Reviewerly/exHarmony huggingface dataset:
Density-Net: A Density-Aware Network for 3D Object Detection

To address the problem of density imbalance between nearby and faraway regions in point clouds, we propose a density-aware 3D single-stage object detector named Density-Net. First, a new 3D data augmentation method is designed to generate low-density areas by using the DBSCAN cluster method to acquire the cluster of point clouds, then by setting several different downsample ratios to stimulate sparse regions. By this way, there will be many sparse regions in point clouds. To restore the representative information, we propose Density-Set-Abstraction (Density-SA) to harmonize the high-level representative feature and low-level spatial feature, which boosts the final object detection performance. Moreover, we design a new Mask Sample branch to accomplish point sampling based on point-wise annotations, which increases the recall rate of meaningful points. We evaluate it on the widely used KITTI dataset. The experiments demonstrate that our method outperforms the well-established and highly-optimized 3DSSD (implemented by MMdetection3D) baseline 2.2% APs in moderate difficulty setting. At the same time, the runtime performance is also on par with 3DSSD. Code: https://github.com/Physu/density-net
Investigation of Convolutional Neural Networks in the Tasks of Medical Images Analysis and Classification of Breast Tumors

The problem of medical images analysis and classification of breast tumors is considered. For its solution the application of different convolutional neural networks CNN is suggested. The experimental investigations of the suggested CNN on the standard data set BreakHis were carried out in problems of binary and multi-class classification. The efficiency of different CNN was estimated and comparison with known results was performed. The best class of CNN for this problem was determined with the highest accuracy.
DESIGN AND DEVELOPMENT OF THE INDOOR MICROCLIMATE CONTROL SYSTEM ON THE ARDUINO PLATFORM

Methods of design and programming of systems are presented. The scheme of the system was implemented. The software part was implemented using the Arduino IDE software package.
Multi-Variable State Prediction: HMM Based Approach for Real-Time Trajectory Prediction

Predicting the motion of observed entities benefits humans almost seamlessly. The same benefits can be proliferated to mobile autonomous systems if we have a reliable, real-time solution to predict the motion of any object of interest, be it the host’s own motion or that of an observed foreign object. In this work, a novel Multi-Variable State Prediction (MVSP) methodology is devised for real-time trajectory prediction. MVSP incorporates cascaded stages of HMM with Viterbi algorithm and probabilistic quantization for accurately predicting the motion characteristics of the moving object. The overall scheme is employed to predict the motion of moving objects in a 3D space. The proposed approach is verified on both synthetically generated data sequences and data-sets captured from real-life experiments. For a practical scenario, the experiments resulted in an RMS error of 0.6m for a predicted distance of ~18m demonstrating the effectiveness and accuracy of the proposed methodology.
Project Management in the Website UX / UI Development: System-Oriented Analysis and Business Process Management Principles

The website interface quality depends on a project management method, since the duration of managerial delays, improper management of large volumes of metrics, excessively increased testing time and other human factors are caused by project management errors. The correct project management methodology can bring economic and social benefits to the users and IT companies. The article describes the BPMS-based project management method and its impact on the interface quality.
FER by Modeling the Conditional Independence between the Spatial Cues and the Spatial Attention Distributions

This paper presents a novel approach for FER. The spatial cues, for example the locations of face components such as eyes and mouth, play an important role to guide the spatial attentions for FER. Traditional approaches define the relations between the spatial cues and the spatial attention distributions based on linear models. However there also exists non-linear relations between them, in which case the spatial cues and the spatial attention distributions can be conditional independent. In this paper we model the conditional independence based on the state-of-the arts framework of the attention models for FER. We design the spatial cues as the hyper-parameters to affect the metric for spatial attention calculation. We exploit the Global-Attention (no spatial cues), Local-Attention (spatial cues affect the attention distributions) and Self-Attention (spatial cues as the hyper-parameters to affect the attention metric) as three different configurations. The experimental results show that the Self-attention achieves the best performances (68.5% on FER2013 Dataset and 49.8% on EmotiW2017 Dataset) which improves the accuracies by 2.8 % (on FER2013) and 1% (on EmotiW2017) compared with the Global-attention. The experimental results support the idea that non-linear modeling the relations between the spatial cues and the spatial attention distributions can improve the performances for FER.
Mine MIMO Depth Receiver: An Intelligent Receiving Model Based on Densely Connected Convolutional Networks

