Reviewerly/exHarmony huggingface dataset:
Scaling Semantic Segmentation Beyond 1K Classes on a Single GPU

The state-of-the-art object detection and image classification methods can perform impressively on more than 9k classes. In contrast, the number of classes in semantic segmentation datasets is relatively limited. This is not surprising when the restrictions caused by the lack of labeled data and high computation demand for segmentation are considered. In this paper, we propose a novel training methodology to train and scale the existing semantic segmentation models for a large number of semantic classes without increasing the memory overhead. In our embedding-based scalable segmentation approach, we reduce the space complexity of the segmentation model’s output from O(C) to O(1), propose an approximation method for ground-truth class probability, and use it to compute cross-entropy loss. The proposed approach is general and can be adopted by any state-of-the-art segmentation model to gracefully scale it for any number of semantic classes with only one GPU. Our approach achieves similar, and in some cases, even better mIoU for Cityscapes, Pascal VOC, ADE20k, COCO-Stuff10k datasets when adopted to DeeplabV3+ model with different backbones. We demonstrate a clear benefit of our approach on a dataset with 1284 classes, bootstrapped from LVIS and COCO annotations, with almost three times better mIoU than the DeeplabV3+. Our source code is available at: https://github.com/shipra25jain/ESSNet.
Towards Distributed Privacy-Preserving Prediction

In privacy-preserving machine learning, individual parties are reluctant to share their sensitive training data due to privacy concerns. Even the trained model parameters or prediction can pose serious privacy leakage. To address these problems, we demonstrate a generally applicable Distributed Privacy-Preserving Prediction (DPPP) framework, in which instead of sharing more sensitive data or model parameters, an untrusted aggregator combines only multiple models' predictions under provable privacy guarantee. Our framework integrates two main techniques to guarantee individual privacy. First, we introduce the improved Binomial Mechanism and Discrete Gaussian Mechanism to achieve distributed differential privacy. Second, we utilize homomorphic encryption to ensure that the aggregator learns nothing but the noisy aggregated prediction. Experimental results demonstrate that our framework has comparable performance to the non-private frameworks and delivers better results than the local differentially private framework and standalone framework.
Do Institutions Promote Firm Performance? Evidence from Sub-Saharan Africa

This article examines the roles of institutions in driving firms’ performance using merged Enterprise Panel Survey data sets collected and compiled by World Bank for 23 sub-Saharan Africa (SSA). The study used labour productivity as a proxy of firms’ performance due to its advantages over total factor productivity. To address the problems of multiple sources of heterogeneity and simultaneity biases, the study employed high-dimensional fixed-effects model together with its instrumental variable version to estimate the impacts of institutions on firms’ performance in SSA. The study established that institutions contribute significantly and positively to firms’ performance in SSA. The study infers that control of corruption, government effectiveness, regulatory quality and rule of law are pro-market institutions that effectively create favourable business and investment climates. Other findings of the study include capital–labour ratio, export, high school, average labour costs, research and development (R&amp;D) and capacity utilisation, which all drive the firm’s performance in SSA. Thus, the findings imply that prevailing and future policies on business, trade and investment in SSA should also incorporate institutional reforms as one of the essential measures to rapidly and sustainably boost economic performance in the region.
Combining Blockchain and Machine Learning to Forecast Cryptocurrency Prices

Blockchain is an emerging technology that enables a vital framework for various cryptocurrency operations such as bitcoin. Notably, without any involvement from third party authorities, blockchain offers a decentralized consensus scheme to process user transactions, fund transfer, and various data records in a secure and reliable way. Furthermore, bitcoin price forecasting has been a vital research trend, where machine learning techniques play a substantial role. A sophisticated and appropriately trained model can be useless if the features being tested are unreliable. Independently, one of the most desirable aspects of a system that utilizes the blockchain is the concrete objectiveness by which each entry is cataloged. Any data collected and reported on the blockchain is unambiguous, and therefore, extremely suitable for a machine learning algorithm. To efficiently forecast bitcoin price movements, in this work, we propose and examine various lenses by which to view this union, each with varying degrees of success.
Agile Framework To Transform Traditional Team

