Reviewerly/exHarmony huggingface dataset:
Agile Transformation: A Case Study on Early Stage of Agile Adoption

Abstract Agile transformation is identified as a facilitator to keep pace with frequent changes within product development. Although initial research exists, the empirical literature on the implementation process of the agile approach, specifically using pilot projects as a change strategy, is scarce. The purpose of this article is to contribute to closing this gap by investigating into effects of piloting agile change projects. To shed light on agile pilots a project within the context of mechatronic system development in the high-tech industry was accompanied over six months. After the initiation of the members and a period of practicing agile a survey was performed. The interviewed team members are bringing up interesting findings, as although they had a different understanding of agile at the beginning of the pilot, they recognized agile values, principles and methods as supportive to the products cycle and specifically development time. Further findings are indicating that professional third-party support is a key success factor. Also piloting, as a change strategy for agile adoption, is proven to be supportive. Although piloting is proven a supportive strategy, the downsides, such as limited scalability caused by extraordinary setups, are identified and analyzed. The limiting factor of this single-case study is the small sample size of data due to the intention of the pilot project to limit impact and risk on the organization.
An Optimized Algorithm for Human Portrait Image Segmentation Using U-Net

Segmentation is a technique used in image analysis that involves the division of an image into smaller, more manageable regions corresponding to distinct objects. Image segmentation can be accomplished in a variety of ways from simple hand-specified regions to intelligent auto-detected regions of interest. Regions of interest can be different objects in an image or different color, foreground, and background of an image. Segmentation process is different for each type of application and there is a lack of a universal process that can be applied to all image segmentation tasks. Experts in the field have proposed many Neural Network-based solutions yet unable to achieve significant results segmenting human portraits. To address this issue, this article proposes the use of U-Net model incorporated with alpha matting, for image segmentation of people, separating foreground and background. For experiments, Matting Human Dataset has been used that is publically available on Kaggle. We evaluated the performance of our proposed model and obtained the Jaccard similarity index 0.95 and Dice similarity index 0.72. Empirically, our proposed model takes the advantages of using U-Net model to accomplish reliable results when compared with the other state of the art methods.
Natural Language Processing (NLP) based Extraction of Tacit Knowledge from Written Communication during Software Development

Software engineering, in general, and Global Software Engineering (GSE), in particular, face challenges such as handling communication and collaboration issues and inappropriate knowledge management. It is difficult to ensure availability of right knowledge at the right time to the right person during software engineering activity. The knowledge needs to be shared across the organization but limitations of knowledge sharing tools or dispersed knowledge sharing media and improper handling of tacit knowledge makes it more challenging to share knowledge. Significant amount of communication during software engineering process takes place via emails and discussion forums. Decisions taken during planning activities are also communicated through emails. These emails and the discussions do not necessarily cover the rationale behind a decision or approach adopted. This leaves a substantial portion of shared knowledge as tacit. Mostly, due to undefined tacit knowledge handling procedures, organizations suffer with the loss of critical knowledge and information. One way to handle this loss of knowledge is to store every information in knowledge repository but this can increase the size of the knowledge repository. This paper presents a three step approach to identify tacit knowledge in the written communication such that the organizations can save the tacit knowledge for future use. The presented approach does not only extract up to 57% of tacit knowledge but also indicates that only 20% of the whole communication need to be externalized hence saving 80% capacity of the knowledge repository, if every communication was to be preserved as potential tacit knowledge.
Anti-social Behavior Detection using Multi-lingual Model

In the current era, social media has emerged as a very useful and reliable means of communication between different people and communities. However, with the leverage of communication platforms and billions of social media users, it became more challenging to stop hateful, abusive, or offensive content spread by extremists that are various aspects of Anti-social Behavior (ASB). Multiple users from several regions use different languages (a mix of native, local and other languages) to express their emotions. Roman Urdu-English and Roman Hindi-English are the two most commonly used languages on social media in the South Asia region. Therefore, the ASB detection with multilingual (multiple languages) model settings represents a wide area of interest for all kinds of social media platforms. Failing to properly address this issue over time on a global scale has already led to morally questionable real-life events, human deaths, and the perpetuation of hate itself. In this paper, we perform a sentimental analysis of the Roman Urdu-English and Roman Hindi-English languages using transformer based mBERT and XLM-R models. Moreover, we process the negatively classified sequences for detection of the ASB.
Multi-Pedestrians Anomaly Detection via Conditional Random Field and Deep Learning

