nils-herrmann/clean_scirepeval huggingface dataset:
Aircraft Type Recognition in Remote Sensing Images Based on Feature Learning with Conditional Generative Adversarial Networks

Aircraft type recognition plays an important role in remote sensing image interpretation. Traditional methods suffer from bad generalization performance, while deep learning methods require large amounts of data with type labels, which are quite expensive and time-consuming to obtain. To overcome the aforementioned problems, in this paper, we propose an aircraft type recognition framework based on conditional generative adversarial networks (GANs). First, we design a new method to precisely detect aircrafts’ keypoints, which are used to generate aircraft masks and locate the positions of the aircrafts. Second, a conditional GAN with a region of interest (ROI)-weighted loss function is trained on unlabeled aircraft images and their corresponding masks. Third, an ROI feature extraction method is carefully designed to extract multi-scale features from the GAN in the regions of aircrafts. After that, a linear support vector machine (SVM) classifier is adopted to classify each sample using their features. Benefiting from the GAN, we can learn features which are strong enough to represent aircrafts based on a large unlabeled dataset. Additionally, the ROI-weighted loss function and the ROI feature extraction method make the features more related to the aircrafts rather than the background, which improves the quality of features and increases the recognition accuracy significantly. Thorough experiments were conducted on a challenging dataset, and the results prove the effectiveness of the proposed aircraft type recognition framework.
Comparison of naming schema in ICN

Information-Centric Networking (ICN) treats content as a first-class entity - each content has a unique identity and ICN routers forward traffic based on content identity rather than the locations of the content. This provides benefits like dynamic request routing, caching and mobility support. The choice of naming schema (flat vs. hierarchical) is a fundamental design choice in ICN which determines the functional separation between the network layer and the application layer. With hierarchical names, the network layer is cognizant of the semantics of hierarchical names. Name space management is also part of network layer. ICN architectures using flat names leave these to the application layer. The naming schema affects the performance and scalability of the network in terms of forwarding efficiency, routing table size and name space size. This paper provides both qualitative and quantitative comparison on the two naming schemas using these metrics, noting that they are interdependent. We seek to understand which naming schema would be better for a high-performance, scalable ICN architecture.
Mitochondrial dysfunction in Parkinson’s disease

Parkinson’s disease (PD) is the second most common neurodegenerative disease, which is characterized by loss of dopaminergic (DA) neurons in the substantia nigra pars compacta and the formation of Lewy bodies and Lewy neurites in surviving DA neurons in most cases. Although the cause of PD is still unclear, the remarkable advances have been made in understanding the possible causative mechanisms of PD pathogenesis. Numerous studies showed that dysfunction of mitochondria may play key roles in DA neuronal loss. Both genetic and environmental factors that are associated with PD contribute to mitochondrial dysfunction and PD pathogenesis. The induction of PD by neurotoxins that inhibit mitochondrial complex I provides direct evidence linking mitochondrial dysfunction to PD. Decrease of mitochondrial complex I activity is present in PD brain and in neurotoxin- or genetic factor-induced PD cellular and animal models. Moreover, PINK1 and parkin, two autosomal recessive PD gene products, have important roles in mitophagy, a cellular process to clear damaged mitochondria. PINK1 activates parkin to ubiquitinate outer mitochondrial membrane proteins to induce a selective degradation of damaged mitochondria by autophagy. In this review, we summarize the factors associated with PD and recent advances in understanding mitochondrial dysfunction in PD.
Control Double Inverted Pendulum by Reinforcement Learning with Double CMAC Network

To accelerate the learning of reinforcement learning, many types of function approximation are used to represent state value. However function approximation reduces the accuracy of state value, and brings difficulty in the convergence. To solve the problems of tradeoff between the generalization and accuracy in reinforcement learning, we represent state-action value by two CMAC networks with different generalization parameters. The accuracy CMAC network can represent values exactly, which achieves precise control in the states around target area. And the generalization CMAC network can extend experiences to unknown area, and guide the learning of accuracy CMAC network. The algorithm proposed in this paper can effectively avoid the dilemma of achieving tradeoff between generalization and accuracy. Simulation results for the control of double inverted pendulum are presented to show effectiveness of the proposed algorithm
Generalized Moog Ladder Filter: Part II–Explicit Nonlinear Model through a Novel Delay-Free Loop Implementation Method