With the increasing computing power of processors, more complex web and mobile applications are being developed by the developers. To meet the consistent high standard software deliverables, software developers need to follow the software design life cycle as a standard practice. The conventional way suffers certain drawbacks in project management that need to be addressed. The newer approach called agile methodology is much efficient and improves the quality of the product if followed by the team members as per the agile values. Agile is one of the software development methodologies that have a lot of features and flexibility which play a vital role to bring improvement in educational as well as industrial sectors. As the traditional methodologies are not flexible with changes, whereas Agile methodologies main advantages are interacting with the customer, respond to changes and strong communication and collaboration. In this research, we have conducted a critical survey based on three fundamental modules including design and learning strategies, team building, and profiling. The consequences showed that the conducted survey brought up the current situation of organization and opening new research direction to bring Agility in existing systems.
Real-time Indoor Object Detection Based on Deep Learning and Gradient Harmonizing Mechanism

Due to the indoor environment is complicated, and the proportion of the background is much larger than the object, the positive and negative categories of the sample are not balanced. This paper proposes that the imbalance of sample categories is due to the imbalance of difficult and easy samples which can be reflected by the gradient norm of the sample. The gradient density is introduced to solve the uneven distribution of gradient norm. The parameter "gradient density" is added to the Yolov3 loss function which is one of the best one-stage object detection frameworks that can balance accuracy and speed in order to improve the sample category imbalance. Considering the high computational complexity of the sample gradient density, the gradient norm is divided into equal-width intervals, and the same gradient density is adopted for the samples falling in same region so as to simplify the calculation and improve the training efficiency. The experimental results demonstrate that the improved approach achieves best detection accuracy and obtains the most accurate bounding boxes of indoor objects to be classified with quick speed. Therefore, the gradient harmonizing mechanism of samples can improve the sample category imbalance. The improved indoor object detection algorithm can be applied in the field of intelligent indoor monitoring, indoor service robots.
How to benefit from newbies' domain ignorance in software development projects

A big problem in a computer-based systems (CBSs) development organization is how to improve the immigration of a newbie (a new hire to the organization or a mover within the organization) into a CBS development project of the organization that is already in progress. The newbie, who is ignorant of the domain of the CBS, must be brought to the same awareness of the domain that the project veterans have (1) without becoming a drain on his or her mentor and (2) while becoming useful to the project as soon as possible. Based on other work that shows that some CBS development activities benefit from the out-of-the-box thinking of a domain ignorant, it is hypothesized that a newbie who starts his or her immigration with CBS development activities requiring or enhanced by domain ignorance has a more successful immigration than a newbie who starts with other activities. The current paper reports a two-part empirical test of the hypothesis. First, a survey was conducted among CBS development managers of varying experience to determine what CBS development activities they thought were helped by domain ignorance. Second, transcripts from fourteen interviews of domain-ignorant immigrants to new CBS development projects at one large company were examined to determine if the activities performed by those with the most successful immigrations were activities that are considered by CBS development managers to be helped by domain ignorance. In the end, there is some modest support for the hypothesis, despite all the opportunities for the data to show a different conclusion because of the existence of many other factors that influence immigration success. Following a discussion of the threats to the validity of the result, the paper concludes with recommendations for tasks to assign to newbies to improve their immigrations, recommendations for project staffing in general, and recommendations for future research.
Semi-commutative Masking: A Framework for Isogeny-Based Protocols, with an Application to Fully Secure Two-Round Isogeny-Based OT

We define semi-commutative invertible masking structures which aim to capture the methodology of exponentiation-only protocol design (such as discrete logarithm and isogeny-based cryptography). We give an instantiation based on the semi-commutative action of isogenies of supersingular elliptic curves, in the style of the SIDH key-exchange protocol. We then construct an oblivious transfer protocol using this new structure and prove that it UC-securely realises the oblivious transfer functionality in the random-oracle-hybrid model against passive adversaries with static corruptions. Moreover, we show that it satisfies the security properties required by the compiler of Döttling et al. (Eurocrypt 2020), achieving the first fully UC-secure two-round OT protocol based on supersingular isogenies.
Proposed Techniques to Optimize the DW and ETL Query for Enhancing data warehouse efficiency

