Reviewerly/exHarmony huggingface dataset:
Effects of Varying Noise Levels and Lighting Levels on Multimodal Speech and Visual Gesture Interaction with Aerobots

This paper investigated the effects of varying noise levels and varying lighting levels on speech and gesture control command interfaces for aerobots. The aim was to determine the practical suitability of the multimodal combination of speech and visual gesture in human aerobotic interaction, by investigating the limits and feasibility of use of the individual components. In order to determine this, a custom multimodal speech and visual gesture interface was developed using CMU (Carnegie Mellon University) sphinx and OpenCV (Open source Computer Vision) libraries, respectively. An experiment study was designed to measure the individual effects of each of the two main components of speech and gesture, and 37 participants were recruited to participate in the experiment. The ambient noise level was varied from 55 dB to 85 dB. The ambient lighting level was varied from 10 Lux to 1400 Lux, under different lighting colour temperature mixtures of yellow (3500 K) and white (5500 K), and different background for capturing the finger gestures. The results of the experiment, which consisted of around 3108 speech utterance and 999 gesture quality observations, were presented and discussed. It was observed that speech recognition accuracy/success rate falls as noise levels rise, with 75 dB noise level being the aerobot’s practical application limit, as the speech control interaction becomes very unreliable due to poor recognition beyond this. It was concluded that multi-word speech commands were considered more reliable and effective than single-word speech commands. In addition, some speech command words (e.g., land) were more noise resistant than others (e.g., hover) at higher noise levels, due to their articulation. From the results of the gesture-lighting experiment, the effects of both lighting conditions and the environment background on the quality of gesture recognition, was almost insignificant, less than 0.5%. The implication of this is that other factors such as the gesture capture system design and technology (camera and computer hardware), type of gesture being captured (upper body, whole body, hand, fingers, or facial gestures), and the image processing technique (gesture classification algorithms), are more important in developing a successful gesture recognition system. Some further works were suggested based on the conclusions drawn from this findings which included using alternative ASR (Automatic Speech Recognition) speech models and developing more robust gesture recognition algorithm.
Integrating Internet of Services and Internet of Things from a Multiagent Perspective

To realize the Internet-based sociotechnical systems, it is necessary to build a comprehensive and effective infrastructure to support the interaction between various cloud services on the Internet and the physical world in which we live. For example, the information produced by the sensors is usually aggregated, processed and analyzed by services in the cloud, which can be used by various stakeholders for decision-making in many different application fields. Therefore, we need to consider integrating the Internet of Services (IoS), which enables the flexible sharing and composition of cloud services on the Internet, with the Internet of Things (IoT), which represents the constellation of things equipped with various sensors and actuators. The integration of IoS and IoT often involves multiple parties and so must deal with complex issues such as interaction, dynamics, scalability and decision making, all of which can be studied from a multiagent perspective. In this paper, we start by discussing the necessities and challenges for integrating IoS and IoT. Then, we propose an integrated architecture and examine it from two multiagent perspectives. One is to regard the integrated architecture of IoS and IoT as a multiagent-based architecture considering various patterns of service composition and interaction. The other is to apply multiagent methodologies when designing sociotechnical systems for various application domains based on the integrated IoS/IoT architecture. Moreover, we use the example application of designing multilingual environments to discuss the above two perspectives with possible future research directions.
Intelligent Tutoring for Informed Feedback in Interactive Learning Environments

An intelligent tutoring system (ITS) is designed to provide and simulate the behavior of the existing educational process. Interactive learning environments deploy simple to complex tools for providing mindful feedback to students. The effect is measured and reflected on learners' academic performance and achievement of subsequent learning outcomes. The study examines contemporary research on instructional designing, feedback strategies and intelligent tutoring systems to understand effect of intelligent tutoring in the form of "informed feedback" on learners reported satisfaction with an interactive learning environment. The study is based on primary data, whereby intervention of intelligent tutoring system (ITS) is included by simulating human-tutors, acting as intelligent-tutors and providing informed feedback as per Interactive Tutoring Feedback Model factors; namely i) Requirement Engineering, ii) Characterization of learners on the basis of meta-cognition and motivation, and iii) Characterization of informed feedback for mindful acceptance. The study was able to conclude that despite being many difficulties in contextual interpretation, intelligent tutoring can bring about positive change in satisfaction score of learners in an interactive learning environment.
Patient necessity notification system based on gesture recognition (Kinect V2) and internet of things using selection frame method

