nils-herrmann/clean_scirepeval huggingface dataset:
Operative management of unstable thoracolumbar burst fractures.

Operative management of unstable burst vertebral fractures is challenging and debatable. This study of such cases was conducted at the Aga Khan Hospital, Karachi from January 1998 to April 2003. All surgically managed spine injuries were reviewed from case notes and operative records. Clinical outcome was assessed by Hanover spine score and correction of kyphosis was measured for radiological assessment. The results were analyzed by Wilcoxon sign rank test for two related samples and p-value < 0.05 was considered significant. Ten patients were identified by inclusion criteria. There was statistically significant difference between mean pre-and postoperative Hanover spine score (p=0.008). Likewise, there was significant difference between mean immediate postoperative and final follow-up kyphosis. (p=0.006). Critical assessment of neurologic and structural extent of injury, proper pre-operative planning and surgical expertise can optimize the outcome of patients.
Security and Efficiency Analysis of One Time Password Techniques

As a result of the rapid growth of available services provided via Internet, as well as multiple accounts a person owns, reliable user authentication schemes are mandatory for security purposes. OTP systems have prevailed as the best viable solution for security over sensitive information and pose an interesting field for research. Although, OTP schemes enhance authentication's security through various algorithmic customizations and extensions, certain compromises should be made; especially since excessively tolerable to vulnerability systems tend to have high computational and storage needs. In order to minimize the risk of a non-authenticated user having access to sensitive data, depending on the use, OTP system's architecture differs; as its tolerance towards already known attack methods. In this paper, the most widely accepted and promising OTP schemes are described and evaluated in terms of resistance against security attacks and in terms of computational intensity (performance efficiency). The results showed that there is a correlation between the security level, the computational efficiency and the storage needs of an OTP system.
Educational Data Mining & Students’ Performance Prediction

It is important to study and analyse educational data especially students’ performance. Educational Data Mining (EDM) is the field of study concerned with mining educational data to find out interesting patterns and knowledge in educational organizations. This study is equally concerned with this subject, specifically, the students’ performance. This study explores multiple factors theoretically assumed to affect students’ performance in higher education, and finds a qualitative model which best classifies and predicts the students’ performance based on related personal and social factors. Data Mining; Education; Students; Performance; Patterns
Smooth Neighbors on Teacher Graphs for Semi-Supervised Learning

The recently proposed self-ensembling methods have achieved promising results in deep semi-supervised learning, which penalize inconsistent predictions of unlabeled data under different perturbations. However, they only consider adding perturbations to each single data point, while ignoring the connections between data samples. In this paper, we propose a novel method, called Smooth Neighbors on Teacher Graphs (SNTG). In SNTG, a graph is constructed based on the predictions of the teacher model, i.e., the implicit self-ensemble of models. Then the graph serves as a similarity measure with respect to which the representations of "similar" neighboring points are learned to be smooth on the low-dimensional manifold. We achieve state-of-the-art results on semi-supervised learning benchmarks. The error rates are 9.89%, 3.99% for CIFAR-10 with 4000 labels, SVHN with 500 labels, respectively. In particular, the improvements are significant when the labels are fewer. For the non-augmented MNIST with only 20 labels, the error rate is reduced from previous 4.81% to 1.36%. Our method also shows robustness to noisy labels.
A multiple case study of design pattern decay, grime, and rot in evolving software systems

Software designs decay as systems, uses, and operational environments evolve. Decay can involve the design patterns used to structure a system. Classes that participate in design pattern realizations accumulate grime—non-pattern-related code. Design pattern realizations can also rot, when changes break the structural or functional integrity of a design pattern. Design pattern rot can prevent a pattern realization from fulfilling its responsibilities, and thus represents a fault. Grime buildup does not break the structural integrity of a pattern but can reduce system testability and adaptability. This research examined the extent to which software designs actually decay, rot, and accumulate grime by studying the aging of design patterns in three successful object-oriented systems. We generated UML models from the three implementations and employed a multiple case study methodology to analyze the evolution of the designs. We found no evidence of design pattern rot in these systems. However, we found considerable evidence of pattern decay due to grime. Dependencies between design pattern components increased without regard for pattern intent, reducing pattern modularity, and decreasing testability and adaptability. The study of decay and grime showed that the grime that builds up around design patterns is mostly due to increases in coupling.
Detection of Lung Cancer Using Backpropagation Neural Networks and Genetic Algorithm

