nils-herrmann/clean_scirepeval huggingface dataset:
Machine Learning for Bioelectromagnetics: Prediction Model using Data of Weak Radiofrequency Radiation Effect on Plants

Plant sensitivity and its bio-effects on non-thermal weak radio-frequency electromagnetic fields (RF-EMF) identifying key parameters that affect plant sensitivity that can change/unchange by using big data analytics and machine learning concepts are quite significant. Despite its benefits, there is no single study that adequately covers machine learning concept in Bioelectromagnetics domain yet. This study aims to demonstrate the usefulness of Machine Learning algorithms for predicting the possible damages of electromagnetic radiations from mobile phones and base station on plants and consequently, develops a prediction model of plant sensitivity to RF-EMF. We used rawdata of plant exposure from our previous review study (extracted data from 45 peer-reviewed scientific publications published between 1996-2016 with 169 experimental case studies carried out in the scientific literature) that predicts the potential effects of RF-EMF on plants. We also used values of six different attributes or parameters for this study: frequency, specific absorption rate (SAR), power flux density, electric field strength, exposure time and plant type (species). The results demonstrated that the adaptation of machine learning algorithms (classification and clustering) to predict 1) what conditions will RF-EMF exposure to a plant of a given species may not produce an effect; 2) what frequency and electric field strength values are safer; and 3) which plant species are affected by RF-EMF. Moreover, this paper also illustrates the development of optimal attribute selection protocol to identify key parameters that are highly significant when designing the in-vitro practical standardized experimental protocols. Our analysis also illustrates that Random Forest classification algorithm outperforms with highest classification accuracy by 95.26% (0.084 error) with only 4% of fluctuation among algorithm measured. The results clearly show that using K-Means clustering algorithm, demonstrated that the Pea, Mungbean and Duckweeds plants are more sensitive to RF-EMF (p ≤ 0.0001). The sample size of reported 169 experimental case studies, perhaps low significant in a statistical sense, nonetheless, this analysis still provides useful insight of exploiting Machine Learning in Bioelectromagnetics domain. As a direct outcome of this research, more efficient RF-EMF exposure prediction tools can be developed to improve the quality of epidemiological studies and the long-term experiments using whole organisms. Machine learning; plants; prediction; mobile phones; base station; radiofrequency electromagnetic fields; RFEMF; plant sensitivity; classification; clustering
Bandwidth enhancement of substrate integrated waveguide (SIW) slot antenna with parasitic dipole

With many advantages of rectangular waveguide and microstrip lines, substrate integrated waveguide (SIW) can be used for design of planar waveguide-like slot antenna. However, the bandwidth of this kind of antenna structure is limited. In this work, a parasitic dipole is introduced and coupled with the SIW radiate slot. The results have indicated that the proposed technique can enhance the bandwidth of the SIW slot antenna significantly. The measured bandwidth of fabricated antenna prototype is about 19%, indicating about 115% bandwidth enhancement than the ridged substrate integrated waveguide (RSIW) slot antenna.
ByLabel: A Boundary Based Semi-Automatic Image Annotation Tool

This paper presents a novel boundary based semiautomatic tool, ByLabel, for accurate image annotation. Given an image, ByLabel first detects its edge features and computes high quality boundary fragments. Current labeling tools require the human to accurately click on numerous boundary points. ByLabel simplifies this to just selecting among the boundary fragment proposals that ByLabel automatically generates. To evaluate the performance of By-Label, 10 volunteers, with no experiences of annotation, labeled both synthetic and real images. Compared to the commonly used tool LabelMe, ByLabel reduces image-clicks and time by 73% and 56% respectively, while improving the accuracy by 73% (from 1.1 pixel average boundary error to 0.3 pixel). The results show that our ByLabel outperforms the state-of-the-art annotation tool in terms of efficiency, accuracy and user experience. The tool is publicly available: http://webdocs.cs.ualberta.ca/~vis/ bylabel/.
Characteristics of Suicide Attempts in Anorexia and Bulimia Nervosa: A Case–Control Study

Compared to other eating disorders, anorexia nervosa (AN) has the highest rates of completed suicide whereas suicide attempt rates are similar or lower than in bulimia nervosa (BN). Attempted suicide is a key predictor of suicide, thus this mismatch is intriguing. We sought to explore whether the clinical characteristics of suicidal acts differ between suicide attempters with AN, BN or without an eating disorders (ED).



Case-control study in a cohort of suicide attempters (n = 1563). Forty-four patients with AN and 71 with BN were compared with 235 non-ED attempters matched for sex, age and education, using interview measures of suicidal intent and severity.



