Reviewerly/exHarmony huggingface dataset:
The Role of Skillset in the Determination of DevOps implementation Strategy

As the trend of interest in DevOps continues to rise significantly among industry practitioners, so does the need for software development organisations to understand how to successfully implement DevOps as they are faced with a wide range of choices, and very few guidelines on how to navigate through a plethora of valuable information.An important question is the skillset required, and its role in the determination of suitable DevOps implementation strategy for organisations. This study contributes to the above and provides insight by critically investigating the DevOps implementation of 14 organisations through interviews with practitioners who lead the transformation in these organisations. Interviews were transcribed, coded and analysed by a method informed by Grounded Theory. We identified six strategies used by organisations to implement DevOps, which we named Platform, Greenfield Application, Monolith Decomposition, Process Improvement, Cultural Improvement, and Advocacy. Based on our analysis, we conclude the following: 1. A striking correlation exists between the skillset and the strategy adopted by organisations to implement DevOps. 2. The inclination of an organisation towards upskilling is relevant for the determination of appropriate DevOps implementation strategy. From our research, we have come to believe that the choice of organisational DevOps implementation strategy should generally be based on a consideration of technologies, processes, culture, and skillsets as top-level concerns.
Automatic Indoor Air Quality Control of COVID -19 Patient Facilities Using Type-2 Fuzzy Controller

Abstract Big facilities can be used to set up cohort wards for severe and critical COVID-19 patients. The purpose of carbon dioxide and humidity control is to provide healthy air for breathing by both diluting the pollutants originating in the building and removing the pollutants from it. In this paper, for proper mechanical ventilation to maintain indoor air quality (IAQ), a Self-tuned Interval Type-2 Fuzzy PI controller (SIT2FPIC) is proposed. 49 fuzzy if-then rules are used to tune the Interval Type-2 Fuzzy PI controller (IT2FPIC) for proper functioning and to reduce the parameter variations. Correlations of CO 2 concentration and air humidity are studied to maintain the air quality in COVID-19 facilities. Mathematical models of CO 2 concentration and air humidity in hospitals or temporary COVID-19 facilities are developed considering the presence of COVID-19 patients, doctors, nurses, etc in the room. The control and monitoring of room air quality are very much essential for any infectious diseases like coronavirus. To maintain the air quality in terms of humidity and carbon dioxide, the proposed SIT2FPIC is applied to the mathematical model of humidity and carbon dioxide for the simulation study. The effectiveness of the proposed controller in mechanical ventilation in the treatment facilities is shown by a comparative study of different transient parameters. Effects of load variations in the model are also studied to check the usefulness of the proposed scheme.
Live Comments Emotional Analysis based on EE-RNN

Live comments, also known as video Danmaku, is a technique through which audiences can express their real-time feelings and opinions with rich emotional information. Emotional analysis results of live comments can more truly reflect the overall characteristics of the video, while user's feedback can be further exploited by extensive applications. Most of the existing live comments emotion classification methods do not fully consider either the real fine granularity or the explicit emotional knowledge of the on-screen comments text. Besides, existing machine learning methods and deep learning methods such as Long Short-Term Memory neural network and Convolutional Neural Network based models do not make full use of the semantic layer representation and emotional features of the text. In this paper, Enhanced ERNIE Deep Recurrent Neural Networks model (EE-RNN) is employed to complete the five-dimensional live comments emotional analysis. The model first obtains the general semantic embedding of the text through ERNIE and introduces external emotional knowledge to further enhance the semantic coding representation, and then uses improved RNN structure as well as attention mechanism to get an emotional enhanced high-level semantic feature representation. Experimental results on the live comments emotional classification dataset and NLPCC2014 emotional classification dataset show that the proposed model greatly improves the classification performance compared with the existing methods and can be used in real applications.
Confine Keypoint Triplets for Object Detection

Corner-based object detection algorithm is a hot topic in academia and industry. Scholars have applied it to different scenarios such as 3D object detection, object tracking, pose estimation and so on. The core of this method is to detect and match corner points. Although a considerable number of papers have been studied on this issue, there are still a lot of missed detection and false detection. In this paper, we proposed a one-stage object detection method with confine key-point triplets(ConfineNet), which applies center pooling and corner matching limit in local feature region. This restrictions can provide stronger correlation features with the object for subsequent prediction of center points, which can help to improve the prediction results of center points and corner matching. In addition, ConfineNet learns the association distance between the upper-left corner point and the lower-right corner point, which can confine the corner matching within a local area, thereby reducing matching errors. The AP value of Confine-Net reaches 48.2% on MS-COCO test-dev. our ConfineNet not only outperforms all existing anchor-free detectors but also achieves comparable performance to the state-of-the-art of two-stage detection approaches.
The Trial of Posit in Shared Offices: Controlling Disclosure Levels of Schedule Data for Privacy by Changing the Placement of a Personal Interactive Calendar