One of the most critical aspects of virtual analog simulation of circuits for music production consists in accurate reproduction of their nonlinear behavior, yet this goal is in many cases difficult to achieve due to the presence of implicit differential equations in circuit models, since they naturally map to delay-free loops in the digital domain. This paper presents a novel and general method for non-iteratively implementing these loops in such a way that the linear response around a chosen operating point is preserved, the topology is minimally affected, and transformation of nonlinearities is not required. This technique is then applied to a generalized model of the Moog ladder filter, resulting in an implementation that outperforms its predecessors with only a modest computational load penalty. This digital version of the filter is shown to offer strong stability guarantees w.r.t. parameter variation, allows the extraction of different frequency response modes by simple mixing of individual ladder stage outputs, and is suitable for real-time sound synthesis and audio effects processing.
SAARC : Origin , Growth , Potential and Achievements

The success of states in today’s world is not so much measured in terms of capacity for defending borders or creating uniquely national institutions, but in terms of ability to adapt to regional and global trends, promote exports, attract investments, and skilled labour, provide a beneficial environment for transnational companies, build attractive institutions of research and higher learning, wield political influence on the regional and global scene, and also brand the nation culturally in the international market-place.
Improving HPC Application Performance in Cloud through Dynamic Load Balancing

Driven by the benefits of elasticity and pay-as-you-go model, cloud computing is emerging as an attractive alternative and addition to in-house clusters and supercomputers for some High Performance Computing (HPC) applications. However, poor interconnect performance, heterogeneous and dynamic environment, and interference by other virtual machines (VMs) are some bottlenecks for efficient HPC in cloud. For tightly-coupled iterative applications, one slow processor slows down the entire application, resulting in poor CPU utilization. In this paper, we present a dynamic load balancer for tightly-coupled iterative HPC applications in cloud. It infers the static hardware heterogeneity in virtualized environments, and also adapts to the dynamic heterogeneity caused by the interference arising due to multi-tenancy. Through continuous live monitoring, instrumentation, and periodic refinement of task distribution to VMs, our load balancer adapts to the dynamic variations in cloud resources. Through experimental evaluation on a private cloud with 64 VMs using benchmarks and a real science application, we demonstrate performance benefits up to 45%. Finally, we analyze the effect of load balancing frequency, problem size, and computational granularity (problem decomposition) on the performance and scalability of our techniques.
Segmenting overlapping cervical cell in Pap smear images

Accurate segmentation of cervical cells in Pap smear images is an important task for automatic identification of pre-cancerous changes in the uterine cervix. One of the major segmentation challenges is the overlapping of cytoplasm, which was less addressed by previous studies. In this paper, we propose a learning-based method to tackle the overlapping issue with robust shape priors by segmenting individual cell in Pap smear images. Specifically, we first define the problem as a discrete labeling task for multiple cells with a suitable cost function. We then use the coarse labeling result to initialize our dynamic multiple-template deformation model for further boundary refinement on each cell. Multiple-scale deep convolutional networks are adopted to learn the diverse cell appearance features. Also, we incorporate high level shape information to guide segmentation where the cells boundary is noisy or lost due to touching and overlapping cells. We evaluate the proposed algorithm on two different datasets, and our comparative experiments demonstrate the promising performance of the proposed method in terms of segmentation accuracy.
A Planar UWB Antenna With a Broadband Feeding Structure

A planar antenna with a broadband feeding structure is presented and analyzed for ultrawideband applications. The proposed antenna consists of a suspended radiator fed by an n-shape microstrip feed. Study shows that this antenna achieves an impedance bandwidth from 3.1-5.1 GHz (48%) for a reflection of coefficient of iotaS11iota < -10 dB, and an average gain of 7.7 dBi. Stable boresight radiation patterns are achieved across the entire operating frequency band, by suppressing the high order mode resonances. This design exhibits good mechanical tolerance and manufacturability.
Clinical leadership development in postgraduate medical education and training: policy, strategy, and delivery in the UK National Health Service

Achieving high quality health care against a background of continual change, increasing demand, and shrinking financial resource is a major challenge. However, there is significant international evidence that when clinicians use their voices and values to engage with system delivery, operational efficiency and care outcomes are improved. In the UK National Health Service, the traditional divide between doctors and managers is being bridged, as clinical leadership is now foregrounded as an important organizational priority. There are 60,000 doctors in postgraduate training (junior doctors) in the UK who provide the majority of front-line patient care and form an "operating core" of most health care organizations. This group of doctors is therefore seen as an important resource in initiating, championing, and delivering improvement in the quality of patient care. This paper provides a brief overview of leadership theories and constructs that have been used to develop a raft of interventions to develop leadership capability among junior doctors. We explore some of the approaches used, including competency frameworks, talent management, shared learning, clinical fellowships, and quality improvement. A new paradigm is identified as necessary to make a difference at a local level, which moves learning and leadership away from developing "leaders", to a more inclusive model of developing relationships between individuals within organizations. This shifts the emphasis from the development of a "heroic" individual leader to a more distributed model, where organizations are "leader-ful" and not just "well led" and leadership is centered on a shared vision owned by whole teams working on the frontline.
SELinux in and out

