Glymphatic Pathway Modelled by Optimal Mass Transport

It was recently shown that the brain-wide cerebrospinal fluid (CSF) and interstitial fluid exchange system designated the ‘glymphatic pathway’ plays a key role in removing waste products from the brain, similarly to the lymphatic system in other body organs . It is therefore important to study the flow patterns of glymphatic transport through the live brain in order to better understand its functionality in normal and pathological states. Unlike blood, the CSF does not flow rapidly through a network of dedicated vessels, but rather through peri-vascular channels and brain parenchyma in a slower time-domain, and thus conventional fMRI or other blood-flow sensitive MRI sequences do not provide much useful information about the desired flow patterns. We have accordingly analyzed a series of MRI images, taken at different times, of the brain of a live rat, which was injected with a paramagnetic tracer into the CSF via the lumbar intrathecal space of the spine. Our goal is twofold: (a) find glymphatic (tracer) flow directions in the live rodent brain; and (b) provide a model of a (healthy) brain that will allow the prediction of tracer concentrations given initial conditions. We model the liquid flow through the brain by the diffusion equation. We then use the Optimal Mass Transfer (OMT) approach to model the glymphatic flow vector field, and estimate the diffusion tensors by analyzing the (changes in the) flow. Simulations show that the resulting model successfully reproduces the dominant features of the experimental data.
Training Simplification and Model Simplification for Deep Learning: A Minimal Effort Back Propagation Method

We propose a simple yet effective technique to simplify the training and the resulting model of neural networks. In back propagation, only a small subset of the full gradient is computed to update the model parameters. The gradient vectors are sparsified in such a way that only the top-k elements (in terms of magnitude) are kept. As a result, only k rows or columns (depending on the layout) of the weight matrix are modified, leading to a linear reduction in the computational cost. Based on the sparsified gradients, we further simplify the model by eliminating the rows or columns that are seldom updated, which will reduce the computational cost both in the training and decoding, and potentially accelerate decoding in real-world applications. Surprisingly, experimental results demonstrate that most of time we only need to update fewer than 5% of the weights at each back propagation pass. More interestingly, the accuracy of the resulting models is actually improved rather than degraded, and a detailed analysis is given. The model simplification results show that we could adaptively simplify the model which could often be reduced by around 9x, without any loss on accuracy or even with improved accuracy.
Some Correlates of Electronic Health Information Management System Success in Nigerian Teaching Hospitals

Nowadays, an electronic health information management system (EHIMS) is crucial for patient care in hospitals. This paper explores the aspects and elements that contribute to the success of EHIMS in Nigerian teaching hospitals. The study adopted a survey research design. The population of study comprised 442 health information management personnel in five teaching hospitals that had implemented EHIMS in Nigeria. A self-developed questionnaire was used as an instrument for data collection. The findings revealed that there is a positive, close relationship between all the identified factors and EHIMS's success: technical factors (r = 0.564, P < 0.05); social factors (r = 0.616, P < 0.05); organizational factors (r = 0.621, P < 0.05); financial factors (r = 0.705, P < 0.05); and political factors (r = 0.589, P < 0.05). We conclude that consideration of all the identified factors was highly significant for the success of EHIMS in Nigerian teaching hospitals.
5G antenna array with wide-angle beam steering and dual linear polarizations

In this paper, we present the design of a switched-beam antenna array at millimeter-wave frequencies for future 5G applications. The proposed antenna array is based on wideband patch antenna elements and a Butler matrix feed network. The patch antenna has a broad radiation pattern for wide-angle beam steering and allows the simultaneous operation with two orthogonal linear polarizations. A combination of two separated Butler matrices provides independent beam steering for both polarizations in the wide operating band. The antenna array has a simple multilayer construction, and it is made on a low-cost Rogers laminate.
Metaphors of human thinking for usability inspection and design

Usability inspection techniques are widely used, but few focus on users' thinking and many are appropriate only for particular devices and use contexts. We present a new technique (MOT) that guides inspection by metaphors of human thinking. The metaphors concern habit, the stream of thought, awareness and associations, the relation between utterances and thought, and knowing. The main novelty of MOT is its psychological basis combined with its use of metaphors to stimulate inspection. The first of three experiments shows that usability problems uncovered with MOT are more serious and more complex to repair than problems found with heuristic evaluation. Problems found with MOT are also judged more likely to persist for expert users. The second experiment shows that MOT finds more problems than cognitive walkthrough, and has a wider coverage of a reference collection of usability problems. Participants prefer using MOT over cognitive walkthrough; an important reason being the wider scope of MOT. The third experiment compares MOT, cognitive walkthrough, and think aloud testing, in the context of nontraditional user interfaces. Participants prefer using think aloud testing, but identify few problems with that technique that are not found also with MOT or cognitive walkthrough. MOT identifies more problems than the other techniques. Across experiments and measures of usability problems' utility in systems design, MOT performs better than existing inspection techniques and is comparable to think aloud testing.
VMTP: a transport protocol for the next generation of communication systems

The Versatile Message Transaction Protocol (VMTP) is a transport-level protocol designed to support remote procedure call, multicast and real-time communication. The protocol is optimized for efficient page-level network file access in particular.
In this paper, we describe the significant aspects of the VMTP design, including the VMTP treatment of sessions, addressing, duplicate suppression, flow control and retransmissions plus its provision for multicast. The VMTP design reflects a change in the use of computer communication as well as a change in the underlying hardware base for the next generation of communication systems. It also challenges certain established notions in the design of protocols.
An Active-Clamp Push–Pull Converter for Battery Sourcing Applications

This paper presents an active-clamp push-pull converter for battery sourcing applications. A pair of auxiliary switches, resonant inductors, and clamping capacitors is added to the primary side of the transformer to clamp voltage spike and recycle the energy trapped in the leakage inductors. In the proposed active-clamp push-pull converter, since both main and auxiliary switches can be turned on with zero-voltage switching, switching loss can be reduced and conversion efficiency therefore can be improved significantly. Furthermore, the proposed converter can eliminate potential flux-imbalance problems existing in the conventional push-pull converter. In this paper, a 1 kW active-clamp push-pull converter was implemented, from which experimental results have shown that efficiency improvement and surge suppression can be achieved effectively. It is relatively feasible for applications to battery sourcing converters.