nils-herrmann/clean_scirepeval huggingface dataset:
BUILDING EXTRACTION FROM REMOTE SENSING DATA USING FULLY CONVOLUTIONAL NETWORKS

Building detection and footprint extraction are highly demanded for many remote sensing applications. Though most previous works have shown promising results, the automatic extraction of building footprints still remains a nontrivial topic, especially in complex urban areas. Recently developed extensions of the CNN framework made it possible to perform dense pixel-wise classification of input images. Based on these abilities we propose a methodology, which automatically generates a full resolution binary building mask out of a Digital Surface Model (DSM) using a Fully Convolution Network (FCN) architecture. The advantage of using the depth information is that it provides geometrical silhouettes and allows a better separation of buildings from background as well as through its invariance to illumination and color variations. The proposed framework has mainly two steps. Firstly, the FCN is trained on a large set of patches consisting of normalized DSM (nDSM) as inputs and available ground truth building mask as target outputs. Secondly, the generated predictions from FCN are viewed as unary terms for a Fully connected Conditional Random Fields (FCRF), which enables us to create a final binary building mask. A series of experiments demonstrate that our methodology is able to extract accurate building footprints which are close to the buildings original shapes to a high degree. The quantitative and qualitative analysis show the significant improvements of the results in contrast to the multy-layer fully connected network from our previous work.
Well-being and problematic Facebook use

Social networking sites are getting more widespread online communication forms all over the world. Especially adolescents and emerging adults use these sites to make connection, and share something. Although, Facebook is one of the most popular sites, research is still in nascent phase on the excessive use of Facebook. Hence, it is fundamental to determine potential predictors of overuse of Facebook. The present study investigated the relationship between well-being and problematic Facebook use. Participants were 311 university students [179(58%) female, 132(42%) male, Mage = 20.86 years, SD = 1.61] who completed the questionnaire packet including the Bergen Facebook Addiction Scale (Andreassen, Torsheim, Brunborg, & Pallesen, 2012), the Satisfaction with Life Scale (Diener, Emmons, Larsen, & Griffin, 1985), the Subjective Vitality Scale (Ryan & Frederick, 1997), the Flourishing Scale (Diener et al., 2010), and the Subjective Happiness Scale (Lyubomirsky & Lepper, 1999). A stepwise regression analyses was used with four independent variables (life satisfaction, subjective vitality, flourishing, and subjective happiness) to explain variance in problematic Facebook use. The results showed that life satisfaction, subjective vitality, flourishing, and subjective happiness were significant negative predictors of problematic Facebook use. The significance and limitations of the results are discussed.
Heart Disease Prediction System Using Data Mining and Hybrid Intelligent Techniques: A Review

Heart disease is one of the main sources of demise around the world and it is imperative to predict the disease at a premature phase. The computer aided systems help the doctor as a tool for predicting and diagnosing heart disease. The objective of this review is to widespread about Heart related cardiovascular disease and to brief about existing decision support systems for the prediction and diagnosis of heart disease supported by data mining and hybrid intelligent techniques .
Extended hierarchical extreme learning machine with multilayer perceptron

For learning in big datasets, the classification performance of ELM might be low due to input samples are not extracted features properly. To address this problem, the hierarchical extreme learning machine (H-ELM) framework was proposed based on the hierarchical learning architecture of multilayer perceptron. H-ELM composes of two parts; the first is the unsupervised multilayer encoding part and the second part is the supervised feature classification part. H-ELM can give higher accuracy rate than of the traditional ELM. However, it still has to enhance its classification performance. Therefore, this paper proposes a new method namely as the extending hierarchical extreme learning machine (EH-ELM). For the extended supervisor part of EH-ELM, we have got an idea from the two-layers extreme learning machine. To evaluate the performance of EH-ELM, three different image datasets; Semeion, MNIST, and NORB, were studied. The experimental results show that EH-ELM achieves better performance than of H-ELM and the other multi-layer framework.
Increase or Decrease Depression : How Body Postures Influence Your Energy Level

