Reviewerly/exHarmony huggingface dataset:
Managing the changing understanding of benefits in software initiatives

When deciding to develop new software, that decision should be based on a clear understanding of the intended benefits the system can lead to. But what if the understanding of those benefits fluctuates. In this article, we investigate the understanding that stakeholders have or build of the benefits of the system under development, and what role that understanding plays. We conducted four studies: an exploratory study based on 22 interviews on nine projects that had explicit incentives to conduct benefits management, a confirmatory study based on a survey (n=110) of concepts of benefits understanding developed in the first study, and two explanatory studies arranged as focus groups (five participants in each) on issues that arouse during analysis of the first two studies. We find that it is common for stakeholders' understanding of benefits to change during and after development (reported by 96% of respondents from their latest digitalization efforts). Predicting and managing the changes to understanding of benefits is not straightforward. This leads to uncertainty, both for investment decisions and how to manage benefits. We conclude that, rather than focusing on cookbook recipes for benefits management, more emphasis should be focused on helping practitioners embrace changes to understanding of benefits.
Evolutionary multi-objective design of autoencoders for compact representation of histopathology whole slide images

The recent success of deep learning coincides with the surge of digital pathology as an effect of the pandemic. However, processing a massive volume of gigapixel histopathology images is daunting. Hence, establishing compact image representations for efficient computational pathology becomes paramount. Deep autoencoders have been frequently used to compress feature vectors extracted from pre-trained networks. However, the topology of autoencoders is generally set heuristically. In this paper, we propose a multi-objective evolutionary framework as a bi-level optimization scheme in which the outer level evolves autoencoders for the goal of dimension reduction while the inner level is to optimize their weights for training. Throughout the evolution process, multiple minimization objectives such as complexity, classification error, and code size are considered. These objectives can obtain compressed models to generate a very small set of deep features with the highest classification accuracy. We use images from The Cancer Genome Atlas (TCGA) repository provided by the National Cancer Institute to train and validate our optimization framework. We increased the classification accuracy by 8% while the image representation is 46,000 times shorter. We apply the framework to demonstrate higher accuracy, substantial memory reduction and faster processing for image retrieval compared to SOTA.
Dual-DIANet: A sharing-learnable multi-task network based on dense information aggregation

Designing neural networks that balance shared features and task-specific ones is a major challenge in multi-task learning. To solve this issue, we aggregate information from both multi-scale and multi-level perspectives for a more comprehensive understanding of the multi-task features, which enables the network to learn a better sharing way. Specifically, we first introduce a basic Multi-scale-focused Dense Information Aggregation Network, where learnable fusion modules are used to connect two task-biased subnets and control the feature sharing. The fusion modules, consisting of densely cascaded dilated convolutions and scale-wise attention blocks, adaptively aggregate multi-scale information for each task. To further exploit multi-level information, the modules at different levels are mutually connected in a dense manner and guided by auxiliary supervision. By combining these two aspects of information aggregation, a Dual Dense Information Aggregation Network with a strong ability to learn appropriate sharing is finally proposed. Comprehensive experiments are reported on NYUDv2, SUN RGB-D, and Mini-Taskonomy to show the effectiveness of our method.
A markless 3D human motion data acquisition method based on the binocular stereo vision and lightweight open pose algorithm

This study aimed to achieve precise 3D human motion data acquisition without the need for markers. Specifically, the proposed system utilized two affordable cameras to capture human motion from diverse angles. It developed a lightweight Open Pose algorithm to extract 2D human body joints from recorded videos. Subsequently, a stereo-matching method was applied to derive accurate 3D human body joint data. Comprehensive experiments with 10 healthy participants showcased the system's exceptional performance, demonstrating superior measurement accuracy compared to established standards like the NOKOV system (RMSE values consistently below 9°) and commonly used Microsoft Kinect devices (V2 and Azure). This innovative binocular vision system presents a new approach to motion capture, potentially offering a cost-effective and precise tool for practical applications across various scenarios (e.g., motor functional assessment, virtual avatar streaming), contributing significantly to both scientific and academic communities.
Attention driven CWT-deep learning approach for discrimination of Radar PRI modulation

