Reviewerly/exHarmony huggingface dataset:
Towards techniques, challenges and efforts of software as a service layer based on business applications in cloud environments

Purpose Cloud computing is qualified to present proper limitless storage and computation resources to users as services throughout the internet. Software as a service (SaaS) layer is the key paradigm perspective in the software layer of the cloud computing. SaaS is connected by business applications to access consumers on existing public, private and hybrid cloud models. This purpose of this paper is to present a discussion and analysis on the SaaS layer based on business applications in the cloud environment in form of a classical taxonomy to recognize the existing techniques, challenges and efforts. Design/methodology/approach Existing techniques, challenges and efforts are classified into four categories: platform-dependent, application-dependent, data-dependent and security-dependent mechanisms. The SaaS layer mechanisms are compared with each other according to the important factors such as the structural properties, quality of service metrics, applied algorithms and measurement tools. Findings The benefits and weaknesses of each research study are analyzed. In the comparison results, the authors observed that the application-based method, the non-heuristic algorithms, the business process method have the highest percentage of the usage in this literature. Originality/value The SaaS layer mechanisms based on business applications have some main features such as high accessibility, compatibility, reusability and collaboration to provide activated application and operation services for user with help of Web browsers. A comprehensive analysis was presented as originality on the SaaS layer mechanisms based on business applications for high level of the cloud environment that 46 peer-reviewed studies were considered.
MAAT

As the Internet of Things (IoT) proliferates, the potential for its opportunistic interaction with traditional mobile apps becomes apparent. We argue that to fully take advantage of this potential, mobile apps must become things themselves, and interact in a smart space like their hardware counterparts. We present an extension to our Atlas thing architecture on smartphones, allowing mobile apps to behave as things and provide powerful services and functionalities. To this end, we also consider the role of the mobile app developer, and introduce actionable keywords (AKWs)---a dynamically programmable description---to enable potential thing to thing interactions. The AKWs empower the mobile app to dynamically react to services provided by other things, without being known a priori by the original app developer. In this paper, we present the mobile-apps-as-things (MAAT) concept along with its AKW concept and programming construct. For MAAT to be adopted by developers, changes to the existing development environments (IDE) should remain minimal to stay acceptable and practically usable, thus we also propose an IDE plugin to simplify the addition of this dynamic behavior. We present details of MAAT, along with the implementation of the IDE plugin, and give a detailed benchmarking evaluation to assess the responsiveness of our implementation to impromptu interactions and dynamic app behavioral changes. We also investigate another study, targeting Android developers, which evaluates the acceptability and usability of the MAAT IDE plugin.
Virtual Phone Discovery for Speech Synthesis Without Text

The objective of this work is to re-synthesize speech directly from the speech signals without using any text in a different speaker's voice. The speech signals are transformed into a sequence of acoustic subword units or virtual phones which are discovered automatically from the given speech signals in an unsupervised manner. The speech signal is initially segmented into acoustically homogeneous segments through kernel-Gram segmentation using MFCC and autoencoder bottleneck features. These segments are then clustered using different clustering techniques. The cluster labels thus obtained are considered as virtual phone units which are used to transcribe the speech signals. The virtual phones for the utterances to be resynthesized are encoded as one-hot vector sequences. Deep neural network based duration model and acoustic model are trained for synthesis using these sequences. A vocoder is used to synthesize speech in target speaker's voice from the features estimated by the acoustic model. The performance evaluation is done on ZeroSpeech 2019 challenge on English and Indonesian language. The bitrate and speaker similarity were found to be better than the challenge baseline with slightly lower intelligibility due to the compact encoding.
Predicting the difficulty of TSP instances using MST

The efforts needed to solve travelling salesman problems (TSP) obviously depend on the problem size. However, also other factors can predict the difficulty of a given problem instance. We present a measure based on the minimum spanning tree (MST). The measure counts the number of knot points, which branch the tree into multiple sub-trees. We show by experiments that the more there are knots in the tree, the more difficult the problem instance is to solve by both humans and computers.
Methodology of short-term planning of work indices in the university department on basis of the reference model using web-portal

Abstract Today, the departments of various universities have implemented databases in the form of Web sites. The information environment of the department is a huge data warehouse. Modern database management systems allow to effectively store and process data, but, unfortunately, these tasks implementation is not enough for personnel management. The trends in the higher education development in Russia indicate that it is necessary not only to store data, but also to control the department development in educational, scientific and other areas of activity. For this it is proposed to implement a system for calculating, maintaining and visualizing the department’s rating and introduce it into the existing Web portal of the department. This system will not only simplify the obtaining, calculation and storage of the required data on the department rating. It will also provide the required indices in a visual and understandable way.
Parity Games: Another View on Lehtinen's Algorithm