To improve time-bound decision capability of any business corporates, they have to perform data-analysis based on historical and current data. For handling this data, data warehouse (DWH) plays a crucial role. Further-more as time passes, the data also grows exponentially, because in data warehouse historical data also needs to be maintained. If we further drill now-a-days Extract Transform and Load (ETL) tools are being used to perform extract, transform and loading of data. These ETL tools are based on any specific language like in case of Informatica ETL tool, which is based on Structured Query Language (SQL) language. So, it's imperative to enhance the efficiency of these languages to enhance the capacity of ETL tools and this will significantly impact the performance of data warehouse ETL work, and by this data can also be provided in faster manner for analysis and reporting purposes. Additionally, data quality is also an important factor, as data-analysis will be performed on the same data supplied by data warehouse. So, it's highly concerned to improve the data quality by applying data-quality checks before supplying for data-analysis purpose. So, we have worked in this direction and gone through various research papers which are mainly focused on query optimization and data quality improvements. After going through all research papers, we have not only summarized the performance tuning tips given in these papers but also worked and experimented various things, and then we have proposed some unique techniques for query optimization and improved data quality techniques. After which we have incorporated it in data warehouse to enhance overall performance of data warehouse. These proposed techniques can be applied in any place wherever queries are being used to extract/transform and/or load the data to enhance the performance.
Mixed-initiative Manned-unmanned Teamwork Using Coactive Design and Graph Neural Network

Mixed-initiative decision making is a flexible and effective way for coherent manned-unmanned teamwork (MUT). It allows the autonomous adjustment of levels of autonomy (LOA) and the human-robot collaboration modes according to the task requirements as well as the states of environments, robots and the human operator. However, it is still difficult for humans and robots to understand each other's intentions and motivations due to the challenges of cognition representation and behavior reasoning. In this paper, we propose a novel mixed-initiative MUT approach using coactive design and graph neural networks (GNN) towards explainable human-robot collaboration. First, an interdependence analysis table is designed for a specific manned-unmanned aerial vehicle task following the coactive design principles of observability, predictability and directablity. Then, a multi-agent dynamic task assignment system is designed based on a task model with key decision-making points. Finally, we have used the Graph Network Library to design a GNN model for adjusting the LOA among the MAV and UAVs in the given task.
The Role of Technology to Teaching and Learning Sign Languages: A Systematic Mapping

Context: The teaching and learning process has become essential for the evolution of the society as a whole. However, there are still major challenges for achieving the global goals of education, especially if we consider the portion of the population with some type of physical disability. In this context, according to World Federation of the Deaf (WFD), deaf children face many difficulties in education due to inappropriate learning environments. Also, this problem is compounded by the lack of consistency worldwide in the provision of sign language interpreting and translation. Motivation: However, the advent of technology is having a significant impact on the way that sign language interpreters and translators work. In this sense, the union between Information and Communication Technologies (ICT) and modern pedagogical practices can be the genesis for the creation of a global learning environment based on sign languages. Objective: In this scenario, this work aims to: (i) obtain an overview of the areas of Software Engineering and their technological solutions in the fields of teaching and learning sign languages; (ii) identify the main educational topics; and (iii) the most investigated sign languages. Method: Therefore, we conduct a Systematic Mapping (SM) with a focus on technologies applied in the teaching and learning of sign languages. Results: We selected 139 primary studies, using a search approach that includes both manual and automated searches, in addition to providing quality criteria for evaluating results. Thereby, we obtained pertinent information about the implemented solutions, in addition to the educational topics and sign languages covered. Conclusions: We conclude that technologies have already contributed to the teaching and learning of sign languages. In this context, different approaches were found, from solutions on traditional platforms (Web, Mobile and Desktop) to the use of Augmented Reality (AR) in smart glasses. However, no solution aims to enable communication between different sign languages, which shows a limitation that can motivate even more innovative research.
Unsupervised learning of global factors in deep generative models

We present a novel deep generative model based on non i.i.d. variational autoencoders that captures global dependencies among observations in a fully unsupervised fashion. In contrast to the recent semi-supervised alternatives for global modeling in deep generative models, our approach combines a mixture model in the local or data-dependent space and a global Gaussian latent variable, which lead us to obtain three particular insights. First, the induced latent global space captures interpretable disentangled representations with no user-defined regularization in the evidence lower bound (as in β-VAE and its generalizations). Second, we show that the model performs domain alignment to find correlations and interpolate between different databases. Finally, we study the ability of the global space to discriminate between groups of observations with non-trivial underlying structures, such as face images with shared attributes or defined sequences of digits images.
OnionNet: Single-View Depth Prediction and Camera Pose Estimation for Unlabeled Video

