Reviewerly/exHarmony huggingface dataset:
Good fences make good neighbours?

Software development is de facto a social activity that often involves people from all places to join forces globally. In such common instances, project managers must face social challenges, e.g., personality conflicts and language barriers, which often amount literally to "culture shock". In this paper, we seek to analyze and illustrate how cultural and geographical dispersion---that is, how much a community is diverse in terms of its members' cultural attitudes and geographical collocation---influence the emergence of collaboration and communication problems in open-source communities, a.k.a. community smells, the socio-technical precursors of unforeseen, often nasty organizational conditions amounting collectively to the phenomenon called social debt. We perform an extensive empirical study on cultural characteristics of GitHub developers, and build a regression model relating the two types of dispersion---cultural and geographical---with the emergence of four types of community smells, i.e., Organizational Silo, Lone Wolf, Radio Silence, and Black Cloud. Results indicate that cultural and geographical factors influence collaboration and communication within open-source communities, to an extent which incites---or even more interestingly mitigates, in some cases---community smells, e.g., Lone Wolf, in development teams. Managers can use these findings to address their own organizational structure and tentatively diagnose any nasty phenomena related to the conditions under study.
Adversarially-Aware Robust Object Detector

Object detection, as a fundamental computer vision task, has achieved a remarkable progress with the emergence of deep neural networks. Nevertheless, few works explore the adversarial robustness of object detectors to resist adversarial attacks for practical applications in various real-world scenarios. Detectors have been greatly challenged by unnoticeable perturbation, with sharp performance drop on clean images and extremely poor performance on adversarial images. In this work, we empirically explore the model training for adversarial robustness in object detection, which greatly attributes to the conflict between learning clean images and adversarial images. To mitigate this issue, we propose a Robust Detector (RobustDet) based on adversarially-aware convolution to disentangle gradients for model learning on clean and adversarial images. RobustDet also employs the Adversarial Image Discriminator (AID) and Consistent Features with Reconstruction (CFR) to ensure a reliable robustness. Extensive experiments on PASCAL VOC and MS-COCO demonstrate that our model effectively disentangles gradients and significantly enhances the detection robustness with maintaining the detection ability on clean images. Our source code and trained models are publicly available at: https://github.com/7eu7d7/RobustDet .
Improving the Reliability for Confidence Estimation

Confidence estimation, a task that aims to evaluate the trustworthiness of the model’s prediction output during deployment, has received lots of research attention recently, due to its importance for the safe deployment of deep models. Previous works have outlined two important qualities that a reliable confidence estimation model should possess, i.e., the ability to perform well under label imbalance and the ability to handle various out-of-distribution data inputs. In this work, we propose a meta-learning framework that can simultaneously improve upon both qualities in a confidence estimation model. Specifically, we first construct virtual training and testing sets with some intentionally designed distribution differences between them. Our framework then uses the constructed sets to train the confidence estimation model through a virtual training and testing scheme leading it to learn knowledge that generalizes to diverse distributions. We show the effectiveness of our framework on both monocular depth estimation and image classification.
Real-time Gaze Tracking with Head-eye Coordination for Head-mounted Displays

High-accuracy, low-latency gaze tracking is becoming one of the indispensable features in augmented reality (AR) head-mounted devices (HMDs). Researchers have proposed different approaches to predict gaze positions from eye images. However, since only the eye modality is focused, these appearance-based algorithms are still struggle to trade off the accuracy and running speed in HMDs. In this paper, we propose a lightweight multi-modal network (HE-Tracker) to regress gaze positions. By fusing head-movement features with eye features, HE-Tracker achieves comparable accuracy (3.655° in all subjects) and $27 \times$ speedup (48 fps in the specialized AR HMD) compared to the state-of-the-art gaze tracking algorithm. We further demonstrate that when applying our head-eye coordination strategy to other baseline models, all these models achieve at least 6.36% performance improvement without a pronounced effect on running speed. Moreover, we construct HE-Gaze, the first multi-modal dataset with eye images and head-movement data for near-eye gaze tracking. This dataset is currently made of 757,360 frames and 15 persons, providing an opportunity to foster research in multi-modal gaze tracking approaches. Our dataset is available at DOWNLOAD LINK <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Parts of Speech Tagging using Conditional Random Field with Long Short Term Memory for English texts

with a rapid growth of technology and data there is a need of processing data with natural language processing techniques. for any type of natural language processing application Parts of Speech tagging is used as preprocessing step. Many algorithms have been proposed for tagging the information with necessary tags. Conditional Random Field is a Classification technique used for POS tagging. To improve the efficiency of the Conditional Random Field algorithm, Long Short Term Memory is used at one of the hidden layer of the Conditional Random Field. with this method good accuracy achieved when compare with these two CRF and LSTM Individually.
One-Trimap Video Matting