The use of gesture recognition integrated with Internet of Things (IoT) as a system for detecting patient needs provides information speed and accuracy for nurses. One tool that is widely used to implement gesture recognition is Kinect (Version 2). For data communication, Kinect will be collaborated with IoT in the process of sending data from the start after gesture recognition to the presentation of information to end users. The problem that arises when Kinect is integrated with IoT is the burden on the network because the average number of frames read by Kinect in one second is 30 frames. In this study, a frame selection method was developed to minimize the number of frames sent to IoT networks by minimizing the number of duplicate frames. The selection frame method on gesture recognition integrated with IoT is then tested for performance with distance and delay parameters. The system implementation results are able to read 100% gesture recognition, and the accuracy of the information sent by the nurse is 100% in the testing environment. The optimal distance that can provide a reading of more than 95% is 2.5 meters. The optimal value of 100% is not achieved because some testing of the falling position cannot read the whole skeleton.
Intelligent Deployable Mini Rover from the Mars Rover for Deep Narrow Scientific Investigation

The paper describes various issues faced by mini rover deployed from main rover in an alien environment and the ways to solve them. The rover features a rocker bogie mechanism with differential drive, collision free distance sensing, wireless XBee control and camera Vision. The rover features a flexible segmented body with a multipurpose arm, corresponding multi wheel mechanism and Kinect module integration for advanced image processing. The system control, for both the Rover as well the robotic arm integrated with it, is done using feasible yet extremely efficient microcontrollers and microprocessors such as Arduino, Raspberry pi etc. Inspired from nature, a reflex mechanism has also been integrated into the rover design to minimize damage, by automated safety reflexes. The six wheeled rocker bogie mechanism ensures that it can traverse over a considerable height greater than the chassis height which could be as much as twice the diameter of the wheels. The rocker bogie mechanism provides traction due to its body weight. The rover finds applications in the exploration of other planets and harsh environments. Such an effort may even prove to be instrumental in detection and study of biological activity in worlds other than ours.
GABoost: A Clustering Based Undersampling Algorithm for Highly Imbalanced Datasets Using Genetic Algorithm

Data sets that have imbalanced class distribution is a challenging problem for many application domains. Learning from imbalanced data can’t be done efficiently using current data mining and machine learning tasks. Instead of merely using those algorithms we have to consider some other techniques to learn from those data set. One solution is to develop some preprocessing methods to balance the data sets and combine it with some existing algorithm. In this paper, we propose a new hybrid clustering based undersampling technique using genetic algorithm and AdaBoost, which is called GABoost, for learning from imbalanced data. This algorithm is an attractive alternative for SMOTEBoost, RUSBoost, CUSBoost. Based on the experimental results obtained from 44 imbalanced datasets we strongly recommend GABoost as a striking alternative for improving the performance of the learned classification model which is built using highly imbalanced dataset.
Breast Cancer Prediction: Importance of Feature Selection

In today’s world, breast cancer is one of the most widespread causes of death in women. According to an estimation, approximately 40,920 women would die in 2018 just because of breast cancer, which is a highly alarming number. Such alarming numbers could be reduced if the cancer is diagnosed at an early stage. With the advent of technology, making such predictions has become an easier task. Machine learning is one of the latest trends, which enables to make predictions related to diseases based on physical or behavioral characteristics. In this paper, we use various machine learning algorithms like decision trees, k-nearest neighbor (KNN), logistic regression, neural networks (NNs), naïve Bayes, random forest, and support vector machine (SVM). The outcome is then compared based on the precision, recall, and F1 score. Furthermore, we identify the least important features in the dataset, implement all these algorithms again after removing those features, and then compare the outcomes for the two implementation stages in order to understand the importance of feature selection in breast cancer prediction.
Combined Belief Propagation-Mean Field Message Passing Algorithm for Dirichlet Process Mixtures

This letter deals with variational inference for Dirichlet process mixtures (DPM) models. We propose a combined message-passing algorithm introducing belief propagation (BP) into the original mean field (MF) rules, which leads to a more precise approximate posterior in DPM. To compute the BP message, we change an exponential distribution to a non-exponential utilizing a flexible expression of Dirac delta function. Therefore, BP rules can be used to handle such functions, resulting to a local exact expectation instead of approximate expectation from the original MF method. Simulation results show that the proposed combined BP-MF algorithm results in a significant performance improvement compared to the state-of-the-art inference methods.
A Model Driven Approach for State Management in Mobile Applications

