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Retrieving health information is a task of search for health-related information from a variety of sources. Gathering self-reported health information may help enrich the knowledge body of the disease and its symptoms. We investigated retrieving symptom mentions in COVID-19-related Twitter posts with a pretrained large language model (GPT-3) without providing any examples (zero-shot learning). We introduced a new performance measure of total match (TM) to include exact, partial and semantic matches. Our results show that the zero-shot approach is a powerful method without the need to annotate any data, and it can assist in generating instances for few-shot learning which may achieve better performance.

Recently, an active area of research in pharmacovigilance is to use social media such as Twitter as an alternative data source to gather patient-generated information pertaining to medication use. Most of thr published work focuses on identifying mentions of adverse effects in social media data but rarely investigating the relationship between a mentioned medication and any mentioned effect expressions. In this study, we treated this relation extraction task as a classification problem, and represented the Twitter text with neural embedding which was fed to a recurrent neural network classifier. The classification performance of our method was investigated in comparison with 4 baseline word embedding methods on a corpus of 9516 annotated tweets.

Recently, an active area of research in pharmacovigilance is to use social media such as Twitter as an alternative data source to gather patient-generated information pertaining to medication use. Most of thr published work focuses on identifying mentions of adverse effects in social media data but rarely investigating the relationship between a mentioned medication and any mentioned effect expressions. In this study, we treated this relation extraction task as a classification problem, and represented the Twitter text with neural embedding which was fed to a recurrent neural network classifier. The classification performance of our method was investigated in comparison with 4 baseline word embedding methods on a corpus of 9516 annotated tweets.

Since the beginning of the COVID-19 pandemic, patients shared their personal experiences of the viral infection on social media. Gathering their symptomatic experiences reported on Twitter may help better understand the infectious disease and supplement our knowledge of the disease gathered by healthcare workers. In this study, we identified personal experience tweets related to COVID-19 infection using a pre-trained and fine-tuned language model, and annotated the machine-identified tweets in order to extract the information of infection status, symptom concepts, and the days the symptomatic experience occurred. Our result shows that the top 10 most common symptoms mentioned in the collected Twitter data are in line with those published by WHO and CDC. The symptoms along with the day information appear to provide additional insight on how the infection progresses in infected individuals.

Since the beginning of the COVID-19 pandemic, patients shared their personal experiences of the viral infection on social media. Gathering their symptomatic experiences reported on Twitter may help better understand the infectious disease and supplement our knowledge of the disease gathered by healthcare workers. In this study, we identified personal experience tweets related to COVID-19 infection using a pre-trained and fine-tuned language model, and annotated the machine-identified tweets in order to extract the information of infection status, symptom concepts, and the days the symptomatic experience occurred. Our result shows that the top 10 most common symptoms mentioned in the collected Twitter data are in line with those published by WHO and CDC. The symptoms along with the day information appear to provide additional insight on how the infection progresses in infected individuals.

One of the keys to making cloud data-centers (CDCs) proliferate impressively is the implementation of efficient task scheduling. Since all the resources of CDCs, even including operating systems (OSes) and application programs, can be stored and managed on remote data-centers, this study first analyzed the task scheduling problem for CDCs and established a mathematical model of the scheduling of two-stage tasks. The Johnson's rule was combined with the genetic algorithm to create a Johnson's-rule-based genetic algorithm (JRGA), which takes into account the characteristics of multiprocessor scheduling in CDCs. New crossover and mutation operations were devised to make the algorithm converge more quickly. In the decoding process, the Johnson's rule is used to optimize the makespan for each machine. Simulations were used to compare the performance of the JRGA with that of the list scheduling algorithm and an improved list scheduling algorithm. The results demonstrate the validity of the JRGA.

First-hand experience related to any changes of one’s health condition and understanding such experience can play an important role in advancing medical science and healthcare. Monitoring the safe use of medication drugs is an important task of pharmacovigilance, and first-hand experience of effects about consumers’ medication intake can be valuable to gain insight into how our human body reacts to medications. Social media have been considered as a possible alternative data source for gathering personal experience with medications posted by users. Identifying personal experience tweets is a challenging classification task, and efforts have made to tackle the challenges using supervised approaches requiring annotated data. There exists abundance of unlabeled Twitter data, and being able to use such data for training without suffering in classification performance is of great value, which can reduce the cost of laborious annotation process. We investigated two semi-supervised learning methods, with different mixes of labeled and unlabeled data in the training set, to understand the impact on classification performance. Our results from both pseudo-label and consistency regularization methods show that both methods generated a noticeable improvement in F1 score when the labeled set was small, and consistency regularization could still provide a small gain even a larger labeled set was used.