Lung Carcinoma is a disease of uncontrolled growth of cancerous cells in the tissues of the lungs. The early detection of lung cancer is the key of its cure. Early diagnosis of the disease saves enormous lives, failing in which may lead to other severe problems causing sudden fatal death. In general, a measure for early stage diagnosis mainly includes X-rays, CT-images, MRI’s, etc. In this system first we would use some techniques that are essential for the task of medical image mining such as Data Preprocessing, Training and testing of samples, Classification using Backpropagation Neural Network which would classify the digital X-ray, CT-images, MRI’s, etc. as normal or abnormal. The normal state is the one that characterizes a healthy patient. The abnormal image will be further considered for the feature analysis. Further for optimized analysis of features Genetic Algorithm will be used that would extract as well as select features on the basis of the fitness of the features extracted. The selected features would be further classified as cancerous or noncancerous for the images classified as abnormal before. Hence this system will help to draw an appropriate decision about a particular patient’s state. BackpopagationNeuralNetworks,Classification, Genetic Algorithm, Lung Cancer, Medical Image Mining.
A hierarchical representation for human action recognition in realistic scenes

BoF statistic-based local space-time features action representation is very popular for human action recognition due to its simplicity. However, the problem of large quantization error and weak semantic representation decrease traditional BoF model’s discriminant ability when applied to human action recognition in realistic scenes. To deal with the problems, we investigate the generalization ability of BoF framework for action representation as well as more effective feature encoding about high-level semantics. Towards this end, we present two-layer hierarchical codebook learning framework for human action classification in realistic scenes. In the first-layer action modelling, superpixel GMM model is developed to filter out noise features in STIP extraction resulted from cluttered background, and class-specific learning strategy is employed on the refined STIP feature space to construct compact and descriptive in-class action codebooks. In the second-layer of action representation, LDA-Km learning algorithm is proposed for feature dimensionality reduction and for acquiring more discriminative inter-class action codebook for classification. We take advantage of hierarchical framework’s representational power and the efficiency of BoF model to boost recognition performance in realistic scenes. In experiments, the performance of our proposed method is evaluated on four benchmark datasets: KTH, YouTube (UCF11), UCF Sports and Hollywood2. Experimental results show that the proposed approach achieves improved recognition accuracy than the baseline method. Comparisons with state-of-the-art works demonstrates the competitive ability both in recognition performance and time complexity.
The relation between 21 st-century skills and digital skills : A systematic literature review

Innovation starts with people, making the human capital within the workforce decisive. In a fastchanging knowledge economy, 21st-century digital skills drive organizations' competitiveness and innovation capacity. Although such skills are seen as crucial, the digital aspect integrated with 21stcentury skills is not yet sufficiently defined. The main objectives of this study were to (1) examine the relation between 21st-century skills and digital skills; and (2) provide a framework of 21st-century digital skills with conceptual dimensions and key operational components aimed at the knowledge worker. A systematic literature review was conducted to synthesize the relevant academic literature concerned with 21st-century digital skills. In total, 1592 different articles were screened from which 75 articles met the predefined inclusion criteria. The results show that 21st-century skills are broader than digital skills e the list of mentioned skills is far more extensive. In addition, in contrast to digital skills, 21st-century skills are not necessarily underpinned by ICT. Furthermore, we identified seven core skills: technical, information management, communication, collaboration, creativity, critical thinking and problem solving. Five contextual skills were also identified: ethical awareness, cultural awareness, flexibility, selfdirection and lifelong learning.
Trisomy 21 causes changes in the circulating proteome indicative of chronic autoinflammation

Trisomy 21 (T21) causes Down syndrome (DS), but the mechanisms by which T21 produces the different disease spectrum observed in people with DS are unknown. We recently identified an activated interferon response associated with T21 in human cells of different origins, consistent with overexpression of the four interferon receptors encoded on chromosome 21, and proposed that DS could be understood partially as an interferonopathy. However, the impact of T21 on systemic signaling cascades in living individuals with DS is undefined. To address this knowledge gap, we employed proteomics approaches to analyze blood samples from 263 individuals, 165 of them with DS, leading to the identification of dozens of proteins that are consistently deregulated by T21. Most prominent among these proteins are numerous factors involved in immune control, the complement cascade, and growth factor signaling. Importantly, people with DS display higher levels of many pro-inflammatory cytokines (e.g. IL-6, MCP-1, IL-22, TNF-α) and pronounced complement consumption, resembling changes seen in type I interferonopathies and other autoinflammatory conditions. Therefore, these results are consistent with the hypothesis that increased interferon signaling caused by T21 leads to chronic immune dysregulation, and justify investigations to define the therapeutic value of immune-modulatory strategies in DS.
Computation of measure-valued solutions for the incompressible Euler equations

