Reviewerly/exHarmony huggingface dataset:
Identifying High Value Users in Twitter Based on Text Mining Approaches

Finding out new potential users for specific products are always the needs of the marketing department in industries. While Traditional ways like RFM model perform poorly in exploring new users. While the popularity of social media like Twitter and Facebook provides advertisers a new way to find, understand and target their users. In this paper, we propose a new method to find out and rank high-value target audience for a specific brand by utilizing machine learning and text mining approach. Overall tweets from 10 accounts in Twitter are collected to build the target and non-target dataset. In order to solve the data imbalance problem, five data resampling methods are assessed. Ensemble learning approaches include Bagging and Boosting algorithm are used to build the classifier. The results show that SMOTE outperforms other resampling method and AdaBoosting algorithm outperform other single classifier and Bagging model. We also find out the existence of marking accounts exists so that a threshold is set to filter these accounts which are not real users. We believe that our approach could be used in industry for identifying high-value users for online marketing purpose.
On the Cost of the Management of user Applications in a Multicloud Environment

Multi-cloud solutions are becoming increasingly popular among enterprises and, today, it is easy to find organizations that use resources and services belonging to two or more cloud providers. It is well-known that the adoption of a multi-cloud infrastructure provides several advantages: it limits vendor lock-in and/or dependence on a single provider, enables cost reduction, grants the access to a wide variety of services and computational resources and allows the exploitation of the geographical location of the servers (e.g., to be consistent with data compliance policies). These advantages, however, have a cost in terms of greater complexity in resources' configuration and management: e.g., security policies have to be adapted and made consistent among all the different cloud providers. This complexity implies that most of the companies that use a multi-cloud infrastructure manage the resources belonging to different providers as independent and autonomous, exploiting only a portion of the advantages that could be benefited if those resources were used in an integrated and optimized way. In this paper, we will describe and discuss some of the aspects related to the inter-networking among resources belonging to multiple providers, a necessary and fundamental step towards a strong integration among different clouds.
Embodying the homunculus in virtual reality

Virtual training has been proposed as a controlled, interactive and efficient way to learn new concepts and acquire new skills. Medical training has shown a fast adoption of virtual reality (VR) technologies in several applications. Although progress has been made to reduce the cost of novel and highly immersive hardware tools, the availability of specialized virtual contents to complement conventional training methods is still limited. In this paper, we present preliminary efforts to create an interactive online platform called the "Neuro VR Lab" which contains different activities aiming to recreate well-known neuroscientific concepts and practical experiments. Particularly, this paper covers the development of the Homunculus VR experience, a hands-on interactive laboratory procedure created to illustrate the concept of mechano-perception and brain mapping. Once in pairs, practitioners use a simple cardboard tool to measure the skin sensitivity of their partners in different parts of the body. By registering this data, practitioners are encouraged to use a VR application that allows visualizing their personalized Homunculus in a recreated virtual laboratory. Full-body tracking through the Kinect V2 sensor allows improving the interactivity with the Homunculus avatar by fostering a more embodied experience. Activities are freely available at the Neuro VR Lab website as well as documents with guidelines to carry out the hands-on activities and the executables to run the interactive applications in VR. Our ultimate goal is to facilitate the adoption of these immersive technologies in schools, universities and education institutions by providing free, virtual and highly interactive applications.
Robust Methods Using Graph and PCA for Detection of Anomalies in Medical Records

Wellbeing in basic words is normalcy in health of human body and disease is unusual condition that influences typical working of human body with no outer wounds. Health care is about the aversion of maladies by finding and treatment. There are several methods and models developed in health care to predict and classify the chronic diseases, but what if the health care record contains some anomalies. A well designed model also would provide wrong results, if the input data contains anomalies. Any wrong decision in health care management would cost the life of the patient. In this work we have modeled Graph method and PCA method to detect anomalies in health care records by the means of frequency of incidences of disease codes in graph and correlation among the disease codes. We have used CMS medi-claim dataset in which diseases are expressed in terms of International disease code (ICD) and Hierarchical Condition Category (HCC) code to indicate the patient health condition. Game theory approach is used for evaluation of the model. The results of this work have been proved to be promising when compared with existing techniques. Since this work is related life of a patient’s, the results should be re looked by the domain experts before taking any decisions.
Complex Network-Based Web Service for Web-API Discovery