Growing uses of opioids for pain management have led to a crisis of addiction and thousands of deaths in the United States. Although many of the opioid effects have been observed and reported, experience directly from the opioid users may help provide additional information in identifying any potentially unreported effects. In this study, we developed a neural embedding-based method to discover potential opioid-effect relations through similar relations of known medication effects. Using a corpus of 3.6 million clean unannotated tweets, a vector space model was learned with word2vec, and the model was used to identify potential opioid effects. The inferred results were further verified against 5 authoritative sources of medication effects. Seven of inferred effects were identified as potentially unreported, demonstrating the power and utility of our method. It is conceivable that our approach can be applied to discovery of potentially unreported effects of other medications.

HUMIRA, a biologic therapy, has been approved to treat autoimmune diseases and been marketed in many countries worldwide. Much like other medications, it demonstrates many effects on the human body. It is important to understand its effects from the information generated by its users, and social media is one of the venues its users share their experience with the medication. To understand what HUMIRA effects were reported on Twitter, we utilized a relational similarity-based approach to infer HUMIRA effects based upon known medication-effect relations of other medications. With a corpus of 3.6 million preprocessed, "clean" tweets, a total of 55 effects were identified, and among them, 46 were previously observed, and nine were potentially unreported after verification with six reliable sources. The results not only indicate that many HUMIRA effects shared by the Twitter users are consistent with those previously reported, but also demonstrate the power and utility of our method, making it applicable to studying effects of other medications shared by Twitter users.

This paper presents an advanced fuzzy C-means (FCM) clustering algorithm to overcome the weakness of the traditional FCM algorithm, including the instability of random selecting of initial center and the limitation of the data separation or the size of clusters. The advanced FCM algorithm combines the distance with density and improves the objective function so that the performance of the algorithm can be improved. The experimental results show that the proposed FCM algorithm requires fewer iterations yet provides higher accuracy than the traditional FCM algorithm. The advanced algorithm is applied to the influence of stars’ box-office data, and the classification accuracy of the first class stars achieves 92.625%.

Identifying influential individuals who lead to faster and wider spreading of influence in social networks is of theoretical significance and practical value to either accelerating the speed of propagation in the case of product promotion, or hindering the pace of diffusion involved in rumors. Conventional methods, ranging from centrality indices to diffusion-based processes, already take into account the number and influences of followers, but fail to make full use of the characteristics of social media. A novel approach called PartitionRank for finding a pre-fixed number of influential individuals in microblogging scenarios is proposed in this study to maximize the impact; it combines interest similarity with social interaction between users via graph partitioning. Experimental results on artificial and real-world microblogging networks illustrate that our scheme outperforms the other state-of-the-art methods in effectiveness and efficiency.

Clustering users with more common interests who interact frequently on social networking sites has attracted much attention from researchers due to the high economic value and further application prospects. Community detection is a widely accepted means of dealing with the challenge of clustering users, but conventional methods are inadequate since there are billions of vertices and various relations in social media. Through the user model, a heterogeneous network containing both undirected and directed edges is built in this study to exactly simulate a social network. A novel approach for overlapping community detection in a heterogeneous social network (OCD-HSN) is proposed, which contains seed selecting and community initializing and expanding to accurately and efficiently unfold modules in parallel. Experimental results on artificial and real-world social networks demonstrate the higher accuracy and lower time consumption of the proposed scheme compared with other existing state-of-the-art algorithms.

Wireless network is crucial for the Mobile Transparent Computing (MTC), in which a mobile device without any Operating System (OS) support needs to load the demanded OSes and applications through accessing the wireless network connection. In this paper, a lightweight approach based on the Boot Management System (BMS) was proposed to ensure the wireless network connection before booting OS. In BMS, the Virtual File System (VFS) technology was used to drive the wireless network card and establish a stable network connection. A prototype of the BMS was tested on ARM11 hardware platform and the results demonstrate the validity of the BMS.

Twitter, a general purpose social media service, has seen growing interests as an active data source for possible use of post-market surveillance of medicinal products. Being able to identify Twitter posts of personal experience related to medication use is as important as being able to identify expressions of adverse medical events/reactions for the surveillance purpose. Identifying personal experience tweets is a challenging task, especially in the aspect of engineering features for classification. Word embedding has become a superior alternative to engineered features in many text classification applications. To investigate if word embedding-based methods can perform constantly better than conventional classification methods with engineered features, we assessed the classification performance of 4 word embedding techniques: GloVe, word2vec, fastText, and wordRank. Using a corpus of 22 million unlabeled tweets for learning of word embedding and a corpus of 12,331 annotated tweets for classification, we discovered that word embedding-based classification methods consistently outperform the engineered feature-based classification methods with statistical significance of p < 0.01, but there exist no significantly statistical differences among the 4 study word embedding methods (p < 0.05).

