nils-herrmann/clean_scirepeval huggingface dataset:
Human actions recognition based on 3D deep neural network

In this paper, a new proposed model has been used to recognize human actions from video frames using a 3D deep neural network (3D DNN). To classify human actions, our recognition process is implemented under different recording conditions from a surveillance camera. By applying Caffe_GoogLeNet framework, we trained our 3D DNN with different training epoch values (TEs). The experiments were then evaluated using three different datasets: KTH, Weizmann, and UCF101 with gray and color resolutions. The results of the experiments demonstrate significantly high performance in the recognition rates by changing the training epoch values (TEs) to accomplish the best classification accuracy with a remarkable short running time. We then compare the classification accuracy results of 3D DNN with other state-of-the-art for three datasets. Classification accuracy resulting is 98.90% for KTH dataset, while Weizmann dataset had an accuracy of 97.02 %. The accuracy of UCF 101 dataset reached to 100% which was the optimum state in our model.
Work system design for patient safety: the SEIPS model.

Models and methods of work system design need to be developed and implemented to advance research in and design for patient safety. In this paper we describe how the Systems Engineering Initiative for Patient Safety (SEIPS) model of work system and patient safety, which provides a framework for understanding the structures, processes and outcomes in health care and their relationships, can be used toward these ends. An application of the SEIPS model in one particular care setting (outpatient surgery) is presented and other practical and research applications of the model are described.
Damage to the superior gluteal nerve after the Hardinge approach to the hip.

We studied prospectively 81 consecutive patients undergoing hip surgery using the Hardinge (1982) approach. The abductor muscles of the hip in these patients were assessed electrophysiologically and clinically by the modified Trendelenburg test. Power was measured using a force plate. We performed assessment at two weeks, and at three and nine months after operation. At two weeks we found that 19 patients (23%) showed evidence of damage to the superior gluteal nerve. By three months, five of these had recovered. The nine patients with complete denervation at three months showed no signs of recovery when reassessed at nine months. Persistent damage to the nerve was associated with a positive Trendelenburg test.
A unified framework for automatic wound segmentation and analysis with deep convolutional neural networks

Wound surface area changes over multiple weeks are highly predictive of the wound healing process. Furthermore, the quality and quantity of the tissue in the wound bed also offer important prognostic information. Unfortunately, accurate measurements of wound surface area changes are out of reach in the busy wound practice setting. Currently, clinicians estimate wound size by estimating wound width and length using a scalpel after wound treatment, which is highly inaccurate. To address this problem, we propose an integrated system to automatically segment wound regions and analyze wound conditions in wound images. Different from previous segmentation techniques which rely on handcrafted features or unsupervised approaches, our proposed deep learning method jointly learns task-relevant visual features and performs wound segmentation. Moreover, learned features are applied to further analysis of wounds in two ways: infection detection and healing progress prediction. To the best of our knowledge, this is the first attempt to automate long-term predictions of general wound healing progress. Our method is computationally efficient and takes less than 5 seconds per wound image (480 by 640 pixels) on a typical laptop computer. Our evaluations on a large-scale wound database demonstrate the effectiveness and reliability of the proposed system.
Very High Resolution Spaceborne SAR Tomography in Urban Environment

Synthetic aperture radar tomography (TomoSAR) extends the synthetic aperture principle into the elevation direction for 3-D imaging. It uses stacks of several acquisitions from slightly different viewing angles (the elevation aperture) to reconstruct the reflectivity function along the elevation direction by means of spectral analysis for every azimuth-range pixel. The new class of meter-resolution spaceborne SAR systems (TerraSAR-X and COSMO-Skymed) offers a tremendous improvement in tomographic reconstruction of urban areas and man-made infrastructure. The high resolution fits well to the inherent scale of buildings (floor height, distance of windows, etc.). This paper demonstrates the tomographic potential of these SARs and the achievable quality on the basis of TerraSAR-X spotlight data of urban environment. A new Wiener-type regularization to the singular-value decomposition method-equivalent to a maximum a posteriori estimator-for TomoSAR is introduced and is extended to the differential case (4-D, i.e., space-time). Different model selection schemes for the estimation of the number of scatterers in a resolution cell are compared and proven to be applicable in practice. Two parametric estimation algorithms of the scatterers' elevation and their velocities are evaluated. First 3-D and 4-D reconstructions of an entire building complex (including its radar reflectivity) with very high level of detail from spaceborne SAR data by pixelwise TomoSAR are presented.
Visiting theories that predict college students' self-disclosure on Facebook

