• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4345-4363
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• 2021

Deep Learning as a Service (DLaaS), utilizing the cloud-based deep neural network models to provide customer prediction services, has been widely deployed on mobile cloud computing (MCC). Such services raise privacy concerns since customers need to send private data to untrusted service providers. In this paper, we devote ourselves to building an efficient protocol to classify users' images using the convolutional neural network (CNN) model trained and held by the server, while keeping both parties' data secure. Most previous solutions commonly employ homomorphic encryption schemes based on Ring Learning with Errors (RLWE) hardness or two-party secure computation protocols to achieve it. However, they have limitations on large communication overheads and costs in MCC. To address this issue, we present LeHE4SCNN, a scalable privacy-preserving and communication-efficient framework for CNN-based DLaaS. Firstly, we design a novel low-expansion rate homomorphic encryption scheme with packing and unpacking methods (LeHE). It supports fast homomorphic operations such as vector-matrix multiplication and addition. Then we propose a secure prediction framework for CNN. It employs the LeHE scheme to compute linear layers while exploiting the data shuffling technique to perform non-linear operations. Finally, we implement and evaluate LeHE4SCNN with various CNN models on a real-world dataset. Experimental results demonstrate the effectiveness and superiority of the LeHE4SCNN framework in terms of response time, usage cost, and communication overhead compared to the state-of-the-art methods in the mobile cloud computing environment.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.1
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• pp.89-98
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• 2016

In this paper, we proposed an automated areal feature matching method based on geometric similarity without user intervention and is applied into areal features of many-to-many relation, for confusion of spatial data-sets of different scale and updating cycle. Firstly, areal feature(node) that a value of inclusion function is more than 0.4 was connected as an edge in adjacency matrix and candidate corresponding areal features included many-to-many relation was identified by multiplication of adjacency matrix. For geometrical matching, these multiple candidates corresponding areal features were transformed into an aggregated polygon as a convex hull generated by a curve-fitting algorithm. Secondly, we defined matching criteria to measure geometrical quality, and these criteria were changed into normalized values, similarity, by similarity function. Next, shape similarity is defined as a weighted linear combination of these similarities and weights which are calculated by Criteria Importance Through Intercriteria Correlation(CRITIC) method. Finally, in training data, we identified Equal Error Rate(EER) which is trade-off value in a plot of precision versus recall for all threshold values(PR curve) as a threshold and decided if these candidate pairs are corresponding pairs or not. To the result of applying the proposed method in a digital topographic map and a base map of address system(KAIS), we confirmed that some many-to-many areal features were mis-detected in visual evaluation and precision, recall and F-Measure was highly 0.951, 0.906, 0.928, respectively in statistical evaluation. These means that accuracy of the automated matching between different spatial data-sets by the proposed method is highly. However, we should do a research on an inclusion function and a detail matching criterion to exactly quantify many-to-many areal features in future.

• Korean journal of applied entomology
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• v.31 no.3
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• pp.205-240
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• 1992

In reality, it is a green revolution of the entire agricultural matrix in Korea that integrated pest control plays an important role in the possible breakthrough in rice self-sufficiency. In paddy agroecosystem as man-modified environment, rice is newly established every year by transplantation under diverse water regimes which affect a microclimate. Standing water benefits rice by regulating the microclimate, but it favors the multiplication of certain pets through the amelioration of the microclimate. Further, the introduction of high yielding varieties with the changing of cultural practices results in changing occurrence pattern of certain pests. In general, japonica type varieties lack genes resistant to most of the important pests and insect-borne virus diseases, whereas indica type possesses more genes conferring varietal resistance. Thus, this differences among indica type, form the background of different approaches to pest management. The changes in rice cultivation such as double cropping, growing high-yielding varieties requiring heavy fertilization, earlier transplanting, intensvie-spacing transplanting, and intensive pesticide use as a consequence of the adoption of improves rice production technology, have intensified the pest problems rather than reduced them. The cultivation of resistant varieties are highly effective to the pest, their long term stability is threathened because of the development of new biotypes which can detroy these varieties. So far, three biotypes of N. lugens are reported in Korea. Since each resistant variety is expected to maintain several years the sequential release of another new variety with a different gene at intervals is practised as a gene rotation program. Another approach, breeding multilines that have more than two genes for resistance in a variety are successfully demonstrated. The average annual rice losses during the last 15 years of 1977-’91 are 9.3% due to insect pests without chemical control undertaken, wehreas there is a average 2.4% despite farmers’insecticide application at the same period. In other words, the average annual losses are prvented by 6.9% when chemical control is properly employed. However, the continuous use of a same group of insecticides is followed by the development of pest resistance. Resistant development of C. suppressalis, L. striatellus and N. cincticeps is observed to organophosphorous insecticides by the mid-1960s, and to carbamates by the early 1970s in various parts of the country. Thus, it is apparent that a scheduled chemical control for rice production systems becomes uneconomical and that a reduction in energy input without impairing the rice yield, is necessarily improved through the implementation of integrated pest management systems. Nationwide pest forecasting system conducted by the government organization is a unique network of investigation for purpose of making pest control timely in terms of economic thresholds. A wise plant protection is expected to establish pest management systems in appropriate integration of resistant varieties, biological agents, cultural practices and other measures in harmony with minimizing use of chemical applications as a last weapon relying on economic thresholds.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.19 no.2
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• pp.121-129
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• 2008