Continuous monitoring the safe use of medication is an important task in pharmacovigilance. The first-hand experiences of medication effects come from the consumers of the pharmaceuticals. Social media have been considered as a possible alternative data source for gathering consumer-generated information of their experience with medications. Identifying personal experience in social media data is a challenging task in natural language processing. In this study, we investigated a method of predicating personal experience tweets using Google’s Bidirectional Encoder Representations from Transformers (BERT) and neural networks, in which BERT models contextually represented the tweet text. Both pre-trained BERT models and our BERT model trained with 3.2 million unlabeled tweets were examined. Our results show that our trained BERT model performs better than Google’s pre-trained models (p < 0.01). This suggests that domain-specific data may contribute to the BERT model yielding better classification performance in predicting personal experience tweets of medication use.

During the last decade, mobile user experience created a new profit structure, thanks to the massive growth in the use of smart mobile devices and the large amount of mobile data it has generated. Owing to this growth, mobile resource management (e.g., bandwidth, computing resources, etc.) has become a big challenge in mobile computing. This article proposes a broker technology that is based on a resource management optimization method and that effectively utilizes local area network (LAN) resources to perform computing tasks for a single mobile client. This technology thus extends the computing paradigm of mobile transparent computing (MTC) with the characteristics of local mobile device computing and remote server storage to include LAN computing and remote server storage as well. Through the cooperation between the management center, request manager, connection manager, and processor manager of a third-party broker (TPB) for managing, respectively the computing resources, user requests, network connections, and other services in MTC, the TPB is able to support mobile computation-intensive and resource-consuming tasks, and handle fluctuation in users' requests. Several experiments were carried out in a testbed environment of MTC to demonstrate the validity and efficiency of the proposed architecture and method. Experiment results showed that the TPB is effective and stable in optimizing resource utilization.

With the development of technology, the processing of natural language texts is increasingly used in medical scenes. For example, speech recognition technology and smart customer service robots can help hospitals to improve efficiency. However, the accuracy rate of the text output of the speech recognition is not satisfactory, especially in the medical scene. As a result, the study of natural language text processing in medical scene is very meaningful. To address this issue, we construct a corpus, a knowledge base, a Pinyin library and a word frequency library. We perform word segmentation operations based on the knowledge base and calculate the word co-occurrence probability based on the word frequency library. The word co-occurrence probability is used to determine whether erroneous words exist or not. If there are words whose co-occurrence probabilities are below a threshold, we assert that there exists erroneous words. And then the clustering analysis is used to locate the erroneous words. The experimental results show that the accuracy rate of erroneous word locating is very high. Therefore, this method has certain practical value.

Understanding medication effects is an important activity in pharmacovigilance in which patients are the most important contributor. Social media, where users share their personal experiences of medication effects, have been recommended as an alternative data source of gathering signal information of suspected medication effects. To discover potential medication-effect relations from Twitter data, we devised a method employing analogical reasoning with neural embedding of Twitter text. The process involves learning the neural embedding from unlabeled tweets and performing vector arithmetic, making it obscure to understand how an inferred relation is derived. To make the process understandable and interpretable and to facilitate the decision making on accepting or rejecting any inferred medication-effect relations, we added explanation(s) to each step of the process. An example of inferred relation is provided to demonstrate the effectiveness of our approach in explaining how the result of each step is derived.

Prevalence of social media has driven a growing number of health related applications with the information shared by online users. It is well known that a gap exists between healthcare professionals and laypeople in expressing the same health concepts. Filling this gap is particularly important for health related applications using social media data. A data-driven, attributional similarity-based method was developed to identify Twitter terms related to side effect concepts. For the 10 most common side effect (symptom) concepts, our method was able to identify a total of 333 Twitter terms, among which only 90 are mapped to those in the consumer health vocabulary (CHV). The identified Twitter terms are specific to Twitter data, indicating a need to expand the existing CHV, and many of them seem to have less ambiguity in word senses than those in CHV.