This study explores factors that may explain information disclosure behavior on Facebook and provides understanding of each factor’s contribution in explaining such behavior. Factors tested in this study are drawn from theories (e.g., social contract theory and uses and gratification theory) and constructs (e.g., trust/self-disclosure relationships, time spent on Facebook, number of Facebook friends, and gender difference). Findings suggest the potential of all the factors examined in this study as frameworks to explain self-disclosure behavior on Facebook. This social media-specific study offers evidence that these theories may have implications that are different from the current e-commerce literature on self-disclosure. Both theoretical and practical implications are discussed.
NFV-based QoS provision for Software Defined Optical Access and residential networks

The promises of SDN and NFV technologies to boost innovation and to reduce the time-to-market of new services is changing the way in which residential networks will be deployed, managed and maintained in the near future. New user-centric management models for residential networks combining SDN-based residential gateways and cloud technologies have already been proposed, providing flexibility and ease of deployment. Extending the scope of SDN technologies to optical access networks and bringing cloud technologies to the edge of the network enable the creation of advanced residential networks in which complex service function chains can be established to provide traffic differentiation. In this context, this paper defines a novel network management model based on a user-centric approach that allows residential users to define and control access network resources and the dynamic provision of traffic differentiation to fulfill QoS requirements.
Heart Failure: Diagnosis, Severity Estimation and Prediction of Adverse Events Through Machine Learning Techniques

Heart failure is a serious condition with high prevalence (about 2% in the adult population in developed countries, and more than 8% in patients older than 75 years). About 3-5% of hospital admissions are linked with heart failure incidents. Heart failure is the first cause of admission by healthcare professionals in their clinical practice. The costs are very high, reaching up to 2% of the total health costs in the developed countries. Building an effective disease management strategy requires analysis of large amount of data, early detection of the disease, assessment of the severity and early prediction of adverse events. This will inhibit the progression of the disease, will improve the quality of life of the patients and will reduce the associated medical costs. Toward this direction machine learning techniques have been employed. The aim of this paper is to present the state-of-the-art of the machine learning methodologies applied for the assessment of heart failure. More specifically, models predicting the presence, estimating the subtype, assessing the severity of heart failure and predicting the presence of adverse events, such as destabilizations, re-hospitalizations, and mortality are presented. According to the authors' knowledge, it is the first time that such a comprehensive review, focusing on all aspects of the management of heart failure, is presented.
Measuring semantic similarity between Gene Ontology terms

Many bioinformatics applications would benefit from comparing proteins based on their biological role rather than their sequence. This manuscript adds two new contributions. First, a study of the correlation between Gene Ontology (GO) terms and family similarity demonstrates that protein families constitute an appropriate baseline for validating GO similarity. Secondly, we introduce GraSM, a novel method that uses all the information in the graph structure of the Gene Ontology, instead of considering it as a hierarchical tree. GraSM gives a consistently higher family similarity correlation on all aspects of GO than the original semantic similarity measures.
Face Recognition Using Gabor Filter And Convolutional Neural Network

Face recognition involves the extraction of different features of human face and its classification for discriminating it from other persons. Face recognition is quite challenging task because faces are complex, multidimensional visual stimuli and also face recognition rate depends on variations in pose, expression, occlusion, resolution, and illumination. Most of the existing face recognition algorithms give poor performance in the presence of high degree variations in human face images. Hence we propose an approach based on deep learning which uses Gabor filter of feature extraction and Convolution Neural Network for classification in order to improve the performance of face recognition with mentioned variations. The experiments conducted on AT & T database and we attained the efficiency of 89.50%.It is seen that 2.5% improvement in the efficiency as reported in literature. Future work is dedicated to evaluate the performance of proposed algorithm on different dataset with varying illumination and its classification.
Arabic Semantic Web Applications – A Survey