Recently, five quasi-polynomial-time algorithms solving parity games were proposed. We elaborate on one of the algorithms, by Lehtinen (2018). Czerwinski et al. (2019) observe that four of the algorithms can be expressed as constructions of separating automata (of quasi-polynomial size), that is, automata that accept all plays decisively won by one of the players, and rejecting all plays decisively won by the other player. The separating automata corresponding to three of the algorithms are deterministic, and it is clear that deterministic separating automata can be used to solve parity games. The separating automaton corresponding to the algorithm of Lehtinen is nondeterministic, though. While this particular automaton can be used to solve parity games, this is not true for every nondeterministic separating automaton. As a first (more conceptual) contribution, we specify when a nondeterministic separating automaton can be used to solve parity games. We also repeat the correctness proof of the Lehtinen's algorithm, using separating automata. In this part, we prove that her construction actually leads to a faster algorithm than originally claimed in her paper: its complexity is $n^{O(\log n)}$ rather than $n^{O(\log d \cdot \log n)}$ (where $n$ is the number of nodes, and $d$ the number of priorities of a considered parity game), which is similar to complexities of the other quasi-polynomial-time algorithms.
Long short-term memory networks based fall detection using unified pose estimation

Falls are one of the major causes of injury and death among elderly globally. The increase in the ageing population has also increased the possibility of re-occurrence of falls. This has further added social and economic burden due to the higher demand for the caretaker and costly treatments. Detecting fall accurately, therefore, can save lives as well as reduce the higher cost by reducing the false alarm. However, recognising falls are challenging as they involve pose translation at a greater speed. Certain activities such as abruptly sitting down, stumble and lying on a sofa demonstrate strong similarities in action with a fall event. Hence accuracy in fall detection is highly desirable. This paper presents a Long Short-Term Memory (LSTM) based fall detection using location features from the group of available joints in the human body. The result from the confusion matrix suggests that our proposed model can detect fall class with a precision of 1.0 which is highly desirable.
Improved non-maximum suppression for detecting overlapping objects

Non-maximum suppression (NMS) is widely used in object detectors for removing imprecise detection boxes. However, NMS can easily discard a part of correct detection boxes when multiple objects are overlapped. To deal with this problem, some methods had been presented, but only for simple overlapping scenes. Therefore, this paper proposes an improved NMS approach to detect objects with high degree of overlap. This method divides all of detection boxes into different clusters to reduce the degree of overlap between boxes. These detection box scores in each cluster are decayed as a function of overlap and no boxes are discarded. The improved NMS is combined with two commonly used object detection networks, namely Faster Region-based Convolutional Neural Networks and Region-based Fully Convolutional Networks. A complex public dataset Microsoft Common Objects in Context is employed to evaluate the performance of the improved NMS. Experimental results show that two metrics average recall and localization performance are improved by the proposed method for these two famous detectors.
Polymorphous Bovine Somatic Cell Recognition Based on Feature Fusion

Microscopic images of bovine milk somatic cells are used to classify neutrophils, epithelial cells, macrophages and lymphocytes. Using pattern recognition technology, the problem of classification and recognition is solved from different nature, levels and spaces. The proposed RKSGA-SVM algorithm is used to realize somatic cell image recognition. First, color, morphological and texture features of four types of cells are extracted separately, including geometric and moment invariant features. Second, ReliefF algorithm is used to calculate the weights of all features. According to preset cumulative contribution rate, the preliminary feature set is obtained. Third, redundant features are eliminated by Kolmogorov–Smirnov (KS) test, and the high-level optimization is obtained. The selected feature sets have remarkable distinguishing ability. Finally, the weighted optimal feature sets are obtained by weighted coefficient method on the advanced optimal feature sets. The overall accuracy of RKSGA-SVM algorithm is 99.00%, and Kappa coefficient is 0.987. The proposed algorithm has the advantages of balancing classification accuracy, eliminating redundancy and reducing feature dimension. On the premise of ensuring high classification accuracy, the feature set can reduce feature dimension and the amount of data calculation, improve operation efficiency and save storage space. Experiments show that the feature selection method proposed in this paper is feasible and more suitable for extracting feature sets in the process of somatic cell classification.
Towards Wide Range Tracking of Head Scanning Movement in Driving