Background As Twitter has become an active data source for health surveillance research, it is important that efficient and effective methods are developed to identify tweets related to personal health experience. Conventional classification algorithms rely on features engineered by human domain experts, and engineering such features is a challenging task and requires much human intelligence. The resultant features may not be optimal for the classification problem, and can make it challenging for conventional classifiers to correctly predict personal experience tweets (PETs) due to the various ways to express and/or describe personal experience in tweets. In this study, we developed a method that combines word embedding and long short-term memory (LSTM) model without the need to engineer any specific features. Through word embedding, tweet texts were represented as dense vectors which in turn were fed to the LSTM neural network as sequences. Results Statistical analyses of the results of 10-fold cross-validations of our method and conventional methods indicate that there exist significant differences (p

Twitter, as a microblogging social media platform, has seen increasing applications of its data for pharmacovigilance which is to monitor and promote safe uses of pharmaceutical products. Medication names are typically used as keywords to query social media data. It is known that medication names are misspelled on social media, and finding the misspellings is challenging because there exists no a priori knowledge as to how people would misspell a medication name. We developed a data-driven, relational similarity-based approach to discover misspellings of medication names. Our approach is based upon the assumption of the identical (or similar) association of a medicine with its effects whether the medication is correctly spelled or misspelled. With distributed representations of the words in tweets posted in recent 24 months, we were able to discover a total of 54 misspellings of 6 medicines whose indications containing headache. Our search results also show that Twitter posts with misspellings of codeine and ibuprofen can be more than 10% of all the tweets associated with each of the medicines. Compared with the phonetics-based approach, our method discovered more actual misspellings used on Twitter.

The number of cloud video surveillance (CVS) systems has been increasing rapidly over the last decade. Since CVS systems are big energy consumers, it is urgent to take the problem of optimizing the energy consumption of CVS systems into consideration. In this study, we build a task scheduling model, and present a method of scheduling that minimizes energy consumption by reducing the number of virtual machines. The optimization problem is first formulated as a multi-dimensional bin-packing problem due to the constrains on the resources (sizes of the bandwidth, the memory, the hard disk, the CPU utilization, etc.). We convert the problem into a one-dimensional bin-packing problem by making use of the relationships between the resources, and solve it using the greedy best-fit search algorithm. This method greatly reduces the computational expense and can be used in a real-time fashion. An experimental system is designed to evaluate the method, and four experiments are carried out to demonstrate the validity of the method. Experimental results show that the method not only largely improved the resource utilization and reduces energy consumption but also the scheduling time was significantly decreased when handling the same number of video tasks. And it is obviously superior to the common approach and First Fit Decreasing (FFD) algorithm. HighlightsWe build a model of power consumption of the cloud video surveillance center.We build a model of video task scheduling of the cloud video surveillance center.We formulate the model of task scheduling into multi-dimensional bin-packing problem.The multi-dimensional problem is converted into a one-dimensional bin-packing problem.A greedy best-fit algorithm is presented with better effective and lower expense.

In the micro-blog scenario, personal user profiling relying on content is limited for recommending desired diverse subjects due to its shortcomings of short text, often leading to a poor recall. Currently, many methods only utilized the personal knowledge from each individual user to represent user profile without considering the neighborhood information. However, resource information related to neighboring friends play an important role in improving the performance of recommender systems. In this paper, we present the personalized expanded user profiling for micro-blog subject recommendation via ontology semantics structure. Next, taking into account diffusion ability of followee friends, we discuss resource perception relationship (RPR) and follow perception relationship (FPR). Finally, we discuss how, by adjusting the importance of RPR and FPR, the neighborhood is selected to construct neighborhood-user profile, which can mine new relevant subjects for target user. Our experimental results demonstrate the effectiveness of our neighborhood-user profiling in comparison to the existing collaborative filtering and personal user profile recommendation approaches on Sina micro-blog platform datasets.

Service matching technology has promoted the development of Business Process Management (BPM). It’s significant for us to know how to retrieve a qualified service from a large number of candidate services in the library, and how to match and compose the services more appropriately. For the first part, this paper introduces the definition of the service model and rules of service matching. Next, in order to accelerate retrieval speed of services, we propose an indexing mechanism for the parameters of the service. Then, we would introduce the matching algorithm of composite services based on indexing mechanism. At the same time, this paper also analyzes the time complexity and space complexity of the algorithm. Through several simulation experiments, we verify the feasibility of the algorithm. After comparing the Back-Front algorithm, Front-Back algorithm, Service Composition Algorithm Based on AND/OR Graph with our algorithm, we find out the comparison results about the matching time and the retrieved number of composite services. The experiment results show that the matching algorithm based on indexing mechanism can greatly improve the matching speed and get more composite services without compromising the quality of service combination.