When expressing personal data on the displays of personal IoT devices, it is important to be intuitively aware of privacy settings and perform ready-to-hand interactions to respond appropriately to various situations occurring in shared spaces. In this paper, we developed Posit, an interactive calendar in which the disclosure level of schedule content can be changed in three stages according to the object's placement by the user. The results of our three-week in-field study with six participants revealed that Posit's interaction was considered to be a simple way of hiding personal schedules quickly, and we could identify the roles of the positional messages in determining the others' gazes on displays. Additionally, we confirmed that social relationships and trust between colleagues affect the use of Posit. Our findings imply new opportunities in designing interactions for the management of personal privacy by applying physical state-changing interaction and understanding social factors in shared spaces.
Anomaly detection method based on multi-criteria evaluation for energy data of steel industry

The stability and integrity of the monitoring data in the energy system of the iron and steel industry is of great significance for ensuring the safety of the system. Aiming at the data with periodic characteristics in the steel energy system, an abnormal data detection method based on multi-criteria evaluation is proposed in this study. The data time series is divided according to its periodic characteristics, and each sub-period sequence are evaluated through the quasi-measures established in this paper. Then the outlier detection of the periodic time series is realized through the AFCM (Adaptive fuzzy C-means) of these evaluation results. The simulation results based on the actual operating data of a steel enterprise show that this method can identify the local abnormal state in the original time series and improve the detection efficiency.
RCBi-CenterNet: An Absolute Pose Policy for 3D Object Detection in Autonomous Driving

3D Object detection is a critical mission of the perception system of a self-driving vehicle. Existing bounding box-based methods are hard to train due to the need to remove duplicated detections in the post-processing stage. In this paper, we propose a center point-based deep neural network (DNN) architecture named RCBi-CenterNet that predicts the absolute pose for each detected object in the 3D world space. RCBi-CenterNet is composed of a recursive composite network with a dual-backbone feature extractor and a bi-directional feature pyramid network (BiFPN) for cross-scale feature fusion. In the detection head, we predict a confidence heatmap that is used to determine the position of detected objects. The other pose information, including depth and orientation, is regressed. We conducted extensive experiments on the Peking University/Baidu-Autonomous Driving dataset, which contains more than 60,000 labeled 3D vehicle instances from 5277 real-world images, and each vehicle object is annotated with the absolute pose described by the six degrees of freedom (6DOF). We validated the design choices of various data augmentation methods and the backbone options. Through an ablation study and an overall comparison with the state-of-the-art (SOTA), namely CenterNet, we showed that the proposed RCBi-CenterNet presents performance gains of 2.16%, 2.76%, and 5.24% in Top 1, Top 3, and Top 10 mean average precision (mAP). The model and the result could serve as a credible benchmark for future research in center point-based object detection.
Robust and Efficient Facial Landmark Localization

Facial landmark localization is an essential component for facial recognition which is a vital technology for patients tracking in the situation of a global pandemic of COVID-19. Parameter amount and computation cost are two key factors affecting the application of facial landmark localization. In addition, the intrinsic imbalance of head pose in existing datasets has a great impact on the model generalization. To address these problems, this paper proposed a lightweight model LiteFace with multi-knowledge distillation and a pose-aware resampling strategy. Specifically, we proposed to extract multi-scale features and enhance the receptive filed of network by delicate model design to obtain an efficient architecture and improved the performance of the lightweight model by multi-knowledge distillation which combines knowledge distillation and mutual learning in a two-stage manner. To further improve the model performance and boost the generalization, we proposed a pose-aware re-sampling strategy that generates samples with different head poses and utilized the LaPa dataset to generate masked face images to increase data diversity. We conducted extensive ablation studies on the model design, multi-knowledge distillation and resampling strategy. The proposed method achieves 1.93% and 3.04% NME on the JD-landmark-mask val and test dataset respectively. Finally, LiteFace wins the third place in the 3rd Grand Challenge of 106-Point Facial Landmark Localization.
Model Proposal for Integrating VR/AR Technologies in Building Construction Project in Architecture Education During Covid-19

