Research on the Copyright of User Content in Online Education Platform

Online interaction plays an important role in online education. At the same time, it also creates a series of copyright issues. How to establish the copyright of the “user content” submitted by the user during the online interaction session, and how the platform and the user negotiate the distribution of rights and interests are all urgently resolved. This paper selects 20 online education platform’s terms of use and privacy policy for research, and concludes that foreign platform have generally signed relevant agreements with users to stipulate the ownership of user content copyrights, while domestic users and platform do not attach importance to the issue of user content copyrights; the relevant agreements for user's content copyrights are detrimental to users,no matter types of contracts，distribution of copyrights or division of responsibilities . In this regard, we should use legal to regulate Agreement content and signing behavior which provided by online education platforms ，protect the user's rights, which has great significance in promoting the development of online education. Keywords:online education, copyright, user, terms of use DOI: 10.7176/JEP/10-18-09 Publication date:June 30th 201

Heterogeneous resources cost-aware geo-distributed data analytics

Many popular cloud service providers deploy tens of data centers (DCs) around the world to reduce user-perceived latency for better user experiences, in which a large amount of data is generated and stored in a geo-distributed manner. Geo-distributed Data Analytics (GDA) has gained great popularity in meeting the growing demand to mine meaningful and timely knowledge from such highly dispersed data. Since GDA systems require a large data migration between DCs via a wide area network (WAN), many existing works invested significant effort to optimize data transfer strategies to efficiently use limited WAN by considering the network pricing policies on the base of infinite compute resources. However, the compute capacities and pricing policies, the limited and heterogeneous resources at different data centers, were ignored in most of the previous works while some compute-intensive workloads such as machine learning, require more compute resources than WAN resources in GDA. Since cloud providers may offer different compute resource capacities and pricing policies at each DC, any cost-agnostic approach can inflate the overall cost that may lead to a cost bottleneck, incurring more costs than their target budgets. To avoid both performances- and cost- bottlenecks, heterogeneous resource capacities, and their costs should be jointly considered. In this research, we propose a heterogeneous cloud resources cost-aware GDA system, called Butler, that exploits heterogeneous resource costs to meet cost-performance goals. To this end, Butler determines optimal task placements given inputs to achieve the best performance with the target budget. Butler provides an easy way to explore a richer cost-performance tradeoff space for various GDA applications

Multi-Sensor Inertial Measurement System for Analysis of Sports Motion

A portable motion analysis system that can accurately measure body movement kinematics and kinetics has the potential to benefit athletes and coaches in performance improvement and injury prevention. In addition, such a system can allow researchers to collect data without limitations of time and location. In this dissertation, a portable multi-sensor human motion analysis algorithm is been developed based on inertial measurement technology. The algorithm includes a newly designed coordinate flow chart analysis method to systematically construct rotation matrices for multi-Inertial Measurement Unit (IMU) application. Using this system, overhead throwing is investigated to reconstruct arm trajectory, arm rotation velocities, as well as torque and force imposed on the elbow and shoulder. Based on this information, different motion features can be established, such as kinematic chain timing as demonstrated in this work. Human subject experiments are used to validate the functionality of the method and the accuracy of the kinematics reconstruction results. Single axis rotation rig experiments are used to shown that this multi-IMU system and algorithm provides an improved in accuracy on arm rotation calculation over the conventional video camera based motion capture system. Finally, a digital filter with switchable cut-off frequency is developed and demonstrated in its application to the IMU-based sports motion signals. The switchable filter method is not limited only to IMUs, but may be applied to any type of motion sensing technology. With the techniques developed in this work, it will be possible in the near future to use portable and accurate sports motion analysis systems in training, rehabilitation and scientific research on sports biomechanics

Smart Windows: Switching Light Transmittance by Responsive Organometallic Poly(ionic liquid)s: Control by Cross Talk of Thermal and Redox Stimuli (Adv. Funct. Mater. 41/2017)

In article number 1702784, G. Julius Vancso and co-workers report an organometallic polymer with sulfonate side groups for transmittance control. The novel polymer, produced by a simple one-step synthesis, exhibits both a lower critical solution temperature (LCST)-type phase transition and an “isothermal” redox-triggered phase transition in aqueous solution, leading to a new type of “smart window” by using thermal and electrical triggers

