Reviewerly/exHarmony huggingface dataset:
Task Allocation and Re-allocation for Big Data Applications in Cloud Computing Environments

Resource allocation for Big data streams in cloud systems involves selecting the appropriate cloud resources. Since incorrect resource allocation results in either under provisioning or over provisioning, accurate resource allocation becomes challenging in Big data applications. Hence, the objective of this work is to design an optimal solution for resource allocation for minimizing the network bandwidth and response delay. In this paper, a task allocation and re-allocation mechanism for Big data applications is designed. It consists of two important agents: RE-allocation Agent (REA) and Resource Agent (RA). The RA is responsible for mapping the user requirements to the available VMs. The REA monitors the resources and chooses the VMs for resource reconfiguration. Then, it dispatches an allocation or de-allocation request to RA, running in the physical system, based on the varying requirements of virtual machines. Experimental results show that the proposed TARA has less execution time and achieves better utilization of resources, when compared to existing tool.
Developer portraying: A quick approach to understanding developers on OSS platforms

Millions of software developers are using open-source software (OSS) platforms to host their code and collaborate with each other. They possess different programming skills, styles, and preferences, etc., and it is important to understand them for making collaborative decisions such as programming task assignment. Existing OSS platforms do not provide sufficient information about developers, and we need to spend significant effort in searching the OSS platforms for such information. Different than the basic developer information displayed on OSS platforms, we propose portraying developers as a quick approach for characterizing and understanding them. We discuss how to build developer portraits to make them concise yet informative. We propose a multi-dimensional developer portrait model to specify the attributes of various aspects concerning software development about developers. Then, a method that leverages text analysis, web data analysis, and code analysis techniques is presented to analyze a developer’s various sources of data on OSS platforms for constructing the portrait. The constructed portraits can be vividly displayed on the web to help people quickly understand developers and make better decisions during collaborative software development. Case studies on two representative problems in the software engineering area—code recommendation and programming task assignment—are conducted, and the results show the improvement in recommendation and the potential for proper assignments when using our portraits. The developer portrait is an effective form to characterize developers. It can help people quickly understand the developers and can be applied to various applications in the software development process.
A "Network Pruning Network" Approach to Deep Model Compression

We present a filter pruning approach for deep model compression, using a multitask network. Our approach is based on learning a a pruner network to prune a pre-trained target network. The pruner is essentially a multitask deep neural network with binary outputs that help identify the filters from each layer of the original network that do not have any significant contribution to the model and can therefore be pruned. The pruner network has the same architecture as the original network except that it has a multitask/multi-output last layer containing binary-valued outputs (one per filter), which indicate which filters have to be pruned. The pruner's goal is to minimize the number of filters from the original network by assigning zero weights to the corresponding output feature-maps. In contrast to most of the existing methods, instead of relying on iterative pruning, our approach can prune the network (original network) in one go and, moreover, does not require specifying the degree of pruning for each layer (and can learn it instead). The compressed model produced by our approach is generic and does not need any special hardware/software support. Moreover, augmenting with other methods such as knowledge distillation, quantization, and connection pruning can increase the degree of compression for the proposed approach. We show the efficacy of our proposed approach for classification and object detection tasks.
A Deep Learning based Framework for Detecting and Reducing onset of Cybersickness

Cybersickness is one of the notable discomforts associated with virtual reality(VR) applications. With the recent advancement of machine learning, we can train deep neural networks to detect cyber-sickness severity from the physiological and VR-image data. Prior researches found a correlation between physiological and VR-image data with cybersickness. In this study, I hypothesize to detect the onset severity of cybersickness. Finally, based on the severity level, automatically apply different cybersickness reduction techniques in real-time.
Machine Learning for Attribution of Heat and Drought in Southwestern Australia

Temperature and precipitation datasets, extending back over 100 years, are analyzed at Perth, Australia. Observational analyses reveal the emergence of hot and dry years since the 1980s, with changes in maximum temperatures ~1.5-2 °C above historical means. These temperatures far exceed recorded natural variability measured in the early 20th century and, in the past few decades, have accelerated above the danger threshold established in the Paris Accords. Permutation testing of mean Perth temperature (precipitation) for the 20-year periods 1979-1998 and 1999-2018 shows an increase (decrease) of 0.855 °C (98.1 mm); p-value 0.001 (0.0087). Attribution of interannual data variability is established by wavelet analyses. Linear and support vector regression (LR, SVR), neural network (NN) and random forests (RF) are used for temperature and precipitation prediction after attribute selection methods are applied to a set of climate drivers. Forecasts on independent testing data show that for temperature and precipitation forecasts, SVR, LR and NN (temperature only) provide more accurate predictions than RF. The features selected by attribute selection and machine learning provide important guidance for climate forecasting, policy planning and management.
Significance of Agile Software Development and SQA Powered by Automation