We combine the spectral (viscosity) method and ensemble averaging to propose an algorithm that computes admissible measure-valued solutions of the incompressible Euler equations. The resulting approximate young measures are proved to converge (with increasing numerical resolution) to a measure-valued solution. We present numerical experiments demonstrating the robustness and efficiency of the proposed algorithm, as well as the appropriateness of measure-valued solutions as a solution framework for the Euler equations. Furthermore, we report an extensive computational study of the twodimensional vortex sheet, which indicates that the computed measure-valued solution is non-atomic and implies possible non-uniqueness of weak solutions constructed by Delort.
Perception without awareness: perspectives from cognitive psychology

Four basic approaches that have been used to demonstrate perception without awareness are described. Each approach reflects one of two types of experimental logic and one of two possible methods for controlling awareness. The experimental logic has been either to demonstrate a dissociation between a measure of perception with awareness and a measure that is sensitive to perception without awareness or to demonstrate a qualitative difference between the consequences of perception with and without awareness. Awareness has been controlled either by manipulating the stimulus conditions or by instructing observers on how to distribute their attention. The experimental findings based on all four approaches lead to the same conclusion; namely, stimuli are perceived even when observers are unaware of the stimuli. This conclusion is supported by results of studies in which awareness has been assessed with either objective measures of forced-choice discriminations or measures based on verbalizations of subjective conscious experiences. Given this solid empirical support for the concept of perception without awareness, a direction for future research studies is to assess the functions of information perceived without awareness in determining what is perceived with awareness. The available evidence suggests that information perceived without awareness both biases what stimuli are perceived with awareness and influences how stimuli perceived with awareness are consciously experienced.
Optimization of astaxanthin production by Phaffia rhodozyma through factorial design and response surface methodology.

Sequential methodology based on the application of three types of experimental designs was used to optimize the astaxanthin production of the mutant strain 25-2 of Phaffia rhodozyma in shake flask cultures. The first design employed was a factorial design 2(5), where the factors studied were: pH, temperature, percent of inoculum, carbon and nitrogen concentrations, each one at two levels. This design was performed in two medium types: rich YM medium and minimal medium, based on date juice (Yucca medium). With this first design the most important factors were determined (carbon concentration and temperature) that were used in the second experimental strategy: the method of steepest ascent was applied in order to rapidly approach the optimum. Finally, a second-order response surface design was applied using temperature and carbon concentration as factors. The optimal conditions stimulating the highest astaxanthin production were: 19.7 degrees C temperature; 11.25 g l(-1) carbon concentration; 6.0 pH; 5% inoculum and 0.5 g l(-1) nitrogen concentration. Under these conditions the astaxanthin production was 8100 microg l(-1), 92% higher than the production under the initial conditions.
A wideband circularly polarized microstrip array antenna at Ka-band

A circularly polarized microstrip array antenna is proposed for Ka-band satellite applications. The antenna element consists of an L-shaped patch with parasitic circular-ring radiator. A sequentially rotated 2×2 antenna array exhibits a wideband 3-dB axial ratio bandwidth of 20.6% (25.75 GHz - 31.75 GHz) and 2;1-VSWR bandwidth of 24.0% (25.5 GHz - 32.5 GHz). A boresight gain of 10-11.8 dBic is achieved across a frequency range from 26 GHz to 32 GHz. An 8×8 antenna array exhibits a boresight gain of greater than 24 dBic over 27.25 GHz-31.25 GHz.
Towards Zero Bit-Error-Rate Physical Unclonable Function: Mismatch-Based vs. Physical-Based Approaches in Standard CMOS Technology

