Reviewerly/exHarmony huggingface dataset:
A Predictive and Evolutionary Approach for Cost-Effective and Deadline-Constrained Workflow Scheduling Over Distributed IaaS Clouds

Clouds provide highly elastic resource provisioning styles through which scientific workflows are allowed to acquire desired resources ahead of the execution and build required software environment on virtual machines (VMs). However, various challenges for cloud workflow, especially its optimal scheduling, are yet to be addressed. Traditional approaches mainly consider VMs to be with non-fluctuating, time-invariant, stochastic, or bounded performance. This work describes workflows to be deployed and executed over distributed infrastructure-as-a-service clouds with time-varying performance of VMs and is aimed at reducing the execution cost of workflow while meeting deadline constraints. For this purpose, the authors employ time-series-based prediction approaches to capture dynamic performance fluctuations, feed an evolutionary algorithm with predicted performance information, and generate schedules at real-time. A case study based on multiple randomly-generated workflow templates and third-party commercial clouds shows that their proposed approach outperforms traditional ones.
Mining Friend Relationship Existing in Microblog Based on Building Relationship Description Dictionary

The character/person relationship is difficult to effectively extract because of microblog characteristics such as massive content, low value density etc. To solve this problem, this paper proposes a microblog friend relationship mining method based on building relationship description dictionary. First, the semantic similarity calculation method of the combination with HowNet and Tongyici Cilin is proposed. Based on the two friend relationship description Dictionary, a more accurate friend relationship description Dictionary is obtained. Then, add core predicates, dependency syntax, and semantic analysis methods when working with text data, the core predicate can determine the entity pair, dependency syntax can describe the relationship between words before and after words, semantic analysis can further mining text information. When combine use them can achieve the mining of friend relationship from microblog. Experimental results show that the friend relationship mining performance is significantly improved.
Click and Sales Prediction for Digital Advertisements: Real World Application for OTAs

Online travel agencies (OTAs) aim to use digital media advertisements within the most efficient way for increasing their market share. One of the most commonly used digital media environments by OTAs are the metasearch bidding engines. In metasearch bidding engines, many OTAs offer daily bids per click for each hotel to get reservations. Therefore, the management of bidding strategies is crucial to minimize the cost and maximize the revenue for OTAs. In this paper, we aim to predict both the impression count and Click-Through-Rate (CTR) metrics of hotel advertisements for an OTA and then use these values to obtain the number of clicks the OTA will take for each hotel. After that using these predictions, we aim to forecast the next day’s sales amount in order to provide an estimate of daily revenue generated per hotel. An important contribution of our study is to use an enriched dataset constructed by combining the most informative features proposed in various related studies for click and sales prediction. In this study, the data which is generated by one of the biggest OTA in Turkey and we provide a real-world application for OTA’s. The results both for Impression, CTR and sales prediction show that enrichment of the input representation with the OTA-specific additional features increases the generalization ability of the prediction models, and tree-based boosting algorithms perform the best results on this task.
Good-Bye Localhost: A Cloud-Based Web IDE for Teaching Java EE Web Development to Non-Computer Science Majors

While cloud-and web-based Integrated Development Environments (IDE) are used in practice and education for some time, these are mainly intended for classical web development. Support for enterprise platforms such as Java Enterprise Edition (EE) is typically lacking. However, in higher education, web-based cloud IDE are particularly useful in software development courses for non-computer science major students, since they could help to overcome the participants' limited IT skills. Thus, a respective easy-to-use web IDE is required. Therefore, in this paper a software platform for a web-based cloud IDE for educational purposes is proposed and evaluated, which in particular supports Java EE and is suitable for non-computer science majors. The platform was evaluated in a classroom experiment. The results indicate that it is useful and beneficial for the students, allowing the lecturer and the students to focus more on the actual learning objectives.
Deep Neural Networks and Hybrid GMDH-Neuro-fuzzy Networks in Big Data Analysis

Deep neural networks (DNN) and their generalizations—hybrid DNN are considered which represent the efficient tools for BD analysis. The properties and drawbacks of deep learning are considered and analyzed. Encoders—decoders and restricted Boltzmann machines are described and their applications for Deep learning implementation are presented. Methods of regularization of DL: penalty functions, Dropout and Bagging are presented. New class of deep learning networks are suggested and presented. so-called GMDH-neo-fuzzy networks representing a combination of self-organization method GMDH and fuzzy neural networks. Due to principle of self-organization and small number of tuning parameters GMDH enables to simplify and accelerate the training of DN. Several variants of this class hybrid networks are considered and algorithms of their structure synthesis based on GMDH are suggested and analyzed. The application of GMDH enables to reduce dimensionality of training DN and accelerate the convergence of training and by this solve some problems of Big Data Analysis. Experimental investigations of hybrid GMDH-neo-fuzzy networks are carried out and their results are presented and analyzed.
Towards Crowd-Sourced API Documentation