The U.S. Food and Drug Administration uses the Center for Food Safety and Applied Nutrition (CFSAN) Adverse Event Reporting System (CAERS) as the primary tool for identifying new and emerging dietary supplement adverse events. Despite mandatory and voluntary reporting of dietary supplement adverse events to CAERS, many continue to go unreported. Availability of social media has enabled dietary supplement consumers to freely share their concerns and experiences online. Such consumer generated information can be a useful source to further monitor the safety of dietary supplements. To study the usefulness of social media (Twitter in particular) for safety surveillance of dietary supplements, we developed a computational processing pipeline: 1) machine learning based identification of potential Twitter posts (tweets) of personal experiences related to the use of dietary supplements, 2) detection of potential supplement events from these tweets using the medpie open source tool, and 3) mapping detected events to effects through the taxonomy provided in SNOMED CT. Using our pipeline, we identified, from a group of 1,244,661 tweets collected, a total of 17,346 personal experience tweets pertaining to 4 dietary supplements. A total of 191 effects were mapped to SNOMED CT and we discovered that 48 of the 191 effects are not listed in either of the two online sources we referenced. However, the effects discovered from the social media data will need to be verified and confirmed with other sources and/or clinical evidences.

Twitter, as a social media platform, has become an increasingly useful data source for health surveillance studies, and personal health experiences shared on Twitter provide valuable information to the surveillance. Twitter data are known for their irregular usages of languages and informal short texts due to the 140 character limit, and for their noisiness such that majority of the posts are irrelevant to any particular health surveillance. These factors pose challenges in identifying personal health experience tweets from the Twitter data. In this study, we designed deep neural networks with 3 different architectural configurations, and after training them with a corpus of 8,770 annotated tweets, we used them to predict personal experience tweets from a set of 821 annotate tweets. Our results demonstrated a significant amount of improvement in predicting personal health experience tweets by deep neural networks over that by conventional classifiers: 37.5% in accuracy, 31.1% in precision, and 53.6% in recall. We believe that our method can be utilized in various health surveillance studies using Twitter as a data source.

Demands for cloud video surveillance systems are growing rapidly. Addressing to the issue of low energy-efficiency in cloud video datacenters, a task scheduling method using a time-clustering-based genetic algorithm is proposed. Firstly, an off-line scheduling model with SLA (service level agreement) time constraint is proposed after the analysis of the constrain relationship between the SLA and surveillance tasks. Then, a time-clustering-based genetic algorithm (TCGA) is proposed to solve the model for an optimal energy-efficient solution. According to the solution, the service quality is guaranteed and the total operating time of virtual machines is minimized. Meanwhile, idle virtual machines are shut down to reduce energy consumption. Simulations of large scale tasks scheduling are conducted. Several comparison experiments verify that the proposed method can improve the resource utilization greatly and achieve energy saving extremely.

The data centers of cloud video surveillance (CVS) systems based on Hadoop have a couple of common problems such as large energy consumption, low power utilization, etc. Addressing to the issue of reducing energy consumption while guaranteeing quality of service, we propose an energy-aware workload balancing method for efficient data storage management in cloud video data centers. A dynamic adjusting algorithm is designed to control the running status of nodes in the data centers according to the access frequency of video data blocks for the purpose of reducing the energy consumption, in order to eliminate the potential influence on the service quality posed by the changes of running status of nodes, the workload balancing between nodes in ring network topology is executed, a nonlinear programming model is established to obtain the minimum number of data blocks transferred during the workload balancing. Experimental results in the GridSim simulation environment show that the proposed method can achieve more energy saving and better performance than the original Hadoop.

Studies have shown that Twitter can be used for health surveillance, and personal experience tweets (PETs) are an important source of information for health surveillance. To mine Twitter data requires a relatively balanced corpus and it is challenging to construct such a corpus due to the labor-intensive annotation tasks of large data sets. We developed a bootstrap method of finding PETs with the use of the machine learning-based filter. Through a few iterations, our approach can efficiently improve the balance of two class dataset with a reduced amount of annotation work. To demonstrate the usefulness of our method, a PET corpus related to effects caused by 4 dietary supplements was constructed. In 3 iterations, a corpus of 8,770 tweets was obtained from 108,528 tweets collected, and the imbalance of two classes was significantly reduced from 1:31 to 1:3. In addition, two out of three classifiers used showed improved performance over iterations. It is conceivable that our approach can be applied to various other health surveillance studies that use machine learning-based classifications of imbalanced Twitter data.

Delivery of an Information Technology curriculum involves utilization of a variety of technologies. Currently hypervisor-based virtualization is widely adopted in classroom and laboratory, satisfying the needs of instructional delivery in Information Technology education. However, it brings new challenges in setting up, and executing on resource-limited hardware. Coupled with this is our situation of shared laboratory computers which have many computationally intensive software applications. To address the issues, we conducted a preliminary investigation of container-based virtualization for use in classroom and laboratory. Our study did discover advantages and limitations of using such an emerging new technology. Although the container-based virtualization does provide many benefits, our observation suggests that at the current stage, it is more appropriate to have a mixture of hypervisor-based and container-based virtualization in Information Technology education.