Today's software industry is fast moving and has daily changing demands. Many organizations are struggling to cope up with these emerging demands and they are looking for change in software development. Agile is most popular software development strategy in today's software industry, Agile provides different methods, organizations can choose suitable method to implement agility. Among all agile methods, Scrum is the most popular as of today. Although, agile is well suitable for large organization with distributed systems. Agile method such as 'scrum' can be improved further and it can work with other process and automation tools coupled with agile artifacts to improve software development team's performance such a solution can be applied to large scale industries. As no one solution is best suitable for today's business, hence the concept of 'Agile Genome' is becoming more popular, which encourages organizations to use combination of other methods and automation tools that can be used based on the application. Hence there is a need of discussing areas where agile and automation can work together and finding different ways of utilizing full potential of agile methods along with automation tools to achieve maximum benefits for the software industries.
HT-Based identification of 3D feature curves and their insertion into 3D meshes

In this paper we present a method for recognising and inserting space curves on 3D shapes. These curves are identified by a set of vertices and approximated with known curves, like helices, which characterise particular curves in the digital models of 3D objects. As the curve recognition method we adopt a generalisation of the Hough Transform (HT), adapting it to space curves. Through the HT we are able to represent generic classes of space curves not necessarily polynomial, like splines and Bézier curves, and we obtain a parametric representation of the curves that allows us to select the points and the curvilinear segments to be inserted directly as constraints in the 3D mesh, thus creating a new hybrid triangle mesh, with both linear and curvilinear elements. The experiments obtained so far on 3D models are encouraging and suggest that the use of such a hybrid mesh is a viable option to avoid to thicken the sampling of the curved parts with respect to the linear parts or to overfit the whole mesh.
Modeling of performance evaluation of educational information based on big data deep learning and cloud platform

The traditional teaching quality evaluation model can only evaluate the data of the corresponding period, does not have the deep learning function, and cannot realize the prediction based on the existing data. In order to improve the operation effect of the teaching evaluation model, this study bui lt a teaching evaluation model by combining data mining with deep learning and constructed a data processing module based on cloud computing. Moreover, this study used the improved incomplete multi-classification algorithm to construct the multi-divider and applied the constructed classifier to the teaching evaluation system to automatically classify the teachers. In addition, after learning the relevant theoretical knowledge of the university evaluation system, this paper also designed a prototype of the teaching quality evaluation framework based on the improved algorithm. Through the analysis of the model effect, it can be seen that the results of this study have certain evaluation effects, which can be applied to practice, and can provide theoretical reference for subsequent related research.
Blockchain Technology to Support Smart Learning and Inclusion: Pre-service Teachers and Software Developers Viewpoints

In support of an open ecosystem for lifelong and smart learning, this study evaluates the perception of educational stakeholders such as pre-service teachers and blockchain developers about the feasibility of the blockchain technology in addressing the numerous gaps in the implementation of smart learning environment. This research was designed within the international project, Smart Ecosystem for Learning and Inclusion (SELI). A total of 491 pre-service teachers and 3 blockchain developers from these countries participated in the study. The study data was collected from a questionnaire and interview. Descriptive statistics and content analysis was performed on the collected data. Results from this study indicates that Blockchain technology in the educational field is rarely known, and the frequency of use is quite low. The pre-service teachers surveyed, for the most part, are unaware of the degree of effectiveness of blockchain technology in education. Blockchain developers are of the opinion that Blockchain is still new to many people and resources for education based application are very rare, even if it is there, yet not many are open-source.
Evaluation of Simulator Sickness for 360° Videos on an HMD Subject to Participants’ Experience with Virtual Reality

Virtual reality (VR) has seen tremendous advances in head-mounted displays (HMDs), optics, media quality, and other improvements that facilitate immersive experiences. With the occurrence of new technologies like Cloud VR and networked VR video services, applications such as 360° video streaming are becoming more popular within the broader consumer markets. As a result, VR content is accessible to customers with rather different levels of experiences with immersive media, i.e., never, sometimes, or often use of VR. The question, therefore, arises to which degree simulator sickness is induced to viewers depending on their experiences with VR on HMDs. In this paper, simulator sickness is evaluated for 360° videos that were shown on an HTC Vive Pro HMD to participants having different levels of experience with VR on HMDs. The modified absolute category rating with hidden reference (M-ACR-HR) method was used in a subjective experiment for video quality assessment within two subsequent sessions along with a simulator sickness questionnaire (SSQ). A statistical analysis of the SSQ scores is performed to reveal the relationship between simulator sickness and participants’ experiences with VR regarding: (1) Individual symptoms, (2) Pairwise comparison of symptoms, and (3) Symptom clusters of nausea, oculomotor, disorientation, and total score. It is shown that the simulator sickness symptoms, in general, are slightly or rarely perceived across the different experience levels for the selected 360° videos. The results indicate that the reported simulator sickness increases in the second session for participants that never used VR on HMDs. Sufficiently long breaks between sessions should therefore be accounted for in the M-ACR-HR method to avoid that simulator sickness influences quality rating.
TEST: an End-to-End Network Traffic Classification System With Spatio-Temporal Features Extraction