This paper compares two types of physical unclonable function (PUF) circuits in terms of reliability, mismatch-based PUF vs. physical-based PUF. Most previous PUF circuits utilize device mismatches for generating random responses. Although they have sufficient random features, there is a reliability issue that some portions of bits are changed over time during operation or under noisy environments. To overcome this issue, we previously proposed the differential amplifier PUF (DA-PUF) which improves the reliability by amplifying the small mismatches of the transistors and rejecting the power supply noise through differential operation. In this paper, we first report the experimental results with the fabricated chips in a 0.35 μm CMOS process. The DA-PUF shows 51.30% uniformity, 50.05% uniqueness, and 0.43% maximum BER. For 0% BER, we proposed the physical-based VIA-PUF which is based on the probability of physical connection between the electrical layers. From the experimental results with the fabricated chips in a 0.18 μm CMOS process, we found the VIA-PUF has 51.12% uniformity and 49.64% uniqueness, and 0% BER throughout 1,000-time repeated measurements. Especially, we have no bit change after the stress test at 25 and 125 °C for 96 hours.
RAGE and amyloid-beta peptide neurotoxicity in Alzheimer's disease.

Amyloid-beta peptide is central to the pathology of Alzheimer's disease, because it is neurotoxic--directly by inducing oxidant stress, and indirectly by activating microglia. A specific cell-surface acceptor site that could focus its effects on target cells has been postulated but not identified. Here we present evidence that the 'receptor for advanced glycation end products' (RAGE) is such a receptor, and that it mediates effects of the peptide on neurons and microglia. Increased expressing of RAGE in Alzheimer's disease brain indicates that it is relevant to the pathogenesis of neuronal dysfunction and death.
Left-Center-Right Separated Neural Network for Aspect-based Sentiment Analysis with Rotatory Attention

Deep learning techniques have achieved success in aspect-based sentiment analysis in recent years. However, there are two important issues that still remain to be further studied, i.e., 1) how to efficiently represent the target especially when the target contains multiple words; 2) how to utilize the interaction between target and left/right contexts to capture the most important words in them. In this paper, we propose an approach, called left-centerright separated neural network with rotatory attention (LCR-Rot), to better address the two problems. Our approach has two characteristics: 1) it has three separated LSTMs, i.e., left, center and right LSTMs, corresponding to three parts of a review (left context, target phrase and right context); 2) it has a rotatory attention mechanism which models the relation between target and left/right contexts. The target2context attention is used to capture the most indicative sentiment words in left/right contexts. Subsequently, the context2target attention is used to capture the most important word in the target. This leads to a two-side representation of the target: left-aware target and right-aware target. We compare our approach on three benchmark datasets with ten related methods proposed recently. The results show that our approach significantly outperforms the state-of-the-art techniques.
Adaptive nonlinear hierarchical control of a quad tilt-wing UAV

Position control of a quad tilt-wing UAV via a nonlinear hierarchical adaptive control approach is presented. The hierarchy consists of two levels. In the upper level, a model reference adaptive controller creates virtual control commands so as to make the UAV follow a given desired trajectory. The virtual control inputs are then converted to desired attitude angle references which are fed to the lower level attitude controller. Lower level controller is a nonlinear adaptive controller. The overall controller is developed for the full nonlinear dynamics of the tilt-wing UAV and thus no linearization is required. In addition, since the approach is adaptive, uncertainties in the UAV dynamics can be handled. Performance of the controller is presented via simulation results.
Using deep learning for Digital Soil Mapping

Digital soil mapping has been widely used as a cost-effective method for generating soil maps. However, current DSM data representation rarely incorporate contextual information of the landscape. DSM models are usually calibrated using point observations intersected with spatially corresponding point covariates. Here, we demonstrate the use of the convolutional neural network model that incorporates contextual information surrounding an observation to significantly improve the prediction accuracy over conventional DSM models. We describe a convolutional neural network (CNN) model that takes inputs 5 as images of covariates and explores spatial contextual information by finding non-linear local spatial relationships of neighbouring pixels. Unique features of the proposed model include: input represented as 3D stack of images, data augmentation to reduce overfitting, and simultaneously predicting multiple outputs. Using a soil mapping example in Chile, the CNN model was trained to simultaneously predict soil organic carbon at multiples depths across the country. The results showed the CNN model reduced the error by 30% compared with conventional techniques that only used point information of covariates. In the 10 example of country-wide mapping at 100 m resolution, the neighbourhood size from 3 to 9 pixels is more effective than at a point location and larger neighbourhood sizes. In addition, the CNN model produces less prediction uncertainty and it is able to predict soil carbon at deeper soil layers more accurately. Because the CNN model takes covariate represented as images, it offers a simple and effective framework for future DSM models. Copyright statement.