Information system ecology: An application of dataphoric ascendancy

Information systems, like biological systems, are susceptible to external perturbations. Similar to flora and fauna in a biome, species of data can be classified within a dataphora. While entropic properties and data geometries can be used to describe local species of data within a dataphora, they are not designed to describe the global properties of an information system or evaluate its stability. Ecologists have used Information Theories to describe macro-level properties of biological ecosystems and statistical tools to evaluate biological systems. This research leverages an ecological perspective to model information systems as a living system. Our findings support the theory of dataphoric ascendancy with Wikipedia having a Diversity Index value of 0.68, within the range of 0.65 and 0.80 that indicates a balanced state. We further support our findings with additional evaluations of other ecosystems including the predicted collapse of the information service known as the Digital Universe. This research allows for an information system’s stability to be (a) characterized and (b) predicted using ecological measures specific to the diversity of data within the ecosystem.     

基于LCP径向偏振光输出的掺镱MOPA脉冲光纤激光器

为实现光纤激光器径向偏振光的高效输出,提出并搭建了基于液晶聚合物(LCP)的纳秒脉冲掺镱(Yb)主振荡器功率放大(MOPA)系统。该系统采用空间相位转换法,利用LCP涡旋半波片将全光纤MOPA激光器输出的高峰值功率、窄线宽、线偏振、高斯形分布的纳秒脉冲信号转换为横向强度呈空心环状分布的拉盖尔-高斯光。MOPA激光器系统由窄线宽连续种子源、电光强度调制器和后续的5级YDF放大器组成,通过实验获得了20.1 W的稳定LP01模输出。而后的LCP涡旋波片用作空间模式转换器,最终获得的平均输出功率为19.5 W,脉宽为10 ns,重复频率为10 kHz,横向剖面呈规则空心环形的径向偏振光输出,模式转换效率可达97%。另外,通过PBS测量法测得径向偏振光的模式纯度约为88.5%,兼具高功率与高纯度的优势。     

Diagnosis of disc bulge and disc desiccation in lumbar MRI using concatenated shape and texture features with random forest classifier

Disc bulge and disc desiccation are the most common abnormalities occurring in the spine, which leads to severe low back pain. Despite computer-aided automatic abnormality diagnostic imaging systems are available still there is a need for betterment in diagnostic accuracy and in processing time. Image processing with combined imaging features like shape and texture has given better diagnostic ability when compared with processing with individual features. In the present study, the desiccated and bulged Intervertebral Discs (IVDs) are diagnosed automatically by combining shape features extracted using Histogram of Oriented Gradients (HOG) and texture feature extracted using novel Local Sub-Rhombus Binary Relation Pattern (LS-RBRP) techniques with Random Forest (RF) classifier. The performance analysis projects that the RF with HOG+LS-RBRP has an overall better accuracy of 94.7% when compared with HOG (87%) and LS-RBRP (90.2%) with RF classifier separately in categorizing the normal IVD, disc bulge and disc desiccation in the lumbar spine MRI.     

A peapod-like ZnO@C design with internal void space to relieve its large-volume-change as lithium-ion battery anode

Peapod-like ZnO@C with internal void space has been synthesized by calcination of ZnO/ZnOHF@polydopamine nanorods. By designing both the large void space between particles and external elastic carbon shell, the large volume change of ZnO during charge-discharge process could be effectively relieved. Moreover, the carbon shell functioned as an electronic conductor and elastic barrier, could accelerate the reaction kinetics and confine stable SEI films formation on the outer protective layer to further improve the structural integrity. Benefiting from these structure advantages, the peapod-like ZnO@C presents a prominent electrochemical performance with a retained discharge capacity of 565.1 mA h g^?1 at 0.2 A g^?1 and high rate capacity of 246.6 mA h g^?1 even at 4 A g^?1.     