Arabic Language is the mother tongue for 23 countries and more than 350 million persons. Moreover, since it is the language of the Holy Quran, many other Islamic countries, like Pakistan, teach Arabic as a second language. Nevertheless, it is noticed that the Arabic content on the web  is  less than what should be expected. The evolution of the semantic web (SW) added a new dimension to this problem. This paper is an attempt to figure out the problem, its causes, and to open avenues to think about the solutions. The survey presented in this paper is concerned with the SW applications regarding the Arabic Language in the domains of Ontology building and using, Arabic WordNet (AWN) exploiting and enrichment, Arabic Named Entities extraction, Holy Quran and Islamic Knowledge semantically representation, and, Arabic Semantic Search Engines. In fact, the study revealed significant deficiencies in the Arabic Language semantically treatment in many aspects. To mention few, most of  the available  tools don't  support Arabic  text. Moreover, very  few  resources are  freely available. Hence,  it  is inevitable to put the Arabic Language  in the category of the  languages which machine can understand  its meaning not  just
Normalizing the Shadows - The Role of Symbolic Models for Individuals' Shadow IT Usage

Employees increasingly use cloud services of third party providers to efficiently perform their job. Interestingly, the majority adopts the cloud without approval of the organization. IT resources like that are commonly referred to as shadow IT. Although widespread in practice, the present literature on shadow IT is scarce lacking a clear definition of the phenomenon and exclusively focusing on the organizational perspective. This research addresses the issue at the individual level. We define shadow IT usage as voluntary usage of any IT resource violating injunctive IT norms at work as reaction to perceived situational constraints with the intent to enhance work performance, but not to harm the organization. Building furthermore on theories of organizational frustration, social learning, and norm salience, we develop a research model of individuals’ shadow IT usage to analyze the inhibiting effect of explicit IT usage restrictions on users’ deviant behaviors in a future laboratory experiment.
Comorbid depression and anxiety spectrum disorders.

The relationship between depression and anxiety disorders has long been a matter of controversy. The overlap of symptoms associated with these disorders makes diagnosis, research, and treatment particularly difficult. Recent evidence suggests genetic and neurobiologic similarities between depressive and anxiety disorders. Comorbid depression and anxiety are highly prevalent conditions. Patients with panic disorder, generalized anxiety disorder, social phobia, and other anxiety disorders are also frequently clinically depressed. Approximately 85% of patients with depression also experience significant symptoms of anxiety. Similarly, comorbid depression occurs in up to 90% of patients with anxiety disorders. Patients with comorbid disorders do not respond as well to therapy, have a more protracted course of illness, and experience less positive treatment outcomes. One key to successful treatment of patients with mixed depressive and anxiety disorders is early recognition of comorbid conditions. Antidepressant medications, including the selective serotonin reuptake inhibitors, tricyclic antidepressants, and monoamine oxidase inhibitors, are highly effective in the management of comorbid depression and anxiety. The high rates of comorbid depression and anxiety argue for well-designed treatment studies in these populations.
Machine Learning-Based Software Quality Prediction Models: State of the Art

Quantification of parameters affecting the software quality is one of the important aspects of research in the field of software engineering. In this paper, we present a comprehensive literature survey of prominent quality molding studies. The survey addresses two views: (1) quantification of parameters affecting the software quality; and (2) using machine learning techniques in predicting the software quality. The paper concludes that, model transparency is a common shortcoming to all the surveyed studies.
Energy Minimization Using Multiple Supply Voltages

We present a dynamic programming technique for solving the multiple supply voltage scheduling problem in both non-pipelined and functionally pipelined data-paths. The scheduling problem refers to the assignment of a supply voltage level to each operation in a data ow graph so as to minimize the average energy consumption for given computation time or throughput constraints or both. The energy model is accurate and accounts for the input pattern dependencies, re-convergent fanout induced dependencies, and the energy cost of level shifters. Experimental results show that using four supply voltage levels on a number of standard benchmarks, an average energy saving of 53% (with a computation time constraint of 1.5 times the critical path delay) can be obtained compared to using one fixed supply voltage level.
RF-MEMS Switches for Reconfigurable Integrated Circuits

This paper deals with a relatively new area of radio-frequency (RF) technology based on microelectromechanical systems (MEMS). RF MEMS provides a class of new devices and components which display superior high-frequency performance relative to conventional (usually semiconductor) devices, and which enable new system capabilities. In addition, MEMS devices are designed and fabricated by techniques similar to those of very large-scale integration, and can be manufactured by traditional batch-processing methods. In this paper, the only device addressed is the electrostatic microswitch—perhaps the paradigm RF-MEMS device. Through its superior performance characteristics, the microswitch is being developed in a number of existing circuits and systems, including radio front-ends, capacitor banks, and time-delay networks. The superior performance combined with ultra-low-power dissipation and large-scale integration should enable new system functionality as well. Two possibilities addressed here are quasi-optical beam steering and electrically reconfigurable antennas.
Time-Frequency Masking in the Complex Domain for Speech Dereverberation and Denoising

