Reviewerly/exHarmony huggingface dataset:
Agile in the Era of Digitalization: A Finnish Survey Study

Agile software development has been applied since the early 2000s. It is now mainstream industrial practice in information and communication technology (ICT) companies and IT organizations. However, recently increasing and even disruptive digitalization has brought new drivers and needs for agility both in software organizations as well as in traditional companies, which are becoming more and more software-intensive. Following that line of developments, based on our recent survey conducted in Finland in 2018, in this paper we explore the current state of the affairs with respect to how different organizations currently address agility and agile development in both IT and non-software industrial sectors. The results show that operative goals (productivity, quality) are considered the most important ones to achieve by agile means. Scrum, Kanban and DevOps are the most frequently reported methods, and SAFe is the dominant scaling model. Lead time metrics are the most typically followed measurements. The operative goals as well as responsiveness are also the most highly ranked future aims. The impacts of digitalization are considered substantial but agile developments are seen to address them well. As a conclusion of this survey study, there is no “one agile way” for all. Different organizations seem to emphasize multiple aspects of agility when they develop, adapt and even transform themselves. Yet, also many commonalities were indicated.
Toward scalable cloud data center simulation using high‐level architecture

Summary Existing simulators are designed to simulate a few thousand nodes due to the tight integration of modules. Thus, with limited simulator scalability, researchers/developers are unable to simulate protocols and algorithms in detail, although cloud simulators provide geographically distributed data centers environment but lack the support for execution on distributed systems. In this paper, we propose a distributed simulation framework referred to as CloudSimScale. The framework is designed on top of highly adapted CloudSim with communication among different modules managed using IEEE Std 1516 (high‐level architecture). The underlying modules can now run on the same or different physical systems and still manage to discover and communicate with one another. Thus, the proposed framework provides scalability across distributed systems and interoperability across modules and simulators.
NARMADA: Near-Memory Horizontal Diffusion Accelerator for Scalable Stencil Computations

Real-world weather forecasting applications consist of compound stencil kernels that do not perform well on conventional architectures. This behavior is due to their complex data access patterns, limited data reusability, and low arithmetic intensity. To overcome these issues, we harness the potential of near-memory computing by offloading a horizontal diffusion kernel, which is a compound stencil kernel, from the COSMO weather prediction application to a reconfigurable fabric. We use a heterogeneous system that comprises a CPU and an FPGA with on-chip SRAM memory and on-board DRAM memory. By introducing a memory hierarchy tailored to the targeted application and using a coherent memory model, we move the computation close to the memory, which improves memory efficiency. Our hardware design on the FPGA uses high-level synthesis techniques and results in an accelerator with IBM CAPI 2.0 (Coherent Accelerator Processor Interface) technology. We evaluate it against a tuned software implementation running on an IBM POWER9 host system. The experimental results show that these kernels on an FPGA can outperform a complete 16-core POWER9 node (configured with 64 threads) by 3.3x. Moreover, our solution provides an 18x improvement in the active energy consumption.
Handling the Class Imbalance in Land-Cover Classification Using Bagging-Based Semisupervised Neural Approach

In this letter, a semisupervised neural network-based approach has been proposed for handling the class-imbalance problem in land-cover classification under a hybrid integration of selective undersampling, oversampling, and a bagging-based ensemble of classifiers. Here, a selective undersampling technique is utilized so as to minimize the loss of information from the majority classes; whereas, the minority class sizes are simultaneously increased by exploiting their presence in the unlabeled test samples. Finally, the imbalanced original training set along with the newly found minority samples is used to classify the remaining unlabeled samples from the test set. Experiments conducted on the patterns collected from multispectral (high as well as very high resolution images) and hyperspectral remote sensing satellite images show encouraging performance of the proposed scheme when compared to other state-of-the-art techniques.
It's not a Sprint, it's a Marathon

During the past few years, all leading cloud providers introduced burstable instances that can sprint their performance for a limited period to address sudden workload variations. Despite the availability of burstable instances, there is no clear understanding of how to minimize the waste of resources by regulating their burst capacity to the workload requirements. This is especially true when it comes to non-CPU-intensive applications. In this paper, we investigate how to limit network and I/O usage to optimize the efficiency of the bursting process. We also study which resource shall be controlled to benefit both cloud providers and end-users. We design MRburst (Multi-Resource burstable performance scheduler) to automatically limit multiple resources (i.e., network, I/O, and CPU) and make the application comply with a user-defined service level objective (SLO) while minimizing wasted resources. MRburst is evaluated on Amazon EC2 using two multi-resource applications: an FTP server and a Ceph system. Experimental results show that MRburst outperforms state-of-the-art approaches by allowing instances to speed up their performance for up to 2.4 times longer period while meeting SLO.
Intelligent, Secure Big Health Data Management Using Deep Learning and Blockchain Technology: An Overview

