nils-herrmann/clean_scirepeval huggingface dataset:
Automated malaria diagnosis using convolutional neural networks in an on-field setting

This study focuses on automated malaria diagnosis in low quality blood smear images, captured by a low-cost smartphone based microscope system. The aim is to localize and classify the healthy and infected erythrocytes (red blood cells) in order to evaluate the parasitaemia in an infected blood smear. Due to the lower quality of the smartphone microscope system compared to traditional high-end light microscopes, conventional algorithms fail to process these images. We propose a framework using a convolutional neural network as a pixel classifier to localize the erythrocytes. Afterwards we classify them accordingly, using a convolutional neural network as an object classifier. Such a system can offer in-the-field malaria diagnosis without human intervention or can act as an aid for human experts to lower workload and increase diagnosis accuracy. The algorithm successfully localizes the erythrocytes with an average sensitivity of 97.31% and precision of 92.21%. Classification performed inadequate, in terms of low agreement with two human experts. This can be due to the low image quality or the small amount of training data available at the time. M.Sc. thesis R. Sorgedrager
Visually-Enabled Active Deep Learning for (Geo) Text and Image Classification: A Review

This paper investigates recent research on active learning for (geo) text and image classification, with an emphasis on methods that combine visual analytics and/or deep learning. Deep learning has attracted substantial attention across many domains of science and practice, because it can find intricate patterns in big data; but successful application of the methods requires a big set of labeled data. Active learning, which has the potential to address the data labeling challenge, has already had success in geospatial applications such as trajectory classification from movement data and (geo) text and image classification. This review is intended to be particularly relevant for extension of these methods to GISience, to support work in domains such as geographic information retrieval from text and image repositories, interpretation of spatial language, and related geo-semantics challenges. Specifically, to provide a structure for leveraging recent advances, we group the relevant work into five categories: active learning, visual analytics, active learning with visual analytics, active deep learning, plus GIScience and Remote Sensing (RS) using active learning and active deep learning. Each category is exemplified by recent influential work. Based on this framing and our systematic review of key research, we then discuss some of the main challenges of integrating active learning with visual analytics and deep learning, and point out research opportunities from technical and application perspectives—for application-based opportunities, with emphasis on those that address big data with geospatial components.
External Computations and Interoperability in the New DLV Grounder

In this paper we focus on some of the most recent advancements in I-DLV, the new intelligent grounder of DLV; the system has been endowed with means aimed at easing the interoperability and integration with external systems and accommodating external source of computation and value invention within ASP programs. In particular, we describe here the support for external computations via explicit calls to Python scripts, and tools for the interoperability with both relational and graph databases.
Enhancement of MRI human thigh muscle segmentation by template-based framework

Image segmentation of anatomic structures is often an essential step in medical image analysis. A variety of segmentation methods have been proposed, but none provides automatic segmentation of the thigh. In magnetic resonance images of the thigh, the segmentation is complicated by factors, such as artifacts (e.g. intensity inhomogeneity and echo) and inconsistency of soft and hard tissue compositions, especially in muscle from older people, where accumulation of intermuscular fat is greater than in young muscles. In this paper, the combination framework that leads to a segmentation enhancement method for region of interest segmentation are demonstrated. Appropriate methods of image pre-processing, thresholding, manual interaction of muscle border, template conversion and deformable contours in combination with image filters are applied. Prior geometrical information in an initial template image is used to automatically derive the muscle outlines by application of snake active contours, in serial images within a single MRI dataset. Our approach has an average segmented output accuracy of 93.34% by Jaccard Similarity Index, and reduced the processing time by 97.73% per image compared to manual segmentation.
Freshmen adaptation to university life: depressive symptoms, stress, and coping.

Attending a university for the first time can be a stressful experience for many new college students. This study examines the relationships among femininity and masculinity, depressive symptomatology, levels of stress, and the types of coping strategies used by college freshmen. Results of this study suggest that these variables were related uniquely for first-year college students. Masculinity and femininity significantly predicted problem-focused coping, and femininity significantly predicted emotion-focused coping. Further, the levels of family and college stress reported by college students, as well as their endorsement of avoidant coping, significantly predicted their levels of depressive symptoms. Overall, the results of this study suggest that understanding the relationships among the gender role, the levels of depressive symptomatology, and the levels of stress exhibited by college freshmen may be important in facilitating their transition and adjustment to university life.
Wireless Power Transmission With Self-Regulated Output Voltage for Biomedical Implant