With the paradigm shift from desktop to mobile applications and the growing demand for mobile devices has awakened the interest of the IT industry on how to tackle the development of mobile applications. Managing application state is hard in building modern mobile applications. As application complexity increases, it becomes increasingly difficult to keep the track of changing state and mapping those changes back to user interface. State management is challenging due to its low-level implementation complexity. To overcome above mentioned issue, there is a strong need to introduce model driven approach for state management in mobile applications. This paper proposes a Unified Modeling Language profile for Mobile Application State Management (UMASM) to simplify mobile application state management requirements. Particularly, UMASM is capable of representing mobile state management requirements at higher abstraction level. This provides the strong basis to automatically generate low level implementations in target platform like Redux from high level UMASM models. The applicability of UMASM is validated through e-banking application case study.
Radar emitter and activity identification using deep clustering methods

Current challenges in spectrum monitoring include radar emitter state identification and the ability to detect changes in radar activity. Recently, large labeled datasets and better compute power have led to improvements in the classification performance of deep neural network (DNN) models on structured data like time series and images. The reliance on large labeled dataset in order to achieve state of the art performance is a hindrance for machine learning applications especially in the area of radar, which tends to have a wealth of noisy and unlabeled data. Due to the abundance of unlabeled data, the problem of radar emitter and activity identification is commonly setup as a clustering problem, which requires no labels. The deep clustering approach uses an underlying deep feature extractor such as an autoencoder to learn a low dimensional feature representation in the service of facilitating a clustering task. In this paper, we will evaluate different clustering loss functions such as K-means for training DNNs, and we use radar emitter state and activity identification as our example task.
Application of Graph Theory in Online Network Services to Determine the Shortest Journey

In this paper, we determine the shortest route in online network services by applying the concept of graph theory.The investigation shows how the journey can be visualized as graphs in the nature of vertices and edges.In our research paper, the smallest distance from starting point to final destination is estimated using google map which provides the smallest cumulative cost.In this paper, our focus is to achieve the process in working of google map services using graph theoretical approach to determine the shortest journey and its implementation in computer science using Floyd Warshall algorithm.In this paper, we also give illustrations and prove some results.
Performance evaluation sql-on-hadoop: a case study of Hortonworks and Cloudera

Currently companies in the world have focused on the Big Data business which has become an invaluable tool in assisting business processes and data analysis. SQL-on-Hadoop is a small part of the Big Data Platform that has been developed to date. Our research implements Big Data Platform on Cloudera and Hortonworks using TPC-H Benchmarks on SQL-on-Hadoop systems and evaluates the characteristics and performance of query processing machines in each scenario applied to each platform. We focuses on evaluating the two Big Data Platforms, Cloudera and Hortonworks, to determine the advantages and disadvantages of each platform based on the TPC-H Benchmark that has been recognized as a Decision Support System to compare the two with four different scenario that run on the same configurations. The results obtained are Cloudera with Impala can process queries with a ratio of up to 41x faster than LLAP-Tez and 200x faster than Hive-Spark.
A Diversified Feature Extraction Approach for Program Similarity Analysis

As code plagiarism becomes more and more prevalent, the need for code similarity detection technology is growing greatly. The feature of program is the basic unit that can represent the procedure and structure. Therefore, the quality of the feature will directly impact the accuracy of the similarity detection results. In this paper, we propose a diversified feature extraction approach, which extracts feature information from attribute counting, statement structure, program structure and program function. In the process of feature extraction, we comprehensively consider multiple factors of program, such as program structure, semantics and data flow. Evaluation results shows that this approach can eliminate the interference caused by multiple plagiarism methods, and it also has certain improvement in accuracy and detection efficiency.
A novel load balancing strategy based on node load comprehensive measuring under cloud computing environment

Although the real-time migration of virtual machines brings about multi-functional management, the service performance will decline to a certain extent during the migration process, so shorter downtime and total migration time are very desirable. The traditional load balancing algorithm reflect the load information of each physical machine, but it does not take into account the actual case of virtual machine migration. Therefore, contraposing to the features of cloud computing, the destination node positioning strategy of cloud computing is proposed. It periodically acquires physical node’s CPU, memory, bandwidth usage and request connection and perform quantification with unified standard. Then, with computation of factors of virtual machine migration the comprehensive load value is determined finally. The approach measures the utilization rate and transfer cost of node computing resources, which can locate target nodes reasonably and efficiently, so it ensures the reasonable and efficient migration of virtual machines and relative load balancing under cloud computing environments.
Interactive system research based on animation visual effects