Gaining environmental awareness through lateral head scanning (yaw rotations) is important for driving safety, especially when approaching intersections. Therefore, head scanning movements could be an important behavioral metric for driving safety research and driving risk mitigation systems. Tracking head scanning movements with a single in-car camera is preferred hardware-wise, but it is very challenging to track the head over almost a 180° range. In this paper we investigate two state-of-the-art methods, a multi-loss deep residual learning method with 50 layers (multi-loss ResNet-50) and an ORB feature-based simultaneous localization and mapping method (ORB-SLAM). While deep learning methods have been extensively studied for head pose detection, this is the first study in which SLAM has been employed to innovatively track head scanning over a very wide range. Our laboratory experimental results showed that ORB-SLAM was more accurate than multi-loss ResNet-50, which often failed when many facial features were not in the view. On the contrary, ORB-SLAM was able to continue tracking as it doesn't rely on particular facial features. Testing with real driving videos demonstrated the feasibility of using ORB-SLAM for tracking large lateral head scans in naturalistic video data.
Automatic Generation of E-Learning Contents Based on Deep Learning and Natural Language Processing Techniques

With the development of automation in all industries, E-Learning automation: automatically generate E-Learning contents gives E-Learning teachers new opportunities to implement effective management of E-Learning systems. And many eLearning professionals already use E-Learning templates and online asset libraries to create cost effective eLearning courses. Moreover, all E-Learning Automation methods have one thing in common—algorithms: used to control various aspects of e-learning course creation. Additionally, another important thing is online exams which are used to get an immediate feedback from learners. This research focuses on automation generation of E-Learning contents, which can be mainly divided into three parts. Firstly, automatically summarize relevant documents using natural language processing and deep learning techniques. Secondly, detecting keywords in generated summaries and then delete them from input summaries. Finally, rearrange output results and fill them into E-Learning system.
Nonlocal means estimation of intrinsic mode functions for speech enhancement

The main aim of this paper is to introduce a new approach to enhance speech signals by exploring the advantages of nonlocal means (NLM) estimation and empirical mode decomposition. NLM, a patch-based denoising method, is extensively used for two-dimensional signals like images. However, its use for one-dimensional signals has been attracting more attention recently. The NLM-based approach is quite useful for removing low-frequency noises based on nonlocal similarities present among samples of the signal. However, there is an issue of under averaging in the high-frequency regions. The temporal and spectral characteristics of the speech signal are changing markedly over time. Thus NLM is conventionally not effective to remove the noise components from the speech signal, unlike image denoising. To address this issue, initially, the speech signal is decomposed into oscillatory components called intrinsic mode functions (IMFs) by using a temporal decomposition technique known as the sifting process. Each IMF represents signal information at a certain scale or frequency band. The IMFs do not have abrupt power spectral changes over time. The decomposed IMFs are processed using NLM estimation based on nonlocal similarities for better speech enhancement. The simulation result shows that the proposed method gives better performance in terms of subjective and objective quality measures. Its performance is evaluated for white, factory, and babble noises at different signal to noise ratios.
Accelerating Gradient Boosting Machine

Gradient Boosting Machine (GBM) is an extremely powerful supervised learning algorithm that is widely used in practice. GBM routinely features as a leading algorithm in machine learning competitions such as Kaggle and the KDDCup. In this work, we propose Accelerated Gradient Boosting Machine (AGBM) by incorporating Nesterov's acceleration techniques into the design of GBM. The difficulty in accelerating GBM lies in the fact that weak (inexact) learners are commonly used, and therefore the errors can accumulate in the momentum term. To overcome it, we design a pseudo residual and fit best weak learner to this corrected pseudo residual, in order to perform the z-update. Thus, we are able to derive novel computational guarantees for AGBM. This is the first GBM type of algorithm with theoretically-justified accelerated convergence rate. Finally we demonstrate with a number of numerical experiments the effectiveness of AGBM over conventional GBM in obtaining a model with good training and/or testing data fidelity.
Learning Class-Specific Features with Class Regularization for Videos

One of the main principles of Deep Convolutional Neural Networks (CNNs) is the extraction of useful features through a hierarchy of kernels operations. The kernels are not explicitly tailored to address specific target classes but are rather optimized as general feature extractors. Distinction between classes is typically left until the very last fully-connected layers. Consequently, variances between classes that are relatively similar are treated the same way as variations between classes that exhibit great dissimilarities. In order to directly address this problem, we introduce Class Regularization, a novel method that can regularize feature map activations based on the classes of the examples used. Essentially, we amplify or suppress activations based on an educated guess of the given class. We can apply this step to each minibatch of activation maps, at different depths in the network. We demonstrate that this improves feature search during training, leading to systematic improvement gains on the Kinetics, UCF-101, and HMDB-51 datasets. Moreover, Class Regularization establishes an explicit correlation between features and class, which makes it a perfect tool to visualize class-specific features at various network depths.
Professional reflection – Mixed reality to augment the next generation of aviation professionals

