nils-herrmann/clean_scirepeval huggingface dataset:
Brain Tumor Extraction from MRI images using a hybridized method of K means Clustering , Watershed segmentation and Morphological operations

449 www.erpublication.org  Brain tumor is an uncontrolled growth of tissues in human brain. This tumor, when turns in to cancer becomes life-threatening. For images of human brain different techniques are used to capture image. These techniques involve X-Ray, Computer Tomography (CT) and Magnetic Resonance imaging MRI. For diagnosis, MRI is used to distinguish pathologic tissue from normal tissue, especially for brain related disorders and has more advantages over other techniques. The fundamental aspect that makes segmentation of medical images difficult is the complexity and variability of the anatomy that is being imaged. It may not be possible to locate certain structures without detailed anatomical knowledge. In this paper, a method to extract the brain tumor from the MRI image using clustering and watershed segmentation is proposed. The proposed method combines K-means clustering and watershed segmentation after applying some morphological operations for better results. The major advantage of watershed segmentation is that it is able to construct a complete division of the image but the disadvantages are over segmentation and sensitivity which was overcome by using K-means clustering to produce a primary segmentation of the image.
Antioxidative, Anticholinesterase and Anti-Neuroinflammatory Properties of Malaysian Brown and Green Seaweeds

Diminished antioxidant defense or increased production of reactive oxygen species in the biological system can result in oxidative stress which may lead to various neurodegenerative diseases including Alzheimer’s disease (AD). Microglial activation also contributes to the progression of AD by producing several proinflammatory cytokines, nitric oxide (NO) and prostaglandin E2 (PGE2). Oxidative stress and inflammation have been reported to be possible pathophysiological mechanisms underlying AD. In addition, the cholinergic hypothesis postulates that memory impairment in patient with AD is also associated with the deficit of cholinergic function in the brain. Although a number of drugs have been approved for the treatment of AD, most of these synthetic drugs have diverse side effects and yield relatively modest benefits. Marine algae have great potential in pharmaceutical and biomedical applications as they are valuable sources of bioactive properties such as anticoagulation, antimicrobial, antioxidative, anticancer and anti-inflammatory. Hence, this study aimed to provide an overview of the properties of Malaysian seaweeds (Padina australis, Sargassum polycystum and Caulerpa racemosa) in inhibiting oxidative stress, neuroinflammation and cholinesterase enzymes. These seaweeds significantly exhibited potent DPPH and moderate superoxide anion radical scavenging ability (P<0.05). Hexane and methanol extracts of S. polycystum exhibited the most potent radical scavenging ability with IC50 values of 0.157±0.004mg/ml and 0.849±0.02mg/ml for DPPH and ABTS assays, respectively. Hexane extract of C. racemosa gave the strongest superoxide radical inhibitory effect (IC50 of 0.386±0.01mg/ml). Most seaweed extracts significantly inhibited the production of cytokine (IL-6, IL-1 β, TNFα) and NO in a concentration-dependent manner without causing significant cytotoxicity to the lipopolysaccharide (LPS)-stimulated microglia cells (P<0.05). All extracts suppressed cytokine and NO level by more than 50% at the concentration of 0.4mg/ml. In addition, C. racemosa and S. polycystum also showed anti-acetylcholinesterase activities with the IC50 values ranging from 0.086-0.115 mg/ml. Moreover, C. racemosa and P. australis were also found to be active against butyrylcholinesterase with IC50 values ranging from 0.1180.287 mg/ml. Anticholinesterase, antioxidative, neuroinflammation, seaweeds. Siti Aisya Gany and Swee Ching Tan are with the School of Postgraduate Studies, International Medical University, Jalan Jalil Perkasa 19, 57000 Kuala Lumpur, Malaysia (e-mail: aisyagany@gmail.com, sweeching_tan89@yahoo.com). Sook Yee Gan is with School of Pharmacy, International Medical University, Jalan Jalil Perkasa 19, 57000 Kuala Lumpur, Malaysia (corresponding author: phone: 603-27317518; fax: 603-86567228; ).
STRATEGIC COMMUNICATION WITH LYING COSTS