Automated video surveillance frameworks quickly distinguish surprising and basic circumstances in a packed climate that would help to pursue sufficient choices for security and crisis control. Hence, In this paper, an innovative method for automatically detect and localize anomalous objects among multi-pedestrian crowds via conditional random field and deep learning is introduced. Initially, necessary preprocessing is performed on extracted frames and then super-pixels are generated using improved watershed transform, the objects are then segmented using a conditional random field. The region of interests are localized using conditional probability and temporal association is implemented to locate the regions with a group of pedestrians and pedestrians with other objects. A deep learning feature pyramid network is then implemented to detect and categorized the objects in each region and finally, the anomalous objects are identified using Jaccard similarity. The effectiveness of proposed framework is assessed on openly accessible UCSD Ped 1 and Ped 2 datasets and it accomplishes an accuracy rate of 94.2% and 95.4% respectively. Extensive experimental data and comparative analysis show that our model outperformed current state-of-the-art models in terms of accuracy.
Manual and Automatic Feature Engineering in Digital Image Forgery Detection Algorithms: Survey

Digital image forgery (DIF) detection has gained importance in recent years. Because of the availability of lowcost, high-resolution digital cameras, issues related to confirming the authenticity of digital images have been highlighted. The term ‘‘ manipulation’’ refers to the changing of any image’s objects or contents without giving any indication that anything has changed. In general, image splicing is one of the most popular techniques used to fake images. Image splicing is the process of adding or hiding things by copying portions of one image and pasting them elsewhere in the same image or into a different image. There have been several image splicing forgery detection surveys throughout the past few decades. However, neither the image forgery detection algorithms-based manual engineering features nor the image forgery detection algorithms-based automatic engineering features are covered by these surveys. This research provided a comparative analysis of image splicing by gathering several techniques and classifying them according to the features they employed, based on manual engineering features and automatic engineering features.
Object Detection and Segmentation for Scene Understanding via Random Forest

In recent days, object detection become a vast topic in computer vision. Accurate object detection and scene understanding is not an easy task due to illumination, viewpoints, and color intensities. Visual features like color, texture, boundaries, and shape, make an image different from another image. The main goal of scene understanding is to machine work like a human and understand the visual information of an image. Currently, researchers working on novel approaches in this field to make a better understanding of the scene. Computer vision portrays a major role in different applications such as health, safety, security surveillance, traffic monitoring, autonomous driving car, object recognition, and tracking. In this paper, we work on a meaningful understanding of an image in the scene. To understand the scene we have done region-based segmentation, and for object detection and labeling, we use the tensor flow algorithm, for geometric features mean of each pixel, harry corner edge detection, and scale-invariant feature transform descriptor. And then object recognition by using random forest. We have performed this experiment on UIUC Sports dataset. The presented model achieved 89.45% recognition accuracy.
Common Problems in Software Requirement Engineering Process: An Overview of Pakistani Software Industry

Requirement engineering is a major phase of software development process. A project's success mainly depends on an efficient and effective requirement engineering process. Practices have been defined to ensure successful requirement engineering of software projects. Yet the professionals face numerous issues during this phase. This paper explores the software requirement engineering practices from in the software industry of Pakistan. It highlights the common problems faced by the software professionals, as well as commonly deployed solutions and practices.
An Analysis of Abnormal Event Detection and Person Identification from Surveillance Cameras using Motion Vectors with Deep Learning

In many places, like museums, banks, and airports, network cameras have taken the place of outdated analogue cameras as a result of advancements in chip technology and the reduced costs of storage and bandwidth equipment, among other factors. In an endeavor to increase public safety and health protection, lower crime, and implement video surveillance, the sector has entered the "blowout" phase. A crucial part of developing an intelligent CCTV system is the detection of abnormal events, human behaviour, and object recognition. These technologies enable the detection of anomalous environmental phenomena, anomalous human behaviour, and the environmental state of alert. These systems use machine learning and machine vision capabilities to recognize and detect specific anomalies that appear in CCTV stream footage. For these systems, supervised learning is a popular training method, and frame-by-frame processing is frequently used. However, supervised learning has been replaced as unsupervised learning as well as semi-supervised learning for the system's training since anomalies come in a variety of forms and because it is not practical to which was before and educate all types of anomalies. By using this technology, the amount of labour that must be done by humans to manually spot anomalies in the CCTV live feed and generate alerts can be reduced or eliminated. Additionally, the method improves storage efficiency by only preserving anomalous occurrences in their original quality and leaving regular circumstances in low quality. Additionally, you may use the Grassmann algorithm to identify faces in CCTV data and alert surrounding security systems. The suggested approach offers better reliability in outlier detection and face detection, according to experimental findings.
Implementation of Underwater Image Enhancement for Corrosion Pipeline Inspection (UIECPI)