The Next Generation of Aviation Professionals entering the industry respond differently to various teaching and learning styles and digital information. The operation and maintenance of modern aircraft requires an understanding of several interrelated human and machine components requiring practice and immersion. The aviation educational experience can be enhanced with serious gaming, augmented, mixed and virtual reality (VR). VR is widely used in the aviation industry and is becoming more and more affordable for end users. Augmented reality (AR) is prevalent in many industries including automotive, education, hospitality, aviation, healthcare, retail, construction, oil mining, and maritime. The advantages of AR extend past aircraft systems, maintenance, pilot and cabin training to many aspects of aviation education, such as weather. ‘Interpreting and understanding weather products is critical to hazardous weather avoidance, and previous studies have indicated that improving understanding of weather and weather products can improve pilot decision making, situational awareness and increase flight safety’.
Characterization of software testing practices: A replicated survey in Costa Rica

Software testing is an essential activity in software development projects for delivering high quality products. In a previous study, we reported the results of a survey of software engineering practices in the Costa Rican industry. To make a more in depth analysis of the specific software testing practices among practitioners, we replicated a previous survey conducted in South America. Our objective was to characterize the state of the practice based on practitioners use and perceived importance of those practices. This survey evaluated 42 testing practices grouped in three categories: processes, activities and tools. A total of 92 practitioners responded to the survey. The participants indicated that: (1) tasks for recording of the results of tests, documentation of test procedures and cases, and re-execution of tests when the software is modified are useful and important for software testing practitioners. (2) Acceptance and system testing are the two most useful and important testing types. (3) Tools for recording defects and the effort to fix them (bug tracking) and the availability of a test database for reuse are useful and important. Regarding use and implementation of practices, the participants stated that (4) Planning and designing of software testing before coding and evaluating the quality of test artifacts are not a regular practice. (5) There is a lack of measurement of defect density and test coverage in the industry; and (6) tools for automatic generation of test cases and for estimating testing effort are rarely used. This study gave us a first glance at the state of the practice in software testing in a thriving and very dynamic industry that currently employs most of our computer science professionals. The benefits are twofold: for academia, it provides us with a road map to revise our academic offer, and for practitioners it provides them with a first set of data to benchmark their practices.
Sentiment Analysis of Customers on Utilizing Online Motorcycle Taxi Service at Twitter with the Support Vector Machine

Online motorcycle taxi is one of the latest public transportation trends to Indonesian people. Although it is still new, its existence is able to change the behavior of Indonesian people. Some people feel that they are being helped by its existence, yet some people do not. Various types of complaints or appreciation about the online motorcycle taxi are frequently discussed on popular social media, namely; twitter. Sentiment Analysis is a part of science in text mining that is able to predict someone’s feeling or feeling as outlined in text. This study uses the support of vector machine method with TF-IDF feature selection. The data set were 1183 that were taken from twitter with the keyword gojekindonesia and grabID through the data crawling process. The data set are divided into 3 classes: positive sentiment class, negative sentiment class and neutral sentiment class. The classification process was done by using five scenarios that were training comparison and Testing 50:50, 60:40, 70:30, 80:20 and 90:10. In addition, the classification process also used four kernels such as linear, rbf, sigmoid and polynomial. The highest accuracy results are in the comparison of 90% as training data and 10% as testing data while using a linear and sigmoid kernel are 0.8060 or about 80%.
A Meaningful Image Encryption Algorithm Based on Prediction Error and Wavelet Transform

Image encryption (IE) is a very useful and popular technology to protect the privacy of users. Most algorithms usually encrypt the original image into an image similar to texture or noise, but texture and noise are an obvious visual indication that the image has been encrypted, which is more likely to cause the attacks of enemy. To overcome this shortcoming, many image encryption systems, which convert the original image into a carrier image with visual significance have been proposed. However, the generated cryptographic image still has texture features. In line with the idea of improving the visual quality of the final password images, we proposed a meaningful image hiding algorithm based on prediction error and discrete wavelet transform. Lots of experimental results and safety analysis show that the proposed algorithm can achieve high visual quality and ensure the security at the same time.