AN patients were more likely to have made a serious attempt (OR = 3.4, 95% CI 1.4-7.9), with a higher expectation of dying (OR = 3.7,95% CI 1.1-13.5), and an increased risk of severity (OR = 3.4,95% CI 1.2-9.6). BN patients did not differ from the control group. Clinical markers of the severity of ED were associated with the seriousness of the attempt.



There are distinct features of suicide attempts in AN. This may explain the higher suicide rates in AN. Higher completed suicide rates in AN may be partially explained by AN patients' higher desire to die and their more severe and lethal attempts.
Constructing a Hybrid Taint Analysis Framework for Diagnosing Attacks on Binary Programs

For the purpose of discovering security flaws in software, many dynamic and static taint analyzing techniques have been proposed. By analyzing information flow at runtime, dynamic taint analysis can precisely find security flaws of software. However, on one hand, it suffers from substantial runtime overhead and is incapable of discovering the potential threats. On the other hand, static taint analysis analyzes program’s code without actually executing it which incurs no runtime overhead, and can cover all the code, but it is often not accurate enough. In addition, since the source code of most software is hard to acquire and intruders simply do not attach target program’s source code in practice, software flaw tracking becomes rather complicated. In order to cope with these issues, this paper proposes HYBit, a novel hybrid framework which integrates dynamic and static taint analysis to diagnose the flaws or vulnerabilities for binary programs. In the framework, the source binary is first analyzed by the dynamic taint analyzer. Then, with the runtime information provided by its dynamic counterpart, the static taint analyzer can process the unexecuted part of the target program easily. Furthermore, a taint behavior filtration mechanism is proposed to optimize the performance of the framework. We evaluate our framework from three perspectives: efficiency, coverage, and effectiveness. The results are encouraging.
Correlation Based Classification of Complex PRI Modulation Types

Classification of pulse repetition interval (PRI) modulation type is an important task of electronic warfare systems. Correct classification of the PRI modulation of an emitter can lead to significant knowledge about the observed emitter and can facilitate an electronic warfare system with applying its functions. Several methods have been proposed in the literature for identifying standard PRI modulation types (random, stable, jittered, dwell \& switch, sliding, wobulated). The last two types, i.e. sliding and wobulated, are also called complex and the classification of their subtypes, i.e. sawtooth, triangle, sine, and saturated sine, remains unaddressed. This paper addresses the classification of complex PRI modulation subtypes. The proposed algorithm evaluates the correlation between a received signal and several ideal complex PRI modulated signals in order to detect such complex modulations. The performance of the proposed classification algorithm is demonstrated using simulated signals.
DropMax: Adaptive Stochastic Softmax

We propose DropMax, a stochastic version of softmax classifier which at each iteration drops non-target classes with some probability, for each instance. Specifically, we overlay binary masking variables over class output probabilities, which are learned based on the input via regularized variational inference. This stochastic regularization has an effect of building an ensemble classifier out of exponential number of classifiers with different decision boundaries. Moreover, the learning of dropout probabilities for non-target classes on each instance allows the classifier to focus more on classification against the most confusing classes. We validate our model on multiple public datasets for classification, on which it obtains improved accuracy over regular softmax classifier and other baselines. Further analysis of the learned dropout masks shows that our model indeed selects confusing classes more often when it performs classification.
Neural computation at the thermal limit

Although several measurements and analyses support the idea that the brain is energy-optimized, there is one disturbing, contradictory observation: In theory, computation limited by thermal noise can occur as cheaply as ~$2.9\cdot 10^{-21}$ joules per bit (kTln2). Unfortunately, for a neuron the ostensible discrepancy from this minimum is startling - ignoring inhibition the discrepancy is $10^7$ times this amount and taking inhibition into account $>10^9$. Here we point out that what has been defined as neural computation is actually a combination of computation and neural communication: the communication costs, transmission from each excitatory postsynaptic activation to the S4-gating-charges of the fast Na+ channels of the initial segment (fNa's), dominate the joule-costs. Making this distinction between communication to the initial segment and computation at the initial segment (i.e., adding up of the activated fNa's) implies that the size of the average synaptic event reaching the fNa's is the size of the standard deviation of the thermal noise. Moreover, defining computation as the addition of activated fNa's, yields a biophysically plausible mechanism for approaching the desired minimum. This mechanism, requiring something like the electrical engineer's equalizer (not much more than the action potential generating conductances), only operates at threshold. This active filter modifies the last few synaptic excitations, providing barely enough energy to allow the last sub-threshold gating charge to transport. That is, the last, threshold-achieving S4-subunit activation requires an energy that matches the information being provided by the last few synaptic events, a ratio that is near kTln2 joules per bit.
An Efficient High-Frequency Drive Circuit for GaN Power HFETs

