Open-Source IT Support for Effective Social Entrepreneurship

To better support its mission, a non-profit organization needs to effectively reach out to the public, collect information and opinions form the public, support effective brainstorming and discussions, implement effective business processes for non-profit operations, and support effective governance of the organization. The latest information technologies have provided better alternative for non-profits to run smoother and more effectively. In this paper we conduct a critical study of two popular open-source contents management systems, Drupal and WordPress, introduce Drupal to social entrepreneurs, and explain how it can support most of the tasks outlined above. Specific guidance is provided for setting up an organization\u27s public website that supports smooth communications and effective governance. This paper also outlines a PHP and Ajaz based real-time information sharing system which can be adapted to support various forms of fast data sharing and brainstorming for organization members through the Internet

A Foreword from the Editor-in-Chief

Over the past decades, Internet has gone beyond information sharing and communication, and become a platform for service reuse and service integration. Computing reuse based on abstraction and divide-and-conquer is at core of computer science and IT industry over the past decades, and the computing reuse granularity has grown from functions/methods to objects, reusable software components, and distributed cloud services. The maturity of container and microservice technologies makes both software system development and deployment truly distributed and reusable. The advancement in speed and security has now also enabled Internet to become an enterprise service integration platform that promote service reuse and management to an even higher level. “Internet +” is one of the layman’s terms to emphasize the new prominent function of Internet in local/regional/national/international scope enterprise service integration and reuse

MiniMax Entropy Network: Learning Category-Invariant Features for Domain Adaptation

How to effectively learn from unlabeled data from the target domain is crucial for domain adaptation, as it helps reduce the large performance gap due to domain shift or distribution change. In this paper, we propose an easy-to-implement method dubbed MiniMax Entropy Networks (MMEN) based on adversarial learning. Unlike most existing approaches which employ a generator to deal with domain difference, MMEN focuses on learning the categorical information from unlabeled target samples with the help of labeled source samples. Specifically, we set an unfair multi-class classifier named categorical discriminator, which classifies source samples accurately but be confused about the categories of target samples. The generator learns a common subspace that aligns the unlabeled samples based on the target pseudo-labels. For MMEN, we also provide theoretical explanations to show that the learning of feature alignment reduces domain mismatch at the category level. Experimental results on various benchmark datasets demonstrate the effectiveness of our method over existing state-of-the-art baselines.Comment: 8 pages, 6 figure

A Case Study of Mobile Health Applications: The OWASP Risk of Insufficient Cryptography

Mobile devices are being deployed rapidly for both private and professional reasons. One area of that has been growing is in releasing healthcare applications into the mobile marketplaces for health management. These applications help individuals track their own biorhythms and contain sensitive information. This case study examines the source code of mobile applications released to GitHub for the Risk of Insufficient Cryptography in the Top Ten Mobile Open Web Application Security Project risks. We first develop and justify a mobile OWASP Cryptographic knowledgegraph for detecting security weaknesses specific to mobile applications which can be extended to other domains involving cryptography. We then analyze the source code of 203 open source healthcare mobile applications and report on their usage of cryptography in the applications. Our findings show that none of the open source healthcare applications correctly applied cryptography in all elements of their applications. As humans adopt healthcare applications for managing their health routines, it is essential that they consider the privacy and security risks they are accepting when sharing their data. Furthermore, many open source applications and developers have certain environmental parameters which do not mandate adherence to regulations. In addition to creating new free tools for security risk identifications during software development such as standalone or compiler-embedded, the article suggests awareness and training modules for developers prior to marketplace software release

Embeddings Among Toruses and Meshes

Toruses and meshes include graphs of many varieties of topologies, with lines, rings, and hypercubes being special cases. Given a d-dimensional torus or mesh G and a c-dimensional torus or mesh H of the same size, we study the problem of embedding G in H to minimize the dilation cost. For increasing dimension cases (d \u3c c) in which the shapes of G and H satisfy the condition of expansion, the dilation costs of our embeddings are either 1 or 2, depending on the types of graphs of G and H. These embeddings a,re optimal except when G is a torus of even size and H is a mesh. For lowering dimension cases (d \u3e c) in which the shapes of G and H satisfy the condition of reduction, the dilation costs of our embeddings depend on the shapes of G and H. These embeddings, however, are not optimal in general. For the special cases in which G and H are square, the embedding results above can always be used to construct embeddings of G in H: these embeddings are all optimal for increasing dimension cases in which the dimension of H is divisible by the dimension of G, and all optimal to within a constant for fixed values of d and c for lowering dimension cases. Our main analysis technique is based on a generalization of Gray code for radix-2 (binary) numbering system to similar sequences for mixed-radix numbering systems

Mobile Software Assurance Informed through Knowledge Graph Construction: The OWASP Threat of Insecure Data Storage

Many organizations, to save costs, are moving to the Bring Your Own Mobile Device (BYOD) model and adopting applications built by third-parties at an unprecedented rate. Our research examines software assurance methodologies specifically focusing on security analysis coverage of the program analysis for mobile malware detection, mitigation, and prevention. This research focuses on secure software development of Android applications by developing knowledge graphs for threats reported by the Open Web Application Security Project (OWASP). OWASP maintains lists of the top ten security threats to web and mobile applications. We develop knowledge graphs based on the two most recent top ten threat years and show how the knowledge graph relationships can be discovered in mobile application source code. We analyze 200+ healthcare applications from GitHub to gain an understanding of their software assurance of their developed software for one of the OWASP top ten mobile threats, the threat of “Insecure Data Storage.” We find that many of the applications are storing personally identifying information (PII) in potentially vulnerable places leaving users exposed to higher risks for the loss of their sensitive data

Evaluating Word Similarity Measure of Embeddings Through Binary Classification

We consider the following problem: given neural language models (embeddings) each of which is trained on an unknown data set, how can we determine which model would provide a better result when used for feature representation in a downstream task such as text classification or entity recognition? In this paper, we assess the word similarity measure through analyzing its impact on word embeddings learned from various datasets and how they perform in a simple classification task. Word representations were learned and assessed under the same conditions. For training word vectors, we used the implementation of Continuous Bag of Words described in [1]. To assess the quality of the vectors, we applied the analogy questions test for word similarity described in the same paper. Further, to measure the retrieval rate of an embedding model, we introduced a new metric (Average Retrieval Error) which measures the percentage of missing words in the model. We observe that scoring a high accuracy of syntactic and semantic similarities between word pairs is not an indicator of better classification results. This observation can be justified by the fact that a domain-specific corpus contributes to the performance better than a general-purpose corpus. For reproducibility, we release our experiments scripts and results

An Efficient Multiway Hypergraph Partitioning Algorithm for VLSI Layout

In this paper, we propose an effective multiway hypergraph partitioning algorithm. We introduce the concept of net gain and embed itin the selection of cell moves. Unlike traditional FM-based iterative improvement algorithms in which the selection of the next cell to move is only based on its cell gain,our algorithm selects a cell based on both its cell gain and the sum of all net gains for those nets incidents to the cell. To escape from local optima and to search broader solution space, we propose a new perturbation mechanism. These two strategies significantly enhance the solution quality produced by our algorithm. Based on our experimental justification, we smoothly decrease the numbers of iteration from pass to pass to reduce the computational effort so that our algorithm can partition large benchmark circuits with reasonable run time. Compared with the recent multiway partitioning algorithms proposed by Dasdan and Aykanat [5], our algorithm significantly outperforms theirs in terms of solution quality (cutsize) and run time: the average improvements in terms of average cutsize over their PLM3 and PFM3 are 47.64% and 36.76% with only 37. 17% and 9.66% of their run time respectively