Information technologies including VR/AR; by providing an immersive environment, contain wide range of innovative opportunities in architecture education, particularly for the Covid-19 pandemic that forced online education. Recent literature reviews show the increase on the adaptation of VR/AR technologies in architecture education, since it improves learners’ outcomes and tutors-learners’ interactions. However, implication of these technologies is frequently seen in design studios, whereas building construction education continues mostly its traditional approach. The study aims to close the gap in this area by proposing a model to integrate VR/AR technologies in building construction education by the assistance of BIM tools in order to boost the effectiveness. This model is developed for the building construction project course, shows step by step integration of related tools and technics to obtain the learning outcomes efficiently during online education in Covid-19 Pandemic. Considering the pandemic conditions, the proposed model involves creative usage of VR/AR tools in terms of distance learning.
Assembling Researchers in Design and the Humanities in a Circular Ecology

This contribution aims at reframing the concept of circular economy (CE) through an experiment with, and reflection on, research-through-design practices. We depart from CE as an efficient process of tidying up or closing circles to open up a discussion of ways cities can productively engage with flows of residual materials as resources in a social-material context. Taking an ecological approach, we investigate interconnected flows of things, people, and perspectives in concrete situations in the city of Rotterdam, the Netherlands. To that end we lean on the logic of assemblage, as developed in Actor- Network Theory, and discuss visualizing tools for putting theory into practice and for assembling researchers in design and the humanities in a circular ecology.
Cyber Range Research Based on Scientific Knowledge Map

The situation of cyber space confrontation is becoming more and more serious. As an important platform for information security ability training, the research on cyber range has become a hot topic in various countries and academia. Based on 1736 articles related to online cyber range collected in the core database of Web of Science in the past 20 years, the knowledge mapping analysis is carried out by using Bibliometric, Citespace, and VOSviewer visualization software from four aspects of the total number of published articles, countries, co-citation and keywords.
TurboBFS: GPU Based Breadth-First Search (BFS) Algorithms in the Language of Linear Algebra

Graphs that are used for modeling of human brain, omics data, or social networks are huge, and manual inspection of these graph is impossible. A popular, and fundamental, method used for making sense of these large graphs is the well-known Breadth-First Search (BFS) algorithm. However, BFS suffers from large computational cost especially for big graphs of interest. More recently, the use of Graphics processing units (GPU) has been promising, but challenging because of limited global memory of GPU's, and irregular structures of real-world graphs. In this paper, we present a GPU based linear-algebraic formulation and implementation of BFS, called TurboBFS, that exhibits excellent scalability on unweighted, undirected or directed sparse graphs of arbitrary structure. We demonstrate that our algorithms obtain up to 40 GTEPs, and are on average 15.7x, 5.8x, and 1.8x faster than the other state-of-the-art algorithms implemented on the SuiteSparse:GraphBLAS, GraphBLAST, and gunrock libraries respectively. The codes to implement the algorithms proposed in this paper are available at https://github.com/pcdslab.
Efficient Partially Policy-Hidden CP-ABE for IoT Assisted Smart Health

With the rapid advancements of Internet of Things (IoT) technologies, wearable devices assisted cloud-based smart health (s-health) has become the promising solution to improve the quality and convenience of healthcare. However, the privacy preserving and data security has not been perfectly addressed. In past few years, ciphertext-policy attribute-based encryption (CP-ABE) proposed as a flexible and powerful cryptographic primitive to realize one-to-many encryption and fine-grained access control has been regarded as a promising solution to the security problem in cloud. But in traditional CP-ABE, the attribute values in access policy are presented in cleartext. This will easily divulge the privacy of the data owners (patients). So in this paper, we propose a efficient partially policy-hidden CP-ABE scheme (PPH-CP-ABE), which can effectively hide the attribute values in ciphertext to protect the sensitive information in access policy. Our access policy in our PPH-CP-ABE scheme can supports both AND and OR gates which is based on the Linear Secret Sharing Scheme (LSSS). And our PPH-CP-ABE scheme is more efficient and lightweight than the other CP-ABE schemes with hidden policies. Furthermore, we give a rigorous security proof and analysis to state that our scheme is selectively indistinguishable secure under chosen plaintext attacks (selectively IND-CPA secure) and resistant to the off-line dictionary attacks. Through comparison with the state-of-art schemes from the perspective of functionality and efficiency, it is easily to observe that our scheme is more practical, secure and efficient in the real s-health scenarios.
Detecting Inconsistent Thrown Exceptions

Exception-handling is a critical mechanism in many programming languages. Although it is beneficial in many programming contexts, the bugs in this mechanism can lead to disastrous consequences. In the literature, researchers have proposed various approaches to detect the bugs in handling exceptions. Meanwhile, the thrown exceptions themselves can also be buggy, but to the best of our knowledge, no prior approach has ever been proposed to detect bugs in thrown exceptions, because it is even difficult for programmers to determine whether a thrown exception is buggy or not. In this paper, we propose the first automatic criterion (meta-oracle) to determine whether a thrown exception contains a bug. Our meta-oracle says that if the messages of two thrown exceptions are quite similar, the type of the two exceptions shall be the same. Based on this meta-oracle, we implement exMiner that is able to detect bugs in thrown exceptions. It introduces sequence mining to identify similar exception messages, and groups thrown exceptions by the patterns that are mined from their messages. From each group of exception messages, it detects the ones whose types are different as bugs in thrown exceptions. For the first time, exMiner detected three bugs from the latest versions of three Apache projects. We reported this new type of bugs to their developers, and all the bugs were confirmed and fixed.
Similar Face Detection for Indian Faces using Siamese Neural Networks