The learnability of an API suffers when the official documentation of the API is incomplete. Developers discuss usage scenarios of APIs in the online developer forums. As such, by automatically mining such crowd-sourced documentation of APIs, we can address the shortcomings of API official documentation. We present a framework to automatically mine usage scenarios about APIs from online developer forums. Each usage scenario of an API consists of a code example, a summary description, and the reactions (i.e., positive and negative opinions) of other developers towards the code example. We evaluate our API usage mining framework by producing a benchmark dataset. We observed a precision of 0.947 and a recall of 1.0 with the linking of a code example to an API mention in the forum.
Application of Micro-Teaching in the Training of Intravenous Indwelling Needle Infusion for Clinical Nursing Students

Objective: To explore the application effect of micro-teaching in the training of intravenous indwelling needle for clinical nursing students.Methods: A total of 102 undergraduate nursing students who were interned in our department from July 2016 to February 2018 were assigned randomly to the experimental group and the control group, 51 for each.The traditional teaching method was used in the control group for the training of intravenous indwelling needle infusion skills, whereas the micro-teaching method was used in the experimental group.Before the end of the internship, the two groups of interns were given a score test of intravenous indwelling needle infusion skills and an anonymous questionnaire survey on teaching effects.Results: The results of the skill test showed that the total scores of the control group and the experimental group were (76.73 ± 1.67) and (84.12 ± 1.90), respectively, and the difference was statistically significant (p < 0.01).According to the questionnaire survey, the percentage of the students in experimental group who had a positive attitude toward teaching methods, interest in learning, self-learning ability, and learning efficiency, was higher than that in the control group, and there was a statistical significance (p < 0.05).Conclusion: Microteaching can effectively promote the nursing students' mastery of intravenous indwelling needle infusion skills, stimulate interest in learning, improve self-learning ability, improve learning efficiency and achieve satisfactory teaching effects.
Mobile Smart-Contract Lifecycle Governance with Incentivized Proof-of-Stake for Oligopoly-Formation Prevention

Permissionless blockchain-enabled smart contracts execute code in a distributed peer-to-peer network system and thereby overcome undesirable effects of system centralization. Smart contracts that use proof-of-stake (PoS) algorithms for the validation of transactions have advantages over proof-of-work (PoW) in that they use less electricity and perform faster. The disadvantage of PoS algorithms is the issue of nothing to stake and the emergence of staking oligopolies. Thus, significant stakeholders might be able to create an oligopoly as miners with significant stakes have the chance to validate the transaction in a dominant position. In current smart contracts, the adoption of mobile devices is another emerging trend to manage mobile smart contracts. The advantage is spreading of a democratization effect as a large number of stakers participate in transaction validation and thereby reduce the risk of oligopolies. In our work, we aim to improve the PoS algorithm to reduce oligopoly formation in smart contracts by addressing the need for creating mobile smart contracts that are governed by a mobile lifecycle management. Additionally, we enhance the scalability and performance of smart contracts by focusing specifically on ways to incentivize PoS algorithms.
TEXT

Traffic congestion is a significant problem for many developing countries. Among a variety of methods proposed to address this problem, extraction of road traffic conditions from microblogging platforms for dissemination to road users has gained popularity. Coupled with the use of Internet of Things (IoT) devices, up-to-date traffic condition reports from road users and road traffic authorities, can be effectively reconciled and communicated to the intended audience. However, the noisy and unstructured nature of Twitter texts causes a decline in the performance of conventional Named Entity Recognition (NER) techniques. NER techniques have traditionally been used to extract location information. In order to extract the corresponding traffic state information for each location, contextual information become important which are not implemented in conventional NER approaches. In this paper, a rule-based NER technique, which considers contextual clues, is proposed for the extraction of location and traffic state entities. The proposed approach has achieved significant improvement to the F1 score, 88.96 and 81.32, for location entity extraction from formal and informal sources, respectively.
Tensor-Based Anomaly Detection for Satellite Telemetry Data