This paper presents a wireless power transfer (WPT) system for powering implantable biomedical devices; the system is configured to achieve high efficiency even with CMOS switches and printed-circuit-board pattern coils and to maintain constant output voltage against coupling and loading variations without any additional blocks. It is shown that the parallel-resonant transmitter (TX) and receiver (RX) topology is advantageous for high efficiency even with lossy but compact components. In addition, the output voltage of the topology is insensitive to coupling and/or loading variations if the operating frequency is automatically adjusted according to coupling variations. A parallel-resonant class-D oscillator TX is developed to track the optimum operating frequency for the constant output voltage. The operating distance for the constant output voltage is also extended using a novel resonator structure, which contains two resonating coils. These proposed schemes allow a compact, efficient, and robust wireless power system. Maximum power of 174 mW can be transmitted with 63% overall efficiency.
Deep Learning with Low Precision by Half-Wave Gaussian Quantization

The problem of quantizing the activations of a deep neural network is considered. An examination of the popular binary quantization approach shows that this consists of approximating a classical non-linearity, the hyperbolic tangent, by two functions: a piecewise constant sign function, which is used in feedforward network computations, and a piecewise linear hard tanh function, used in the backpropagation step during network learning. The problem of approximating the widely used ReLU non-linearity is then considered. An half-wave Gaussian quantizer (HWGQ) is proposed for forward approximation and shown to have efficient implementation, by exploiting the statistics of of network activations and batch normalization operations. To overcome the problem of gradient mismatch, due to the use of different forward and backward approximations, several piece-wise backward approximators are then investigated. The implementation of the resulting quantized network, denoted as HWGQ-Net, is shown to achieve much closer performance to full precision networks, such as AlexNet, ResNet, GoogLeNet and VGG-Net, than previously available low-precision networks, with 1-bit binary weights and 2-bit quantized activations.
Multifacet's general execution-driven multiprocessor simulator (GEMS) toolset

The Wisconsin Multifacet Project has created a simulation toolset to characterize and evaluate the performance of multiprocessor hardware systems commonly used as database and web servers. We leverage an existing full-system functional simulation infrastructure () as the basis around which to build a set of timing simulator modules for modeling the timing of the memory system and microprocessors. This simulator infrastructure enables us to run architectural experiments using a suite of scaled-down commercial workloads . To enable other researchers to more easily perform such research, we have released these timing simulator modules as the Multifacet General Execution-driven Multiprocessor Simulator (GEMS) Toolset, release 1.0, under GNU GPL .
Efficient Algorithms for Convolutional Sparse Representations

When applying sparse representation techniques to images, the standard approach is to independently compute the representations for a set of overlapping image patches. This method performs very well in a variety of applications, but results in a representation that is multi-valued and not optimized with respect to the entire image. An alternative representation structure is provided by a convolutional sparse representation, in which a sparse representation of an entire image is computed by replacing the linear combination of a set of dictionary vectors by the sum of a set of convolutions with dictionary filters. The resulting representation is both single-valued and jointly optimized over the entire image. While this form of a sparse representation has been applied to a variety of problems in signal and image processing and computer vision, the computational expense of the corresponding optimization problems has restricted application to relatively small signals and images. This paper presents new, efficient algorithms that substantially improve on the performance of other recent methods, contributing to the development of this type of representation as a practical tool for a wider range of problems.
A Low-Complexity Full-Duplex Radio Implementation With a Single Antenna

This paper presents a novel low-complexity full-duplex radio design, which only uses a single patch antenna without any duplexer or circulator for passive suppression of self-interference, and a computationally efficient technique for linear digital cancellation. The proposed full-duplex design is tested for IEEE 802.11g Wireless Standard on the WARP (v3) software-defined radio implementation platform. It is shown that this design provides a total suppression of 88 dB, which is sufficient for low-power or short-range full-duplex communication. The dual polarized slot coupled patch antenna used in our design provides an interport isolation as high as 60 dB in 2.4 GHz band. Additionally, the digital domain cancellation utilizes a frequency domain-based estimation and reconstruction approach, which not only offers up to <inline-formula><tex-math notation="LaTeX">$61\%$</tex-math></inline-formula> reduction in the computational complexity but also provides a <inline-formula><tex-math notation="LaTeX">$5-7$ </tex-math></inline-formula> dB better digital cancellation performance in highly selective channel conditions, as compared to the time-domain-based techniques. The proposed full-duplex implementation can be easily applied in OFDM-based wireless systems, such as IEEE 802.11, which is the considered air interface in this paper.
Gaze estimation method based on an aspherical model of the cornea: surface of revolution about the optical axis of the eye