The requirements for driving gallium nitride (GaN) heterostructure field-effect transistors (HFETs) and the design of a resonant drive circuit for GaN power HFET switches are discussed in this paper. The use of wideband III-nitride (such as GaN) devices today is limited to telecom and low-power applications. The current lack of high-frequency high-power drivers prevents their application in power converters. The proposed circuit is based upon resonant switching transition techniques, by means of an LC tag, to recover part of the power back into the voltage source in order to reduce the power loss. This circuit also uses level shifters to generate the zero and negative gate-source voltages required to turn the GaN HFET on and off, and it is highly tolerant to input-signal timing variances. The circuit reduces the overall power consumed in the driver and thus reduces the power loss. This is particularly important for high-frequency driver operation to take full advantage, in terms of efficiency, of the superior switching speed of GaN devices. In this paper, the topology of the low-power-loss high-speed drive circuit is introduced. Some simulation results and preliminary experimental measurements are discussed.
A Kalman Filtering and Nonlinear Penalty Regression Approach for Noninvasive Anemia Detection with Palpebral Conjunctiva Images

Noninvasive medical procedures are usually preferable to their invasive counterparts in the medical community. Anemia examining through the palpebral conjunctiva is a convenient noninvasive procedure. The procedure can be automated to reduce the medical cost. We propose an anemia examining approach by using a Kalman filter (KF) and a regression method. The traditional KF is often used in time-dependent applications. Here, we modified the traditional KF for the time-independent data in medical applications. We simply compute the mean value of the red component of the palpebral conjunctiva image as our recognition feature and use a penalty regression algorithm to find a nonlinear curve that best fits the data of feature values and the corresponding levels of hemoglobin (Hb) concentration. To evaluate the proposed approach and several relevant approaches, we propose a risk evaluation scheme, where the entire Hb spectrum is divided into high-risk, low-risk, and doubtful intervals for anemia. The doubtful interval contains the Hb threshold, say 11 g/dL, separating anemia and nonanemia. A suspect sample is the sample falling in the doubtful interval. For the anemia screening purpose, we would like to have as less suspect samples as possible. The experimental results show that the modified KF reduces the number of suspect samples significantly for all the approaches considered here.
Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches

Stochastic neural net weights are used in a variety of contexts, including regularization, Bayesian neural nets, exploration in reinforcement learning, and evolution strategies. Unfortunately, due to the large number of weights, all the examples in a mini-batch typically share the same weight perturbation, thereby limiting the variance reduction effect of large mini-batches. We introduce flipout, an efficient method for decorrelating the gradients within a mini-batch by implicitly sampling pseudo-independent weight perturbations for each example. Empirically, flipout achieves the ideal linear variance reduction for fully connected networks, convolutional networks, and RNNs. We find significant speedups in training neural networks with multiplicative Gaussian perturbations. We show that flipout is effective at regularizing LSTMs, and outperforms previous methods. Flipout also enables us to vectorize evolution strategies: in our experiments, a single GPU with flipout can handle the same throughput as at least 40 CPU cores using existing methods, equivalent to a factor-of-4 cost reduction on Amazon Web Services.
The Planning and Development of Green Public Places in Urban South Africa : A Child-Friendly Approach

The impact that urban green spaces have on sustainability and quality of life is phenomenal. This is also true for the local South African environment. However, in reality green spaces in urban environments are decreasing due to growing populations, increasing urbanization and development pressure. This further impacts on the provision of child-friendly spaces, a concept that is already limited in local context. Child-friendly spaces are described as environments in which people (children) feel intimately connected to, influencing the physical, social, emotional, and ecological health of individuals and communities. The benefits of providing such spaces for the youth are well documented in literature. This research therefore aimed to investigate the concept of childfriendly spaces and its applicability to the South African planning context, in order to guide the planning of such spaces for future communities and use. Child-friendly spaces in the urban environment of the city of Durban, was used as local case study, along with two international case studies namely Mullerpier public playground in Rotterdam, the Netherlands, and Kadidjiny Park in Melville, Australia. The aim was to determine how these spaces were planned and developed and to identify tools that were used to accomplish the goal of providing successful child-friendly green spaces within urban areas. The need and significance of planning for such spaces was portrayed within the international case studies. It is confirmed that minimal provision is made for green space planning within the South African context, when there is reflected on the international examples. As a result international examples and disciples of providing child-friendly green spaces should direct planning guidelines within local context. The research concluded that childfriendly green spaces have a positive impact on the urban environment and assist in a child’s development and interaction with the natural environment. Regrettably, the planning of these childfriendly spaces is not given priority within current spatial plans, despite the proven benefits of such. Built environment, child-friendly spaces, green spaces. public places, urban area. E. J. Cilliers is a Professor at the North West University, Unit for Environmental Sciences and Management, Urban and Regional Planning, Potchestroom, 2531, South Africa (e-mail: juanee.cilliers@nwu.ac.za). Z. Goosen is a PhD student with the North West University, Unit for Environmental Sciences and Management, Urban and Regional Planning, Potchestroom, 2531, South Africa (e-mail: goosenzhangoosen@gmail.com). This research (or parts thereof) was made possible by the financial contribution of the NRF (National Research Foundation) South Africa. The opinions, findings and conclusions or recommendations expressed in this material are those of the authors and therefore the NRF does not accept any liability in regard thereto.
Sigsoftmax: Reanalysis of the Softmax Bottleneck