The treatment of depression has predominantly focused on medication or cognitive behavioral therapy and has given little attention to the effect of body movement and postures. This study investigated how body posture during movement affects subjective energy level. One hundred and ten university students (average age 23.7) rated their energy level and then walked in either a slouched position or in a pattern of opposite arm and leg skipping. After about two to three minutes, the students rated their subjective energy level, then walked in the opposite movement pattern and rated themselves again. After slouched walking, the participants experienced a decrease in their subjective energy (p , .01); after opposite arm leg skipping they experienced a significant increase in their subjective energy (p , .01). There was a significantly greater decrease (p , .05) in energy at the end of the slouched walk for the 20% of the participants who had the highest self-rated depression scores, as compared to the lowest 20%. By changing posture, subjective energy level can be decreased or increased. Thus the mind-body relationship is a two way street: mind to body and body to mind. The authors discuss clinical and teaching implications of body posture.
Erp Implementation Failure: A Case Study

While it is true that successful implementation of an enterprise resource planning (ERP) system is a task of Herculean proportions, it is not impossible. If your organization is to reap the benefits of ERP, it must first develop a plan for success. But “prepare to see your organization reengineered, your staff disrupted, and your productivity drop before the payoff is realized.”1 Implementing ERP must be viewed and undertaken as a new business endeavor and a team mission, not just a software installation. Companies must involve all employees, and unconditionally and completely sell them on the concept of ERP for it to be a success.2 A successful implementation means involving, supervising, recognizing, and retaining those who have worked or will work closely with the system. Without a team attitude and total backing by everyone involved, an ERP implementation will end in less than an ideal situation.3 This was the situation for a soft drink bottler that tried to cut corners and did not recognize the importance of the people so heavily involved and depended on.
Self-Alignment with Copper Pillars Micro-Bumps for Positioning Optical Devices at Submicronic Accuracy

This paper studies the self-alignment properties between two chips that are stacked on top of each other with copper pillars micro-bumps. The chips feature alignment marks used for measuring the resulting offset after assembly. The accuracy of the alignment is found to be better than 0.5 µm in × and y directions, depending on the process. The chips also feature waveguides and vertical grating couplers (VGC) fabricated in the front-end-of-line (FEOL) and organized in order to realize an optical interconnection between the chips. The coupling of light between the chips is measured and compared to numerical simulation. This high accuracy self-alignment was obtained after studying the impact of flux and fluxless treatments on the wetting of the pads and the successful assembly yield. The composition of the bump surface was analyzed with Time-of-Flight Secondary Ions Mass Spectroscopy (ToF-SIMS) in order to understand the impact of each treatment. This study confirms that copper pillars micro-bumps can be used to self-align photonic integrated circuits (PIC) with another die (for example a microlens array) in order to achieve high throughput alignment of optical fiber to the PIC.
Robotic techniques for upper limb evaluation and rehabilitation of stroke patients

This paper presents two robot devices for use in the rehabilitation of upper limb movements and reports the quantitative parameters obtained to characterize the rate of improvement, thus allowing a precise monitoring of patient's recovery. A one degree of freedom (DoF) wrist manipulator and a two-DoF elbow-shoulder manipulator were designed using an admittance control strategy; if the patient could not move the handle, the devices completed the motor task. Two groups of chronic post-stroke patients (G1 n=7, and G2 n=9) were enrolled in a three week rehabilitation program including standard physical therapy (45 min daily) plus treatment by means of robot devices, respectively, for wrist and elbow-shoulder movements (40 min, twice daily). Both groups were evaluated by means of standard clinical assessment scales and a new robot measured evaluation metrics that included an active movement index quantifying the patient's ability to execute the assigned motor task without robot assistance, the mean velocity, and a movement accuracy index measuring the distance of the executed path from the theoretic one. After treatment, both groups improved their motor deficit and disability. In G1, there was a significant change in the clinical scale values (p<0.05) and range of motion wrist extension (p<0.02). G2 showed a significant change in clinical scales (p<0.01), in strength (p<0.05) and in the robot measured parameters (p<0.01). The relationship between robot measured parameters and the clinical assessment scales showed a moderate and significant correlation (r>0.53 p<0.03). Our findings suggest that robot-aided neurorehabilitation may improve the motor outcome and disability of chronic post-stroke patients. The new robot measured parameters may provide useful information about the course of treatment and its effectiveness at discharge.
Surgical site infections in neonates and infants: is antibiotic prophylaxis needed for longer than 24 h?

