Cryptographic Lightweight Encryption Algorithm with Dimensionality Reduction in Edge Computing

Edge Computing is one of the radically evolving systems through generations as it is able to effectively meet the data saving standards of consumers, providers and the workers. Requisition for Edge Computing based items have been increasing tremendously. Apart from the advantages it holds, there remain lots of objections and restrictions, which hinders it from accomplishing the need of consumers all around the world. Some of the limitations are constraints on computing and hardware, functions and accessibility, remote administration and connectivity. There is also a backlog in security due to its inability to create a trust between devices involved in encryption and decryption. This is because security of data greatly depends upon faster encryption and decryption in order to transfer it. In addition, its devices are considerably exposed to side channel attacks, including Power Analysis attacks that are capable of overturning the process. Constrained space and the ability of it is one of the most challenging tasks. To prevail over from this issue we are proposing a Cryptographic Lightweight Encryption Algorithm with Dimensionality Reduction in Edge Computing. The t-Distributed Stochastic Neighbor Embedding is one of the efficient dimensionality reduction technique that greatly decreases the size of the non-linear data. The three dimensional image data obtained from the system, which are connected with it, are dimensionally reduced, and then lightweight encryption algorithm is employed. Hence, the security backlog can be solved effectively using this method.     

优化GD-模糊规则的滑坡预报模型研究

为了提高滑坡灾害预报准确率，改善传统的滑坡监测和预报中存在的参数选取困难及模糊控制系统作为预报模型精确度不高的问题，首先采用山体结构稳定性分析法进行滑坡灾害参数的选取，得出降雨量、含水率、土压力及岩土表面位移增量作为预报参数的结论；其次将选取的参数作为模糊系统的输入，建立滑坡灾害发生概率模型，并引入优化的GD算法修正预报模型中的动态参数，使模糊控制模型具有自适应性；同时与未优化的模糊控制模型以及单独模糊控制模型进行仿真对比，仿真结果表明，该控制算法收敛速度快，具有很好的收敛性；最后将该模型在某滑坡重点灾区实验区进行实验测试，实验结果显示该模型具有较好的收敛性，且预报精度达到90%。     

正则化图形模糊聚类及鲁棒分割算法

针对现有图形模糊聚类算法合理性差和抗噪能力弱的问题，提出嵌入对称正则项的图形模糊聚类鲁棒算法。将样本聚类所对应的中立度与拒分度相结合构造对称正则项，嵌入现有图形模糊聚类所对应的目标函数；同时，利用像素邻域所对应的均值信息辅助当前像素聚类并构造了空间信息约束正则项，采用拉格朗日乘子法获得正则化图形模糊聚类鲁棒分割算法。不同噪声干扰图像分割结果表明，所建议的分割算法是有效的，相比现有的鲁棒模糊聚类分割算法具有更强的抑制噪声能力。     

基于光谱分类模型的保险杠物证无损研究

采用中红外光谱结合化学计量学的方法对车用保险杠碎片进行鉴别，分别对52个车用保险杠碎片样本的全波段光谱数据、指纹区光谱数据和主成分分析降维后的光谱数据建立Fisher判别分析和K近邻算法2种分类模型，并对分类结果进行比较。结果表明，主成分分析提取特征变量后构建的分类模型，分类的准确率更高，对聚丙烯（PP）、PP/滑石粉、PP/滑石粉/碳酸钙（CaCO₃）3种类型的样本分类准确率达到92.3 %，对PP/滑石粉类型中的10种品牌样本分类准确率达到88.9 %，分类结果理想；在构建的2种分类模型中，Fisher判别分析模型的分类率远高于K近邻算法模型，分析认为K近邻算法模型受到样本不均衡的影响；中红外光谱结合化学计量学可以实现对车用保险杠碎片的准确区分，且满足快速、无损的检验要求。     

Critical state model for structured soil

Structure is an evident determinant for macroscopic behaviors of soils. However, this is not taken into account in most constitutive models, as structure is a rather complex issue in models. For this, it is important to develop and implement simple models that can reflect this important aspect of soil behavior. This paper tried to model structured soils based on well-established concepts, such as critical state and sub-loading. Critical state is the core of the classic Cam Clay model. The sub-loading concept implies adoption of an inner (sub-loading) yield surface, according to specific hardening rules for some internal strain-like state variables. Nakai and co-workers proposed such internal variables for controlling density (ρ) and structure (ω), using a modified stress space, called t_ij. Herein, similar variables are used in the context of the better-known invariants (p and q) of the Cam Clay model. This change requires explicit adoption of a non-associated flow rule for the sub-loading surface. This is accomplished by modifying the dilatancy ratio of the Cam Clay model, as a function of the new internal variables. These modifications are described and implemented under three-dimensional (3D) conditions. The model is then applied to simulating laboratory tests under different stress paths and the results are compared to experiments reported for different types of structured soils. The good agreements show the capacity and potential of the proposed model.     