Security Enhanced Linux (SELinux) is a widely used Mandatory Access Control system which is integrated in the Linux kernel. It is an added layer of security mechanism on top of the standard Discretionary Access Control system that Unix/Linux and other major operating systems have. SELinux does not nullify DAC but in fact supports DAC and its checks are performed after DAC's. If DAC allows an operation then SELinux checks that operation by comparing it with the set of specified rules that it has and decides based on those rules only. If DAC denies some access then SELinux checks are not performed. Because DAC allows users to have full control over files that they own, they could unwantedly set any permission on the files that they own, at their own discretion, which could prove dangerous so for this reason SELinux brings the Mandatory Access Controls (MAC) mechanism which enforces rules based on a specified policy and denies access operations if policy in use do not allow it, even if the file permissions were world-accessible using DAC In this paper we discuss various SELinux policies and provide a statistical comparison using standard Delphi method.
Blood Pressure Prediction via Recurrent Models with Contextual Layer

Recently, the percentage of people with hypertension is increasing, and this phenomenon is widely concerned. At the same time, wireless home Blood Pressure (BP) monitors become accessible in people’s life. Since machine learning methods have made important contributions in different fields, many researchers have tried to employ them in dealing with medical problems. However, the existing studies for BP prediction are all based on clinical data with short time ranges. Besides, there do not exist works which can jointly make use of historical measurement data (e.g. BP and heart rate) and contextual data (e.g. age, gender, BMI and altitude). Recurrent Neural Networks (RNNs), especially those using Long Short-Term Memory (LSTM) units, can capture long range dependencies, so they are effective in modeling variable-length sequences. In this paper, we propose a novel model named recurrent models with contextual layer, which can model the sequential measurement data and contextual data simultaneously to predict the trend of users’ BP. We conduct our experiments on the BP data set collected from a type of wireless home BP monitors, and experimental results show that the proposed models outperform several competitive compared methods.
Critical Success Factors for ERP Projects in Small and Medium-Sized Enterprises - The Perspective of Selected ERP System Vendors

The aim of our study was to provide a contribution to the research field of the critical success factors (CSFs) of ERP projects, with specific focus on smaller enterprises (SMEs). Therefore, we conducted a systematic literature review in order to update the existing reviews of CSFs. On the basis of that review, we led several interviews with ERP consultants experienced with ERP implementations in SMEs. As a result, we showed that all factors found in the literature also affected the success of ERP projects in SMEs. However, within those projects, technological factors gained much more importance compared to the factors that most influence the success of larger ERP projects. For SMEs, factors like the Organizational fit of the ERP system as well as ERP system tests were even more important than Top management support or Project management, which were the most important factors for large-scale companies.
Anticipatory scheduling: A disk scheduling framework to overcome deceptive idleness in synchronous I/O

Disk schedulers in current operating systems are generally work-conserving, i.e., they schedule a request as soon as the previous request has finished. Such schedulers often require multiple outstanding requests from each process to meet system-level goals of performance and quality of service. Unfortunately, many common applications issue disk read requests in a synchronous manner, interspersing successive requests with short periods of computation. The scheduler chooses the next request too early; this induces deceptive idleness, a condition where the scheduler incorrectly assumes that the last request issuing process has no further requests, and becomes forced to switch to a request from another process.We propose the anticipatory disk scheduling framework to solve this problem in a simple, general and transparent way, based on the non-work-conserving scheduling discipline. Our FreeBSD implementation is observed to yield large benefits on a range of microbenchmarks and real workloads. The Apache webserver delivers between 29% and 71% more throughput on a disk-intensive workload. The Andrew filesystem benchmark runs faster by 8%, due to a speedup of 54% in its read-intensive phase. Variants of the TPC-B database benchmark exhibit improvements between 2% and 60%. Proportional-share schedulers are seen to achieve their contracts accurately and efficiently.
Deep Bidirectional Cross-Triplet Embedding for Online Clothing Shopping