Multiple-input multiple-output (MIMO) systems suffer from high BER in the mining environment. In this paper, the mine MIMO depth receiver model is proposed. The model uses densely connected convolutional networks for feature extraction and constructs multiple binary classifiers to recover the original information. Compared with conventional MIMO receivers, the model has no error accumulation caused by processes such as decoding and demodulation. The experimental results show that the model has better performance than conventional decoding methods under different modulation codes and variations in the number of transmitting terminals. Furthermore, we demonstrate that the model can still achieve effective decoding and recover the original information with some data loss at the receiver.
Integrated Cloud Computing Environment for Upstream Geoscience Workflows

Abstract Extensive computing resources are required to leverage todays advanced geoscience workflows that are used to explore and characterize giant petroleum resources. In these cases, high-performance workstations are often unable to adequately handle the scale of computing required. The workflows typically utilize complex and massive data sets, which require advanced computing resources to store, process, manage, and visualize various forms of the data throughout the various lifecycles. This work describes a large-scale geoscience end-to-end interpretation platform customized to run on a cluster-based remote visualization environment. A team of computing infrastructure and geoscience workflow experts was established to collaborate on the deployment, which was broken down into separate phases. Initially, an evaluation and analysis phase was conducted to analyze computing requirements and assess potential solutions. A testing environment was then designed, implemented and benchmarked. The third phase used the test environment to determine the scale of infrastructure required for the production environment. Finally, the full-scale customized production environment was deployed for end users. During testing phase, aspects such as connectivity, stability, interactivity, functionality, and performance were investigated using the largest available geoscience datasets. Multiple computing configurations were benchmarked until optimal performance was achieved, under applicable corporate information security guidelines. It was observed that the customized production environment was able to execute workflows that were unable to run on local user workstations. For example, while conducting connectivity, stability and interactivity benchmarking, the test environment was operated for extended periods to ensure stability for workflows that require multiple days to run. To estimate the scale of the required production environment, varying categories of users’ portfolio were determined based on data type, scale and workflow. Continuous monitoring of system resources and utilization enabled continuous improvements to the final solution. The utilization of a fit-for-purpose, customized remote visualization solution may reduce or ultimately eliminate the need to deploy high-end workstations to all end users. Rather, a shared, scalable and reliable cluster-based solution can serve a much larger user community in a highly performant manner.
Self-supervised Variational Autoencoder for Recommender Systems

Variational autoencoder (VAE) is considered as an emerging model for ensuring competitive performance in recom-mender systems. However, its performance is severely limited by the amount of training examples and, as a result, existing VAE models may fail to provide satisfactory recommendation results in presence of highly sparse user-item interactions. In this paper, we propose a self-supervised VAE model, SSVAE in short, to improve the generalization ability of VAE model on the sparse interaction datasets. Concretely, we first build multiple views for each user by data augmentation, and then design a pretext task to align the representations learned from different views of each user. Particularly, SSVAE aims to optimize a combined objective of recommendation task and pretext task, making them to rein-force each other during the learning process. Our encouraging experimental results on three real-world benchmarks validate the superiority of our SSVAE model to state-of-the-art VAE style recommendation techniques.
Placement of CPU-Intensive Virtual Machines in High Resource Utilization Cloud Datacenters. An Economical Revenue Maximization Analysis

The enormous growth in the use of Cloud Service Providers (CSPs) leads to an increasing consideration of the optimization of Virtual Machine Placement (VMP) to host services for clients. This work aims to study VMP resolution algorithms in cloud datacenters with high resource utilization and CPU-intensive requested VMs for economical revenue maximization. Experiments were carried out in 64 different experimental scenarios. From the four evaluated algorithms in the experimental results, it can be seen that A1 offers the best results considering a centralized decision approach, First-Fit for the iVMP phase, Memetic Algorithm (MA) for the VMPr phase, prediction-based method for VMPr Triggering and update-based method for VMPr recovering. A1 slightly outperforms the other algorithms that also perform well for the analyzed scenarios considering average, maximum and minimum objective function evaluation metrics.
Assessing learners’ needs for practising spoken Icelandic L2 with the mobile app TALAÐU!