Satellites is the bird’s-eyes that enable us to view massive areas of earth at the same time, satellites can gather more data, more quickly, than tools on the ground. Satellites also can view into space better than telescopes at earth’s surface. Development of such artificial satellites which composed of many subsystem, requires a lot of time and money that any deadly failure is unacceptable, the satellite operating in a remote environment, so it is practically very hard or impossible to repair it once a severe failure occurs, so detecting the anomalies of the subsystems measurement values (Telemetry data) is the first step in satellite failure protection and early warning. Traditional spectral-based methods like PCA is traditional for detecting anomalies in a variety of domains and problems. However, if the collected data contains tensor (multiway) structure, for example space-time-measurements values, such as the satellites subsystems measurement, some significant anomalies may stay hidden with these traditional methods. Tensor-based anomaly detection (TAD) applied in a variety set of disciplines over the recent years, although it is not recognized yet as an official category of anomaly detection techniques. This work target to highlight the candidate of tensor-based technique as a new approach for identification and detection of abnormalities and dud in the satellite telemetry data.
On the Bayes Filter for Shared Autonomy

The Bayes filter is the basis for many state-of-the-art robot localization algorithms. In the literature, its derivation typically requires the robot controls to be chosen independently of all other variables. However, this assumption is not valid for a robotic system that is to act purposefully. The contribution of this letter is twofold: We prove that the Bayes filter is also exact for an autonomous system that chooses the controls depending on any subset of all observable variables. We further show how to augment the filter if a human agent chooses controls on the basis of parts of the state space that are not directly accessible to the robot. In this case, modeling the agent's purpose improves the pose estimate as the control selection provides additional information about the hidden state space. A careful derivation of the Bayes filter then leads to an additional pseudo measurement update step. Simulation and real-world experiments with a teleoperated mobile robot as well as evaluations on the KITTI dataset show that the localization accuracy significantly improves if we augment a particle filter with the proposed pseudo measurement update. Finally, we present an analytical example for an augmented Kalman filter, which leads to a more accurate estimate than the standard Kalman filter.
Addressing Autonomy in Conceptual Design

Aircraft design is an iterative process of creating a design concept from a set of requirements. Conceptual design is an early phase in the process, during which preliminary decisions and trade studies are made from a set of requirements related to mission objective and costs. Although much attention has been paid to applying autonomy technologies to robotic systems, including air vehicles, there has been little attention paid to incorporating autonomy as part of the conceptual design process. Consequently, designing for autonomy tends to be retrofitted to a vehicle that has already gone through a complete design process rather than as part of the initial process. This derivative approach to designing autonomous systems is suboptimal, and there is evidence that this has hindered the acceptance of autonomy technologies. This article proposes an approach to conceptual design for aircraft that incorporates autonomy into the conceptual design process. To illustrate the principles introduced, we consider the example of configuring an autonomous small unmanned aerial vehicle for searching and tracking a target of interest.
Declarative Modelling of Transactions for IS Development

In the development life cycle of an information system (IS) - from initial user wishes up to a running IS - an intermediate mathematical model is very useful, both as a clear and unambiguous capture of the user wishes regarding the functional requirements as well as a formal model of the system to be built. Based on decades of experience, we tackle the problem to develop a suitable, practical modelling method for formal, declarative, and implementation-independent specifications of information systems that can serve as a clear, unambiguous capture of the user wishes regarding the functional requirements. The theory should integrate data and transactions in a uniform way, because data and transactions are closely related. The theory should also be suitable for incremental and agile development, where we must quickly determine where, what, and how to change when necessary. The notion of an information machine turns out to be very suitable for this goal. Several related notions will be defined. We introduce a general structure of transactions which includes a generic rollback, taking consistency into account. Several notions are generalizations of notions from database theory, such as transaction, query, and view. We also explore some common practical structures for states and transactions in an information machine because they can become rather subtle in practice. In particular, transactions can be very subtle and complicated, also because they can be ‘rolled back’ and/or can be ‘compound’. Our formal, declarative specifications of transactions must (and do) account for that. Fortunately, many of the transactions in practice have more or less the same form. We study and formally define several of such common transaction patterns in a declarative way. We first sketch a development path for functional requirements which is straightforward. In order to handle the inherent complexity of development, the path enables ‘stepwise clarification’, ‘stepwise specification’, and traceability. It gradually goes from the informal natural language and way of thinking of users to a formal model (with inputs, outputs, procedures, parameters, etc.).
Association Matrix Method and Its Applications in Mining DNA Sequences

Many mining algorithms have been presented for business big data such as marketing baskets, but they cannot be effective or efficient for mining DNA sequences, any of which is typically with a small alphabet but a much long sizes. This paper will design a compact data structure called Association Matrix, and give an algorithm to specially mine long DNA sequences. The Association Matrix is novel in-memory data structure, which can be so compact that it can deal with super long DNA sequences in a limited memory spaces. Such, based on the Association Matrix structure, we can design the algorithms for efficiently mining key segments from DNA sequences. Additionally, we will show our related experiments and results in this paper.
Rational Function Model Based Color Correction in Image Stitching Applications