In this article, we address the cross-domain (i.e., street and shop) clothing retrieval problem and investigate its real-world applications for online clothing shopping. It is a challenging problem due to the large discrepancy between street and shop domain images. We focus on learning an effective feature-embedding model to generate robust and discriminative feature representation across domains. Existing triplet embedding models achieve promising results by finding an embedding metric in which the distance between negative pairs is larger than the distance between positive pairs plus a margin. However, existing methods do not address the challenges in the cross-domain clothing retrieval scenario sufficiently. First, the intradomain and cross-domain data relationships need to be considered simultaneously. Second, the number of matched and nonmatched cross-domain pairs are unbalanced. To address these challenges, we propose a deep cross-triplet embedding algorithm together with a cross-triplet sampling strategy. The extensive experimental evaluations demonstrate the effectiveness of the proposed algorithms well. Furthermore, we investigate two novel online shopping applications, clothing trying on and accessories recommendation, based on a unified cross-domain clothing retrieval framework.
All-printed flexible organic transistors enabled by surface tension-guided blade coating.

A combination of surface energy-guided blade coating and inkjet printing is used to fabricate an all-printed high performance, high yield, and low variability organic thin film transistor (OTFT) array on a plastic substrate. Functional inks and printing processes were optimized to yield self-assembled homogenous thin films in every layer of the OTFT stack. Specifically, we investigated the effect of capillary number, semiconductor ink composition (small molecule-polymer ratio), and additive high boiling point solvent concentrations on film fidelity, pattern design, device performance and yields.
Werdy: Recognition and Disambiguation of Verbs and Verb Phrases with Syntactic and Semantic Pruning

Word-sense recognition and disambiguation (WERD) is the task of identifying word phrases and their senses in natural language text. Though it is well understood how to disambiguate noun phrases, this task is much less studied for verbs and verbal phrases. We present Werdy, a framework for WERD with particular focus on verbs and verbal phrases. Our framework first identifies multi-word expressions based on the syntactic structure of the sentence; this allows us to recognize both contiguous and non-contiguous phrases. We then generate a list of candidate senses for each word or phrase, using novel syntactic and semantic pruning techniques. We also construct and leverage a new resource of pairs of senses for verbs and their object arguments. Finally, we feed the so-obtained candidate senses into standard word-sense disambiguation (WSD) methods, and boost their precision and recall. Our experiments indicate that Werdy significantly increases the performance of existing WSD methods.
Simulation of single phase SPWM ( Unipolar ) inverter

In this paper, a simulation of SPWM (Unipolar) strategy is presented for single phase full bridge inverter. The simulation of the single-phase unipolar voltage switching inverter device model is simulated in Matlab/Simulink. The modulation ratio change from 0.4 to 0.9 by varying amplitude of modulating signal. The outputs voltage and current %THD waveforms for variable AC voltages and modulation index are observed on scope, and also see the THD.SPWM techniques are characterized by constant amplitude pulses with different duty cycle for each period. The width of this pulses are modulated in order to obtain inverter output voltage control and to reduce its harmonic content. Sinusoidal pulse width modulation or SPWM is the most common method in motor control and inverter application. Conventionally, to generate the signal, triangle wave as a carrier signal is compared with the sinusoidal wave, whose frequency is the desired frequency. In next the hardware design for unipolar voltage switching for inverter for fixed modulation index and is tested for different resistive loads. Sinusoidal pulse width modulation (SPWM), Bipolar, Total Harmonic distortion (THD). Pulse Width Modulation (PWM), UPS (uninterruptible power supplies).
Comparing leaning-based motion cueing interfaces for virtual reality locomotion

In this paper, we describe a user study comparing five different locomotion interfaces for virtual reality locomotion. We compared a standard non-motion cueing interface, Joystick (Xbox), with four motion cueing interfaces, NaviChair (stool with springs), MuvMan (sit/stand active stool), Head-Directed (Oculus Rift DK2), and Swivel Chair (everyday office chair with leaning capability). Each interface had two degrees of freedom to move forward/backward and rotate using velocity (rate) control. The aim of this mixed methods study was to better understand relevant user experience factors and guide the design of future locomotion interfaces. This study employed methods from HCI to provide an understanding of why users behave a certain way while using the interface and to unearth any new issues with the design. Participants were tasked to search for objects in a virtual city while they provided talk-aloud feedback and we logged their behaviour. Subsequently, they completed a post-experimental questionnaire on their experience. We found that the qualitative themes of control, usability, and experience echoed the results of the questionnaire, providing internal validity. The quantitative measures revealed the Joystick to be significantly more comfortable and precise than the motion cueing interfaces. However, the qualitative feedback and interviews showed this was due to the reduced perceived controllability and safety of the motion cueing interfaces. Designers of these interfaces should consider using a backrest if users need to lean backwards and avoid using velocity-control for rotations when using HMDs.