Exploiting Ontology for Software License Agreements

Software end-users need to sign licenses to seal an agreement with the product providers. Habitually, users agree with the license (i.e. terms and conditions) without fully understanding the agreement. To address this issue, an ontological model is developed to formulate the user requirements and license agreements formally. This paper, introduces ontological model that includes the abstract license ontology of common features found in di?erent license agreements. The abstract license ontology is then extended to a few real world license agreements. The resulting model can be used for di?erent purposes such as querying the appropriate licenses for a speciˉc requirement or checking the license terms and conditions with user requirements.     

移动自组织网中基于最优距离的概率广播方案

广播是移动Adhoc网络提供路由发现、地址解析等网络功能的一项重要操作．为了减少了泛洪广播算法中的端到端延迟,有效利用有限的带宽资源,解决广播风暴问题,文中提出了一种距离感知的广播技术,即基于最优距离的概率广播方案．本方案通过结合概率算法和距离感知来解决广播风暴问题,以高可达率和低延迟在网络中传播广播数据包．仿真结果显示与主流的泛洪算法和概率广播算法相比,本方案在可到达率、端到端延迟及重播概率等方面的性能有所提高．     

Enhanced halftone-based secure and improved visual cryptography scheme for colour/binary Images

Visual Cryptography (VC) is gaining attraction during the past few years to secure the visual information in the transmission network. It enables the visual data i.e. handwritten notes, photos, printed text, etc. to encrypt in such a way that their decryption can be done through the human visual framework. Hence, no computational assistance is required for the decryption of the secret images they can be seen through naked eye. In this paper, a novel enhanced halftoning-based VC scheme is proposed that works for both binary and color images. Fake share is generated by the combination of random black and white pixels. The proposed algorithm consists of 3 stages i.e., detection, encryption, and decryption. Halftoning, Encryption, (2, 2) visual cryptography and the novel idea of fake share, make it even more secure and improved. As a result, it facilitates the original restored image to the authentic user, however, the one who enters the wrong password gets the combination of fake share with any real share. Both colored and black images can be processed with minimal capacity using the proposed scheme.

     

Deep learning for graphics recognition: document understanding and beyond

International Journal on Document Analysis and Recognition (IJDAR) -     

NN-based Prediction Interval for Nonlinear Processes Controller

Neural networks (NNs) are extensively used in modelling, optimization, and control of nonlinear plants. NN-based inverse type point prediction models are commonly used for nonlinear process control. However, prediction errors (root mean square error (RMSE), mean absolute percentage error (MAPE) etc.) significantly increase in the presence of disturbances and uncertainties. In contrast to point forecast, prediction interval (PI)-based forecast bears extra information such as the prediction accuracy. The PI provides tighter upper and lower bounds with considering uncertainties due to the model mismatch and time dependent or time independent noises for a given confidence level. The use of PIs in the NN controller (NNC) as additional inputs can improve the controller performance. In the present work, the PIs are utilized in control applications, in particular PIs are integrated in the NN internal model-based control framework. A PI-based model that developed using lower upper bound estimation method (LUBE) is used as an online estimator of PIs for the proposed PI-based controller (PIC). PIs along with other inputs for a traditional NN are used to train the PIC to predict the control signal. The proposed controller is tested for two case studies. These include, a chemical reactor, which is a continuous stirred tank reactor (case 1) and a numerical nonlinear plant model (case 2). Simulation results reveal that the tracking performance of the proposed controller is superior to the traditional NNC in terms of setpoint tracking and disturbance rejections. More precisely, 36% and 15% improvements can be achieved using the proposed PIC over the NNC in terms of IAE for case 1 and case 2, respectively for setpoint tracking with step changes.

     

哈尔滨地区汛期径流量变化趋势与复杂性分析

区域水文要素复杂性研究是当今的热点问题,为了研究哈尔滨地区汛期径流量的复杂性特征及变化趋势,将R/S分析法和等概率粗粒化LZC算法引入到区域径流复杂性分析与变化趋势预测中,对地区内10个水文站点的汛期径流序列的复杂性进行诊断并对其变化趋势进行分析。研究结果表明:哈尔滨、通河、阿城、依兰、烟囱山各站未来的汛期径流量呈减少趋势,岔林河、莲花、西北河、延寿、四平山各站未来的汛期径流量呈增长趋势;各站点汛期径流序列的复杂度由高到低依次为:四平山、烟囱山、岔林河、阿城、西北河、依兰、莲花、通河、哈尔滨、延寿;从河流集水面积对其径流长程相关性和复杂性的影响可以看出,站点的河流集水面积越大,其径流量的长程相关性越强,复杂性指数越小。研究成果揭示了本地区汛期径流量的变化趋势以及复杂性演变特征,该特征可为区域径流预测及水资源优化配置提供科学依据。     

The dynamic contact area of elastomers at different velocities

The friction in tribo-systems that contain viscoelastic materials, such as elastomers, is relevant for a large number of applications. Examples include tyres, hoses, transmission and conveyor belts. To quantify the friction in these applications, one must first understand the contact behaviour of such viscoelastic materials, both in static and in dynamic situations. This work discusses an experimental study into the change of the contact area with the sliding velocity and relates the change in contact area with the mechanical properties of the elastomer. The results show that for a tribo-system containing an elastomer, there is a threshold velocity, above which the size of the contact area significantly reduces.