I study a model of strategic communication between an informed Sender and an uninformed Receiver. The Sender bears a cost of lying, or more broadly, of misrepresenting his private information. The main results show that inflated language naturally arises in this environment, where the Sender (almost) always claims to be a higher type than he would under complete information. Regardless of the intensity of lying cost, there is incomplete separation, with some pooling on the highest messages. The degree of language inflation and how much information is revealed depends upon the intensity of lying cost. The analysis delivers a framework to span a class of cheap talk and verifiable disclosure games, unifying the polar predictions they make under large conflicts of interest. I apply the model to highlight how the degree of manipulability of information can affect the tradeoff between delegation and communication. Date: December 12, 2008 (First version: November 2003).
Internet of Things for Smart Cities

The Internet of Things (IoT) shall be able to incorporate transparently and seamlessly a large number of different and heterogeneous end systems, while providing open access to selected subsets of data for the development of a plethora of digital services. Building a general architecture for the IoT is hence a very complex task, mainly because of the extremely large variety of devices, link layer technologies, and services that may be involved in such a system. In this paper we focus specifically to an urban IoT systems that, while still being quite a broad category, are characterized by their specific application domain. Urban IoTs, in fact, are designed to support the Smart City vision, which aims at exploiting the most advanced communication technologies to support added-value services for the administration of the city and for the citizens.
Progressively Diffused Networks for Semantic Image Segmentation

This paper introduces Progressively Diffused Networks (PDNs) for unifying multi-scale context modeling with deep feature learning, by taking semantic image segmentation as an exemplar application. Prior neural networks such as ResNet [11] tend to enhance representational power by increasing the depth of architectures and driving the training objective across layers. However, we argue that spatial dependencies in different layers, which generally represent the rich contexts among data elements, are also critical to building deep and discriminative representations. To this end, our PDNs enables to progressively broadcast information over the learned feature maps by inserting a stack of information diffusion layers, each of which exploits multi-dimensional convolutional LSTMs (Long-Short-Term Memory Structures). In each LSTM unit, a special type of atrous filters are designed to capture the short range and long range dependencies from various neighbors to a certain site of the feature map and pass the accumulated information to the next layer. From the extensive experiments on semantic image segmentation benchmarks (e.g., ImageNet Parsing, PASCAL VOC2012 and PASCAL-Part), our framework demonstrates the effectiveness to substantially improve the performances over the popular existing neural network models, and achieves state-of-the-art on ImageNet Parsing for large scale semantic segmentation.
Business Model Innovation of Startups Developing Multisided Digital Platforms

Platforms are defined as multisided marketplaces with business models that enable producers and users to create value together by interacting with each other. In recent years, platforms have benefited from the advances of digitalization. Hence, digital platforms continue to triumph, and continue to be attractive for companies, also for startups. In this paper, we first explore the research of platforms compared to digital platforms. We then proceed to analyze digital platforms as business models, in the context of startups looking for business model innovation. Based on interviews conducted at a technology startup event in Finland, we analyzed how 34 startups viewed their business model innovations. Using the 10 sub-constructs from the business model innovation scale by Clauss in 2016, we found out that the idea of business model innovation resonated with startups, as all of them were able to identify the source of their business model innovation. Furthermore, the results indicated the complexity of business model innovation as 79 percent of the respondents explained it with more than one sub-construct. New technology/equipment, new processes and new customers and markets got the most mentions as sources of business model innovation. Overall, the emphasis at startups is on the value creation innovation, with new proposition innovation getting less, and value capture innovation even less emphasis as the source of business model innovation.
CIoTA: Collaborative IoT Anomaly Detection via Blockchain