Softmax is an output activation function for modeling categorical probability distributions in many applications of deep learning. However, a recent study revealed that softmax can be a bottleneck of representational capacity of neural networks in language modeling (the softmax bottleneck). In this paper, we propose an output activation function for breaking the softmax bottleneck without additional parameters. We re-analyze the softmax bottleneck from the perspective of the output set of log-softmax and identify the cause of the softmax bottleneck. On the basis of this analysis, we propose sigsoftmax, which is composed of a multiplication of an exponential function and sigmoid function. Sigsoftmax can break the softmax bottleneck. The experiments on language modeling demonstrate that sigsoftmax and mixture of sigsoftmax outperform softmax and mixture of softmax, respectively.
Compact atomic magnetometer for global navigation (NAV-CAM)

Northrop Grumman is developing an atom-based magnetometer technology that has the potential for providing a global position reference independent of GPS. The NAV-CAM sensor is a direct outgrowth of the Nuclear Magnetic Resonance Gyro under development by the same technical team. It is capable of providing simultaneous measurements of all 3 orthogonal axes of magnetic vector field components using a single compact vapor cell. The vector sum determination of the whole-field scalar measurement achieves similar precision to the individual vector components. By using a single sensitive element (vapor cell) this approach eliminates many of the problems encountered when using physically separate sensors or sensing elements.
A general framework for error analysis in measurement-based GIS

This paper is Part 2 of a four-part series of our research on the development of a general framework for error analysis in measurement-based geographic information systems (MBGIS). In this paper, we discuss the problem of point-in-polygon analysis under randomness, i.e., with random measurement error (ME). It is well known that overlay is one of the most important operations in GIS, and point-in-polygon analysis is a basic class of overlay and query problems. Though it is a classic problem, it has, however, not been addressed appropriately. With ME in the location of the vertices of a polygon, the resulting random polygons may undergo complex changes, so that the point-in-polygon problem may become theoretically and practically ill-defined. That is, there is a possibility that we cannot answer whether a random point is inside a random polygon if the polygon is not simple and cannot form a region. For the point-in-triangle problem, however, such a case need not be considered since any triangle can always forms its an interior or region. To formulate the general point-in-polygon problem in a suitable way, a conditional probability mechanism is first introduced in order to accurately characterize the nature of the problem and establish the basis for further analysis. For the point-in-triangle problem, four quadratic forms in the joint coordinate vectors of a point and the vertices of the triangle are constructed. The probability model for the point-in-triangle problem is then established by the identification of signs of these quadratic form variables. Our basic idea for solving a general point-in-polygon (concave or convex) problem is to convert it into several point-in-triangle A general framework for error analysis in measurement-based GIS ------Part 2 2 problems under a certain condition. By solving each point-in-triangle problem and summing the solutionsm up, the probability model for a general point-in-polygon analysis is constructed. The simplicity of the algebra-based approach is that from using these quadratic forms, we can circumvent the complex geometrical relations between a random point and a random polygon (convex or concave) that one has to deal with in any geometric methods when the probability is computed. The theoretical arguments are substantiated by simulation experiments.
Students' perceptions on game-based classroom response system in a computer programming course

The use of game-based learning in the classroom has turned out to be a trend nowadays. Most game-based learning tools and platforms are based on a quiz concept where the students can score points if they can choose the correct answer among multiple answers. Based on our experience in Faculty of Electrical Engineering, Universiti Teknologi MARA, most undergraduate students have difficulty to appreciate the Computer Programming course thus demotivating them in learning any programming related courses. Game-based learning approach using a Game-based Classroom Response System (GCRS) tool known as Kahoot is used to address this issue. This paper presents students' perceptions on Kahoot activity that they experienced in the classroom. The study was carried out by distributing a survey form to 120 students. Based on the feedback, majority of students enjoyed the activity and able to attract their interest in computer programming.