With more encrypted network traffic gets involved in the Internet, how to effectively classify network traffic has become a more and more important task in the field. Accurate classification of the network traffic is the footstone of basic network services, say QoE, bandwidth allocation, and Intrusion Detection System (IDS). Previous classification methods have many shortcomings since they cannot deal with the encrypted network traffic but require human experts to select tons of features to attain a relatively decent accuracy. Therefore, in this paper, we present a Deep Learning based end-to-end network traffic classification framework, termed TEST, to avoid the aforementioned problems. CNN and LSTM are combined and implemented to help the network intrusion detection systems automatically extract features from both special and time-related features of the raw traffic. The structure of our framework has two layers, which made it possible to attain a remarkable accuracy on encrypted traffic classification tasks. Compared to the LeNet model with the accuracy of 80.27% and the LSTM model with the accuracy of 81.96%, terimental results demonstrate that our model can outperform previous methods with a state-of-the-art accuracy of 99.98%.
Cognitive Anthropomorphism of AI: How Humans and Computers Classify Images

Modern artificial intelligence (AI) image classifiers have made impressive advances in recent years, but their performance often appears strange or violates expectations of users. This suggests that humans engage in cognitive anthropomorphism: expecting AI to have the same nature as human intelligence. This mismatch presents an obstacle to appropriate human-AI interaction. To delineate this mismatch, I examine known properties of human classification, in comparison with image classifier systems. Based on this examination, I offer three strategies for system design that can address the mismatch between human and AI classification: explainable AI, novel methods for training users, and new algorithms that match human cognition.
Ramp loss for twin multi-class support vector classification

Twin K-class support vector classification (TKSVC) adopts 'One-vs.-One-vs.-Rest' structure to utilise all the samples to increase the prediction accuracy. However, TKSVC is sensitive to noises or outliers due to the use of the Hinge loss function. To reduce the negative influence of outliers, in this paper, we propose a more robust algorithm termed as Ramp loss for twin K-class support vector classification (Ramp-TKSVC) where we use the Ramp loss function to substitute the Hinge loss function in TKSVC. Because the Ramp-TKSVC is a non-differentiable non-convex optimisation problem, we adopt Concave–Convex Procedure (CCCP) to solve it. To overcome the drawbacks of conventional multi-classification methodologies, the TKSVC is utilised as a core of our Ramp-TKSVC. In the Ramp-TKSVC, the outliers are prevented from becoming support vectors, thus they are not involved in the construction of hyperplanes, making the Ramp-TKSVC more robust. Besides, the Ramp-TKSVC is sparser than the TKSVC. To verify the validity of our Ramp-TKSVC, we conduct experiments on 12 benchmark datasets in both linear and nonlinear cases. The experimental results indicate that our algorithm outperforms the other five compared algorithms.
Unsupervised Tumor Characterization via Conditional Generative Adversarial Networks

Grading for cancer, based upon the degree of cancer differentiation, plays a major role in describing the characteristics and behavior of the cancer and determining treatment plan for patients. The grade is determined by a subjective and qualitative assessment of tissues under microscope, which suffers from high inter- and intra-observer variability among pathologists. Digital pathology offers an alternative means to automate the procedure as well as to improve the accuracy and robustness of cancer grading. However, most of such methods tend to mimic or reproduce cancer grade determined by human experts. Herein, we propose an alternative, quantitative means of assessing and characterizing cancers in an unsupervised manner. The proposed method utilizes conditional generative adversarial networks to characterize tissues. The proposed method is evaluated using whole slide images (WSIs) and tissue microarrays (TMAs) of colorectal cancer specimens. The results suggest that the proposed method holds a potential for quantifying cancer characteristics and improving cancer pathology.
Hybrid ABFA-APSO Algorithm