Sensor-based health data collection, remote access to health data to render real-time advice have been the key advantages of smart and remote healthcare. Such health monitoring and support are getting immensely popular among both patients and doctors as it does not require physical movement which is always not possible for elderly people who lives mostly alone in current socio-economic situations. Healthcare Informatics plays a key role in such circumstances. The huge amount of raw data emanating from sensors needs to be processed applying machine learning and deep learning algorithms for useful information extraction to develop an intelligent knowledge base for providing an appropriate solution as and when required. The real challenge lies in data storage and retrieval preserving security, privacy, reliability and availability requirements. Health data saved in Electronic medical record (EMR) is generally saved in a client-server database where central coordinator does access control like create, access, update, or delete of health records. But in smart and remote healthcare supported by enabling technologies such as Sensors, Internet of Things (IoT), Cloud, Deep learning, Big data, etc. EMR needs to be accessed in a distributed manner among multiple stakeholders involved such as hospitals, doctors, research labs, patients’ relatives, insurance provider, etc. Hence, it is to be ensured that health data be protected from unauthorized access specifically to maintain data integrity using advanced distributed security techniques such as blockchain.
An Automated Approach for Developing a Convolutional Neural Network Using a Modified Firefly Algorithm for Image Classification

Convolutional neural network (CNN) is a basic configuration of neural networks that can perform deep learning. There are many applications based on CNN in fields of image processing, machine learning, and data analysis. The CNN is a complex neural network with a various number of hidden layers (depth) and a large number of neurons. The depth of the CNN is the essential factor that determines how the network can perform a complicated task. The design phase of CNNs requires potential from non-experts of machine learning. In this chapter, a fully automated algorithm to develop CNN was proposed based on firefly optimization. The proposed method can design a CNN structure with any number of layer depth without any limitation on the depth value. The proposed method employed the skip connection as a fundamental building block of CNN. A modified firefly algorithm was presented base on the $$k-$$nearest neighbor attraction model to reduce the computational complexity of the firefly. The CIFAR-10 and CIFAR-100 were used for the training and validation of the proposed method to perform image classification. The proposed method provided high accuracy when compared to the cutting-edge approaches.
Adversarial Autoencoders for Denoising Digitized Historical Documents: The Use Case of Incunabula

Historical document denoising is the most challenging step in the field of image processing and computer vision. In this paper, we propose a novel end-to-end adversarial autoencoder (AAE) to generate clean images and to show how adversarial autoencoders can be used in historical document denoising. We used the Adversarial Autoencoder (AAE), which uses the generative adversarial networks (GAN) so as to suit the aggregated posterior of the hidden code vector of the autoencoder with an arbitrary prior. The experiments results prove that our approach functions more positively than the cutting-edge approaches on synthetic and real world images at a lower computational cost as well.
Sample Balancing for Deep Learning-Based Visual Recognition

Sample balancing includes sample selection and sample reweighting. Sample selection aims to remove some bad samples that may lead to bad local optima. Sample reweighting aims to assign optimal weights to samples to improve performance. In this article, we integrate a sample selection method based on self-paced learning into deep learning frameworks and study the influence of different sample selection strategies on training deep networks. In addition, most of the existing sample reweighting methods mainly take per-class sample number as a metric, which does not fully consider sample qualities. To improve the performance, we propose a novel metric based on the multiview semantic encoders to reweight the samples more appropriately. Then, we propose an optimization mechanism to embed sample weights into loss functions of deep networks, which can be trained in end-to-end manners. We conduct experiments on the CIFAR data set and the ImageNet data set. The experimental results demonstrate that our proposed sample balancing method can improve the performances of deep learning methods in several visual recognition tasks.
Performance on the Verbal Naming Test among healthy, community-dwelling older adults