The purpose is to determine whether use of perioperative antibiotics for more than 24 h decreases the incidence of SSI in neonates and infants. We studied neonates and infants who had clean–contaminated or contaminated gastrointestinal operations from 1996 to 2006. Patient- and operation-related variables, duration of perioperative antibiotics, and SSI within 30 days were ascertained by retrospective chart review. In assessing the effects of antibiotic duration, we controlled for confounding by indication using standard covariate adjustment and propensity score matching. Among 732 operations, the incidence of SSI was 13 %. Using propensity score matching, the odds of SSI were similar (OR 1.1, 95 % CI 0.6–1.9) in patients who received ≤24 h of postoperative antibiotics compared to >24 h. No difference was also found in standard covariate adjustment. This multivariate model identified three independent predictors of SSI: preoperative infection (OR 3.9, 95 % CI 1.4–10.9) and re-operation through the same incision, both within 30 days (OR 3.5, 95 % CI 1.7–7.4) and later (OR 2.3, 95 % CI 1.4–3.8). In clean–contaminated and contaminated gastrointestinal operations, giving >24 h of postoperative antibiotics offered no protection against SSI. An adequately powered randomized clinical trial is needed to conclusively evaluate longer duration antibiotic prophylaxis.
Wideband Circularly Polarized Substrate Integrated Cavity-Backed Antenna Array

In this letter, a 4 ×4 substrate integrated waveguide (SIW)-fed circularly polarized (CP) antenna array with a broad axial-ratio (AR) bandwidth is designed and fabricated by multilayer printed circuit board (PCB) technology. The antenna array consists of 16 sequentially rotated elliptical cavities fed by slots on the SIW acting as the radiating elements, four 1-to-4 SIW power dividers, and a transition from a coaxial cable to the SIW. The widened AR bandwidth of the antenna array is achieved by using an improved SIW power divider. The antenna prototype was fabricated and measured, and the discrepancies between simulations and measurements are carefully analyzed.
Inhaled Therapy in Respiratory Disease: The Complex Interplay of Pulmonary Kinetic Processes

The inhalation route is frequently used to administer drugs for the management of respiratory diseases such as asthma or chronic obstructive pulmonary disease. Compared with other routes of administration, inhalation offers a number of advantages in the treatment of these diseases. For example, via inhalation, a drug is directly delivered to the target organ, conferring high pulmonary drug concentrations and low systemic drug concentrations. Therefore, drug inhalation is typically associated with high pulmonary efficacy and minimal systemic side effects. The lung, as a target, represents an organ with a complex structure and multiple pulmonary-specific pharmacokinetic processes, including (1) drug particle/droplet deposition; (2) pulmonary drug dissolution; (3) mucociliary and macrophage clearance; (4) absorption to lung tissue; (5) pulmonary tissue retention and tissue metabolism; and (6) absorptive drug clearance to the systemic perfusion. In this review, we describe these pharmacokinetic processes and explain how they may be influenced by drug-, formulation- and device-, and patient-related factors. Furthermore, we highlight the complex interplay between these processes and describe, using the examples of inhaled albuterol, fluticasone propionate, budesonide, and olodaterol, how various sequential or parallel pulmonary processes should be considered in order to comprehend the pulmonary fate of inhaled drugs.
Deciphering the Mechanism of Action of Wrightia tinctoria for Psoriasis Based on Systems Pharmacology Approach.