Introduction This paper describes a process for developing an outcome based objectives model (Helps, Lunt, & Anthony, 2005) for implementing an undergraduate computer information technology (CIT) curriculum. Outcome based objectives are an integral part of the ABET (Accreditation Board for Engineering and Technology) accreditation guidelines. The literature supporting information technology education (Aasheim, Lee, & Reichgelt, 2005; Dark, Ekstrom, & Lunt, 2005; Hazem et al., 2004; Said et al., 2004; Stockman, Chaytor, et al., 2004; Stockman, Christopherson, Said, & Nyland, 2004) and supporting using outcome based objectives (Abernethy, Treu, Piegari, & Reichgelt, 2005; Longenecker Jr. & Feinstein, 2005) to define the graduating student, then the specific requisite knowledge, skills, and abilities (KSA's) of the student's primary focus area. A graduate of the program should have specific sets of KSA's spread across the curricula. Utilization of this process model allows the specific academic unit (department, school, discipline) to easily identify and address accreditation goals. We first decide what skills the students will need for a given program, which, in turn, defines the specialization for the discipline. Courses are developed within the specialization to teach the specific skills. The implementation is a matter of selecting the appropriate courses within the specialization that teach the desired skill set for the discipline. Designing a program is the last step in the process since the idea is to provide a flexible framework around which an individually tailored student major can be customized. The Special Interest Group for Information Technology Education (SIGITE) is an accreditation development group comprised of academics, professionals, and industry within the Association of Computing Machinery (ACM) who are responsible for creating the guidelines and requirements contained in the Information Technology Education volume. For those readers who are aware of CC2001 ("Computing curricula 2001", 2001) and previous volumes of the Computer Science education accreditation guidelines this is a similar effort on the part of SIGITE. The process of creating the SIGITE guidelines are integral to a larger effort to update all of the volumes of engineering and technology education (Shackelford et al., 2004). When completed these guidelines will be accepted and utilized by ABET (Accreditation Board for Engineering and Technology) for accrediting programs. The concept behind an outcome based objectives curriculum model is analogous to reading a story backwards. You end up defining the conclusion before constructing the plot. Based on the requirements of "what must be accomplished" we define what must be taught. Stepping sequentially through the process, a story of what a student needs as KSA's emerges. The Curriculum Development Process The process (see Figure 1) of creating the curriculum included meeting with specific industry subject matter experts, and reviewing the needs assessment of those industry experts and hiring agencies. Specific requirements for hiring graduates of the program were assessed by the industry panelists and cross referenced with the SIGITE knowledge areas. These diverse groups became the stake holders for the curriculum project. These knowledge areas were then adopted and incorporated into the curricula (Lawson, Lidtke, & Price, 2004). Where appropriate current courses were adapted, or modified to create a seamless adoption of the curriculum. However, where required, new courses were created to satisfy the needs of the stake holders. [FIGURE 1 OMITTED] Each of the outcome based learning objectives of the courses was processed for level of skill based on the Bloom Taxonomy (Anderson, Krathwohl, & Bloom, 2001; Bloom, Krathwohl, Masia, & Emgelhart, 1956) for education. The objectives were then looked at for areas of expertise as applied to the individual curricula guidelines. …

Commercially available mobile devices with various kinds of hardware and software platforms have resulted in a huge amount of mobile services. This has a new challenge in designing multiple services that are compatible with heterogeneous devices and operating systems (OSes). This paper presents a method of multi-services mobile cloud computing (MSMCC) for mobile device, which regards OS as a service to improve user experience. A multi-services pre-boot firmware (MSPF) is designed to stream multi-OS images and multi-application images to the mobile devices over wireless network. After that, we use the MSMCC method to run on the S3C6410 board equipped with MSPF, results illustrated that MSPF is able to support remote boot and streaming execution of multi-services with stable performance.

Online social networks have found a significant increase in their popularity in recent years. All the networks have community structure, and one of the research problems mostly frequently tackled is the discovery of communities. An overlapping community is a network structure that allows one node to be a member of multiple communities. The method presented in this paper aims at detecting overlapping communities in social networks, and its novelty lies in that it combines with the Microblog User Model (MUM) which can reflect the interest of the user accurately. First, the MUM network, which is an undirected and weighted network, is constructed by computing the similarity among MUMs. Afterwords, Overlapping Community Detection based on MUM (OCD-MUM) is performed to partition the network. A community stops expanding when the fitness function reaches a local maximum. The communities detected are locally optimized. A user's interest is not only decided by the MUM, but it is also affected by the communities the user belongs to. The community model can reflect the interest of the community. The MUM is updated with community model of its communities, and therefore the interest of the user can be predicted by these communities. Our experiment result shows that OCD-MUM has a higher modularity Q value than traditional methods and the predicted interest is more close to the real world situations.