Objective: The Verbal Naming Test (VNT) is a nonvisual measure of word finding with stimuli chosen based on rare frequency of usage in spoken English. The purpose of the current study was to evaluate the psychometric properties of the VNT and test the feasibility of telephone administration. In addition, regression-based normative data were obtained for the VNT as well as other measures.Method: Eighty-one community-dwelling older adults 61–92 years old (mean = 74.19 years) completed the VNT, the Naming subtests of the Neuropsychological Assessment Battery (NAB), the WIAT-III Sentence Repetition subtest, and the Montreal Cognitive Assessment (MoCA).Results: As evidence of construct validity, the VNT had large correlations with the NAB Naming test and medium correlations with the MoCA and WIAT-III Sentence Repetition test. Cronbach's alpha in this sample was 0.621. Age, education, and gender were entered into linear regression equations and regression-based normative equations are presented. Lastly, administration of the VNT over the telephone was found to be feasible.Conclusions: The VNT is a valid measure of naming among community-dwelling older adults. Regression-based normative data for the measure will enable its use in the neuropsychological assessment of naming with a wide range of older adults.
Reproducibility of Deep CNN for Biomedical Image Processing Across Frameworks and Architectures

With the increasing spread of easy and effective frameworks, in recent years Deep Learning approaches are becoming more and more used in several application fields, including computer vision (such as natural and biomedical image processing), automatic speech recognition (ASR) and time-series analysis. If, on one hand, the availability of such frameworks allows developers to use the one they feel more comfortable with, on the other, it raises questions related to the reproducibility of the designed model across different hardware and software configurations, both at training and at inference times. The reproducibility assessment is important to determine if the resulting model produces good or bad outcomes just because of luckier or blunter environmental training conditions. This is a non-trivial problem for Deep Learning based applications, not only because their training and optimization phases strongly rely on stochastic procedures, but also because of the use of some heuristic considerations (mainly speculative procedures) at training time that, although they help in reducing the required computational effort, tend to introduce non-deterministic behavior, with a direct impact on the results and on the model’s reproducibility. Usually, to face this problem, designers make use of probabilistic considerations about the distribution of data or focus their attention on very huge datasets. However, this kind of approach does not really fit some application field standards (such as medical imaging analysis with Computer-Aided Detection and Diagnosis systems – CAD) that require strong demonstrable proofs of effectiveness and repeatability of results across the population. It is our opinion that in those cases it is of crucial importance to clarify if and to what extent a Deep Learning based application is stable and repeatable as well as effective, across different environmental (hardware and software) configurations. Therefore, the aim of this work is to quantitatively analyze the reproducibility problem of Convolutional Neural Networks (CNN) based approaches for the biomedical image processing, in order to highlight the impact that a given software framework and hardware configurations might have when facing the same problem by the same means. In particular, we analyzed the problem of breast tissue segmentation in DCE-MRI by using a modified version of a 2D U-Net CNN, a very effective deep architecture for semantic segmentation, using two Deep Learning frameworks (MATLAB and TensorFlow) across different hardware configurations.
Spatial-Temporal Recurrent Neural Network for Anomalous Trajectories Detection

Aiming to improve the quality of taxi service and protect the interests in passengers, anomalous trajectory detection attracts increasing attention. Most of the existing methods concentrate on the coordinate information about trajectories and learn the similarities between anomalous trajectories from a large number of coordinate sequences. These methods ignore the relationship of spatial-temporal and ignore the particularity of the whole trajectory. Through data analysis, we find that there are significant differences between normal trajectories and anomalous trajectories in terms of spatial-temporal characteristic. Meanwhile Recurrent Neural Network can use trajectory embedding to capture the sequential information on the trajectory. Consequently, we propose an efficient method named Spatial-Temporal Recurrent Neural Network (ST-RNN) using coordinate sequence and spatial-temporal sequence. ST-RNN combines the advantages of the Recurrent Neural Network (RNN) in learning sequence information and adds attention mechanism to the RNN to improve the performance of the model. The application of Spatial-Temporal Laws in anomalous trajectory detection also achieves a positive influence. Several experiments on a real-world dataset demonstrate that the proposed ST-RNN achieves state-of-the-art performance in most cases.
On Training Deep Networks for Satellite Image Super-Resolution

The capabilities of super-resolution (SR) reconstruction (i.e., techniques for enhancing image spatial resolution) have been boosted recently by the use of deep convolutional neural networks. For SR, they are learned using huge training sets composed of original images, each of which is coupled with a low-resolution counterpart. In this paper, we explore how the SR performance depends on the procedure employed to obtain the training data. Up to date, this has not been given much attention-commonly, bicubic downsampling is used. Our extensive experimental study indicates that the training data characteristics have a large impact on the reconstruction accuracy, and the widely-adopted approach is not the most effective for dealing with satellite images. Overall, we argue that developing better training data preparation routines may be pivotal in making SR suitable for real-world applications.
Concept Drift Adaption for Online Anomaly Detection in Structural Health Monitoring