With the proliferation of radio frequency (RF) systems and radar applications, Electronic Warfare (EW) is receiving increasing importance. The analysis of the radar signals is a critical EW task that decides the nature of counter employments. In an Electronic Support (ES) system, the challenge is to detect hostile radiation sources efficiently and trigger a counter response. Detection of types of Pulse Repetition Interval (PRI) modulation of radar signal significantly facilitates the manifestation of RF emitters during recognition which is difficult in a dense EW environment. Recent developments in artificial intelligence (AI) methods suggest that this emerging technology can be effective for such purposes. In this direction, an automatic approach for recognizing several kinds of complex PRI modulation based on Continuous Wavelet Transform (CWT) and a combination of the vanilla Convolutional Neural Network (CNN), a multi-head self-attention (MHSA) mechanism and the popular Long Short-Term Memory (LSTM) is proposed. The CWT is used to decompose the PRI modulation sequence and obtain different time–frequency components. Further, aided by the proposed CNN-MHSA-LSTM combination, the features extracted from the CWT 2D-scalograms are used to execute PRI modulation discrimination. In this method, the vanilla CNN is employed for the extraction of deep features to figure out the class details while capturing the spatial attributes. Thereafter, to improve the discriminative power of the entire framework a MHSA mechanism is used. The temporal attributes are acquired by the LSTM which works in concert with the CNN for executing the detection of the PRI classes based on the extracted features. Also to assess the effectiveness of the proposed method, three models based on ResNet, popular CNN and SqueezeNet are implemented for benchmark comparison in terms of overall performance and complexity. The simulation results show that the proposed method enhances performance and achieves robustness in the noise-filled and imperfect channel knowledge environment. The best recognition accuracy is 98.3% with 50% spurious pulses in the environment which fluctuates with imperfect channel knowledge cases.
Enhanced radial base deep learning algorithm for predicting multimedia security issues

From online social networks to life-or-death security systems, multimedia files (photos, movies, and audio recordings) have grown common in today’s digital culture. Protecting people, businesses and infrastructure requires strict adherence to the encryption and decryption of multimedia data. We suggested an Ensemble Whale Optimized Recurrent Neural Network (EWO-RNN)used in this study to overcome these issues. With the help of this study, multimedia security will be evaluated in more accurate and comprehensive manner. Smarter decisions and proactive security measures may follow as a result of this. To increase the system quality and the overall performance, the collected data is pre-processed for normalized data by using Min-Max Normalization. Pre-processed data is extracted by using Kernel Principle Component Analysis (K-PCA). The EWO-RNN evaluates the effectiveness and efficiency of an approach by analyzing the performance of Accuracy (97.85%), Precision (92.2%), F1-score (96.1%), Mean Square Error (MSE) (0.086), Root Mean Square (RMSE) (0.12%) and Sensitivity (95%). The Enhanced Radial Base Deep Learning Algorithm for Predicting Multimedia Security Issues proposes a solution with improved resilience, accuracy, generalization, and decision-making capabilities. In a dynamic and evolving digital environment, this makes the algorithm a viable tool for multimedia security assessments.
Automated code-based test case reuse for software product line testing

A software product line (SPL) grows in size as a new product is developed. A new product in an SPL should be tested extensively for quality assurance. For the efficient testing, previous studies suggested reusing the existing test cases of a product family. However, either their methods were not efficient because interventions from human experts, specifications, architecture and/or traceabilities for test cases were required. To address these limitations, we propose an Automated Code-based Test case reuse for SPLs (ActSPL). ActSPL automatically identifies reusable test cases for new products of a product family using source code and test cases. ActSPL automatically constructs a hash-based traceability links between test cases and source code of a product family. Using the traceability links, ActSPL selects reusable test cases for a given new product from existing test cases of the product family. We evaluated ActSPL in terms of the effectiveness and cost reduction of reusing test cases with five open-source SPLs. The evaluation results showed that ActSPL, on average, achieved 100 % precision and 62 % recall. In addition, ActSPL, on average, saved 47.5 % of time required for testing a new product from scratch. Our study shows the feasibility of ActSPL reusing SPL test cases based on source code and test cases. Our results can be a basis for successive studies for automated code-based SPL testing.
Pixel-wise annotated and high-quality synthesized image datasets for semi-supervised truck segmentation with limited raw images

Deep-learning-based segmentation methods have emerged as effective paradigms for intelligent monitoring, such as the loading status and locations of trucks at construction sites. However, the time-consuming and labor-intensive data preparation hinders the implementation of these methods. We propose a novel semi-supervised method to automatically synthesize high-quality and pixelwise annotated datasets for trucks using limited raw images. Ada-StyleGAN2 is initially employed to synthesize high-quality images from limited raw images. Subsequently, a label generator derived from DatasetGAN is integrated with atrous spatial pyramid pooling (ASPP) and a positional embedding module to generate pixel-level masks. Finally, a "crop and paste" module is proposed to address the scale variations. With only 5000 raw images and 18 annotated samples, the method can synthesize high-quality and pixelwise annotated images with a Fréchet inception distance (FID) of 5.40 in 30 min. Other annotations, such as pose estimation and activity recognition, have potential for future research.
Differential privacy scheme using Laplace mechanism and statistical method computation in deep neural network for privacy preservation

