Reviewerly/exHarmony huggingface dataset:
ST3D++: Denoised Self-Training for Unsupervised Domain Adaptation on 3D Object Detection

In this paper, we present a self-training method, named ST3D++, with a holistic pseudo label denoising pipeline for unsupervised domain adaptation on 3D object detection. ST3D++ aims at reducing noise in pseudo label generation as well as alleviating the negative impacts of noisy pseudo labels on model training. First, ST3D++ pre-trains the 3D object detector on the labeled source domain with random object scaling (ROS) which is designed to reduce target domain pseudo label noise arising from object scale bias of the source domain. Then, the detector is progressively improved through alternating between generating pseudo labels and training the object detector with pseudo-labeled target domain data. Here, we equip the pseudo label generation process with a hybrid quality-aware triplet memory to improve the quality and stability of generated pseudo labels. Meanwhile, in the model training stage, we propose a source data assisted training strategy and a curriculum data augmentation policy to effectively rectify noisy gradient directions and avoid model over-fitting to noisy pseudo labeled data. These specific designs enable the detector to be trained on meticulously refined pseudo labeled target data with denoised training signals, and thus effectively facilitate adapting an object detector to a target domain without requiring annotations. Finally, our method is assessed on four 3D benchmark datasets (i.e., Waymo, KITTI, Lyft, and nuScenes) for three common categories (i.e., car, pedestrian and bicycle). ST3D++ achieves state-of-the-art performance on all evaluated settings, outperforming the corresponding baseline by a large margin (e.g., 9.6% ∼ 38.16% on Waymo → KITTI in terms of AP[Formula: see text]), and even surpasses the fully supervised oracle results on the KITTI 3D object detection benchmark with target prior. Code is available at https://github.com/CVMI-Lab/ST3D.
AN IMPROVED TRANSFER LEARNING APPROACH TOWARDS BREAST CANCER CLASSIFICATION ON DEEP RESIDUAL NETWORK

Breast Cancer is considered a significant health threat in women of all ages, and the incidence has been rapidly increasing, particularly in the last decade.Usually, for diagnosis, characteristics such as skin texture, color (redness), size variation, pain, and gene mutation are used.Binary classification is the most frequent classification type to classify benign and malignant tumors.A deep learning-based mechanism has been hugely popular and significant improvisation in binary classification, especially CNN-based mechanism.Moreover, to take CNN advantage, several breakthroughs have been observed for image classification, such as integrating layer-based features with the classifier.However, due to limited images available for medical research, several areas are exposed, such as overfitting issues, memory issues, and network architecture issues.Hence, in this research work, the design and development of an Improvised-DeepResidualNetwork, aka IDRN, for binary classification on mammogram.IDRN adopts the Deep Residual Network and enforces the improved transfer learning approach for training and testing the model.IDRN follows the novel architecture, forcing the transfer learning-based task-specific layer to be integrated to enhance the features and improvise the classification process.Further, IDRN is evaluated considering the INbreast dataset considering the important metrics like accuracy, precision, recall, F1-score, and AUC; a comparative analysis is also carried out with a state-of-art model of deep learning model.
Shared Knowledge as a Part of Open Science

This work attempts to discuss knowledge sharing as part of the Open Science movement. It attempts to briefly analyze its historical development and the options for modern implementation of the open science idea. It discusses the process of knowledge sharing as an element of recognition of scientific works and part of social development. It seeks a solution to the established national and other restrictions for sharing of knowledge, as well as its importance in the situation of limited sharing.
Interconnected web platform with autonomous robots for helping patients in ICU sections

Smart devices play an important role in today's society, from TVs, to vacuum cleaners, people are starting to automate more and more their daily activities. Given that the pandemic context has forced humanity not to limit itself to existing conditions, but to find new solutions in order to get over it, a special attention has been paid to the medical area. In this context, it was observed that most of the time the medical staff was overwhelmed by the large masses of patients from the ICU wards who required more attention and constant complex care. Through this paper we aim to identify the current needs of the medical sector and, based on our analysis we will propose a possible solution to reduce the overload of ICU departments. At the same time, we will focus on the impact of the pandemic on communication and interpersonal relationships, analysing the case of patients unable to connect with their families. Finally, the focus will be on the possible implementation of a solution that meets all quality standards and norms in order to be used in hospitals and to help medical staff, patients and families.
Creativity, Readiness for Changes and Tolerance for Ambiguity