Despite its success for anomaly detection in the scenario where only data representing normal behavior are available, one-class support vector machine (OCSVM) still has challenge in dealing with non-stationary data stream, where the underlying distributions of data are time-varying. Existing OCSVM-based online learning methods incrementally update the model to address the challenge, however, they solely rely on the location relationship between a test sample and error support vectors. To better accommodate normal behavior evolution, online anomaly detection in non-stationary data stream is formulated as a concept drift adaptation problem in this paper. It is proposed that OCSVM-based incremental learning is only performed in the case of a normal drift. For an incoming sample, its relative relationship with three sets of vectors in OCSVM, namely margin support vectors, error support vectors, and reserve vectors is fully utilized to estimate whether a normal drift is emerging. Extensive experiments in the field of structural health monitoring have been conducted and the results have shown that the proposed simple approach outperforms the existing OCSVM-based online learning algorithms for anomaly detection.
Semantics-Aware Autoencoder

Recommender Systems are widely adopted in nowadays services such as e-commerce websites, multimedia streaming platforms, and many others. They help users to find what they are looking for by suggesting relevant items leveraging their past preferences. Deep Learning models are very effective in solving the recommendation problem; as a matter of fact, many deep learning architectures have been proposed over the years. Even if deep learning models outperform many state-of-the-art algorithms, the worst disadvantage is about their interpretability: explaining the reason a specific item has been recommended to a user is quite a difficult task since the model is not interpretable. Accuracy in the recommendation is no more enough since users are also expecting a useful explanation for the suggested items. Users, on the other hand, want to know why. In this paper, we present SemAuto, a novel approach based on an Autoencoder Neural Network that makes it possible to semantically label neurons in hidden layers, thus paving the way to the model's interpretability and consequently to the explanation of a recommendation. We tested our semantics-aware approach with respect to other state-of-the-art algorithms to prove the recommendation's accuracy. Furthermore, we performed an extensive A/B test with real users to evaluate the explanation we generate.
A novel method to compensate systematic errors due to undermatched shape functions in digital image correlation

Undermatched shape functions have always been the primary source of systematic errors for non-uniform deformation measurement in digital image correlation, especially for large curvature displacement. All the other errors arising from grey-level interpolation, subset size, and speckle pattern are overwhelmed by the systematic errors due to undermatched shape functions. The effect of the first-order shape functions on polynomials is analyzed, and based on this analysis, a novel and easy to implement displacement post-processing algorithm is proposed to compensate the systematic errors. The direct digital image correlation displacement result is processed by multiple filters with the same window size as the subset, and the resulting data is employed to recover the actual displacement field. Theoretically, the proposed method is capable of recovering the original continuous displacement function of any order with no bias. The numerical results of elementary function and simulated images show that the proposed algorithm is effective in correcting the displacement systematic bias and improving the subpixel accuracy of digital image correlation. Finally, the algorithm is applied to the displacement measurement of shear band.
EyeClouds

In this paper, we discuss and evaluate the advantages and disadvantages of several techniques to visualize and analyze eye movement data tracked and recorded from public transport map viewers in a formerly conducted eye tracking experiment. Such techniques include heat maps and gaze stripes. To overcome the disadvantages and improve the effectiveness of those techniques, we present a viable solution that makes use of existing techniques such as heat maps and gaze stripes, as well as attention clouds which are inspired by the general concept of word clouds. We also develop a web application with interactive attention clouds, named the EyeCloud, to put theory into practice. The main objective of this paper is to help public transport map designers and producers gain feedback and insights on how the current design of the map can be further improved, by leveraging on the visualization tool. In addition, this visualization tool, the EyeCloud, can be easily extended to many other purposes with various types of data. It could be possibly applied to entertainment industries, for instance, to track the attention of the film audiences in order to improve the advertisements.
Mixing realities for sketch retrieval in Virtual Reality

Users within a Virtual Environment often need support designing the environment around them with the need to find relevant content while remaining immersed. We focus on the familiar sketch-based interaction to support the process of content placing and specifically investigate how interactions from a tablet or desktop translate into the virtual environment. To understand sketching interaction within a virtual environment, we compare different methods of sketch interaction, i.e., 3D mid-air sketching, 2D sketching on a virtual tablet, 2D sketching on a fixed virtual whiteboard, and 2D sketching on a real tablet. The user remains immersed within the environment and queries a database containing detailed 3D models and replace them into the virtual environment. Our results show that 3D mid-air sketching is considered to be a more intuitive method to search a collection of models; while the addition of physical devices creates confusion due to the complications of their inclusion within a virtual environment. While we pose our work as a retrieval problem for 3D models of chairs, our results are extendable to other sketching tasks for virtual environments.