A novel gaze estimation method based on a novel aspherical model of the cornea is proposed in this paper. The model is a surface of revolution about the optical axis of the eye. The calculation method is explained on the basis of the model. A prototype system for estimating the point of gaze (POG) has been developed using this method. The proposed method has been found to be more accurate than the gaze estimation method based on a spherical model of the cornea.
Identifying Appropriate Support for Propositions in Online User Comments

The ability to analyze the adequacy of supporting information is necessary for determining the strength of an  This is especially the case for online user comments, which often consist of arguments lacking proper substantiation and reasoning. Thus, we develop a framework for automatically classifying each proposition as UNVERIFIABLE, VERIFIABLE NONEXPERIENTIAL, or VERIFIABLE EXPERIENTIAL2, where the appropriate type of support is reason, evidence, and optional evidence, respectively3. Once the existing support for propositions are identified, this classification can provide an estimate of how adequately the arguments have been supported. We build a goldstandard dataset of 9,476 sentences and clauses from 1,047 comments submitted to an eRulemaking platform and find that Support Vector Machine (SVM) classifiers trained with n-grams and additional features capturing the verifiability and experientiality exhibit statistically significant improvement over the unigram baseline, achieving a macro-averaged F1 of 68.99%.
Performance characteristics of dipstick and microscopic urinalysis for diagnosis of urinary tract infection

Although the urine culture is used as the reference standard to determine presence or absence of urinary tract infection, the culture is an expensive and time-consuming method. The objectives of the study were to compare dipstick urinalysis with microscopic urinalysis and to compare dipstick and microscopic urinalysis results with urine culture results, by calculating performance characteristics of these tests.  The 250 morning urine specimens were performed by using dipstick and microscopic urinalysis and cultured. Laboratory urinalysis included semi-automated dipstick reading by a Miditron-M Reflectance photometer and microscopic examination.  Thirty-five point six percent (89/250) of patients had urine cultures with 105 colonies/mL or greater. Sensitivity and specificity of microscopic urinalysis were 91% and 68%, whereas in dipstick urinalysis they were 80% and 60%, respectively. Negative predictive values were 84% for urine dipsticks and 93% for microscopic urinalysis. Positive predictive values were 52% and 61% for dipstick and microscopic urinalysis, respectively.  The study has suggested that both urinalysis methods can be used for rapid diagnosis. Urine culture is an expensive test for routine use and should not be applied unless the result of the nitrite, leukocyte or bloods are positive in dipstick or leukocyte, erythrocyte or bacteria are positive in microscopic examination.
An Integrated Dual MIMO Antenna System With Dual-Function GND-Plane Frequency-Agile Antenna

In this letter, an integrated multiple-input–multiple-output (MIMO) antenna system is presented. The proposed design consists of a two-element printed inverted F-shape (IFA) MIMO antenna integrated with a two-element frequency-agile annular slot-based MIMO antenna system. The IFA operates at the WLAN band, while the annular slots act as an isolation enhancement structure between the two IFA elements. Furthermore, the annular slots are tuned over the bands from 1.73 to 2.28 GHz with a minimum bandwidth of 60 MHz. The slots are made reconfigurable using varactor diodes. The integrated MIMO antenna system is designed on commercially available FR-4 substrate with dimensions $\text{50} \times \text{110} \times \text{1.56}\,\text{mm}^{3}$ . All the antenna elements are of small size, low profile, and planar in structure and hence can easily be accommodated in wireless devices for second-generation cognitive radio applications.
The Limitations of Deep Learning in Adversarial Settings

Deep learning takes advantage of large datasets and computationally efficient training algorithms to outperform other approaches at various machine learning tasks. However, imperfections in the training phase of deep neural networks make them vulnerable to adversarial samples: inputs crafted by adversaries with the intent of causing deep neural networks to misclassify. In this work, we formalize the space of adversaries against deep neural networks (DNNs) and introduce a novel class of algorithms to craft adversarial samples based on a precise understanding of the mapping between inputs and outputs of DNNs. In an application to computer vision, we show that our algorithms can reliably produce samples correctly classified by human subjects but misclassified in specific targets by a DNN with a 97% adversarial success rate while only modifying on average 4.02% of the input features per sample. We then evaluate the vulnerability of different sample classes to adversarial perturbations by defining a hardness measure. Finally, we describe preliminary work outlining defenses against adversarial samples by defining a predictive measure of distance between a benign input and a target classification.
COMPARING PROGRAMMER PRODUCTIVITY IN OPENACC AND CUDA: AN EMPIRICAL INVESTIGATION