The corrosion penetration rate (CPR) during crude oil transportation procedures or gas transportation by carbon steel pipelines is one of the most important critical issue problems for any oil and gas sector today. Several studies have been conducted on these topics using various methods. The major purpose of this research is to use computer vision concepts which is underwater image enhancement for corrosion pipeline inspection to develop a robust and capable model that can accurately detect corrosion using certain algorithms and operating parameters. A reliable algorithm is developed to enhance the input images. The results from this detection model showed that, with small set of examples image, the underwater image enhancement for corrosion pipeline inspection (UIECPI) was able readily distinguished.
A Peculiar Reading System for Blind People using OCR Technology

There are numerous ways to assist blind people as they will not be able to detect text in all fonts, numbers, or low-light situations. To address this, an innovative model has been designed to detect various fonts, numbers, and so on. A camera has also been embedded to identify and capture text from printed text. Further, the image will be analyzed with Tesseract-Optical Character Recognition. Finally, to implement it in real-time, the Text-to-speech software is used.
Breast Cancer Classification using SVM and KNN

The leading cause of death for women is now breast cancer. Researchers are encouraged to use studies in extracting unidentified patterns from healthcare datasets by the accessibility of healthcare datasets and data analysis. The main intention for this study is to know the best classification technique among the both techniques Support Vector Machines and K-Nearest Neighbor. As well as comparing the SVM and KNN classifiers, we propose a system for classifying breast cancer in this research. After receiving the findings from the Breast Cancer dataset using Support Vector Machine (SVM) and K-Nearest Neighbors (KNN), a performance evaluation and comparison between these classifiers is done. Following that, we train the SVM and KNN classifiers separately using the collected features. The major goal of this study is to identify the best effective machine-learning algorithms for predicting and diagnosing breast cancer in terms of accuracy, precision, recall, and f1-score. Conducting training is a prerequisite for this strategy. The evaluation and comparison of the SVM and KNN classification models included evaluation methods such as heat map and scatter-plot. The comparison results show that the KNN and SVM algorithms perform well for identifying breast cancer.
Lion Swarm Optimization with Deep Learning Driven Predictive Model on Blockchain Financial Product Return Rates

Recently, financial globalization is an extremely improved in distinct manners for enhancing service quality with advanced resources. An effectual application of bitcoin Blockchain (BC) approaches allows the shareholders to be concern regarding the risk and return of financial product. The shareholders mainly concentrate on the predictive of risk and return rates of financial product. Thus, an automated return rate bitcoin predictive method develops vital for BC financial product (FP). A newly planned machine learning (ML) and deep learning (DL) techniques offers a way for the return rate predictor systems. This work designs a Lion Swarm Optimization with Deep Learning Driven Predictive Model on Blockchain Financial Product Return Rates (LSODL-BFPRR) technique. The projected LSODL-BFPRR technique lies in the effectual forecasting of return rates in the BC financial sector. In the presented LSODL-BFPRR technique, stacked bidirectional gated recurrent unit (SBiGRU) approach was exploited for return rate classification. To modify the hyperparameters based on the SBiGRU approach, the LSO algorithm is used. The LSODL- BFPRR technique exploits Ethereum (ETH) return rate as the target. The experimental outcomes of the LSODL-BFPRR technique are tested using a series of simulations and the results demonstrate the effectual predicting results of the LSODL-BFPRR technique over other ones.
Semantics-enhanced early action detection using dynamic dilated convolution