Mountainous amounts of information are now available in hospitals, finance, counter-terrorism, education and many other sectors. Those information can offer a rich source for analysis and decision making. Such information contains user's sensitive and personal data as well. This emanates direct conflict with the user's privacy. Individual's privacy is their right. The existing privacy preserving algorithms works mainly on the numerical data and doesn't care about the categorical data. In addition, there is a heavy trade-off between privacy preservation and data utility. To overcome these issues, a deep neural network - statistical differential privacy (DNN−SDP) algorithm is proposed as the solution to disguise the individual's private and sensitive data. Both the numerical and categorical based human-specific data are considered and fed to the input layer of the neural network. The statistical methods weight of evidence and information value is applied in the hidden layer along with the random weight (wi) to get the initial perturbed data. This initially perturbed data is taken by Laplace computation based differential privacy mechanism as the input and provides the final perturbed data. Census income, bank marketing and heart disease datasets are used for experimentation. While comparing with the state-of-the-art methods, DNN−SDP algorithm provides 97.4% of accuracy with 98.2% of precision, 99% of recall rate and 98.7% of F-measure value. In addition, the fall-out rate, miss rate and false omission rate of the proposed algorithm are less than 4.1%. The DNN−SDP algorithm guarantees the privacy preservation along with data utility.
Highly Efficient Self-Checking Matrix Multiplication on Tiled AMX Accelerators

General Matrix Multiplication (GEMM) is a computationally expensive operation that is used in many applications such as machine learning. Hardware accelerators are increasingly popular for speeding up GEMM computation, with Tiled Matrix Multiplication (TMUL) in recent Intel processors being an example. Unfortunately, the TMUL hardware is susceptible to errors, necessitating online error detection. The Algorithm-based Error Detection (ABED) technique is a powerful technique to detect errors in matrix multiplications. In this article, we consider implementation of an ABED technique that integrates seamlessly with the TMUL hardware to minimize performance overhead. Unfortunately, rounding errors introduced by floating-point operations do not allow a straightforward implementation of ABED in TMUL. Previously an error bound was considered for addressing rounding errors in ABED. If the error detection threshold is set too low, it will a trigger false alarm, while a loose bound will allow errors to escape detection. In this article, we propose an adaptive error threshold that takes into account the TMUL input values to address the problem of false triggers and error escapes and provide a taxonomy of various error classes. This threshold is obtained from theoretical error analysis but is not easy to implement in hardware. Consequently, we relax the threshold such that it can be easily computed in hardware. While ABED ensures error-free computation, it does not guarantee full coverage of all hardware faults. To address this problem, we propose an algorithmic pattern generation technique to ensure full coverage for all hardware faults. To evaluate the benefits of our proposed solution, we conducted fault injection experiments and show that our approach does not produce any false alarms or detection escapes for observable errors. We conducted additional fault injection experiments on a Deep Neural Network (DNN) model and find that if a fault is not detected, it does not cause any misclassification.
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This note studies the problem of estimating frequencies of items over data streams. We propose a simple streaming algorithm for the problem in small space complexity. Our algorithm is a counter-based algorithm with the aid of probabilistic counting. We show that our algorithm with k counters computes, with probability at least 1−δ, the estimation with relative error at most (1+ε)N/k, taking O(klog⁡log⁡Nk+klog⁡(ε−1δ−1k)+klog⁡ℓ) space in expectation, where N is the total number of items and ℓ is the number of different items.
OneSpace: Detecting cross-language clones by learning a common embedding space

Identifying clone code fragments across different languages can enhance the productivity of software developers in several ways. However, the clone detection task is often studied in the context of a single language and less explored for code snippets spanning different languages. In this paper, we present OneSpace, a new cross-language clone detection approach. OneSpace projects different programming languages to the same embedding space using both code and API data. OneSpace, hence, leverages a Siamese Network to infer the similarity of the embedded programs. We evaluate OneSpace by detecting clones across three language pairs; JAVA-Python, Java-C++ and Java-C. We compared OneSpace with the other state-of-art techniques, SupLearn and CLCDSA. In our evaluation, OneSpace provided higher effectiveness than the state of the art. Our ablation study validated some of our intuitions in designing OneSpace, particularly that using a single embedding space (as opposed to separate ones) provides higher effectiveness. Additionally, we designed a variant of OneSpace that uses Word-Mover-Distance Algorithm and provides lower effectiveness, but is much more efficient. We also found that OneSpace provides higher effectiveness than the state of the art, even for: complex implementations, single-method implementations, varying ratios of positive to negative clones in training, varying amounts of training data, and for additional programming languages.
Harmonizing DevOps taxonomies — A grounded theory study