Signature of Meyer-Neldel compensation rule in iso-conversional crystallization under the influence of γ-ray irradiation

In the current article, the influence of three different doses of gamma-rays on the thermally assisted crystal growth in a novel ternary chalcogenide glassy Se₇₈Te₂₀Sn₂ semiconductor has been reported in terms of the re-crystallization. The iso-conversional kinetic approach is used in the present study. The variation of crystal growth rate with temperature obeys the Arrhenius relation for all the doses of gamma-rays irradiation. Further analysis confirms that pre-factor K₀ of crystal growth rate and the corresponding energy ΔE involved in thermally governed crystallization follows Meyer-Neldel compensation rule (MNCR). Further, we have observed a linear correlation i.e., Further Meyer-Neldel compensation rule (FMNCR) between Meyer-Neldel pre-factor K₀₀ and Meyer-Neldel energy kT₀. The observed correlations between these significant parameters (MNCR between K₀ & ΔE and FMNCR between K₀₀ & kT₀) may open a new gateway for revealing the external effects on crystal growth rate during the iso-conversional analysis of crystallization kinetics.     

GA-based learning for rule identification in fuzzy neural networks

Employing an effective learning process is a critical topic in designing a fuzzy neural network, especially when expert knowledge is not available. This paper presents a genetic algorithm (GA) based learning approach for a specific type of fuzzy neural network. The proposed learning approach consists of three stages. In the first stage the membership functions of both input and output variables are initialized by determining their centers and widths using a self-organizing algorithm. The second stage employs the proposed GA based learning algorithm to identify the fuzzy rules while the final stage tunes the derived structure and parameters using a back-propagation learning algorithm. The capabilities of the proposed GA-based learning approach are evaluated using a well-examined benchmark example and its effectiveness is analyzed by means of a comparative study with other approaches. The usefulness of the proposed GA-based learning approach is also illustrated in a practical case study where it is used to predict the performance of road traffic control actions. Results from the benchmarking exercise and case study effectively demonstrate the ability of the proposed three stages learning approach to identify relevant fuzzy rules from a training data set with a higher prediction accuracy than alternative approaches.     

A proposed expert system for word sense disambiguation: deductive ambiguity resolution based on data mining and forward chaining

One of the major issues in the process of machine translation is the problem of choosing the proper translation for a multi‐sense word referred to as word sense disambiguation (WSD). Two commonly used approaches to this problem are statistical and example‐based methods. In statistical methods, ambiguity resolution is mostly carried out by making use of some statistics extracted from previously translated documents or dual corpora of source and target languages. Example‐based methods follow a similar approach as they also make use of bilingual corpora. However, they perform the task of matching at run‐time (i.e. online matching). In this paper, by looking at the WSD problem from a different viewpoint, we propose a system, which consists of two main parts. The first part includes a data mining algorithm, which runs offline and extracts some useful knowledge about the co‐occurrences of the words. In this algorithm, each sentence is imagined as a transaction in Market Basket Data Analysis problem, and the words included in a sentence play the role of purchased items. The second part of the system is an expert system whose knowledge base consists of the set of association rules generated by the first part. Moreover, in order to deduce the correct senses of the words, we introduce an efficient algorithm based on forward chaining in order to be used in the inference engine of the proposed expert system. The encouraging performance of the system in terms of precision and recall as well as its efficiency will be analysed and discussed through a set of experiments.     

{X,Y}-Cyclacene Graphs with Next Nearest Neighbor Interactions

Eigensolutions of {X( = C,B,N),Y( = C,B,N)}-cyclacene graphs with next nearest neighbor (nnn) interactions have been obtained in analytical forms by adapting n-fold rotational symmetry followed by two-fold rotational symmetry (or a plane of symmetry). Expressions of eigensolution indicate the subspectral relationship among such cyclacenes with an even number of hexagonal rings e.g., eigenvalues of {X,Y}-di-cyclacene are found in the eigenspectra of all such even cyclacenes. Total π-electron energies and highest occupied molecular orbital and lowest unoccupied molecular orbital (HOMO–LUMO) gaps are calculated using the analytical expressions obtained and are found to vary negligibly with the variation of nnn interactions in such cyclacenes. Total π-electron energy is found to increase due to increase in restriction intensity of nnn interactions, whereas the HOMO–LUMO gap of polyacenecs having the even number of hexagonal rings and with one electron at each site (atom) decreases with increase in the restriction intensity since such systems contain degenerate half-filled HOMO (bonding or nonbonding) that are much more vulnerable for perturbations imposed through nnn interactions.