With the rapid and continual increase in the number and diversity of Web-APIs currently available on the Web, finding most appropriate Web-APIs to speed-up software development is becoming increasingly challenging. At the moment, Web-API consumers including mashup developers normally rely on Web-APIs repositories such as ProgrammableWeb and Mashapes to discover API of their interest. However, these registries are considered ineffective because: (a) Web APIs registered on these directories are in general isolated, as they are registered by diverse providers independently and progressively, without considering relevant dynamic information or continuous social interactions that exist among the services, which could influence their discovery (b) they cannot effectively respond to complex, mashup-oriented Web-API requests. In this paper, we address the above challenges from complex network perspective by constructing an evolving, complex-network-based Web service that leverages an online Google custom search service for recommending Web-APIs for mashup development. We conduct our study in three phases: First, we study the Web service ecosystem topological attributes using network analysis, and build an evolving network of Web service (Web-API) based on our findings using the theoretical procedure of the Barabási-Albert complex network model. Secondly, we dynamically grow the network and publish both nodes (Web-APIs) and edges (social connections) via an active web domain. Finally, we employ Google Page-Ranking feature to facilitate node ranking based on term frequency, functionality and node popularity information. To evaluate the performance of our framework, we create synthetic mashup requests based on original mashup profile. We validate our approach using ProgrammableWeb dataset, and experimental results show that our proposed framework is effective and outperform not only ProgrammableWeb approach but several other state-of-the-art methods.
Design and Development of a Hardware Efficient Image Compression Improvement Framework

Background: In the region of image processing, a varied number of methods have already initiated the concept of data sciences optimization, in which, numerous global researchers have put their efforts upon the reduction of compression ratio and increment of PSNR. Additionally, the efforts have also separated into hardware and processing sections, that would help in emerging more prospective outcomes from the research. In this particular paper, a mystical concept for the image segmentation has been developed that helps in splitting the image into two different halves’, which is further termed as the atomic image. In-depth, the separations were done on the bases of even and odd pixels present within the size of the original image in the spatial domain. Furthermore, by splitting the original image into an atomic image will reflect an efficient result in experimental data. Additionally, the time for compression and decompression of the original image with both Quadtree and Huffman is also processed to receive the higher results observed in the result section. The superiority of the proposed schemes is further described upon the comparison basis of performances through the conventional Quadtree decomposition process. Objective: The objective of this present work is to find out the minimum resources required to reconstruct the image after compression. Method: The popular method of quadtree decomposition with Huffman encoding used for image compression. Results: The proposed algorithm was implemented on six types of images and got maximum PSNR of 30.12dB for Lena Image and a maximum compression ratio of 25.96 for MRI image. Conclusion: Different types of images are tested and a high compression ratio with acceptable PSNR was obtained.
A Combination of Prefixed-Itemset and Database Optimization to Improve Apriori Algorithm on Hadoop Cluster

Apriori algorithm is a classical algorithm in the field of data mining. It is widely used in the research of mining the association rules, but it also has some disadvantages. In this paper, for the three main steps in the execution of the Apriori algorithm, we propose a novel method that combines the storage structure of the prefixed-itemset with the database optimization to improve the Apriori algorithm on the Hadoop cluster. First, we used the storage structure of the prefixed-itemset to improve the implementation methods of the connection step and the pruning step in the traditional Apriori algorithm to increase the execution efficiency of the algorithm. Second, we changed the storage schema of the database. And we converted the original transaction database into the transaction-state matrix to transform the storage pattern of transaction data and enhance the efficiency of the traditional Apriori algorithm in the counting step. Then, we combined the properties of the frequent itemsets to improve the iterative termination condition of the algorithm, thus reduced the running time of the algorithm. Finally, we performed MapReduce parallelization improvement on the Apriori algorithm optimized by the above steps based on the Hadoop distributed architecture. The experimental results show that compared with the traditional Apriori algorithm, the improved Apriori algorithm on the Hadoop cluster has improved the execution efficiency greatly.
An augmented reality platform targeted to promote learning about planetary systems

We present a mobile augmented reality platform targeted to promote learning about planetary systems. The architecture of this platform includes a mobile application and a back-office that allows teachers to choose the planetary system they intend to present to their students. Furthermore, they can introduce information about celestial bodies, such as stars or planets and develop a set of multiple-choice questions to assess student's learning about the subject matters they teach. Also, after playing the game, the data collected by the application is sent to the information system that processes it and makes it available to teachers. With these functionalities, this paper intends to propose this platform as a resource to be implemented in any level of school syllabus.
Research on Improved Adaptive ViBe Algorithm For Vehicle Detection