Under the conditions of precarious situation, caused by the global pandemic and unprecedented restrictions, aimed at countering it, productivity of specialists in various fields of work is reducing significantly. This is particularly true of activities, conducted through direct communication between concerned parties. In order to counter instability, workers have to develop creativity, readiness for changes and tolerance for ambiguity. The goal of research is to establish correlations between the type of professional activity and creativity, readiness for changes and tolerance for ambiguity. The respondents of the study were 260 people of working age –the staff of event, tourism, restaurant business, trade, IT spheres. The research procedure included organizational, target-oriented, empirical, final stages. Time limits of the study – April – July 2020. For psychodiagnostics the article uses the Torrance test of creative thinking, diagnostics of personal creativity by Tunik, the methodology “personal readiness for changes”, the scale of individual’s tolerance for ambiguity by McLain. The study found clear correlations between the indicators of creativity, readiness for changes and tolerance for ambiguity. The original model of a creative specialist (endowed with originality, adaptability, optimism and common sense) in the conditions of changes and uncertainty was formed in the research. The results of psychodiagnostics showed the highest indicators of creativity among IT workers, readiness for changes–among the staff of IT and event spheres, tolerance for ambiguity–among retail workers. In the conditions of pandemic destruction, the workers of the tourism industry were the least creative, while the workers of the event sphere turned out to be unprepared for changes and the workers of the event and tourism industries –intolerant for ambiguity. The results of the study can be used to develop correctional programs to increase the staff’s creativity, readiness for changes, tolerance for ambiguity. It is the development and implementation of effective psycho-correctional programs for the use of real communication and digital tools that are the prospect of further scientific research on the ways to solve the problem.
Efficient Unbiased Volume Path Tracing on the GPU

We present a set of optimizations that improve the performance of high-quality volumetric path tracing. We build upon unbiased volume sampling techniques, i.e., null-collision trackers [16, 8, 15], with voxel data stored in an OpenVDB [14, 13] tree. The presented optimizations achieve an overall 2* to 3* speedup when implemented on a modern GPU, with an approximately 6.5* reduction in memory footprint. The improvements primarily stem from a multi-level digital differential analyzer (DDA) [1, 11, 6] to step through a grid of precomputed bounds; a replacement of the top levels of the OpenVDB tree with a dense indirection texture, similar to virtual textures [3, 4, 17], while preserving some sparsity; and quantization of the voxel data, encoded using GPU-supported block compression. Finally, we examine the isolated effect of our optimizations, covering stochastic filtering, the use of dense indirection textures, compressed voxel data, and singleversus multi-level DDAs.
Enhancing Delphi Method with Algorithmic Estimates for Software Effort Estimation: An Experimental Study

Literature review shows that more accurate software effort and cost estimation methods are needed for software project management success. Expert judgment and algorithmic model estimation are two predominant methods discussed in the literature. Both are reported almost at the comparable level of accuracy performance. The combination of the two methods is suggested to increase the estimation accuracy. Delphi method is an encouraging structured expert judgment method for software effort group estimation but surprisingly little was reported in the literature. The objective of this study is to test if the Delphi estimates will be more accurate if the participants in the Delphi process are exposed to the algorithmic estimates. A Delphi experiment where the participants in the Delphi process were exposed to three algorithmic estimates –Function Points, COCOMO estimates, and Use Case Points, was therefore conducted. The findings show that the Delphi estimates are slightly more accurate than the statistical combination of individual expert estimates, but they are not statistically significant. However, the Delphi estimates are statistically significant more accurate than the individual estimates. The results also show that the Delphi estimates are slightly less optimistic than the statistical combination of individual expert estimates but they are not statistically significant either. The adapted Delphi experiment shows a promising technique for improving the software cost estimation accuracy.
Analysis of English Performance Rating Based on Machine Learning and Optimized BP Network Technology

In order to improve the intelligent effect of English performance rating, this paper analyzes the English performance rating process and builds the corresponding algorithm model based on the wisdom of teaching ideas, and it combines BP spatial network technology with spatial sampling and spatial statistical algorithms to construct the system kernel algorithm. Moreover, this paper extracts students’ English answers through text recognition and compares them with the standard library. Among them, objective questions are mainly scored directly through standard answer comparison, and subjective questions compare student scores with standard answers. In addition, this paper uses a needs analysis to construct the framework of the entire English performance rating system and evaluates the practical effect of the system constructed in the paper through experimental research. According to statistical research data, the English performance rating system in this paper is known to have certain effects.
Gyroscope-Based Remote Text Entry Interface

This paper analyzes the problem of remote text entry using a gyroscope. Its rotary movements are mapped to the screen coordinate system and employed for a virtual laser pointer behaviour. We analyze different placement for the unit and choose held in a hand smartphone with a buil-in gyroscope for further investigations. In the study, the analysis of different modes of text entry is examined, including simple point and click procedure on different layouts, and trajectory-based stroke or gesture typing.
The Transformation Of Marketing By Ai