Interactive animation based on motion capture is a relatively new animation technique that implements the action of transforming an actor’s actions into a model within the computer in real time. To solve this problem, a visualized interactive computer vision system was developed. A brief introducti on to the design of the entire system and some important technologies, focusing on the principles of node-based visual programming, the integration of functional modules, and the plug-in architecture of the system. The experimental results show that the process is simple, fast and real-time, especially in the field of entertainment and education.
Performance evaluation of lightweight Block Ciphers in soft-core processor

The Internet of Things (IoT) and RFID devices are essential parts of the new information technology generation. They are mostly characterized by their limited power and computing resources. In order to ensure their security under computing and power constraints, a number of lightweight cryptography algorithms has emerged. This paper outlines the performance analysis of six lightweight blocks crypto ciphers with different structures - LED, PRESENT, HIGHT, LBlock, PICCOLO and TWINE on a LEON3 open source processor. We have implemented these crypto ciphers on the FPGA board using the C language and the LEON3 processor. Analysis of these crypto ciphers is evaluated after considering various benchmark parameters like throughput, execution time, CPU performance, AHB bandwidth, Simulator performance, and speed. These metrics are tested with different key sizes provided by each crypto algorithm.
The genesis of the revolution in Contract Law

Smart Contracts, which derive from Blockchain technology, are one of the most peremptory applications of this. However, as it is a very recent technology, investors' uncertainty and fear makes their development slowly and cautiously. There are, therefore, a number of questions and legal problems that arise. The paper identifies what revolution Blockchain adds to the contractual processes, exploring its potential and analysing the main legal difficulties it is facing. It seeks the analysis of the pros and cons of this new technology, specially the questions about enforcement, nature and form, consent and (in) flexibility. The purpose of this investigation is to recognize the nature of this technology and its impact on the Contract Law. And because of this, the impact and challenges that it brings to the Governance of a nation as well as on private companies and individuals. Therefore this study is based mostly in the review of already existing embryonic laws about Blockchain technology or similar, authors that wrote about Smart Contracts and also some recent news to show the social and economic impact in the world. In short, the subject that this paper presents is the beginning of a true technological, social and economic revolution that must be anticipated and prepared by the law and those who work with it.
Advanced Weighted Exact Matching Algorithm (AWEMA)

Enhanced Weighted Exact Matching Algorithm (EWEMA) is an exact string matching algorithm that finds the pattern `P' in the text `T.' EWEMA contains two phases, the preparing phase, and the matching phase. This paper aims to enhance EWEMA and reduce its time complexity using the proposed algorithm called Advanced Weighted Exact Matching Algorithm (AWEMA). The simulation results presented the improvement in the algorithm AWEMA over EWEMA.
Innovative Project Technologies in High School

The relevance of the topic is due to the need to improve the quality of professional training for students of a technical university in the context of modernizing the educational process. Increasing the competitiveness and relevance of graduates in the labor-market at the current stage is determined by a number of factors, including the ability to apply the knowledge gained in practice, creative thinking, and the possession of a foreign language. The creation of a virtual educational environment as an innovative project technology contributes to the solution of this problem, within the framework of which a language environment is formed that contributes to improving the learning process, developing communicative competence, motivation for learning foreign language for specific purposes. In order to prove the relevance of the use of this method, the application of VR models as a new direction in the industry of modern technologies was considered and statistical data of various foreign and Russian consulting companies were provided. The analysis of the development of the Russian VR technology market is given in three segments, which leads to a conclusion about the dynamics and prospects of its development. Advantages of using this information technology, contributing to a full immersion in the professional environment, are highlighted. The authors come to the conclusion about the advisability of using virtual reality technologies as one of the most effective innovative project technologies in the educational process.
A Proactive Approach to Evaluate Performance of BPEL Workflows by Queuing Theory and Stochastic Petri Net

Service oriented architecture is a novel paradigm for developing software based on composition of components or services. This innovation in the context of software engineering facilitates reusability of services according to loosely coupled relations. Meanwhile, BPEL standard accelerates composition of services. Although many efforts have been made to prove functional correctness of composed services through formal verification techniques, prediction of non-functional properties such as performance, reliability, and availability received less attention. Since BPEL service is similar to a workflow, it has been modeled with Stochastic Petri Net. We have extended SPN to fit our purpose. In this paper, we have presented an analytical modeling approach based on SPN where it uses a sequence of matrix calculations in a statistical space. The calculations are according to exponential distribution for transitions and Poisson distribution for arcs. Simulation results have validated the model for performance of a complex example. This approach can also be deployed for prediction of reliability and availability.