In this paper, we present a fast analysis of multilayer microstrip fractal structure by using the fast multipole method (FMM). In the analysis, accurate spatial green's functions from the real-axis integration method(RAIM) are employed to solve the mixed potential integral equation(MPIE) with FMM algorithm. MoM's iteration and memory requirement is $O(N^2)$ in case of calculation using the green function. the problem is the unknown number N can be extremely large for calculation of large scale objects and high accuracy. To improve these problem is fast algorithm FMM. FMM use the addition theorem of green function. So, it reduce the complexity of a matrix-vector multiplication and reduce the cost of calculation to the order of $O(N^{1.5})$, The efficiency is proved from comparing calculation results of the moment method and Fast algorithm.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.3
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• pp.105-112
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• 2017

PCI Express is a bus technology that connects the processor and the peripheral I/O devices that widely used as an industry standard because it has the characteristics of high-speed, low power. In addition, PCI Express is system interconnect technology such as Ethernet and Infiniband used in high-performance computing and computer cluster. PGAS(partitioned global address space) programming model is often used to implement the one-sided RDMA(remote direct memory access) from multi-host systems, such as computer clusters. In this paper, we design and implement a OpenSHMEM API based on PCI Express maintaining the existing features of OpenSHMEM to implement RDMA based on PCI Express. We perform experiment with implemented OpenSHMEM API through a matrix multiplication example from system which PCs connected with NTB(non-transparent bridge) technology of PCI Express. The PCI Express interconnection network is currently very expensive and is not yet widely available to the general public. Nevertheless, we actually implemented and evaluated a PCI Express based interconnection network on the RDK evaluation board. In addition, we have implemented the OpenSHMEM software stack, which is of great interest recently.

• The Korean Journal of Nuclear Medicine
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• v.30 no.3
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• pp.332-337
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• 1996

Purpose : It has been known that the activity of extracellular matrix degradative enzymes such as collagenase correlate well with the metastatic potential of various tumor cells in experimental study. This study was aimed at comparing the activities of type IV collagenase with bone scan findings in patients with breast cancer. Materials and Methods : We retrospectively correlated bone scan findings with the results of immunohistochemical staining for 92kDa, 72kDa type IV collagenase in 28, and 30 patients with metastatic breast cancer, respectively, as well as 23, and 27 patients with primary breast cancer, respectively. The immunohistochemical staining was performed with tissue specimens obtained from primary or metastatic breast tumor lesions. The amounts of the enzyme were graded from 0 to 4 and scored by multiplication with the percentage of tumor cells. The confidence of bone scan interpretation for metastasis was also scored from 1 to 5 with increasing probability. Results : There was a significant difference in enzyme scores between patients with and without metastasis. In patients with primary breast cancer group, the frequency of patients with enzyme score of less than 170 were 96%(26/27) and 100%(26/26) with 92kDa and 72kDa collagenase, respectively. In contrast, in patients with metastatic breast cancer group, the frequency of patients with enzyme score of more than 200 were 93%(28/30) and 87%(26/30) with 92kDa and 72kDa collagenase, respectively. All patients with each enzyme score of less than 170 show no active bony metastasis, however, there were variable bone scan findings in patients with each enzyme score of more than 200. Conclusion : Bone scan is useful to confirm, localize or follow up of bony metastasis in patients with each enzyme scores of more than 200. Acitve metastatic lesions were hardly seen on the bone scintigraphy in patients with collagenase scores of less than 170.