Due to their rapid growth and deployment, Internet of things (IoT) devices have become a central aspect of our daily lives. However, they tend to have many vulnerabilities which can be exploited by an attacker. Unsupervised techniques, such as anomaly detection, can help us secure the IoT devices. However, an anomaly detection model must be trained for a long time in order to capture all benign behaviors. This approach is vulnerable to adversarial attacks since all observations are assumed to be benign while training the anomaly detection model. In this paper, we propose CIoTA, a lightweight framework that utilizes the blockchain concept to perform distributed and collaborative anomaly detection for devices with limited resources. CIoTA uses blockchain to incrementally update a trusted anomaly detection model via self-attestation and consensus among IoT devices. We evaluate CIoTA on our own distributed IoT simulation platform, which consists of 48 Raspberry Pis, to demonstrate CIoTA’s ability to enhance the security of each device and the security of the network as a whole.
Deep Residual Feature Learning for Action Prediction

Though action recognition based on complete videos has achieved great success recently, action prediction remains a challenging task as the information provided by partial videos is not discriminative enough for classifying actions. In this paper, we propose a Deep Residual Feature Learning (DeepRFL) framework to explore more discriminative information from partial videos, achieving similar representations as those of complete videos. The proposed method is based on residual learning, which captures the salient differences between partial videos and their corresponding full videos. The partial videos can attain the missing information by learning from features of complete videos and thus improve the discriminative power. Moreover, our model can be trained efficiently in an end-to-end fashion. Extensive evaluations on the challenging UCF101 and HMDB51 datasets demonstrate that the proposed method outperforms state-of-the-art results.
Defining Cyberbullying: A Qualitative Research into the Perceptions of Youngsters

Data from 53 focus groups, which involved students from 10 to 18 years old, show that youngsters often interpret "cyberbullying" as "Internet bullying" and associate the phenomenon with a wide range of practices. In order to be considered "true" cyberbullying, these practices must meet several criteria. They should be intended to hurt (by the perpetrator) and perceived as hurtful (by the victim); be part of a repetitive pattern of negative offline or online actions; and be performed in a relationship characterized by a power imbalance (based on "real-life" power criteria, such as physical strength or age, and/or on ICT-related criteria such as technological know-how and anonymity).
A Quadruped Robot Exhibiting Spontaneous Gait Transitions from Walking to Trotting to Galloping

The manner in which quadrupeds change their locomotive patterns—walking, trotting, and galloping—with changing speed is poorly understood. In this paper, we provide evidence for interlimb coordination during gait transitions using a quadruped robot for which coordination between the legs can be self-organized through a simple “central pattern generator” (CPG) model. We demonstrate spontaneous gait transitions between energy-efficient patterns by changing only the parameter related to speed. Interlimb coordination was achieved with the use of local load sensing only without any preprogrammed patterns. Our model exploits physical communication through the body, suggesting that knowledge of physical communication is required to understand the leg coordination mechanism in legged animals and to establish design principles for legged robots that can reproduce flexible and efficient locomotion.
Resource management for Infrastructure as a Service (IaaS) in cloud computing: A survey

The cloud phenomenon is quickly becoming an important service in Internet computing. Infrastructure as a Service (IaaS) in cloud computing is one of the most significant and fastest growing field. In this service model, cloud providers offer resources to users/machines that include computers as virtual machines, raw (block) storage, firewalls, load balancers, and network devices. One of the most pressing issues in cloud computing for IaaS is the resource management. Resource management problems include allocation, provisioning, requirement mapping, adaptation, discovery, brokering, estimation, and modeling. Resource management for IaaS in cloud computing offers following benefits: scalability, quality of service, optimal utility, reduced overheads, improved throughput, reduced latency, specialized environment, cost effectiveness and simplified interface. This paper focuses on some of the important resource management techniques such as resource provisioning, resource allocation, resource mapping and resource adaptation. It brings out an exhaustive survey of such techniques for IaaS in cloud computing, and also put forth the open challenges for further research.
Association between muscle function and low back pain in relation to pregnancy.