D-STEM: a Design led approach to STEM innovation

Advances in the Science, Technology, Engineering and Maths (STEM) disciplines offer opportunities for designers to propose and make products with advanced, enhanced and engineered properties and functionalities. In turn, these advanced characteristics are becoming increasingly necessary as resources become ever more strained through 21st century demands, such as ageing populations, connected communities, depleting raw materials, waste management and energy supply. We need to make things that are smarter, make our lives easier, better and simpler. The products of tomorrow need to do more with less. The issue is how to maximize the potential for exploiting opportunities offered by STEM developments and how best to enable designers to strengthen their position within the innovation ecosystem. As a society, we need designers able to navigate emerging developments from the STEM community to a level that enables understanding and knowledge of the new material properties, the skill set to facilitate absorption into the design ‘toolbox’ and the agility to identify, manage and contextualise innovation opportunities emerging from STEM developments. This paper proposes the blueprint for a new design led approach to STEM innovation that begins to redefine studio culture for the 21st Century

Identification of morphological fingerprint in perinatal brains using quasi-conformal mapping and contrastive learning

The morphological fingerprint in the brain is capable of identifying the uniqueness of an individual. However, whether such individual patterns are present in perinatal brains, and which morphological attributes or cortical regions better characterize the individual differences of ne-onates remain unclear. In this study, we proposed a deep learning framework that projected three-dimensional spherical meshes of three morphological features (i.e., cortical thickness, mean curvature, and sulcal depth) onto two-dimensional planes through quasi-conformal mapping, and employed the ResNet18 and contrastive learning for individual identification. We used the cross-sectional structural MRI data of 682 infants, incorporating with data augmentation, to train the model and fine-tuned the parameters based on 60 infants who had longitudinal scans. The model was validated on 30 longitudinal scanned infant data, and remarkable Top1 and Top5 accuracies of 71.37% and 84.10% were achieved, respectively. The sensorimotor and visual cortices were recognized as the most contributive regions in individual identification. Moreover, the folding morphology demonstrated greater discriminative capability than the cortical thickness, which could serve as the morphological fingerprint in perinatal brains. These findings provided evidence for the emergence of morphological fingerprints in the brain at the beginning of the third trimester, which may hold promising implications for understanding the formation of in-dividual uniqueness in the brain during early development

PEGylated graphene oxide for tumor-targeted delivery of paclitaxel.

AIM: The graphene oxide (GO) sheet has been considered one of the most promising carbon derivatives in the field of material science for the past few years and has shown excellent tumor-targeting ability, biocompatibility and low toxicity. We have endeavored to conjugate paclitaxel (PTX) to GO molecule and investigate its anticancer efficacy. MATERIALS & METHODS: We conjugated the anticancer drug PTX to aminated PEG chains on GO sheets through covalent bonds to get GO-PEG-PTX complexes. The tissue distribution and anticancer efficacy of GO-PEG-PTX were then investigated using a B16 melanoma cancer-bearing C57 mice model. RESULTS: The GO-PEG-PTX complexes exhibited excellent water solubility and biocompatibility. Compared with the traditional formulation of PTX (Taxol®), GO-PEG-PTX has shown prolonged blood circulation time as well as high tumor-targeting and -suppressing efficacy. CONCLUSION: PEGylated graphene oxide is an excellent nanocarrier for paclitaxel for cancer targeting

A Novel Floating High-Voltage Level Shifter with Pre-Storage Technique

This paper proposes a novel floating high-voltage level shifter (FHV-LS) with the pre-storage technique for high speed and low deviation in propagation delay. With this technology, the transmission paths from input to output are optimized, and thus the propagation delay of the proposed FHV-LS is reduced to as low as the sub-nanosecond scale. To further reduce the propagation delay, a pull-up network with regulated strength is introduced to reduce the fall time, which is a crucial part of the propagation delay. In addition, a pseudosymmetrical input pair is used to improve the symmetry of FHV-LS structurally to balance between the rising and falling propagation delays. Moreover, a start-up circuit is developed to initialize the output state of FHV-LS during the VDDH power up. The proposed FHV-LS is implemented using 0.3-µm HVCMOS technology. Post-layout simulation shows that the propagation delays and energy per transition of the proposed FHV-LS are 384 ps and 77.7 pJ @VH = 5 V, respectively. Finally, the 500-points Monte Carlo are performed to verify the performance and the stability