Electronic Product Code has been widely used to identify items with RFID tags. To the best of our knowledge, however, few works focus on exploring the value of textural data in the EPC Network that pertains to EPC. In this paper we propose the concept of the content-based query for tagged items by applying information retrieval method to the EPC network resources. The objective of the content-based query is to discover a set of class-level tagged items of interest which correlate with the user-specific keyword or EPC in question. Accordingly, we describe the strategies in terms of content integration, modelling and similarity measurement for the content-based queries. Our experimental results demonstrate the effectiveness of our approach.

Energy efficiency has become an increasingly prominent issue in cloud computing. Task consolidation is an effective way to maximise utilisation of cloud computing resources and reduce energy consumption. Presented in this paper is an improved energy-aware task consolidation algorithm to optimise the scheduling of tasks in cloud computing data centres. Our algorithm was developed based on the linear relationship between energy consumption and the CPU utilisation. Instead of consolidating tasks to a service node until its CPU utilisation reaches 100%, our algorithm uses an optimal point, where the CPU utilisation of a service node reaches 70%, and ensures that every task is preferentially assigned to service nodes that satisfy the 70% CPU utilisation. Our experiments demonstrate that our algorithm can significantly reduce the energy consumption of cloud computing data centres without performance degradation while maintaining good performance compared to the energy-conscious task consolidation (ECTC) approach.

Adverse drug reactions have become one of the top causes of deaths. For surveillance of adverse drug events, patients have gradually become involved in reporting their experiences with medications through the use of dedicated and structured systems. The emerging of social networking provides a way for patients to describe their drug experiences online in less-structured free text format. We developed a computational approach that collects, processes and analyzes Twitter data for drug effects. Our approach uses a machine-learning-based classifier to classify personal experience tweets, and use NLM’s MetaMap software to recognize and extract word phrases that belong to drug effects. Our results on 5 medications demonstrate the validity of our approach, and its ability to correctly extract potential drug effects from the Twitter data. It is conceivable that social media data can serve to complement and/or supplement traditional time-consuming and costly surveillance methods.

Background Audit trails have been used widely to ensure quality of study data and have been implemented in computerized clinical trials data systems. Increasingly, there is a need to audit access to study participant identifiable information to provide assurance that study participant privacy is protected and confidentiality is maintained. In the United States, several federal regulations specify how the audit trail function should be implemented. Purpose To describe the development and implementation of a comprehensive audit trail system that meets the regulatory requirements of assuring data quality and integrity and protecting participant privacy and that is also easy to implement and maintain. Methods The audit trail system was designed and developed after we examined regulatory requirements, data access methods, prevailing application architecture, and good security practices. Results Our comprehensive audit trail system was developed and implemented at the database level using a commercially available database management software product. It captures both data access and data changes with the correct user identifier. Documentation of access is initiated automatically in response to either data retrieval or data change at the database level. Limitations Currently, our system has been implemented only on one commercial database management system. Although our audit trail algorithm does not allow for logging aggregate operations, aggregation does not reveal sensitive private participant information. Careful consideration must be given to data items selected for monitoring because selection of all data items using our system can dramatically increase the requirements for computer disk space. Evaluating the criticality and sensitivity of individual data items selected can control the storage requirements for clinical trial audit trail records. Conclusions Our audit trail system is capable of logging data access and data change operations to satisfy regulatory requirements. Our approach is applicable to virtually any data that can be stored in a relational database.

Natural disasters, such as hurricanes, tsunamis and earthquakes cause huge loss of life, enormous amounts of property damage, and lengthy recovery times. Although it is impossible to avoid the costs of disasters, human sufferings can be minimized through effective disaster management and decision support system that can facilitate and expedite the resource distribution process more efficiently and effectively. In this paper, we utilized the high performance computing resources at Purdue University Calumet (PUC) to develop a disaster data management, visualization and decision supporting system that focuses on North West Indiana region. This paper focuses on three research objectives: 1) development of web-based disaster data management and communication system, 2) fast distributed computation of disaster related damage assessment and resource distribution strategy utilizing Miner Cluster, 3) interactive visualization of disaster affected populations, resource centers, as well as the resource distribution strategy. This research provides an efficient way for the local government agencies to process and manage disaster related information under adverse disaster conditions.