The aim of this chapter is to propose improvement to the adaptation of bacterial foraging algorithm (BFA) and to hybridize it with accelerated particle swarm optimization (APSO) in order to accelerate its convergence. In the proposed algorithm, the random walk in the chemotaxis stage of the ABFA is updated through the velocity equation of the APSO.
Smart contracts and over-enforcement: Analytical considerations on Smart Contracts as Legal Contracts

Smart contracts are a common topic in scientific discussions held by computer scientists. Due to the dynamic nature of its applications, smart contracts must be considerably more adaptable, responsive and controllable. Yet the longstanding focus on security issues has affected negatively the development of smart contracts in the transfer of ownership of the real estate.In this paper, we study the possibilities and the limitations of how to transfer properties on the block chain using a digital record, as a back- support in case of litigation. We propose storing a registry catalog to handle the property historical data and criminal record of the owners. The execution of the smart contract is enforced using a digital identity of the two parties. We design the components of what the registry should contain to integrate with block chain infrastructure and we also analyze and discuss the government endorsement needed for the success of the implementation.
Partially Zero-shot Domain Adaptation from Incomplete Target Data with Missing Classes

We tackle a domain adaptation problem under partially zero-shot setting. In this setting, a certain subset of classes is missing in the unlabeled target data, while all classes appear in the labeled source data, and the goal is to discriminate all classes at the target domain. To solve this problem, we utilize an adversarial training scheme and adopt instance weighting to estimate the loss related to unavailable target data in the missing classes. The instance weight is computed on the basis of the prediction of deep neural networks, implying which instance would be similar to unseen data and having useful information for the loss estimation. This estimation makes it possible to explicitly consider all classes during the domain adaptation training even in the partially zero-shot setting, which leads to accurate adaptation between domains. Experimental results with several benchmark datasets validate the advantage of our method.
Auto-adaptive multi-scale Laplacian Pyramids for modeling non-uniform data

Kernel-based techniques have become a common way for describing the local and global relationships of data samples that are generated in real-world processes. In this research, we focus on a multi-scale kernel based technique named Auto-adaptive Laplacian Pyramids (ALP). This method can be useful for function approximation and interpolation. ALP is an extension of the standard Laplacian Pyramids model that incorporates a modified Leave-One-Out Cross Validation procedure, which makes the method stable and automatic in terms of parameters selection without extra cost. This paper introduces a new algorithm that extends ALP to fit datasets that are non-uniformly distributed. In particular, the optimal stopping criterion will be point-dependent with respect to the local noise level and the sample rate. Experimental results over real datasets highlight the advantages of the proposed multi-scale technique for modeling and learning complex, high dimensional data.
Fukunaga–Koontz Convolutional Network with Applications on Character Classification

Abstract Several convolutional neural network architectures have been proposed for handwritten character recognition. However, most of the conventional architectures demand large scale training data and long training time to obtain satisfactory results. These requirements prevent the use of these methods in a broader range of applications. As an alternative to cope with these problems, we present a new convolutional network for handwritten character recognition based on the Fukunaga–Koontz transform (FKT). Our approach lies in the assumption that Fukunaga–Koontz convolutional kernels can be efficiently learned from subspaces and directly employed to produce high discriminant features in a shallow network architecture. When representing image classes by subspaces, the within-class separability is reduced, since the subspaces form clusters in a low-dimensional space. To increase the between-class separability, we compute a discriminative space from the training subspaces using FKT. By learning convolutional kernels from subspaces, it is possible to extract representative and discriminative features from an image with only a few parameters. Another contribution of the proposed network is the use of pooling layers, which further improves its performance. The proposed method, called Fukunaga–Koontz Network (FKNet), is suitable for solving practical problems, especially when training and processing times are constraints. Four publicly available handwritten character datasets are employed to evaluate the advantages of FKNet. In addition, we demonstrate the flexibility of the proposed method by experiments on LFW dataset.
User Authentication Method Based on MKL for Keystroke and Mouse Behavioral Feature Fusion

In order to improve the recognition rate of users with single behavioral feature and prevent impostors from restricting an input device to avoid detection, a dual-index user authentication method based on Multiple Kernel Learning (MKL) for keystroke and mouse behavioral feature fusion was proposed in this paper. Due to the heterogeneity between the keystroke features and the mouse features, we argue that each type of features is mapped to a suitable kernel and the weights of each kernel are obtained through computing and then summed to obtain a compound kernel that implements the multifeature fusion. The dataset used in this paper was collected under complete uncontrolled condition from some volunteers by using our data collection program. The experimental results show that the proposed method can obtain the best recognition accuracy of 89.6%. Compared to the traditional methods of single feature, the dual-index method can get more stable and effective authentication. Therefore, the proposed method in this paper fully demonstrates the reliability of dual-index user authentication.