• Asia pacific journal of information systems
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• v.21 no.2
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• pp.89-116
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• 2011

In collaborative tagging systems such as Delicious.com and Flickr.com, users assign keywords or tags to their uploaded resources, such as bookmarks and pictures, for their future use or sharing purposes. The collection of resources and tags generated by a user is called a personomy, and the collection of all personomies constitutes the folksonomy. The most significant need of the folksonomy users Is to efficiently find useful resources or experts on specific topics. An excellent ranking algorithm would assign higher ranking to more useful resources or experts. What resources are considered useful In a folksonomic system? Does a standard superior to frequency or freshness exist? The resource recommended by more users with mere expertise should be worthy of attention. This ranking paradigm can be implemented through a graph-based ranking algorithm. Two well-known representatives of such a paradigm are Page Rank by Google and HITS(Hypertext Induced Topic Selection) by Kleinberg. Both Page Rank and HITS assign a higher evaluation score to pages linked to more higher-scored pages. HITS differs from PageRank in that it utilizes two kinds of scores: authority and hub scores. The ranking objects of these pages are limited to Web pages, whereas the ranking objects of a folksonomic system are somewhat heterogeneous(i.e., users, resources, and tags). Therefore, uniform application of the voting notion of PageRank and HITS based on the links to a folksonomy would be unreasonable, In a folksonomic system, each link corresponding to a property can have an opposite direction, depending on whether the property is an active or a passive voice. The current research stems from the Idea that a graph-based ranking algorithm could be applied to the folksonomic system using the concept of mutual Interactions between entitles, rather than the voting notion of PageRank or HITS. The concept of mutual interactions, proposed for ranking the Semantic Web resources, enables the calculation of importance scores of various resources unaffected by link directions. The weights of a property representing the mutual interaction between classes are assigned depending on the relative significance of the property to the resource importance of each class. This class-oriented approach is based on the fact that, in the Semantic Web, there are many heterogeneous classes; thus, applying a different appraisal standard for each class is more reasonable. This is similar to the evaluation method of humans, where different items are assigned specific weights, which are then summed up to determine the weighted average. We can check for missing properties more easily with this approach than with other predicate-oriented approaches. A user of a tagging system usually assigns more than one tags to the same resource, and there can be more than one tags with the same subjectivity and objectivity. In the case that many users assign similar tags to the same resource, grading the users differently depending on the assignment order becomes necessary. This idea comes from the studies in psychology wherein expertise involves the ability to select the most relevant information for achieving a goal. An expert should be someone who not only has a large collection of documents annotated with a particular tag, but also tends to add documents of high quality to his/her collections. Such documents are identified by the number, as well as the expertise, of users who have the same documents in their collections. In other words, there is a relationship of mutual reinforcement between the expertise of a user and the quality of a document. In addition, there is a need to rank entities related more closely to a certain entity. Considering the property of social media that ensures the popularity of a topic is temporary, recent data should have more weight than old data. We propose a comprehensive folksonomy ranking framework in which all these considerations are dealt with and that can be easily customized to each folksonomy site for ranking purposes. To examine the validity of our ranking algorithm and show the mechanism of adjusting property, time, and expertise weights, we first use a dataset designed for analyzing the effect of each ranking factor independently. We then show the ranking results of a real folksonomy site, with the ranking factors combined. Because the ground truth of a given dataset is not known when it comes to ranking, we inject simulated data whose ranking results can be predicted into the real dataset and compare the ranking results of our algorithm with that of a previous HITS-based algorithm. Our semantic ranking algorithm based on the concept of mutual interaction seems to be preferable to the HITS-based algorithm as a flexible folksonomy ranking framework. Some concrete points of difference are as follows. First, with the time concept applied to the property weights, our algorithm shows superior performance in lowering the scores of older data and raising the scores of newer data. Second, applying the time concept to the expertise weights, as well as to the property weights, our algorithm controls the conflicting influence of expertise weights and enhances overall consistency of time-valued ranking. The expertise weights of the previous study can act as an obstacle to the time-valued ranking because the number of followers increases as time goes on. Third, many new properties and classes can be included in our framework. The previous HITS-based algorithm, based on the voting notion, loses ground in the situation where the domain consists of more than two classes, or where other important properties, such as "sent through twitter" or "registered as a friend," are added to the domain. Forth, there is a big difference in the calculation time and memory use between the two kinds of algorithms. While the matrix multiplication of two matrices, has to be executed twice for the previous HITS-based algorithm, this is unnecessary with our algorithm. In our ranking framework, various folksonomy ranking policies can be expressed with the ranking factors combined and our approach can work, even if the folksonomy site is not implemented with Semantic Web languages. Above all, the time weight proposed in this paper will be applicable to various domains, including social media, where time value is considered important.