From the ages, marketing has been a tough nut to crack as to how thoughtful to deal with the customer’s needs and to reach the specified products to them. Marketing is a vast area to deal with which is a crucial part of any business. In this decade we have a significant innovation to manage such issues effectively, which is Artificial Intelligence. Artificial Intelligence is quite possibly the most brilliant region of science today and can undoubtedly be utilized in the acts of marketing. Platforms for multimedia (social networks, news, images, video, Newsletters, infographics, podcasts, blogs, e-books.) are no longer accessible today are not just for the contact between users or users and companies, but also for companies to guide all aspects of business, collect and identify data of paramount importance. The artificial intelligence marketing technique has become climacteric for companies to find consumer conduct and needs. In this paper, I will walk you through the artificial intelligence marketing technique which transformed marketing into a whole new level. By the end of this paper, you will have a brief idea of how marketing has changed by knowing consumer conduct and needs using artificial intelligence.
Transformational Leadership and Performance: Testing Mediation and Moderation in Qatari Hospitality Industry

This study aimed to determine organisational performance with the perspective of employees' service performance by leadership in the hospitality industry. For doing so, we hypothesised the relationship of transformational leadership and employees' service performance via two roots, service climate and organisational identification. Furthermore, to examine the leadership role in service climate development and organisational identification, we used proactive personality as the boundary condition to these relationships. Data for the study was collected from a diverse range of organisations operating in the Qatari hospitality industry. Findings showed that the service climate fully mediates the relationship, while organisational identification failed to mediate the relationship. Considering the moderating effects, results suggested that strong proactive personalities will strengthen transformational leadership on service climate and organisational identification. This study als0 discussed various theoretical and managerial implications.
Research on the Construction of University Music Teaching Cloud Platform Based on Data Mining Technology

Many colleges and universities still use the traditional recording and broadcasting system to record the courseconstruction and high-quality courseware. For data mining technology. As far as time is concerned, the proportionof music education that colleges and universities can give is very few and scarce. Therefore, it is necessary tosupplement and further enhance the cultivation of teenagers' artistic accomplishment, music appreciation andexpressive ability in the art education system of universities. With the integration of global social culture andtechnological progress, music education has increasingly become an interdisciplinary subject integratingmusicology, pedagogy, ethics and related technical sciences. At the same time, it also faces the restriction of talenttraining and teaching mode. The resources and audiences of music education are extremely rich, diverse andextensive. Information based education not only brings convenience to teachers and students, but also stores a lot ofinformation generated by the application platform of teachers and students, which is the biggest feature ofinformation-based teaching itself. At the same time, on the basis of cloud computing, we put forward the researchideas for the model architecture of big data mining platform. Enterprises or operators can use the framework of themodel. This paper mainly proposes to build a teaching resource management platform under the cloud computingmode. Using information technology, combined with the knowledge characteristics of music teaching, it constructsthe music teaching resource database and its cloud platform.
MÜHENDİSLİK TASARIM PROBLEMLERİNİ ÇÖZMEK İÇİN KISIT-YÖNETİMİ MEKANİZMALARININ KARŞILAŞTIRMALI BİR ANALİZİ

Optimization problems have numerous real-life applications in science and engineering. The engineering design problems are usually subject to various constraints. Although many state-of-the-art metaheuristic optimization algorithms have been developed during the last decades, these algorithms require additional constraint-handling mechanisms to cope with constrained optimization problems. Therefore, selecting a suitable constraint-handling mechanism requires extensive trial-and-error experiments, which is time-consuming and demanding. In this study, a comparative analysis of the eight constraint handling mechanisms is carried out, guiding decision-makers in their optimization practices. The constraint-handling techniques are used along with the whale optimization algorithm, and 19 real-life mechanical design problems, which are also part of the CEC2020 benchmark suite, are tested in the experimental analysis. The nonparametric statistical analysis incorporating Nemenyi and Holm post-hoc procedures shows that the inverse tangent constraint-handling and eclectic penalty methods exhibit high performance in real-life mechanical design problems.
Design of intelligent kitchen waste sorting machine based on Pro/E and Adams

Abstract In order to effectively solve the problem that the subsequent waste treatment is affected by foreign bodies such as bones and hard shells in kitchen waste, reduce the treatment cost and improve the utilization efficiency of kitchen waste, this paper designs a small kitchen waste Intelligent Classification Recycling and reprocessing device which can be applied to school canteens, communities and township waste treatment plants. The device is mainly composed of mechanical body, central processing unit, detection and sensing system and post-processing system design. It can further separate kitchen waste on the basis of solid-liquid separation, realize reprocessing and recycling, and maximize the recycling value of kitchen waste, so as to achieve the purpose of energy conservation, emission reduction and sustainable development.
ReBLUR: A Hierarchical Recurrent Denoiser