This paper proposes a new pipeline to perform early action detection from skeleton-based untrimmed videos. Our pipeline includes two new technical components. The first is a new Dynamic Dilated Convolutional Network (DDCN), which supports dynamic temporal sampling and makes feature learning more robust against temporal scale variance in action sequences. The second is a new semantic referencing module, which uses identified objects in the scene and their co-existence relationship with actions to adjust the probabilities of inferred actions. Such semantic guidance can help distinguish many ambiguous actions, which is a core challenge in the early detection of incomplete actions. Our pipeline achieves state-of-the-art performance in early action detection in two widely used skeleton-based untrimmed video benchmarks. The source codes are available at: https://github.com/Powercoder64/DDCN_SRM.
Quantifying multiparticle entanglement with randomized measurements

Randomized measurements constitute a simple measurement primitive that exploits the information encoded in the outcome statistics of samples of local quantum measurements defined through randomly selected bases. In this work we exploit the potential of randomized measurements in order to probe the amount of entanglement contained in multiparticle quantum systems as quantified by the multiparticle concurrence. We further present a detailed statistical analysis of the underlying measurement resources required for a confident estimation of the introduced quantifiers using analytical tools from the theory of random matrices. The introduced framework is demonstrated by a series of numerical experiments analyzing the concurrence of typical multiparticle entangled states as well as of ensembles of output states produced by random quantum circuits. Finally, we examine the multiparticle entanglement of mixed states produced by noisy quantum circuits consisting of single- and two-qubit gates with non-vanishing depolarization errors, thus showing that our framework is directly applicable in the noisy intermediate-scale regime.
Un/Making the Plastic Straw: Designerly Inquiries into Disposability

This article proposes un/making as a designerly response to urgent environmental issues. By focusing on the simultaneous constructive and destructive aspects of design, this effort attempts to challenge design’s dominant focus on making new things. The implications and potentialities of un/making are explored through a designerly inquiry into ongoing and emerging attempts to ban the plastic straw. Based on this inquiry, the article proposes an approach to un/making that is driven by speculative, what if questions, informed by the history of the plastic straw: from coming into being to becoming preferable and now emerging as a matter of concern. Through a series of speculative design artifacts, the authors articulate matters at stake in the un/making of the plastic straw. They also show how these matters are a stake in the un/making of disposability as part of a preferable future. Rather than proposing one preferable future, the article highlights the frictions that emerge in un/making.
Balanced Off-Chain Payment Channel Network Routing Strategy Based On Weight Calculation

Abstract Off-chain payment channel network is an effective solution to deal with low throughput and high load on the blockchain. However, the quality of the routing scheme will directly affect the performance of the off-chain network system. Existing routing schemes cannot select an appropriate transaction path according to the actual needs of user nodes. And they may also lead to network congestion, channel imbalance and node centralization. This paper proposes a new balanced routing selection scheme based on weight calculation called BRBW, which builds a weight model by Analytic Hierarchy Process. BRBW comprehensively considers the channel capacity, handling fee and path length to improve transaction success rate, reduce channel congestion and maintain the long-term sustainability of the payment channel. It uses a modified maximum flow algorithm to find paths with sufficient capacity and selects the transaction path for a large payment through linear programming. Finally, we do some experiments and compare with state-of-the-art approaches in the test environment. Simulation results show that under the same network environment, the payment success rate of the BRBW is above 75%, and the channel utilization rate is about 10% higher than other schemes.
Attention‐based network embedding with higher‐order weights and node attributes

Abstract Network embedding aspires to learn a low‐dimensional vector of each node in networks, which can apply to diverse data mining tasks. In real‐life, many networks include rich attributes and temporal information. However, most existing embedding approaches ignore either temporal information or network attributes. A self‐attention based architecture using higher‐order weights and node attributes for both static and temporal attributed network embedding is presented in this article. A random walk sampling algorithm based on higher‐order weights and node attributes to capture network topological features is presented. For static attributed networks, the algorithm incorporates first‐order to k ‐order weights, and node attribute similarities into one weighted graph to preserve topological features of networks. For temporal attribute networks, the algorithm incorporates previous snapshots of networks containing first‐order to k ‐order weights, and nodes attribute similarities into one weighted graph. In addition, the algorithm utilises a damping factor to ensure that the more recent snapshots allocate a greater weight. Attribute features are then incorporated into topological features. Next, the authors adopt the most advanced architecture, Self‐Attention Networks, to learn node representations. Experimental results on node classification of static attributed networks and link prediction of temporal attributed networks reveal that our proposed approach is competitive against diverse state‐of‐the‐art baseline approaches.