Psoriasis is a chronic immune-mediated disorder of the skin. The disease manifests itself with red or silvery scaly plaques distributing over the lower back, scalp, and extensor aspects of limbs. Several medications are available for the treatment of psoriasis; however, high rates of remission and side-effects still persist as a major concern. Siddha, one of the traditional systems of Indian medicine offers cure to many dermatological conditions, including psoriasis. The oil prepared from the leaves of Wrightia tinctoria is prescribed by many healers for the treatment of psoriasis. This work aims to decipher the mechanism of action of the W. tinctoria in curing psoriasis and its associated comorbidities.



The work integrates various pharmacology approaches such as drug-likeness evaluation, oral bioavailability predictions, and network pharmacology approaches to understand the roles of various bioactive components of the herb.



This work identified 67 compounds of W. tinctoria interacting with 238 protein targets. The compounds were found to act through synergistic mechanism in reviving the disrupted process in the diseased state.



The results of this work not only shed light on the pharmacological action of the herb but also validate the usage of safe herbal drugs.
A review on transfer learning for brain-computer interface classification

Due to the non-stationarity nature and poor signal-to-noise ratio (SNR) of brain signals, repeated time-consuming calibration is one of the biggest problems for today's brain-computer interfaces (BCIs). In order to reduce calibration time, many transfer learning methods have been proposed to extract discriminative or stationary information from other subjects or prior sessions for target classification task. In this paper, we review the existing transfer learning methods used for BCI classification problems and organize them into three cases based on different transfer strategies. Besides, we list the datasets used in these BCI studies.
LH-CAM: Logic-Based Higher Performance Binary CAM Architecture on FPGA

Binary content-addressable memory (BiCAM) is a popular high speed search engine in hardware, which provides output typically in one clock cycle. But speed of CAM comes at the cost of various disadvantages, such as high latency, low storage density, and low architectural scalability. In addition, field-programmable gate arrays (FPGAs), which are used in many applications because of its advantages, do not have hard IPs for CAM. Since FPGAs have embedded IPs for random-access memories (RAMs), several RAM-based CAM architectures on FPGAs are available in the literature. However, these architectures are especially targeted for ternary CAMs, not for BiCAMs; thus, the available RAM-based CAMs may not be fully beneficial for BiCAMs in terms of architectural design. Since modern FPGAs are enriched with logical resources, why not to configure them to design BiCAM on FPGA? This letter presents a logic-based high performance BiCAM architecture (LH-CAM) using Xilinx FPGA. The proposed CAM is composed of CAM words and associated comparators. A sample of LH-CAM of size  ${64\times 36}$  is implemented on Xilinx Virtex-6 FPGA. Compared with the latest prior work, the proposed CAM is much simpler in architecture, storage efficient, reduces power consumption by 40.92%, and improves speed by 27.34%.
Variable developmental delays and characteristic facial features-A novel 7p22.3p22.2 microdeletion syndrome?

Isolated 7p22.3p22.2 deletions are rarely described with only two reports in the literature. Most other reported cases either involve a much larger region of the 7p arm or have an additional copy number variation. Here, we report five patients with overlapping microdeletions at 7p22.3p22.2. The patients presented with variable developmental delays, exhibiting relative weaknesses in expressive language skills and relative strengths in gross, and fine motor skills. The most consistent facial features seen in these patients included a broad nasal root, a prominent forehead a prominent glabella and arched eyebrows. Additional variable features amongst the patients included microcephaly, metopic ridging or craniosynostosis, cleft palate, cardiac defects, and mild hypotonia. Although the patients' deletions varied in size, there was a 0.47 Mb region of overlap which contained 7 OMIM genes: EIP3B, CHST12, LFNG, BRAT1, TTYH3, AMZ1, and GNA12. We propose that monosomy of this region represents a novel microdeletion syndrome. We recommend that individuals with 7p22.3p22.2 deletions should receive a developmental assessment and a thorough cardiac exam, with consideration of an echocardiogram, as part of their initial evaluation.
Classification of Malware programs using autoencoders based deep learning architecture and its application to the microsoft malware Classification challenge (BIG 2015) dataset