In the field of image processing, color correction is usually used when there are significant differences in tone or brightness in the original images. This method is also often used as an image preprocessing method to assist many other applications. This paper introduces a method of color correction using rational function model and histogram. And we use two different mapping methods to get best stitching results according to different application scenarios. The problem of color discontinuity in many color correction algorithms is solved, and the experimental results show that our method has a faster time efficiency.
Development of Balmer Series Experiment Simulator in Mobile and Android Applications

The need for application of android-based experimental physics is urgent, beside to facilitate students to do the lab, students can reach the simulator through gadget. Previously Balmer series experimental physics applications have been made in the form of Java Applet with the concept of 2D Virtual Reality and it has been used since 2010 until 2016. However, for security reasons, since 2015 the development of Internet technology is no longer support Applet in its web browser. Therefore, the Balmer series Experimental Physics simulator needs to be overhauled into another form that is easier and safer to access in to the form of Android applications. In this research android applications engine generator has been made for Balmer series Experimental Physics simulator so that applications based on Java Applet on PC can be run in Android gadget. The engine consists of image processing, object transformation, browser connector, interactor and visualizer. In this report we show the Balmer series Experimental Physics modules that have been successfully created in the form of android applications.
Salient object detection method for breast tumor in ultrasound images based on absorbing Markov chain

Automatic detection of tumor in breast ultrasound (BUS) images is important for the subsequent image processing and has been researched for decades. However, there still lacks a robust method due to poor quality of BUS images.To propose and test a salient object detection method for BUS images.BUS image is preprocessed by an adaptively selective replacement and speckle reducing anisotropic diffusion (SRAD) algorithm. Then, the preprocessed image is segmented into super pixels by a simple linear iterative clustering (SLIC) algorithm to form a graph model, and the saliency of the nodes in the graph is calculated by using the absorbed time of absorbing Markov chain (AMC). Finally, the initial saliency map is optimized by the recurrent time of ergodic Markov chain (EMC) and a distance weighting formula.Results of the proposed method were compared both qualitatively and quantitatively with two saliency detection models. It was observed that the proposed method outperformed the comparison models and yielded the highest Accuracy value (97.49% vs. 86.63% and 90.33%) using a dataset of 1000 BUS images.After the adaptively selective replacement, AMC can effectively distinguish tumors from background by random walks.
Causes and Effects of Climate Change via a Fuzzy Inference System

Climate change is the most large scale problem and the well-known vexed question in the modern era. The increase of global temperature on Earth’s surfaces due to the higher emission of greenhouse gasses is an indication of climate change. This paper presents a Fuzzy Inference System (FIS) to predict the relationship between effects and causes of climate change. The Fuzzy Inference System opens a baseline for understanding the causes of climate change, with expert knowledge, which reflects the relationship between multiple environmental factors. Based on a Multiple-Input-Multiple-Output Mamdani Fuzzy Inference System the repercussions of the climate change can be examined by acquiring an anticipated result with respect to a variety of climate scenarios. Here, CO2 (Carbon Dioxide), Global Temperature Changes, Snow Cover, Percentage of Forestlands, and Net Radiation are considered as inputs; while the considered outputs are Ozone Layer Changes, Arctic Ice Sheet Level, and Sea Level. Three membership functions of the “generalized bell function” type have been considered for each input and output. This paper shows that multiple inputs and multiple outputs in a Fuzzy Inference System can lead the way for understanding climate change causes and effects. Furthermore, these results are in agreement with results from other authors approaches; however, the use of a FIS allows to include elements of uncertainty and vagueness in the input variables considered.
A Lightweight Linked Data Reasoner Using Jena and Axis2

Semantic Web is rapidly becoming a reality through the development of Linked Data in recent years. Linked Data uses RDF data model to describe statements that link arbitrary data resources on the Internet. It can facilitate to infer new data resources at runtime through the RDF links, and then provide more complete answers as new data resources appear on the Internet. Linked Data provides the means to reach the goal of Semantic Web. At present, Linked Data being used only in the promotion of information sharing or exchange is not a semantic inference due to the lack of an easily shared inference engine. This study addresses the issue developing a Lightweight Linked Data Reasoner (LLDR) which is based on Jena reasoner and is implemented in the apache Axis2. To illustrate the LLDR application, this study developed the Vehicle Ontology to annotate project document from heterogeneous and distributed project resources as Linked Data.