Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 6

embedding Related Abstracts

6 A Novel Approach of Secret Communication Using Douglas-Peucker Algorithm

Authors: R. Kiruthika, A. Kannan


Steganography is the problem of hiding secret messages in 'innocent – looking' public communication so that the presence of the secret message cannot be detected. This paper introduces a steganographic security in terms of computational in-distinguishability from a channel of probability distributions on cover messages. This method first splits the cover image into two separate blocks using Douglas – Peucker algorithm. The text message and the image will be hided in the Least Significant Bit (LSB) of the cover image.

Keywords: Steganography, Douglas-Peucker algorithm, lsb, embedding

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5 Challenges of Embedding Entrepreneurship in Modibbo Adama University of Technology Yola, Nigeria

Authors: Michael Ubale Cyril


Challenges of embedding entrepreneurship in tertiary institutions in Nigeria requires a consistent policy for equipping schools with necessary facilities like establishing incubating technology centre, the right calibres of human resources, appropriate pedagogical tools for teaching entrepreneurship education and exhibition grounds where products and services will be delivered and patronised by the customers. With the death of facilities in public schools in Nigeria, educators are clamouring for a way out. This study investigated the challenges of embedding entrepreneurship education in Modibbo Adama University of Technology Yola, Nigeria. The population for the study was 201 comprising 34 industrial entrepreneurs, 76 technical teachers and 91 final year undergraduates. The data was analysed using means of 3 groups, standard deviation, and analysis of variance. The study found out, that technical teachers have not been trained to teach entrepreneurship education, approaches to teaching methodology, were not varied and lack of infrastructural facilities like building was not a factor. It was recommended that technical teachers be retrained to teach entrepreneurship education, textbooks in entrepreneurship should be published with Nigerian outlook.

Keywords: Challenges, embedding, entrepreneurship pedagogical, technology incubating centres

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4 Stereotyping of Non-Western Students in Western Universities: Applying Critical Discourse Analysis to Undermine Educational Hegemony

Authors: Susan Lubbers


This study applies critical discourse analysis to the language used by educators to frame international students of Asian backgrounds in Anglo-Western universities as quiet, shy, passive and unable to think critically. Emphasis is on the self-promoted ‘internationalised’ Australian tertiary context, where negative stereotypes are commonly voiced not only in the academy but also in the media. Parallels are drawn as well with other Anglo-Western educational contexts. The study critically compares the discourse of these persistent negative stereotypes, with in-class and interview discourses of international students of Asian and Western language, cultural and educational backgrounds enrolled in a Media and Popular Culture unit in an Australian university. The focus of analysis of the student discourse is on their engagement in critical dialogic interactions on the topics of culture and interculturality. The evidence is also drawn from student interviews and focus groups and from observation of whole-class discussion participation rates. The findings of the research project provide evidence that counters the myth of student as problem. They point rather to the widespread lack of intercultural awareness of Western educators and students as being at the heart of the negative perceptions of students of Asian backgrounds. The study suggests the efficacy of an approach to developing intercultural competence that is embedded, or integrated, into tertiary programs. The presentation includes an overview of the main strategies that have been developed by the tertiary educator (author) to support the development of intercultural competence of and among the student cohort. The evidence points to the importance of developing intercultural competence among tertiary educators and students. The failure by educators to ensure that the diverse voices, ideas and perspectives of students from all cultural, educational and language backgrounds are heard in our classrooms means that our universities can hardly be regarded or promoted as genuinely internationalised. They will continue as undemocratic institutions that perpetrate persistent Western educational hegemony.

Keywords: Critical thinking, Critical Discourse Analysis, intercultural competence, embedding, interculturality, international student, internationalised education

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3 Automatic Aggregation and Embedding of Microservices for Optimized Deployments

Authors: Pablo Chico De Guzman, Cesar Sanchez


Microservices are a software development methodology in which applications are built by composing a set of independently deploy-able, small, modular services. Each service runs a unique process and it gets instantiated and deployed in one or more machines (we assume that different microservices are deployed into different machines). Microservices are becoming the de facto standard for developing distributed cloud applications due to their reduced release cycles. In principle, the responsibility of a microservice can be as simple as implementing a single function, which can lead to the following issues: - Resource fragmentation due to the virtual machine boundary. - Poor communication performance between microservices. Two composition techniques can be used to optimize resource fragmentation and communication performance: aggregation and embedding of microservices. Aggregation allows the deployment of a set of microservices on the same machine using a proxy server. Aggregation helps to reduce resource fragmentation, and is particularly useful when the aggregated services have a similar scalability behavior. Embedding deals with communication performance by deploying on the same virtual machine those microservices that require a communication channel (localhost bandwidth is reported to be about 40 times faster than cloud vendor local networks and it offers better reliability). Embedding can also reduce dependencies on load balancer services since the communication takes place on a single virtual machine. For example, assume that microservice A has two instances, a1 and a2, and it communicates with microservice B, which also has two instances, b1 and b2. One embedding can deploy a1 and b1 on machine m1, and a2 and b2 are deployed on a different machine m2. This deployment configuration allows each pair (a1-b1), (a2-b2) to communicate using the localhost interface without the need of a load balancer between microservices A and B. Aggregation and embedding techniques are complex since different microservices might have incompatible runtime dependencies which forbid them from being installed on the same machine. There is also a security concern since the attack surface between microservices can be larger. Luckily, container technology allows to run several processes on the same machine in an isolated manner, solving the incompatibility of running dependencies and the previous security concern, thus greatly simplifying aggregation/embedding implementations by just deploying a microservice container on the same machine as the aggregated/embedded microservice container. Therefore, a wide variety of deployment configurations can be described by combining aggregation and embedding to create an efficient and robust microservice architecture. This paper presents a formal method that receives a declarative definition of a microservice architecture and proposes different optimized deployment configurations by aggregating/embedding microservices. The first prototype is based on i2kit, a deployment tool also submitted to ICWS 2018. The proposed prototype optimizes the following parameters: network/system performance, resource usage, resource costs and failure tolerance.