In real-world situations, speech is masked by both background noise and reverberation, which negatively affect perceptual quality and intelligibility. In this paper, we address monaural speech separation in reverberant and noisy environments. We perform dereverberation and denoising using supervised learning with a deep neural network. Specifically, we enhance the magnitude and phase by performing separation with an estimate of the complex ideal ratio mask. We define the complex ideal ratio mask so that direct speech results after the mask is applied to reverberant and noisy speech. Our approach is evaluated using simulated and real room impulse responses, and with background noises. The proposed approach improves objective speech quality and intelligibility significantly. Evaluations and comparisons show that it outperforms related methods in many reverberant and noisy environments.
Basic aspects of food preservation by hurdle technology.

Hurdle technology is used in industrialized as well as in developing countries for the gentle but effective preservation of foods. Previously hurdle technology, i.e., a combination of preservation methods, was used empirically without much knowledge of the governing principles. Since about 20 years the intelligent application of hurdle technology became more prevalent, because the principles of major preservative factors for foods (e.g., temperature, pH, a(w), Eh, competitive flora), and their interactions, became better known. Recently, the influence of food preservation methods on the physiology and behaviour of microorganisms in foods, i.e. their homeostasis, metabolic exhaustion, stress reactions, are taken into account, and the novel concept of multitarget food preservation emerged. In the present contribution a brief introduction is given on the potential hurdles for foods, the hurdle effect, and the hurdle technology. However, emphasis is placed on the homeostasis, metabolic exhaustion, and stress reactions of microorganisms related to hurdle technology, and the prospects of the future goal of a multitarget preservation of foods.
DataSite: Proactive visual data exploration with computation of insight-based recommendations

Effective data analysis ideally requires the analyst to have high expertise as well as high knowledge of the data. Even with such familiarity, manually pursuing all potential hypotheses and exploring all possible views is impractical. We present DataSite, a proactive visual analytics system where the burden of selecting and executing appropriate computations is shared by an automatic server-side computation engine. Salient features identified by these automatic background processes are surfaced as notifications in a feed timeline. DataSite effectively turns data analysis into a conversation between analyst and computer, thereby reducing the cognitive load and domain knowledge requirements. We validate the system with a user study comparing it to a recent visualization recommendation system, yielding significant improvement, particularly for complex analyses that existing analytics systems do not support well.
Author Profiling with Doc2vec Neural Network-Based Document Embeddings

To determine author demographics of texts in social media such as Twitter, blogs, and reviews, we use doc2vec document embeddings to train a logistic regression classifier. We experimented with age and gender identification on the PAN author profiling 2014–2016 corpora under both singleand cross-genre conditions. We show that under certain settings the neural network-based features outperform the traditional features when using the same classifier. Our method outperforms existing state of the art under some settings, though the current stateof-the-art results on those tasks have been quite weak.
Motives for using Facebook, patterns of Facebook activities, and late adolescents' social adjustment to college.

Previous studies have confirmed that Facebook, the leading social networking site among young people, facilitates social connections among college students, but the specific activities and motives that foster social adjustment remain unclear. This study examined associations between patterns of Facebook activity, motives for using Facebook, and late adolescents' social adjustment to the college environment. Anonymous self-report survey data from 193 mostly European American students (M age = 20.32; 54 % female) attending a major Midwestern university indicated that motives and activity patterns were associated directly with social adjustment, but the association between one activity, status updating, and social adjustment also was moderated by the motive of relationship maintenance. Findings provide a more comprehensive portrait of how Facebook use may foster or inhibit social adjustment in college.
PhoneyC: A Virtual Client Honeypot

The number of client-side attacks has grown significantly in the past few years, shifting focus away from defendable positions to a broad, poorly defended space filled with vulnerable clients. Just as honeypots enabled deep research into server-side attacks, honeyclients can permit the deep study of client-side attacks. A complement to honeypots, a honeyclient is a tool designed to mimic the behavior of a userdriven network client application, such as a web browser, and be exploited by an attacker’s content. These systems are instrumented to discover what happened and how.  By using dynamic analysis, PhoneyC is able to remove the obfuscation from many malicious pages. Furthermore, PhoneyC emulates specific vulnerabilities to pinpoint the attack vector. PhoneyC is a modular framework that enables the study of malicious HTTP pages and understands modern vulnerabilities and attacker techniques.