Recent studies made great progress in video matting by extending the success of trimap-based image matting to the video domain. In this paper, we push this task toward a more practical setting and propose One-Trimap Video Matting network (OTVM) that performs video matting robustly using only one user-annotated trimap. A key of OTVM is the joint modeling of trimap propagation and alpha prediction. Starting from baseline trimap propagation and alpha prediction networks, our OTVM combines the two networks with an alpha-trimap refinement module to facilitate information flow. We also present an end-to-end training strategy to take full advantage of the joint model. Our joint modeling greatly improves the temporal stability of trimap propagation compared to the previous decoupled methods. We evaluate our model on two latest video matting benchmarks, Deep Video Matting and VideoMatting108, and outperform state-of-the-art by significant margins (MSE improvements of 56.4% and 56.7%, respectively). The source code and model are available online: https://github.com/Hongje/OTVM .
Vector Field-based Collision Avoidance for Moving Obstacles with Time-Varying Elliptical Shape

This paper presents an algorithm for local motion planning in environments populated by moving elliptical obstacles whose velocity, shape and size are fully known but may change with time. We base the algorithm on a collision avoidance vector field (CAVF) that aims to steer an agent to a desired final state whose motion is described by a double integrator kinematic model. In addition to handling multiple obstacles, the method is applicable in bounded environments for more realistic applications (e.g., motion planning inside a building). We also incorporate a method to deal with agents whose control input is limited so that they safely navigate around the obstacles. To showcase our approach, extensive simulations results are presented in 2D and 3D scenarios.
Domain Infused Conversational Response Generation for Tutoring based Virtual Agent

Recent advances in deep learning typically, with the introduction of transformer based models has shown massive improvement and success in many Natural Language Processing (NLP) tasks. One such area which has leveraged immensely is conversational agents or chatbots in open-ended (chit-chat conversations) and task-specific (such as medical or legal dialogue bots etc.) domains. However, in the era of automation, there is still a dearth of works focused on one of the most relevant use cases, i.e., tutoring dialog systems that can help students learn new subjects or topics of their interest. Most of the previous works in this domain are either rule based systems which require a lot of manual efforts or are based on multiple choice type factual questions. In this paper, we propose EDICA (Educational Domain Infused Conversational Agent), a language tutoring Virtual Agent (VA). EDICA employs two mechanisms in order to converse fluently with a student/user over a question and assist them to learn a language: (i) Student/Tutor Intent Classification (SIC-TIC) framework to identify the intent of the student and decide the action of the VA, respectively, in the on-going conversation and (ii) Tutor Response Generation (TRG) framework to generate domain infused and intent/action conditioned tutor responses at every step of the conversation. The VA is able to provide hints, ask questions and correct student's reply by generating an appropriate, informative and relevant tutor response. We establish the superiority of our proposed approach on various evaluation metrics over other baselines and state of the art models.
Virtual and Augmented Reality Technology for the Treatment of Mental Health Disorders: An Overview

Augmented and Virtual Reality plays an important role in the field of medical health science. According to the study of past decades, Human computer interaction helps the patients to recover from their issues very quickly. Virtual Reality is used in several fields nowadays and the study of this paper is in the field of clinical sector. The purpose of using Virtual Reality concepts on the patients is that it helps to change the functioning of the brain and also change the behavior, mood, and perception of the person. For example phobias, and addicted person, when person is in an abnormal situation and their mind is not working properly at that time they need a virtual environment. There is a requirement to show the virtual videos to the patients according to their problems. So to analyze the problem of the patients, use virtual reality concept to check the level of the stress, anxiety, and phobias.
A Machine Learning Based Approach to Predict Online Gaming Addiction in the Context of Bangladesh