This article presents results from a human-centred approach study to designing a mobile app TALAÐU!, which aims at assisting learners of Icelandic as a second language (L2) with spoken language practice. The main goal of the app would be to encourage learners to have real-life conversations with Icelandic speakers by accomplishing conversational missions. In the first stage of development, a qualitative deep-method needs study in the form of interviews was conducted with 19 migrants from 14 different countries, who permanently live in Iceland. Results indicated that learners have five core needs for practising spoken language: connection, confidence, clarity, growth, and convenience. Based on different learning styles and confidence in speaking rather than language ability, three main learner profiles were indicated: ‘diving in’, ‘learning with caution’ and ‘dipping a toe’. Both the learners’ needs and the profiles would drive the design of the learning app to personalise and maximise the learner experience with practising spoken L2. This human-centred study approach suggests an innovative strategy in designing a learning app for practising L2 in a specific language and culture environment.
Offline Handwritten Signature Recognition based on Discrete Cosine Transform and Artificial Neural Network

Abstract- Signature recognition is of great importance in our daily life as well as in automatic identification systems. It is an acceptable biometric method used to identify a person, because each person is distinguished by its signature. This paper presents a technique based on Discrete Cosine Transform to capture features to distinguish signatures handwritten. Discrete Cosine Transform parameters are extracted from signature image and these parameters are entered for the Artificial Neural Network for the purpose of classification. This work was tested with sigcomp2011 database and a comparison made against some existing techniques, and promising results have been obtained.
Identifying ”good” practices of developers using corrective and preventive actions and their impact on software metrics

Abstract This work presents a literature review, an analysis on how certain actions of software developers impact certain software metrics (such as defect density), and an attempt to highlight good (most efective in terms of software metrics) development practices based on the corrective and preventive actions extracted from the literature. Across multiple relevant studies, defect density was the most used metric, that is why this metric was used to identify the good practices. The most used practices are those that were encountered the most during data extraction from the relevant literature. The extracted actions were categorized using CMMI taxonomy. Overall, 115 unique actions were identifed falling into 53 CMMI taxonomy categories. There were 30 good and the most used practices identifed that fell into 4 CMMI categories.
A Blockchain-Based Software Always-On System

Moving towards Industry 5.0, the Industrial Internet of Things (IIoT) has become an indispensable role. IIoT devices could help manufacturing companies to observe various production statuses hence improving production efficiency. However, its limitation on resources and features makes robust security implementation a big challenge. Implementing a secure, general and scalable system to adopt across industries is one of the most critical topics for IIoT. This paper proposed a blockchain-based system architecture to auto-update, monitor, and fix the IIoT devices' software. We take the software status snapshots and store them as a blockchain ledger to protect the software's integrity and the software from unauthorized modification. To achieve auto-update and auto fix, we also simulate the concept of a blockchain contract to create the ledger whenever there are changes. As a result, the changes could continuously be tracked. The performance and scalability are also evaluated. The result shows that the system could also be deployed in larger-scale IIoT devices. The software update and fix could be guaranteed authenticated, and the unauthorized software could be monitored and detected.
Text Pared into Scene Graph for Diverse Image Generation

Although significant recent advances in condition generative model have shown remarkable improvements for controlled image generation, the image generation for multiple complex objects is still a challenge. To address the challenge, we propose a module of text description parsed into scene graph, which can generate reasonable scene layout to ensure the generated image and object realistic. Our proposed method enhances the interaction between objects and global semantics by concatenates each object embedding with text embedding To preserve the local image semantics, the Spatially-adaptive normalization(SPADE) layer is added into the generator of our model. We validate our method on Visual Genome and COCO-Stuff, where qualitative results and ablation study demonstrate the ability of our model in generating images with multiple objects and complex relationships.
Application of Machine Learning in Flight Safety Event Reasoning

QAR (quick access recorder) data exceedance and statistical analyses is used to flight safety event reasoning. Machine learning algorithms are applied to fully automatically classify pilot behaviors into classes. Both classifications (supervised) and clustering (non-supervised) methods are tried, for instance, gentle Adaboost (Adaptive Boosting) classification, K-means and Gustafson-Kessel (G-K) clustering. Routine exceedance events are assigned to gentle adaboost labels for training model and evaluating testing result, while K-means and G-K clustering are conducted in free-style. All results are compared, and findout unveils that each method emphasizes different series of pilot performances given by a method named Minimum Redundancy Maximum Relevance (mRMR). After theoretical study, we realize in our cases, G-K clustering is much more accurate than K-means in clustering methods.
EssenceProject: Software Project Planning Tools based on OMG’s Essence

This research proposes a software project management tool to help project managers with software project planning. EssenceProject ver 2.0 is the improvement of EssenceProject ver 1.0 that only focuses on project monitoring. The new version adds planning functionalities, especially in scope and time management. Existing tools apply specific software development methods. Those methods sometimes are not suited for software development teams. Therefore, teams have to adjust it to meet their needs. EssenceProject is a generic tool that accepts any method described in Essence. Essence is a standard to define practices and methods described by OMG (Object Management Group). Using Essence, a team can define their method to suit their needs. Furthermore, they can use EssenceProject to manage their software project.
Visual Saliency Computing with Color Feature Integration Based on Psychological Experiment