Abstract Vehicle detection is an important step in vehicle tracking and recognition in video environment. Vibe algorithm is a moving target detection algorithm based on background difference method. Based on the traditional ViBe algorithm, this paper introduces three-frame difference method combined with ViBe algorithm to speed up the elimination of ghosts, and proposes an adaptive Vibe algorithm, which defines two kinds of vehicle detection errors and their corresponding error functions. Then, according to the range of these two errors, a set of reasonable judgment methods are determined to adjust the unreasonable threshold, which ensures the adaptive updating of the background model. It improves the environmental adaptability of vehicle detection and ensures higher accuracy of vehicle detection under complex illumination conditions.
Human Activity Recognition with Streaming Smartphone Data

With the widely used smartphones, dynamic data coming from built in sensors, such as human activity data, can be easily obtained. Many applications' developments, such as applications in healthcare, fitness monitoring, and elder monitoring, are based on this kind of dynamic data. Although there are many offline methods that have made a great progress in analyzing these kinds of data, it still has a big challenge to get good results from a streaming data perspective. In this paper, we use an online method called Very Fast Decision Tree (VFDT) to mimic the real scenario. There are two main improvements from the existing models: 1) we train the model online and only use the examples data once for training instead of using them more than once; 2) after building VFDT, the model can be adjusted to identify new activities by adding only small amount of labeled observations. Our experiment on the same existing activities shows that the proposed algorithm achieves an average accuracy of 85.9% for all subjects and single subject accuracy rates are between 60.5% and 99.3%. Moreover, the average accuracy of learning new activity from a different data is 84% and single subject accuracy rate goes to as high as 100%.
A hybrid variable selection algorithm for multi-layer perceptron with nonnegative garrote and extremal optimization

In the paper, a new hybrid variable selection algorithm for nonlinear regression multi-layer perceptron (MLP) is proposed. The proposed algorithm applies nonnegative garrote (NNG) to compress the input weights of the MLP. The zero input weights dependent variables will be removed from the initial dataset. Next, a further variable selection is carried out by extremal optimization (EO) algorithm. The new variable selection algorithm integrates powerful global selection ability of NNG and accurate local search ability of EO. Finally, two examples of artificial data sets and an industrial application for a debutanizer column are implemented to demonstrate the performance of the new algorithm. The simulation result demonstrates that the developed algorithm presents d better model performance along with less input variable selected than other state-of-art variable selection methods.
A Modified Medical Information Retrieval System

Physicians often seek scientific evidence regarding how to best care for their patients, while making clinical decisions These scientific evidence are available in the form of published medical articles, reports and clinical trials. Considering the volume of already existing medical literature and the pace at which medical research is growing, getting the most relevant information will be a tedious task. In this paper, we describe an empirical approach to fetch relevant medical articles from the PubMed (about 733,328 articles of 45.2 gigabytes) collection, based on a given query. Our IR system comprises of three parts: inverted indexing using Lucene, lexical query expansion to increase recall with MetaMap and reranking aimed at optimizing the system. Word sense of ambiguous terms are introduced to limit the negative effects that synonymy-based query expansion may have on precision. The subsequent ranked list was then re-ranked with learning to rank algorithms. We evaluated our system using 30 medical queries and the results show that our system can handle various medical queries effectively and efficiently. Also, the final results demonstrate that the ensemble approach performs better than the Lucene baseline by boosting the ranking of articles that are near the top of several single ranked lists.
Exploiting Equivalence to Efficiently Enhance the Accuracy of Cognitive Services

Equivalent services deliver the same functionality with dissimilar non-functional characteristics, including latency, accuracy, and cost. With these dissimilarities in mind, developers can exploit the combined execution of equivalent services to increase accuracy, shorten latency, or reduce cost. However, it remains unknown how to effectively combine equivalent services to satisfy application requirements. With the recent surge in popularity of machine learning, different vendors offer a plethora of equivalent services, whose characteristics are mostly undocumented. As a result, developers cannot make an informed decision about which service to select from a set of equivalent services. To address this problem, we explore different service combination strategies (i.e., majority voting, weighted-majority voting, stacking, and custom) to ascertain their impact on non-functional characteristics. In particular, we study how these strategies impact the accuracy, cost, and latency of the face detection task and validate our findings on the sentiment analysis task. We consider the combined executions of commercial web services, deployed in the cloud, and open-source implementations, deployed as edge services. Our evaluation reveals that the combined execution of equivalent services is most effective for improving cost and latency. Informed by our experimental results, we formulate practical guidelines to help developers identify the best execution strategy for a given set of services.
A multi-robot simulator for the evaluation of formation control algorithms