Online games are hazardous to one's health and well-being. Addiction to video games has now become a concern to Bangladeshi youth. Our lives are negatively impacted by online gaming. We must keep an eye on our country's youth to ensure that they do not become addicted to any kind of internet gaming. We must refrain from playing games in order to avoid being hooked on them. Machine learning will be used to forecast the likelihood of becoming addicted to video games. We begin by reviewing relevant studies, journals, and internet publications, and then we speak with online gaming addictions. We discovered certain common elements that are linked to gaming addiction and Lemmen’s gaming addiction scale to measure the whether a person is addicted or not. Then, based on those criteria, such as age, gender, mental pressure, and so on, we collect data. We gather information from both addicts and nonaddicts. There are two possible outcomes: We processed all of the data once it was collected and developed a processed dataset. On our processed dataset, we used machine learning techniques. In different predictions and detection systems, machine learning, artificial intelligence and deep learning are applied. KNN (K-Nearest Neighbor), LR (Logistic Regression), Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), Random Forest (RF), Adaptive Boosting (ADA Boosting), Decision Tree (DT), XGBOOST and Gradient Boosting (GB) classifier are some of the methods we utilize. Adaptive Boosting outperformed the other nine methods in our study. All the nine classifiers predict with high accuracy and among them, Adaptive Boosting (ADA Boosting) gives the maximum accuracy which is 93.00%.
An Approach to Software Assets Reusing

The modern software development methodologies require systematic reuse of software assets. It is expected that reuse becomes a cause of reducing efforts. From the other hand, nowadays reuse procedure is connected with plenty of problems and risks, for example, how to choose the best software asset from the set of available for reuse? What criterion should be considered to estimate internal structure of software asset? How to perform such an operation quickly and effectively? Paper proposes the approach allowing predicting estimation of effectiveness for further reuse of software asset. Approach is based on matching software assets to requirement specification considering their semantic attributes, namely OCL expressions. Software asset is associated with some problem domain process through keywords. Points of requirement specification are associated with the same keywords and completed by OCL expressions. Similarity of requirement specification and software assets from repository is defined by means of comparing corresponding keywords and OCL expressions. Model for approximate comparison of OCL expressions and its estimation are proposed. Evaluation of the proposed approach according to IBM reuse maturity model is represented.
Resources for Comparative Analysis of IDN Authoring Tools

Authoring tools are a crucial component in the practice and research of interactive digital narrative design, yet there is no contemporary knowledge base to evaluate and comparatively analyze the great many tools that currently exist. This paper takes on the tasks of constructing a framework for the description and comparison of IDN authoring tools and their defining characteristics and affordances, and of developing this framework into a community resource. We propose a descriptive framework and an online resource meant to facilitate the development of a tool database and curate its properties. Our framework is composed of 30 tool descriptors, addressing (among other factors) the tools' basic identify, business model, use context, technical information, interface affordances and unique design elements. Values were additionally created to define the answer range for most descriptors and streamline the process of tool-logging via the online form we created. In this paper, we explain and demonstrate our framework and present the online database and examples of the sort of meta-analysis it can generate, testifying to the potential usefulness of our framework to the research and practice community.
A Skeleton-based Dynamic Hand Gesture Recognition for Home Appliance Control System

In recent years, advances in 3D sensors have dramatically promoted the development of dynamic hand gesture recognition research. On the other side, the task of hand pose estimation has seen significant progress due to the powerful feature extraction capabilities based on Convolutional Neural Networks (CNNs). In this paper, we present a lightweight CNNs method on hand gesture recognition for home appliance control system. We propose a two-stage CNN model to facilitate it. At the first stage, we utilize DetNet to detect the hand and generate 3D hand skeleton locations. At the second stage, a skeleton-based dynamic hand gesture recognition model is developed. We have 99.4% accuracy by the trained CNN model with the testing dataset. Besides, we implement this system on the Nvidia Jetson AGX Xavier to control the on/off of the fan and the light.
NNNet: New Normal Guided Depth Completion From Sparse LiDAR Data and Single Color Image

In this paper, we propose new normal guided depth completion from sparse LiDAR data and single color image, named NNNet. Sparse depth completion often uses normal maps as a constraint for model training. However, direct construction of a normal map from the color image causes a lot of noise in the normal map and reduces the model performance. Thus, we use a new normal map as an intermediate constraint to promote the fusion of multi-modal features. We generate the new normal map from the sparse LiDAR depth data to use it as a constraint for network training. The new normal map is generated by converting the input depth into a grayscale image, constructing a normal map, replacing the Z channel of the normal map with the original depth, and finally adding a mask. Based on the new normal map, we construct an end-to-end network NNNet for sparse depth completion guided by its corresponding color image. NNNet consists of two branches. The one branch generates the new normal map from the depth image and its corresponding color image, while the other branch constructs a dense depth image from the sparse depth and the predicted new normal map. The two branches fully merge the features through skip connection. In loss function, we use L2 loss to ensure that the new normal map plays a restrictive role. Finally, we generate the dense depth image by refining it with a spatial propagation network. Experimental results show that the new normal map provides effective constraints for sparse depth completion. Moreover, NNNet achieves 724.14 in terms of RMSE and outperforms most of the current state-of-the-art methods.
Joint Participant Selection and Learning Scheduling for Multi-Model Federated Edge Learning