Distinguishing and classifying different types of malware is important to better understanding how they can infect computers and devices, the threat level they pose and how to protect against them. In this paper, a system for classifying malware programs is presented. The paper describes the architecture of the system and assesses its performance on a publicly available database (provided by Microsoft for the Microsoft Malware Classification Challenge BIG2015) to serve as a benchmark for future research efforts. First, the malicious programs are preprocessed such that they are visualized as gray scale images. We then make use of an architecture comprised of multiple layers (multiple levels of encoding) to carry out the classification process of those images/programs. We compare the performance of this approach against traditional machine learning and pattern recognition algorithms. Our experimental results show that the deep learning architecture yields a boost in performance over those conventional/standard algorithms. A hold-out validation analysis using the superior architecture shows an accuracy in the order of 99.15%.
A Helping Hand: Transfer Learning for Deep Sentiment Analysis

Deep convolutional neural networks excel at sentiment polarity classification, but tend to require substantial amounts of training data, which moreover differs quite significantly between domains. In this work, we present an approach to feed generic cues into the training process of such networks, leading to better generalization abilities given limited training data. We propose to induce sentiment embeddings via supervision on extrinsic data, which are then fed into the model via a dedicated memorybased component. We observe significant gains in effectiveness on a range of different datasets in seven different languages.
Diminished reality for augmented reality interior design

A modular real-time diminished reality pipeline for indoor applications is presented. The pipeline includes a novel inpainting method which requires no prior information of the textures behind the object to be diminished. The inpainting method operates on rectified images and adapts to scene illumination. In typically challenging illumination situations, the method produces more realistic results in indoor scenes than previous approaches. Modularity enables using alternative implementations in different stages and adapting the pipeline for different applications. Finally, practical solutions to problems occurring in diminished reality applications, for example interior design, are discussed.
Analysis of the Yokeless And Segmented Armature Machine

This paper presents a new type of axial flux motor, the yokeless and segmented armature (YASA) topology. The YASA motor has no stator yoke, a high fill factor and short end windings which all increase torque density and efficiency of the machine. Thus, the topology is highly suited for high performance applications. The LIFEcar project is aimed at producing the world's first hydrogen sports car, and the first YASA motors have been developed specifically for the vehicle. The stator segments have been made using powdered iron material which enables the machine to be run up to 300 Hz. The iron in the stator of the YASA motor is dramatically reduced when compared to other axial flux motors, typically by 50%, causing an overall increase in torque density of around 20%. A detailed Finite Element analysis (FEA) analysis of the YASA machine is presented and it is shown that the motor has a peak efficiency of over 95%.
Hands Off the Wheel in Autonomous Vehicles?: A Systems Perspective on over a Million Miles of Field Data

Autonomous vehicle (AV) technology is rapidly becoming a reality on U.S. roads, offering the promise of improvements in traffic management, safety, and the comfort and efficiency of vehicular travel. The California Department of Motor Vehicles (DMV) reports that between 2014 and 2017, manufacturers tested 144 AVs, driving a cumulative 1,116,605 autonomous miles, and reported 5,328 disengagements and 42 accidents involving AVs on public roads. This paper investigates the causes, dynamics, and impacts of such AV failures by analyzing disengagement and accident reports obtained from public DMV databases. We draw several conclusions. For example, we find that autonomous vehicles are 15 - 4000Ã— worse than human drivers for accidents per cumulative mile driven; that drivers of AVs need to be as alert as drivers of non-AVs; and that the AVs' machine-learning-based systems for perception and decision-and-control are the primary cause of 64% of all disengagements.