This chapter presents ReBLUR: a hierarchical recurrent denoiser, currently deployed in recent RTX game titles, including Cyberpunk 2077 and Watch Dogs: Legion. By aggressively reusing information both spatially and temporally, the denoiser produces noise-free images from only a few samples per pixel. In this chapter, we describe the internal structure of the denoiser in detail, and we hope that the basic blocks of this architecture can be useful to improve future denoisers.
Build TensorFlow Input Pipelines

We introduce you to TensorFlow input pipelines with the tf.data API, which enables you to build complex input pipelines from simple, reusable pieces. Input pipelines are the lifeblood of any deep learning experiment because learning models expect data in a TensorFlow consumable form. It is very easy to create high-performance pipelines with the tf.data.Dataset abstraction (a component of the tf.data API) because it represents a sequence of elements from a dataset in a simple format.
Trustworthiness of spam email addresses using machine learning

Cybercriminals have increasingly used spam email to send scams, phishing, malware and other frauds to organisations and people. They design sophisticated and contextualised emails to make them look trustworthy for users, being the sender addresses an essential part. Although cybersecurity agencies and companies develop products and organise courses for people to detect emails patterns, spam attacks are not totally avoided yet.
Sample Reduction Using $$\ell _1$$-Norm Twin Bounded Support Vector Machine

Twin support machine (TSVM) has a lower time complexity than support vector machine (SVM), but it has a poor ability to perform sample reduction. In order to improve the ability of TSVM to reduce sample, we propose an $$\ell _1$$ -norm twin bounded support machine ( $$\ell _1$$ -TBSVM) inspired by the sparsity of $$\ell _1$$ -norm in feature space. The objective function of $$\ell _1$$ -TBSVM contains the hinge loss and the $$\ell _1$$ -norm terms, both which can induce sparsity. We solve the primal programming problems of $$\ell _1$$ -TBSVM to prevent the disappearance of sparsity and avoid the situation that the inverse of matrix does not exist. Thus, $$\ell _1$$ -TBSVM has a good sparsity, or a good ability to reduce sample. Experimental results on synthetic and UCI datasets indicate that $$\ell _1$$ -TBSVM has a good ability to perform sample reduction and simultaneously enhances the classification performance.
An Edge Detection Method for UAV Image Based on the Enhancing ABC

In this paper, we propose a novel ABC method called as EABC to improve the efficiency and quality for color UAV image edge detection based on quaternion. We propose a RGB image pixel as a pure quaternion form and applying the principle of rotation quaternion to determine the edge points of the color UAV image. For the artificial bee colony algorithm, two new search equations are presented to generate candidate solutions in the employed bee phase and the onlookers phase, respectively. Then an enhancing artificial bee colony algorithm (EABC) is proposed. Further, a more reasonable ABC parameters is proposed to improve the performance of ABC. Finally, we combine the advantages of quaternion-based color UAV image and enhancing artificial bee colony algorithm to develop color UAV image edge detection. By comparing the performance and computing time processing different images, the proposed method has superior performance which not only provides the best color UAV image edge detection results but also yields better color UAV image quality com-pared to other widely used algorithm.
Design of a motion system for 3D printed snakebot

This article presents the results of work related to design, analysis and selection of the electric motors, servos and elements of motion system for 3D printed snakebot. Electric motors and servos had to meet a number of requirements like dimensions, torque, RPM. The drivetrain allowed to drive the snakebot and rotate system allowed to torsional movement between adjacent robot modules. CAD model and analysis allowed to select the proper elements of drivetrain and rotate system. We built test stands and after verification we built the prototype. Next step after building the robot was to carry out tests to verify the mobility of the snake robot. We checked, among others, movement of servos in different planes, snakebot speed, driving at angle (up and down).
Establishing a large amount of point correspondences using patch-based affine-scale invariant feature transform for fisheye images

Affine-scale invariant feature transform (ASIFT) has performed very well for perspective images, but the intrinsic nonlinear distortion makes it difficult to utilize ASIFT directly for fisheye images to find a large amount of correspondences. We reuse ASIFT and propose a new feature matching method for uncorrected fisheye images. First, we employ the Gaussian hemi-image to divide fisheye images into patches. In this process, we introduce Plücker coordinates and side operator to establish the correspondences between the hemispherical projection model, Gaussian hemi-image, and fisheye images. Simultaneously, maximum stable extreme region algorithm is used to detect the target regions. After that, each patch in these regions is simulated at different orientation parameters in ASIFT, and SIFT algorithm is applied to all simulated patches. Experiments on real-world images show that the proposed method can achieve good performance: the numbers of the point correspondences increase greatly with satisfactory accuracy, reliability, and efficiency.