Chromatic adaptation in a 2D photograph demonstrated by a D‐up viewer

It is said that we cannot have color constancy in a photograph. The concept of recognized visual space of illumination (RVSI) asserts that chromatic adaptation occurs when one perceives the illumination that is filling a space and not the objects in the space. It predicts then that if one perceives a 3D scene in a photograph, then color constancy will occur in the photograph. In this work, a dimension‐up (D‐up) viewer was developed to perceive a 3D scene on a 2D photograph, and the effect of chromatic adaptation was measured by the color appearance of a gray patch placed at the center of the photograph. Subjects saw the patch as a vivid color when they saw a photograph that had been taken under colored illumination, which is a normal experience in a real space observation. When the color appearance was measured by the elementary color naming method, the amount of chromaticness of the patch in percentage and the apparent hue were very similar to those observed in the 2‐room technique, thus confirming the prediction by the RVSI theory.     

Arabic sign language intelligent translator

ABSTRACT

Arabic sign language (ArSL) is method of communication between deaf communities in Arab countries; therefore, the development of systemsthat can recognize the gestures provides a means for the Deaf to easily integrate into society. In this research we implemented a computational structurefor an intelligent interpreter that automatically recognizes the isolated dynamic gestures. The proposed system recognizes and translates gesturesperformed with one or both hands. It comprises five subsystems, building dataset, video processing, feature extraction, mapping between ArSL and Arabictext, and text generation. To apply the system, 100-signs of ArSL was used, which was applied on 1500 video files. It's were divided into five classes:alphabet, numbers, "prepositions, pronouns and question words", Arabic life expressions, and "nouns and verbs". The evaluation indicated that thesystem automatically recognizes and translates isolated dynamic ArSL gestures by highly accurate manner. The results showed that the system accuracy is 95.8%.     

Food recognition using neural network classifier and multiple hypotheses image segmentation

ABSTRACT

This paper proposes the multiple-hypotheses image segmentation and feed-forward neural network classifier for food recognition to improve the performance. Initially, the food or meal image is given as input. Then, the segmentation is applied to identify the regions, where a particular food item is located using salient region detection, multi-scale segmentation, and fast rejection. Then, the features of every food item are extracted by the global feature and local feature extraction. After the features are obtained, the classification is performed for each segmented region using a feed-forward neural network model. Finally, the calorie value is computed with the aid of (i) food volume and (ii) calorie and nutrition measure based on mass value. The experimental results and performance evaluation are validated. The outcome of the proposed method attains 0.947 for Macro Average Accuracy (MAA) and 0.959 for Standard Accuracy (SA), which provides better classification performance.     

MOOC Learner’s Final Grade Prediction Based on an Improved Random Forests Method

Massive Open Online Course (MOOC) has become a popular way of online learning used across the world by millions of people. Meanwhile, a vast amount of information has been collected from the MOOC learners and institutions. Based on the educational data, a lot of researches have been investigated for the prediction of the MOOC learner’s final grade. However, there are still two problems in this research field. The first problem is how to select the most proper features to improve the prediction accuracy, and the second problem is how to use or modify the data mining algorithms for a better analysis of the MOOC data. In order to solve these two problems, an improved random forests method is proposed in this paper. First, a hybrid indicator is defined to measure the importance of the features, and a rule is further established for the feature selection; then, a Clustering-Synthetic Minority Over-sampling Technique (SMOTE) is embedded into the traditional random forests algorithm to solve the class imbalance problem. In experiment part, we verify the performance of the proposed method by using the Canvas Network Person-Course (CNPC) dataset. Furthermore, four well-known prediction methods have been applied for comparison, where the superiority of our method has been proved.     

基于特征贡献率的机械故障分类方法

为提高往复压缩机、航空发动机等复杂机械故障分类的准确率，依据特征参数对不同故障的敏感度存在差异的特性，提出一种狄利克雷过程混合模型(Dirichlet process mixture model,简称DPMM)与贝叶斯推断贡献(Bayesian inference contribution,简称BIC)相结合的分析方法。采用DPMM方法自学习机械振动信号高维特征的统计分布模型，并依据BIC理论计算得到各特征参数对模型的贡献率，通过对比观测数据与各类故障数据特征贡献率间的差异实现故障分类。试验结果表明，该方法的平均分类准确率比基于高斯混合模型(Gaussian mixture model,简称GMM)的故障诊断方法的平均分类准确率提高19.29%，比基于Relief算法的故障诊断方法的平均分类准确率提高32.71%，且该方法的时效性高，泛化性能强，能够更有效地进行复杂机械故障分类。