To investigate the association of muscle function and subgroups of low back pain (no low back pain, pelvic girdle pain, lumbar pain and combined pelvic girdle pain and lumbar pain) in relation to pregnancy.



Prospective cohort study.



Consecutively enrolled pregnant women seen in gestational weeks 12-18 (n = 301) and 3 months postpartum (n = 262).



Classification into subgroups by means of mechanical assessment of the lumbar spine, pelvic pain provocation tests, standard history and a pain drawing. Trunk muscle endurance, hip muscle strength (dynamometer) and gait speed were investigated.



In pregnancy 116 women had no low back pain, 33% (n = 99) had pelvic girdle pain, 11% (n = 32) had lumbar pain and 18% (n = 54) had combined pelvic girdle pain and lumbar pain. The prevalence of pelvic girdle pain/combined pelvic girdle pain and lumbar pain decreased postpartum, whereas the prevalence of lumbar pain remained stable. Women with pelvic girdle pain and/or combined pelvic girdle pain and lumbar pain had lower values for trunk muscle endurance, hip extension and gait speed as compared to women without low back pain in pregnancy and postpartum (p < 0.001-0.04). Women with pelvic girdle pain throughout the study had lower values of back flexor endurance compared with women without low back pain.



Muscle dysfunction was associated with pelvic girdle pain, which should be taken into consideration when developing treatment strategies and preventive measures.
A survey and assessment of the capabilities of Cubesats for Earth observation

In less than a decade, Cubesats have evolved from purely educational tools to a standard platform for technology demonstration and scientific instrumentation. The use of COTS (Commercial-Off-The-Shelf) components and the ongoing miniaturization of several technologies have already led to scattered instances of missions with promising scientific value. Furthermore, advantages in terms of development cost and development time with respect to larger satellites, as well as the possibility of launching several dozens of Cubesats with a single rocket launch, have brought forth the potential for radically new mission architectures consisting of very large constellations or clusters of Cubesats. These architectures promise to combine the temporal resolution of GEO missions with the spatial resolution of LEO missions, thus breaking a traditional tradeoff in Earth observation mission design. This paper assesses the current capabilities of Cubesats with respect to potential employment in Earth observation missions. A thorough review of Cubesat bus technology capabilities is performed, identifying potential limitations and their implications on 17 different Earth observation payload technologies. These results are matched to an exhaustive review of scientific requirements in the field of Earth observation, assessing the possibilities of Cubesats to cope with the requirements set for each one of 21 measurement categories. Based on this review, several Earth observation measurements are identified that can potentially be compatible with the current state-of-the-art of Cubesat technology although some of them have actually never been addressed by any Cubesat mission. Simultaneously, other measurements are identified which are unlikely to be performed by Cubesats in the next few years due to insuperable constraints. Ultimately, this paper is intended to supply a box of ideas for universities to design future Cubesat missions with high
Data-Driven Color Augmentation Techniques for Deep Skin Image Analysis

Dermoscopic skin images are often obtained with different imaging devices, under varying acquisition conditions. In this work, instead of attempting to perform intensity and color normalization, we propose to leverage computational color constancy techniques to build an artificial data augmentation technique suitable for this kind of images. Specifically, we apply the shades of gray color constancy technique to color-normalize the entire training set of images, while retaining the estimated illuminants. We then draw one sample from the distribution of training set illuminants and apply it on the normalized image. We employ this technique for training two deep convolutional neural networks for the tasks of skin lesion segmentation and skin lesion classification, in the context of the ISIC 2017 challenge and without using any external dermatologic image set. Our results on the validation set are promising, and will be supplemented with extended results on the hidden test set when available.
Molecular activity underlying working memory.