Autonomous/unmanned driving has the capability to provide numerous benefits such as better traffic management, increased safety, reduced emission, and enhanced transportation network. Once autonomous ground vehicles (AGVs) are deployed, they will have to interact with other such vehicles. Interaction between multiple AGVs is an important area of research where analysis on the performance of algorithms/control schemes of AGVs is carried out. Performing real-world experiments with teams of autonomous vehicles is a challenging task due to cost and complexity. On the other hand, a simulation can emulate reality and provide an inexpensive and less time-consuming development process compared to the real world robots' testing. Therefore, a simulation tool is developed for multi-robot navigation. This simulator is based on open-source Robot Operating Systems (ROS) and natively supported robotics simulator Gazebo.
Early Stage Detection of Malignant Cells: A Step Towards Better Life

Cancer is a collection of diseases, which is driven by change in cells of the body by increasing the normal growth and control. Its prevalence is increasing year by year, and is accordingly advancing along with it to counter the occurrences and provide solution. Breast Cancer is considered to be a deadly disease and is one of the crucial reasons of demise among the women globally. Early detection of breast cancer increases the probability of better treatment and viability. Research has been done mostly on mammogram images. Although, sometimes these images are inaccurate and may show fallacious detection. Thus, it can risk the patient's well-being. It is, therefore, important to obtain substitutes that are trouble-free, economical, secure, and can generate a more genuine prediction. Presently, Machine Learning approaches are being widely used in breast cancer detection. Machine Learning enables the system to master based on former occurrences and decide using a variety of statistical and probabilistic techniques with a minimum human intrusion. This research work showcase the use of five machine learning methods, which are SVM (Support Vector Machine), KNN (K-Nearest Neighbor), K-SVM (Kernel Support Vector Machine), Random Forest Tree, Decision Tree and the accuracy achieved for breast cancer detection has been 97.20%, 95.10%, 96.50%, 98.60%, 95.80% respectively. As per the results, Random Forest Tree offers the highest accuracy in comparison with other algorithms when applied to the Wisconsin breast cancer detection dataset which has been taken from a machine learning repository.
Evaluating the strength of a multilingual passphrase policy

A number of studies have advocated for the use of long passwords (passphrases) with the aim of attaining a balance between security and usability. This study investigated the security gains of using a multilingual passphrase policy in user generated passphrases that are based on African and Indo-European languages. The research on passwords has been largely focused on the Global North where English is often the first or only language. Targeted password guessing of English and Chinese-based passwords shows that a user's mother tongue language can influence password structure, something that reflects on security. Given a multilingual user group, for example in Africa, it is interesting to establish whether such a population can generate secure multilingual passphrases. Accordingly, the findings of this study could be extrapolated to other contexts with multilingual users. In this study, a total of 224 university students in Southern Africa were invited to take part in an experiment that involved the generation of passwords and passphrases guided by a short password and multilingual passphrase policy. The results show that English language-oriented passwords dominated the short password corpora. Moreover, the use of a multilingual passphrase policy reduced the dominance of English language-oriented passwords. Security tests using a Probabilistic Context-Free Grammar (PCFG) suggest that short passwords are weaker, with marginally more than 50% of the short passwords being guessed while none of the multilingual passphrases were guessed. Further analysis shows that short passwords oriented towards an Indo-European language were easier to guess when compared to short passwords based on African languages. Hence, this study encourages orienting passwords to other languages, with the use of a multilingual passphrase policy expected to offer more security.
Comparative Study on SVD, DCT and Fuzzy Logic of NOAA Satellite Data to Detect Convective Clouds

Information available in the Infrared and visible NOAA (National Oceanic Atmosphere Administration) satellite imagery is in the form of brightness temperature and albedo. Three Techniques are constructed to retrieve the convective cloud portion using Singular Value Decomposition (SVD), 2D-DCT (Discrete Cosine Transform) and fuzzy logic methods provided within MATLAB®, and compared the accuracy of these methods. The results delineates that there is tradeoff between accuracy of effective cloud amount and execution time. Though the fuzzy logic method takes the time to train the data but gives the accurate detection and classification of clouds