DevOps responds to the growing need of companies to streamline the software development process and thus has experienced widespread adoption in the past few years. However, the successful adoption of DevOps requires companies to address important cultural and organizational changes. Nevertheless, it is crucial to recognize that various DevOps taxonomies exist, both from academic and practitioner perspectives, which may lead to misleading or failed adoption of DevOps. This paper presents empirical research on the structure of DevOps teams in software-producing organizations. The goal is to better understand the organizational structure and characteristics of teams adopting DevOps by harmonizing the existing knowledge. To achieve this, we employed a grounded theory approach with collaborative coding, involving two research groups. Inter-Coder Agreement (ICA) was utilized to guide the discussion rounds. We conducted a comprehensive analysis of existing studies on DevOps teams and taxonomies to gain a deeper understanding of the subject. From the analysis, we built a substantive and analytic theory of DevOps taxonomies. The theory is substantive in that the scope of validity refers to the ten secondary studies processed and analytic in that it analyzes “what is” rather than explaining causality or attempting predictive generalizations. A public repository with all the data related to the products resulting from the analysis and generation of the theory is available. We built a theory on DevOps taxonomies and tested whether it harmonizes the existing taxonomies, i.e., whether our theory can instantiate the others. This is the first step to define which taxonomies are best suited to approach DevOps culture and practices according to the companies’ objectives and capabilities. Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board.
Improved generative adversarial network with deep metric learning for missing data imputation

Incomplete data are ubiquitous in real-world computer vision tasks. Imputing missing data is crucial for modeling machine learning algorithms. Although existing methods, such as MisGAN, have achieved considerable success, two problems remain: (1) generating a complete image from random noise weakens the model's imputation capability; (2) owing to the influence of random noise, the training of the generator is unstable. Therefore, this study proposes a novel approach that uses deep metric learning to enable MisGAN to perform multi-tasking missing data imputation. First, an image feature extraction network is applied to extract the semantic representation of the images. Then, deep metric learning is performed to learn good features embedding by maximizing inter-class variation and minimizing intra-class variation. Such an embedding replaces the random noise and is fed into MisGAN to obtain an improved generated complete image. Several experiments are conducted on datasets, demonstrating that the proposed method can significantly outperform baseline methods.
Features based adaptive augmentation for graph contrastive learning

Self-supervised learning aims to eliminate the need for expensive annotation in graph representation learning, where graph contrastive learning (GCL) is trained with the self-supervision signals containing data-data pairs. These data-data pairs in GCL are generated with augmentation employing stochastic functions on the original graph. We argue that some features can be more critical than others depending on the downstream task, and applying stochastic function uniformly vandalizes the influential features, leading to diminished accuracy. To fix this issue, we introduce a Feature Based Adaptive Augmentation (FebAA) approach, which identifies and preserves potentially influential features and corrupts the remaining ones. We implement FebAA as a plug-and-play layer and use it with state-of-the-art Deep Graph Contrastive Representation Learning (GRACE) and Large-Scale Representation Learning on Graphs via Bootstrapping (BGRL). We successfully improved the accuracy of GRACE and BGRL on eight graph representation learning benchmark datasets.
Multi-layered self-attention mechanism for weakly supervised semantic segmentation

Weakly Supervised Semantic Segmentation (WSSS) provides efficient solutions for semantic image segmentation using image-level annotations. WSSS requires no pixel-level labeling that Fully Supervised Semantic Segmentation (FSSS) does, which is time-consuming and label-intensive. Most WSSS approaches have leveraged Class Activation Maps (CAM) or Self-Attention (SA) to generate pseudo pixel-level annotations to perform semantic segmentation tasks coupled with fully supervised approaches (e.g., Fully Convolutional Network). However, those approaches often provides incomplete supervision that mainly includes discriminative regions from the last convolutional layer. They may fail to capture regions of low- or intermediate-level features that may not be present in the last convolutional layer. To address the issue, we proposed a novel Multi-layered Self-Attention (Multi-SA) method that applies a self-attention module to multiple convolutional layers, and then stack feature maps from the self-attention layers to generate pseudo pixel-level annotations. We demonstrated that integrated feature maps from multiple self-attention layers produce higher coverage in semantic segmentation than using only the last convolutional layer through intensive experiments using standard benchmark datasets.