The prefrontal cortex is necessary for directing thought and planning action. Working memory, the active, transient maintenance of information in mind for subsequent monitoring and manipulation, lies at the core of many simple, as well as high-level, cognitive functions. Working memory has been shown to be compromised in a number of neurological and psychiatric conditions and may contribute to the behavioral and cognitive deficits associated with these disorders. It has been theorized that working memory depends upon reverberating circuits within the prefrontal cortex and other cortical areas. However, recent work indicates that intracellular signals and protein dephosphorylation are critical for working memory. The present article will review recent research into the involvement of the modulatory neurotransmitters and their receptors in working memory. The intracellular signaling pathways activated by these receptors and evidence that indicates a role for G(q)-initiated PI-PLC and calcium-dependent protein phosphatase calcineurin activity in working memory will be discussed. Additionally, the negative influence of calcium- and cAMP-dependent protein kinase (i.e., calcium/calmodulin-dependent protein kinase II (CaMKII), calcium/diacylglycerol-activated protein kinase C (PKC), and cAMP-dependent protein kinase A (PKA)) activities on working memory will be reviewed. The implications of these experimental findings on the observed inverted-U relationship between D(1) receptor stimulation and working memory, as well as age-associated working memory dysfunction, will be presented. Finally, we will discuss considerations for the development of clinical treatments for working memory disorders.
Ramp-based soft-start circuit with soft-recovery for DC-DC buck converters

A soft-start circuit with soft-recovery function for DC-DC converters is presented in this paper. The soft-start strategy is based on a linearly ramped-up reference and an error amplifier with minimum selector implemented with a three-limb differential pair skillfully. The soft-recovery strategy is based on a compact clamp circuit. The ramp voltage would be clamped once the feedback voltage is detected lower than a threshold, which could control the output to be recovered slowly and linearly. A monolithic DC-DC buck converter with proposed circuit has been fabricated with a 0.5μm CMOS process for validation. The measurement result shows that the ramp-based soft-start and soft-recovery circuit have good performance and agree well with the theoretical analysis.
Nanoionics-based resistive switching memories Many metal – insulator –

RaineR WaseR1,2* and Masakazu aono3,4 1Institut für Werkstoffe der Elektrotechnik 2, RWTH Aachen University, 52056 Aachen, Germany 2Institut für Festkörperforschung/CNI—Center of Nanoelectronics for Information Technology, Forschungszentrum Jülich, 52425 Jülich, Germany 3Nanomaterials Laboratories, National Institute for Material Science, 1-1 Namiki, Tsukuba, Ibaraki 305-0044, Japan 4ICORP/Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama 332-0012, Japan *e-mail: r.waser@fz-juelich.de
Understanding software project risk: a cluster analysis

Understanding software project risk can help in reducing the incidence of failure. Building on prior work, software project risk was conceptualized along six dimensions. A questionnaire was built and 507 software project managers were surveyed. A cluster analysis was then performed to identify aspects of low, medium, and high risk projects. An examination of risk dimensions across the levels revealed that even low risk projects have a high level of complexity risk. For high risk projects, the risks associated with requirements, planning and control, and the organization become more obvious. The influence of project scope, sourcing practices, and strategic orientation on project risk dimensions was also examined. Results suggested that project scope affects all dimensions of risk, whereas sourcing practices and strategic orientation had a more limited impact. A conceptual model of project risk and performance was presented.
A position-varied plate utilized for a Thai license plate recognition

This paper presents a position-varied plate utilized for Thai license plate recognition using back propagation neural netwo rk (BPNN). In this method, a dimension image of the car is suitably decreased by image resizing (e.g. interpolation method), and then they are converted to gray images for inputs to plate localization process. The plate localization process is used to find the area position of the license plate for inputting to image segmentation process which is used to find edges of main characters in the license plate. After that, each of image characters received from character segmentation process is inserted into neural network to analyze the probable characters and numbers. In this experiment, the images of numbers and Thai characters are cross-validated by BPNN (training, validation and testing sets), and then 100 images of Thai license plate are used for testing. The results reveal that an accuracy of analysis is at approximately 97 % for the distance of the car and camera between 0.5m to 1m, and the angle of inclined plate varied from ±13 degrees.