In real scenes, humans can easily infer their positions and distances from other objects with their own eyes. To make the robots have the same visual ability, this article presents an unsupervised OnionNet framework, including LeafNet and ParachuteNet, for single-view depth prediction and camera pose estimation. In OnionNet, for speeding up OnionNet’s convergence and concretizing objects against the gradient locality and moving objects in videos, LeafNet adopts two decoders and enhanced upconvolution modules. Meanwhile, for improving the robustness of fast camera movement and rotation, ParachuteNet uses and integrates three pose networks to estimate multiview camera pose parameters by combining with the modified image preprocess. Different from existing methods, single-view depth prediction and camera pose estimation are trained view by view, where the variations between views is gradual reduction of view range and outer pixels disappear in next view, similar to onion peeling. Moreover, the LeafNet is optimized with pose parameter from each pose network in turn. Experimental results on the KITTI data set show the outstanding effectiveness of our method: single-view depth performs better than most supervised and unsupervised methods which contain two same subtasks, and pose estimation gets the state-of-the-art performance compared with existing methods under the comparable input settings.
Unsupervised Feature Selection based on Constructing Virtual Cluster’s Representative

The data readability, complexity reduction of learning algorithms and increase predictability are the most important reasons for using feature selection methods, especially when there exist lots of features. In recent years, unsupervised feature selection techniques are well explored. In this paper, we proposed an unsupervised feature selection algorithm using multivariate-symmetrical-uncertainty based feature clustering, Feature Selection-based Virtual Feature Representative (FSVFR). The main idea of FSVFR is as follows: First, it selects the cluster centers based on the similarity density of the neighbors of each feature; after assigning the features to the clusters, the virtual representative is generated in such a way that contains maximum common information with cluster's members and minimum similarity with other representatives. These steps continues until there is no more change in the representatives. Second, a feature that has the most common information in each cluster is selected as its representative. The experimental results on benchmark datasets demonstrate the effectiveness of our approaches as compared to the two common methods.
Hybrid Cloud Architecture for Higher Education System

As technology improves, several modernization efforts are taken in the process of teaching and learning.An effective education system should maintain global connectivity, federate security and deliver self-access to its services.The cloud computing services transform the current education system to an advanced one.There exist several tools and services to make teaching and learning more interesting.In the higher education system, the data flow and basic operations are almost the same.These systems need to access cloud-based applications and services for their operational advancement and flexibility.Architecting a suitable cloud-based education system will leverage all the benefits of the cloud to its stakeholders.At the same time, educational institutions want to keep their sensitive information more secure.For that, they need to maintain their on-premises data center along with the cloud infrastructure.This paper proposes an advanced, flexible and secure hybrid cloud architecture to satisfy the growing demands of an education system.By sharing the proposed cloud infrastructure among several higher educational institutions, there is a possibility to implement a common education system among organizations.Moreover, this research demonstrates how a cloud-based education architecture can utilize the advantages of the cloud resources offered by several providers in a hybrid cloud environment.In addition, a reference architecture using Amazon Web Service (AWS) is proposed to implement a common university education system.
Loss Scheduling for Class-Imbalanced Image Segmentation Problems

When training a classifier the choice of loss function heavily influences the characteristics of the resulting model. The most commonly used loss function for classification is cross entropy. In image segmentation problems where each pixel is assigned to a particular class, overlap-based losses have recently been shown to improve classifier performance especially for datasets with an imbalanced class distribution. This is particu-larly relevant to segmentation because class imbalance mitigation strategies used in regular classification are often not applicable. Overlap-based losses, however, have different drawbacks. We are aiming at combining the upsides of different losses with a simple scheduling scheme during training while minimizing their downsides. Gradually transitioning from an overlap-based dice loss to cross entropy allows to reliably select a distinct minimum in the optimization landscape as a valuable alternative to results obtained from traditional unscheduled loss functions. We demonstrate the efficacy of our approach on different combinations of loss functions, datasets, and models.
Unsupervised Evaluation of Lidar Domain Adaptation

In this work, we investigate the potential of latent representations generated by Variational Autoencoders (VAE) to analyze and distinguish between real and synthetic data. Although the details of the domain adaptation task are not the focus of this work, we use the example of simulated lidar data adapted by a generative model to match real lidar data. To assess the resulting adapted data, we evaluate the potential of latent representations learned by a VAE. During training, the VAE aims to reduce the input data to a fixed-dimensional feature vector, while also enforcing stochastic independence between the latent variables. These properties can be used to define pseudometrics to make statements about generative models that perform domain adaptation tasks. The variational autoencoder is trained on real target data only and is subsequently used to generate distributions of feature vectors for data coming from different data sources such as simulations or the output of Generative Adversarial Networks.