Now a days facial recognition is a very common yet highly significant task all around the globe. Most of the times facial detection or facial similarity detection would lead the thought towards complex neural networks like CNN's. But the task of working with CNN's would be very computationally expensive and also require huge amounts of data for better results. In this work we focus on implementing the task of facial similarity detection using Siamese neural networks, a rather simple architecture which is capable of one-shot learning. A new dataset has been created and tested for facial similarity using Siamese neural networks and the model is able to predict the top similar images of a given image very accurately.
AI Waste Prevention

Artificial Intelligence (AI) has changed our daily lives. The evolution from centralised cloud-hosted services towards embedded and mobile devices has shifted the focus from quality-related aspects towards the resource demand of machine learning. Its pervasiveness demands for "green" AI---both the development and the operation of AI models still include significant resource investments in terms of processing time and power demand. In order to prevent such AI Waste, this paper presents Precious, an approach, as well as practical implementation, that estimates execution time and power draw of neural networks (NNs) that execute on a commercially-available off-the-shelf accelerator hardware (i.e., Google Coral Edge TPU). The evaluation of our implementations shows that Precious accurately estimates time and power demand.
Cybersecurity, Computer Networks Phishing, Malware, Ransomware, and Social Engineering Anti-Piracy Reviews

With the rapid development of technology, connection to the internet is an indispensable element of life. With the Covid-19 Pandemic outbreak, online education with a computer and tablet mobile phone has become mandatory due to the remote training. The security of these tools used for connectivity has become critical. In this process, online shopping from homes has increased considerably. Due to this use among users for the first time and due to fast service, security vulnerabilities expose users to a difficult situation and cyberbullying. The fact that the devices are on the internet as wired or wireless makes it inevitable that they are vulnerable. The exposure of such devices in your home to cyberattacks means a violation of your privacy, along with many of your personal data. Nowadays, even the fine details of such technologies are trying to steal personal data by using the need to believe, such as phone fraud or pandemic vaccines. It is becoming increasingly important to inform end users of internet technology and security weaknesses in this field and to create awareness. If these are increased, the number of people exposed to cyber attacks will decrease. The first method for this is to reach these people and education.
A comparison of statistical relational learning and graph neural networks for aggregate graph queries

Abstract Statistical relational learning (SRL) and graph neural networks (GNNs) are two powerful approaches for learning and inference over graphs. Typically, they are evaluated in terms of simple metrics such as accuracy over individual node labels. Complex aggregate graph queries (AGQ) involving multiple nodes, edges, and labels are common in the graph mining community and are used to estimate important network properties such as social cohesion and influence. While graph mining algorithms support AGQs, they typically do not take into account uncertainty, or when they do, make simplifying assumptions and do not build full probabilistic models. In this paper, we examine the performance of SRL and GNNs on AGQs over graphs with partially observed node labels. We show that, not surprisingly, inferring the unobserved node labels as a first step and then evaluating the queries on the fully observed graph can lead to sub-optimal estimates, and that a better approach is to compute these queries as an expectation under the joint distribution. We propose a sampling framework to tractably compute the expected values of AGQs. Motivated by the analysis of subgroup cohesion in social networks, we propose a suite of AGQs that estimate the community structure in graphs. In our empirical evaluation, we show that by estimating these queries as an expectation, SRL-based approaches yield up to a 50-fold reduction in average error when compared to existing GNN-based approaches.
Convergence analysis of rule-generality on the XCS classifier system

The XCS classifier system adaptively controls a rule-generality of a rule-condition through a rule-discovery process. However, there is no proof that the rule-generality can eventually converge to its optimum value even under some ideal assumptions. This paper conducts a convergence analysis of the rule-generality on the rule-discovery process with the ternary alphabet coding. Our analysis provides the first proof that an average rule-generality of rules in a population can converge to its optimum value under some assumptions. This proof can be used to mathematically conclude that the XCS framework has a natural pressure to explore rules toward optimum rules if XCS satisfies our derived conditions. In addition, our theoretical result returns a rough setting-up guideline for the maximum population size, the mutation rate, and the GA threshold, improving the convergence speed of the rule-generality and the XCS performance.