Keywords: Resource Allocation, deployment, aggregation, embedding

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2 A Supervised Embedding and Clustering Anomaly Detection Method for Classification of Mobile Network Faults

Authors: Raziyeh Mosayebi, Hanif Kia, Aseman Kianpour Raki, Seyed Mohamadhasan Zoee


Several nodes and elements of the mobile network are continuously generating different alarm logs. The most frequent alarm types are normal and do not require special attention. However, a small portion of these alarms may result in anomaly and fault. In the network operating center, the operators monitor the enormous amount of alarm logs in their dashboards to acknowledge any possible anomaly; however, due to the large volume of alarms, the high rate of miss identification is inevitable. Recently, by moving toward the full automation of telecommunication networks, machine learning techniques have come to assist operators for anomaly detection and problem identification. In this paper, we propose a Supervised Embedding and Clustering Anomaly Detection (SEMC-AD) method to identify the faulty alarm logs in a fraction of a second, reduce the operator workload and enhance the network maintenance by reducing the time for problem resolution. However, because anomalies contain a very small portion of total data, so anomaly detection would be an imbalanced classification problem and the process of identification is quite challenging. Also, the dataset has numerous categorical features with enormous different values for each one. As a result, the ordinary one hot encoding approach will produce a large number of features. To overcome the curse of dimensionality while we work with numerical data, the history of alarm logs and their labels (anomaly and normal) are used in a supervised embedding approach based on a deep neural network to extract a numerical representation of each alarm log. To examine the robustness of the embedding, the scatter plot of the two most significant principle components of the embedded alarm logs is used. The plot shows that the alarms in the anomaly group are scattered in the same neighborhood, which means the embedding vectors of the anomalies are similar. The multivariate normal Gaussian clustering method is then applied to the two most important principle components of embedded alarm logs. The different number of clusters are examined to estimate the best possible clusters for anomalies. The clusters in which the ratio of the number of anomalies to normal alarms are higher than 90 percent are labeled as anomaly groups. The new alarm log will be classified as an anomaly if the two most significant principle components of its embedded vector are classified in clusters with the label of an anomaly. Considering the values of recall and precision metrics, the performance of the proposed SEMC_AD method is 18% better than the ordinary random forest and Gradient boosting method without embedding. 99% percent of anomalies are detected by SEMC-AD method, while 86% and 81% of anomalies are detected by random forest and XGBoost, respectively. Although it seems that in the labeled datasets, the supervised classification methods may have higher performance in anomaly detection, but the results demonstrate that in our dataset with high number of categorical features, the SEMC_AD method can classify the anomalies more efficiently.

Keywords: Mobile Network, Deep learning, Anomaly Detection, alarm, fault, embedding

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1 Index t-SNE: Tracking Dynamics of High-Dimensional Datasets with Coherent Embeddings

Authors: Gaelle Candel, David Naccache


t-SNE is an embedding method that the data science community has widely used. It helps two main tasks: to display results by coloring items according to the item class or feature value; and for forensic, giving a first overview of the dataset distribution. Two interesting characteristics of t-SNE are the structure preservation property and the answer to the crowding problem, where all neighbors in high dimensional space cannot be represented correctly in low dimensional space. t-SNE preserves the local neighborhood, and similar items are nicely spaced by adjusting to the local density. These two characteristics produce a meaningful representation, where the cluster area is proportional to its size in number, and relationships between clusters are materialized by closeness on the embedding. This algorithm is non-parametric. The transformation from a high to low dimensional space is described but not learned. Two initializations of the algorithm would lead to two different embeddings. In a forensic approach, analysts would like to compare two or more datasets using their embedding. A naive approach would be to embed all datasets together. However, this process is costly as the complexity of t-SNE is quadratic and would be infeasible for too many datasets. Another approach would be to learn a parametric model over an embedding built with a subset of data. While this approach is highly scalable, points could be mapped at the same exact position, making them indistinguishable. This type of model would be unable to adapt to new outliers nor concept drift. This paper presents a methodology to reuse an embedding to create a new one, where cluster positions are preserved. The optimization process minimizes two costs, one relative to the embedding shape and the second relative to the support embedding’ match. The embedding with the support process can be repeated more than once, with the newly obtained embedding. The successive embedding can be used to study the impact of one variable over the dataset distribution or monitor changes over time. This method has the same complexity as t-SNE per embedding, and memory requirements are only doubled. For a dataset of n elements sorted and split into k subsets, the total embedding complexity would be reduced from O(n²) to O(n²=k), and the memory requirement from n² to 2(n=k)², which enables computation on recent laptops. The method showed promising results on a real-world dataset, allowing to observe the birth, evolution, and death of clusters. The proposed approach facilitates identifying significant trends and changes, which empowers the monitoring high dimensional datasets’ dynamics.

Keywords: Data Visualization, monitoring, unsupervised learning, Dimension Reduction, reusability, embedding, concept drift, t-SNE

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