This paper introduced a method of visual saliency computing with color feature integration based on the psychological experiment. Firstly, based on the results of the psychological experiment of the saliency features, the color saliency features with the highest selected frequency were extracted. Then, the saliency map of each saliency feature was generated with the combination center “of-surround” theory from Itti model. Finally, the feature integration mechanism was adopted to combine each saliency feature to integrate the saliency map. The gray value of each pixel in the saliency map reflected the computing value of visual saliency. The results shows that the proposed method is highly sensitive to color and can effectively recognize the saliency objects of the color static images. In addition, when detecting the saliency objects from different types, it has a stable performance with the good generalization.
Strategic Digital Informing and Its Challenges in the 21st Century

This chapter examines the challenges faced by digital informing technologies and civilization in the 21st century. The chapter begins by analyzing (1) the stages of development of strategic information technologies from the early 20th century up to the present as well as (2) the strategies adopted by informing science specializations (such as cognitive science, software engineering, etc.). Next, the chapter surveys major innovations in the history of strategic information technologies. This is followed by an analysis and evaluation of the concept of a laborless economy. The chapter concludes by positing a set of rules for workers in the digital economy that will ensure the wise development of civilization.
THE STUDY OF THE NEGATIVE IMPACT OF VIRTUAL REALITY ON THE HUMAN CONDITION IN THE LEARNING PROCESS

A study of the degree of influence of negative factors on the physical condition of a person in the course of training with the use of virtual reality was conducted. The main factors of influence of virtual reality on the learning process of specialists were considered. Specialized software was developed to collect the necessary data on the process of interaction with virtual reality. Tests were conducted on a focus group of students and young professionals to calculate the pattern of manifestation of negative factors of virtual reality in the learning process. Based on the collected data, the dynamics of changes in objective indicators of focus group participants was analyzed, and subjective evaluations of users’ quality of interaction with virtual reality were processed. The obtained values of quantitative and qualitative metrics made it possible to assess the success of using virtual reality technologies in the process of training specialists.
Linear Domain-aware Log-scale Post-training Quantization

In recent years, various convolutional neural networks (CNNs) are widely used in practical applications. These networks require a lot of hidden layers to achieve high accuracy, resulting in a significant amount of computation. Therefore, studies related to network compression to support the real-time operation of CNNs in mobile devices have been actively conducted. In particular, log-scale quantization is receiving a lot of attention because it can bring many advantages in the computation amount and power consumption by changing the multiplication operations to the addition operations in the hardware accelerator design process. However, in conventional log-scale quantization methods, the parameters (e.g., weight, activation) are quantized after applying log transformation, which results in considerable quantization loss. To address this problem, this paper proposes a new technique that minimizes the accuracy degradation due to log-scale quantization by applying log transformation after performing quantization. As a result of verifying the proposed method with Cifar-10 dataset in Resnet-56 network, we have achieved much better performance than the conventional method by reducing the error rate due to quantization to within 1%.
Enriching Resource Rooms for Students with Learning Disabilities: A Case of Transparent, Semi-Transparent, and Non-Transparent Concepts

The aim of this study is to illustrate how to enrich resource rooms with specialized instructional materials for the concepts of “transparent,” “semi-transparent,” and “non-transparent.” Given the special situations of students with learning disabilities, the specialized instructional materials use their own avatars to ensure a sense of belonging for each student to the study. In addition, they have the opportunity to follow the QR-coded experimental videos repeatedly, after performing hands-on experiments. Thus, the specialized instructional materials not only help them understand the concepts of transparent, semi-transparent and non-transparent but also develop their social skills.
Track Estimation and Maintenance for the BP-CBMeMBer Filter

Aiming at the disadvantage that the box particle cardinality balanced multi-target multi-Bernoulli (BP-CBMeMBer) filter can not output the target tracks, a labeled BP-CBMeMBer (LBP-CBMeMBer) filter is proposed. First, each box particle is assigned a label, and the box particles sampled from the same target are assigned the same label. In this way, each label can be used as the identification of each target identity. Then, with the recursion of the filter, each label is transmitted in real time with each corresponding box particle. In the state estimation step, when the state of each target is estimated, the label of each target is also estimated together. Finally, the track of each target is formed by connecting the target states with the same label at different times. Simulation shows that the proposed filter can not only estimate the target number and target states, but also output the target tracks.