As edge computing complements the cloud to enable computational services right at the network edge, federated learning (FL) can also benefit from close-by edge computing infrastructure. However, most prior works on federated edge learning (FEL) mainly focus on one shared global model during the federated training in edge systems. In a real edge computing scenario, there may co-exist multiple various FL models that are owned by different entities and used by different applications. Simultaneously training these models competes both computing and networking resources in the shared edge system. Therefore, in this work, we consider a multi-model federated edge learning where multiple FEL models are being trained in the edge network and edge servers can act as either parameter servers or workers of these FEL models. We formulate a joint participant selection and learning scheduling problem, which is a non-linear mixed-integer program, aiming to minimize the total cost of all FEL models while satisfying the desired convergence rate of trained FEL models and the constrained edge resources. We then design several algorithms by decoupling the original problem into two or three sub-problems which can be solved respectively and iteratively. Extensive simulations with real-world training datasets and FEL models show that our proposed algorithms can efficiently reduce the average total cost of all FEL models in a multi-model FEL setting compared with existing algorithms.
Depth-Guided Sparse Structure-from-Motion for Movies and TV Shows

Existing approaches for Structure from Motion (SfM) produce impressive 3-D reconstruction results especially when using imagery captured with large parallax. However, to create engaging video-content in movies and TV shows, the amount by which a camera can be moved while filming a particular shot is often limited. The resulting small-motion parallax between video frames makes standard geometry-based SfM approaches not as effective for movies and TV shows. To address this challenge, we propose a simple yet effective approach that uses single-frame depth-prior obtained from a pretrained network to significantly improve geometry-based SfM for our small-parallax setting. To this end, we first use the depth-estimates of the detected keypoints to reconstruct the point cloud and camera-pose for initial two-view reconstruction. We then perform depth-regularized optimization to register new images and triangulate the new points during incremental reconstruction. To comprehensively evaluate our approach, we introduce a new dataset (StudioSfM) consisting of 130 shots with 21K frames from 15 studio-produced videos that are manually annotated by a professional CG studio. We demonstrate that our approach: (a) significantly improves the quality of 3-D reconstruction for our small-parallax setting, (b) does not cause any degradation for data with large-parallax, and (c) maintains the generalizability and scalability of geometry-based sparse SfM. Our dataset can be obtained at https://github.com/amazon-researchlsmall-baseline-camera-tracking.
Virtual Experiences of Metaverse Using Mobile Type Head-Mounted Displays and Their Applicability

The concept of a 3D experience using the Google Cardboard VR headset and cell phones is discussed in terms of its educational effect. As for the video component, content on YouTube as a 360°VR video is used. The following three topics are included in this project: skydiving, lunar exploration, and deep-sea exploration. In some organizations, groups of faculty and student members are asked to participate in such an activity to experience 3D using a cell phone and YouTube along with the VR headset. The authors plan to analyze the results using a 5-point scale. A similar experiment is already in progress. It involves the use of a different VR headset tool (Oculus Quest). Since the contents of both activities are similar, the purpose of this experiment is to compare and verify the advantages and disadvantages of using a VR cardboard headset and a cell phone, as well as to identify the advantages and disadvantages of using a cell phone.
A Optimized Taxonomy on Spot Sale Services Using Mathematical Methodology

In reality, the most appropriate cloud standards are significant ideas for application administrations. OSCCS technique calculation gives a spot-like market in virtual space to the end clients. The OSCCS gives an on-spot showcasing framework in the virtual universe of processing. As fluffy hypothesis is giving the specific outcomes and close to correct outcomes that is the reason that authors have taken the idea of fluffy unpleasant set hypothesis in their OSCCS approach. This sort of approach depends on the determination of the best ideal cost and season of asset allotment to an end client by the suppliers in virtual advertising. The allotments of assets depend on powerful on-request demand upkeep so the end-client gets the cloud administration on a business premise like in continuous shopping advertising on events. By the OSCCS deals approach framework, an end client can get the assistance at the ideal expense. The OSCCS calculation is mimicked in cloud test systems.