There is a great need in industry to mitigate the risks and threats that our computer infrastructure faces. The answer is to incorporate a better strategy of software assurance within the development process. This requires knowledge and skills that is not offered to many college level students. A software assurance course offered at a university was observed and evaluated on the course content and the learning strategy. The content was compared to the Software Assurance: A Curriculum Guide to the Common Body of Knowledge to Produce, Sustain, and Acquire Software. The course content focuses on security principles and vulnerabilities in coding. The laboratory exercises require students to develop an application, learn formalized testing methods, and then evaluate and fix the vulnerabilities found from the testing methods. The course was very effective in teaching students the importance of software assurance. The observation of the course found that with additional areas added to the course content a well rounded course in software assurance could be offered to students.

Software assurance is not a new discipline, but industry is expressing a need for a stronger knowledge of and improved skills in software assurance. Poor quality software is a serious problem that continues to grow. This requires a skill that few professionals can provide. Training and education are important factors to increase knowledge and skill. Software assurance topics in academia are limited, so there is a need for additional research on the subject.This research focuses on the need for software assurance topics in academia. The results identified the gap of knowledge in the education of software assurance. Important software assurance concepts have been disregarded in the college curriculum leaving industry struggling for skilled professionals. These concepts are identified and must be incorporated into a college curriculum to close the gap.A comparison was made to analyze the topics that are not discussed in academia. The comparison between computing curricula and the Software Assurance: A Curriculum Guide to the Common Body of Knowledge to Produce, Sustain, and Acquire Software (CBK) resulted in a guide of topics to be addressed in academia. The CBK was not entirely addressed in any of the computing curriculum. The topics that were poorly covered in the computing curricula were identified and categorized. These results should be the primary topics incorporated into computer courses and utilized in the development of new software assurance courses.

Natural disasters, such as hurricanes, tsunamis and earthquakes cause huge loss of life, enormous amounts of property damage, and lengthy recovery times. Although it is impossible to avoid the costs of disasters, human sufferings can be minimized through effective disaster management and decision support system that can facilitate and expedite the resource distribution process more efficiently and effectively. This paper is to present the design of GIS based disaster data management, visualization and decision support system for North West Indiana region utilizing grid computing and visualization resources at Purdue University Calumet (PUC). One of the key factors in the disaster management system is to provide damage assessment maps in timely manner. We propose a parallel image processing algorithm utilizing computational grid to compute the disaster damage assessment map from pre- and post- disaster satellite images. Disaster decision support system will compute the effective resource distribution strategy and prioritize the rescue areas by utilizing disaster specific geospatial information system. This research will lay a foundation for disaster preparedness, management and decision support system at local government agency level.

In most organizations, there still exists a variety of enterprise-level applications that were developed at different times, with different technologies, and on different platforms. One of the job challenges our IT graduates face is to be involved in the projects of integrating those applications, and such a challenge requires particular technical knowledge and skills.Driven by such a market need, inspired by the IT body of knowledge, and motivated by our continuing effort of IT curriculum development and improvement, we have designed a senior level course titled "Application Integration". The course content spans from basic principles and architectures of various application integration approaches, to the foundations of modern application integration that include all the basics of XML technology, and to service-oriented architecture computing paradigm.The course involves extensive laboratory exercises and a semester project. The lab assignments require students to design and make their own "dog food" early in the semester so that it will be processed (manipulated) over time. The diverse of their initial designs provides a forum for active participation and learning as well as discussions among the students and instructor. The semester project is either a single phase or a two-phase project. The two-phase project consists of an earlier phase involving the creation of standalone applications by each individual student with different technologies and platforms, and a later phase dealing with the development of a dashboard type application that integrates individually developed applications.The feedback from students who took the course has been very positive and encouraging. Students were not only actively immersed in learning the knowledge and technology of application integration, but they were also immersed in their great learning experience to better prepare for their careers.

Comparative study of biological pathway structures and composition can aid us in elucidating the functions of newly discovered pathways, understanding evolutionary traits, and determining missing pathway elements. A method has been developed to perform pair-wise comparison and similarity search of biological pathways. The comparison determines the differences of each pair of pathways represented in the XML format. The similarity search uses a scoring mechanism to rank the similarities of the pathway in question against those in the pathway repository. To achieve a reasonably good performance, the method is being implemented using the Condor high performance computing environment.

The growing accumulation of biological pathway datasets is making it possible to study pathways at genomic scale by exploration and visualization, facilitating the shift of biomedical research to systems-level analyses. Presented in this paper is our ongoing work on developing a system for exploring and visualizing biological pathways for large scale pathway analyses. A pathway repository with a standard data format was developed for storing various types of pathways, mechanisms for navigating among different pathway datasets and to external data sources were also designed, and a rendering engine is being developed to allow user for visualization and exploration.