OpenACC has been touted as a "high productivity" API designed to make GPGPU programming accessible to scientific programmers, but to date, no studies have attempted to verify this quantitatively. In this paper, we conduct an empirical investigation of program productivity comparisons between OpenACC and CUDA in the programming time, the execution time and the analysis of independence of OpenACC model in high performance problems. Our results show that, for our programs and our subject pool, this claim is true. We created two assignments called Machine Problem 3(MP3) and Machine Problem 4(MP4) in the classroom environment and instrumented the WebCode website developed by ourselves to record details of students’ coding process. Three hypotheses were supported by the statistical data: for the same parallelizable problem, (1) the OpenACC programming time is at least 37% shorter than CUDA; (2) the CUDA running speed is 9x faster than OpenACC; (3) the OpenACC development work is not significantly affected by previous CUDA experience
Adaptive Manifold Learning

Manifold learning algorithms seek to find a low-dimensional parameterization of high-dimensional data. They heavily rely on the notion of what can be considered as local, how accurately the manifold can be approximated locally, and, last but not least, how the local structures can be patched together to produce the global parameterization. In this paper, we develop algorithms that address two key issues in manifold learning: 1) the adaptive selection of the local neighborhood sizes when imposing a connectivity structure on the given set of high-dimensional data points and 2) the adaptive bias reduction in the local low-dimensional embedding by accounting for the variations in the curvature of the manifold as well as its interplay with the sampling density of the data set. We demonstrate the effectiveness of our methods for improving the performance of manifold learning algorithms using both synthetic and real-world data sets.
A Comparison of Internet Protocol (IPv6) Security Guidelines

The next generation of the Internet Protocol (IPv6) is currently about to be introduced in many organizations. However, its security features are still a very novel area of expertise for many practitioners. This study evaluates guidelines for secure deployment of IPv6, published by the U.S. NIST and the German federal agency BSI, for topicality, completeness and depth. The later two are scores defined in this paper and are based on the Requests for Comments relevant for IPv6 that were categorized, weighted and ranked for importance using an expert survey. Both guides turn out to be of practical value, but have a specific focus and are directed towards different audiences. Moreover, recommendations for possible improvements are presented. Our results could also support strategic management decisions on security priorities as well as for the choice of security guidelines for IPv6 roll-outs.
Pellet: An OWL DL Reasoner

Reasoning capability is of crucial importance to many applications developed for the Semantic Web.  Logics provide sound and complete reasoning algorithms that can effectively handle the DL fragment of the Web Ontology Language (OWL). However, existing DL reasoners were implemented long before OWL came into existence and lack some features that are essential for Semantic Web applications, such as reasoning with individuals, querying capabilities, nominal support, elimination of the unique name assumption and so forth. With these objectives in mind we have implemented an OWL DL reasoner and deployed it in various kinds of applications.
Epidemic Algorithms for Replicated Database Maintenance

When a database is replicated at many sites, maintaining mutual consistency among the sites in the face of updates is a significant problem. This paper describes several randomized algorithms for distributing updates and driving the replicas toward consistency. The algorithms are very simple and require few guarantees from the underlying communication system, yet they ensure that the effect of every update is eventually reflected in all replicas. The cost and performance of the algorithms are tuned by choosing appropriate distributions in the randomization step. The algorithms are closely analogous to epidemics, and the epidemiology literature aids in understanding their behavior. One of the algorithms has been implemented in the Clearinghouse servers of the Xerox Corporate Internet, solving long-standing problems of high traffic and database inconsistency. An earlier version of this paper appeared in the Proceedings of the Sixth Annual ACM Symposium on Principles of Distributed Computing, Vancouver, August 1987, pages 1-12.
Regional Attention Based Deep Feature for Image Retrieval

Many approaches using Convolutional Neural Network (CNN) for efficient image retrieval have concentrated on feature aggregation rather than feature embedding over recent years, since convolutional features have been found to be reasonably discriminative. Nonetheless, we found that a well-known region-based feature aggregation method, RMAC, for image retrieval is suffered from the background clutter and varying importance of regions. In this work, we tackle these problems with a simple and effective, contextaware regional attention network that weights an attentive score of a region considering global attentiveness. We conduct various experiments on well-known retrieval datasets, and confirm that our method does not only improve the R-MAC baseline significantly, but also present new state-of-the-art results in the category of “pre-trained single-pass”. Furthermore, we show that our method shows higher accuracy improvement combined over prior methods, when combined with the query expansion method. These results are attributed by our novel regional-attention network integrated with R-MAC.