Reviewerly/exHarmony huggingface dataset:
Comprehensive Framework of Early and Late Fusion for Image–Sentence Retrieval

Image–text retrieval is one challenging task to bridge the modality gap between vision and language. Although the mainstream late fusion schemes could facilitate intramodality correlations, it would result in heavy burden of computation resources and insufficient intermodal alignment. In this work, we propose comprehensive framework of early and late fusion (CFELF), a universal framework to collaborate early fusion with late fusion. To enhance cross-modal correspondence, CFELF fuses local visual regions with global sentences at the early stage to aggregate on late fusion backbones. Therefore, fusions on two phases of the feature process could be complementary to each other to capture salient information in intramodality while encouraging intermodal alignments. We have extensively evaluated CFELF on four advanced late fusion backbones and compare with other early fusion modules. The results on two public image–text datasets demonstrate the effectiveness of the comprehensive fusion framework in retrieval performance with convergence accelerating.
EC-360: Speeding Up 360° Video Streaming Using Tile-based Online Erasure Coding

360&#x00B0; video services require extremely high bitrate and frame rate videos for a good immersive experience. Traditional solutions for adaptive bitrate streaming are still limited by currently insufficient and fluctuating bandwidth. Besides, viewpoint-aware or tile-based solutions would lead more rebuffering due to the short viewpoint prediction window. In this paper, we present EC-360, a novel video streaming framework to speed up 360&#x00B0; video streaming. Specifically, it creatively integrates tilebased online erasure coding into multi-source content delivery to mitigate &#x201C;cask effect&#x201D; or impact of straggler node, which oftentimes leads to failure to improve delivery speed in multisource streaming. We formulate the critical tile-based request scheduling problem in EC-360 based on the Combinatorial Multiarmed Bandit (CMAB) model and develop a low complexity online algorithm. We further theoretically verify the efficiency of the CMAB-based algorithm by deriving its regret upper bound. Extensive experiments based on prototype implementation and real network traces corroborate the efficiency, flexibility, and lightweight of proposed solution. EC-360 achieves superior performance improvement compared to state-of-the-art works in various network scenes and system settings.
Introduction to Federated Learning

The idea of federated learning is given by Google in 2016, to develop local-to-global machine learning models by using data locally. Federated learning is a machine learning approach where the objective is to train a centralized machine learning model without aggregating training data from a large number of clients (typically mobile phones). In this approach, each client independently trained the local model by using its local data and sending update to a central server, where the updates from clients are combined to evolve a new high-quality global model. This chapter includes brief overview of the decentralized approach federated learning and its applications.
Providing Physical and Virtual Mobility for a Regional University-Based Technology Park

This research is focused on such an aspect of engineering pedagogy as training provided in Russia by regional technology parks (technoparks). Technoparks have been organized in various regions of the country so that schoolchildren could get access to engineering activities when still at secondary school, then continue their training at the level of higher education and become qualified engineers contributing to the development of their respected regions, as well as their country and the world in total. However, this opportunity is sufficiently present only in the cities, particularly in the regional centres. Therefore, technology parks need to have sufficient mobility in order to be accessible from various, even distant, parts of the regions where they are based. The purpose of this research is to analyze the opportunities that a regional technology park – particularly a technopark based at university in Russia – can have and use in respect of its mobility aimed at providing engineering education from secondary to higher education level. There are two kinds of mobility in this respect. The first one is physical mobility, when, for example, the technology park equipment and/or staff is physically transported from a university (where it is normally located) to a remote village school. The second one is virtual mobility, when all the technology park equipment and/or staff stays in its regular place(s) but, at the same time, is accessible from remote districts and settlements of the region. These mobility kinds have their peculiar benefits, as well as prerequisites and limits. They are studied in details, taking into consideration the experience of various regional technology parks of Russia, particularly the regional school technology park based at Kuban State Technological University in the city of Krasnodar (one of Russian regional administrative centres).
Teatime: Exploring ways to support diverse narratives on sustainability through design

In the design research community, diverse narratives and ontologies are discussed in relation to sustainability. Relational ontology is proposed as an alternative to the dominant dualist ontology as a way to reconnect people with their ecological embeddedness and responsibility. This work presents a dialogical tool called ‘teatime’ created to introduce diverse, immaterial perspectives on sustainability in a co-design project with youth and researchers. The study explores the role of the teatime design in eliciting diverse narratives and forming a dialogical space. The results show that the teatime supported reflections on immaterial perspectives, bringing out relational and social values related to the ecological crises. This study uses a systematic evaluation to reveal a micro-material perspective on ways in which the teatime design and facilitation supported the inquiry process. We propose that the design practitioners take on the role of crafting dialogical spaces that support social relationships and evoke immaterial perspectives.
Secured Online Learning in COVID-19 Pandemic using Deep Learning Methods

With the outbreak of the global pandemic covid-19, most educational institutions across India have moved towards the usage of online teaching platforms Viz., Google Meet, Zoom, Webex, and Microsoft teams to process learning continuity. In research and development, it is observed that meager importance is given to address the issues of securing e- learning systems. Securing an e-learning system is a unique challenge faced in India as many systems are accessed and managed through the internet by numerous users distributed over diverse networks. Moreover, the online teaching platforms are open, distributed, and interactive; hence, it becomes challenging to ensure that every user has access to the correct information. Building trust will leverage the usage of online teaching systems in terms of security, usability, and protection of personal information. The key focus of this paper is to analyze the existing online and remote learning tools and identify the level of cyberattacks. This article also explores recent progress in novel ICT engineering paradigms in cyber assurance and protection. The paper proposes a cyber-security framework using cutting-edge technologies like AI and Deep Learning to fight against cyber-attacks.
Color Transfer of 3D Point Clouds For XR Applications

In this paper, we analyse and compare four different color transfer algorithms, which were originally developed for 2D images, for 3D point clouds. In particular, we transfer the color distribution of a given reference 3D point cloud to a source 3D point cloud in order to change the illumination of the scene. Color transfer of 3D models might become an important task in AR and VR applications where e.g., an existing 3D model needs to be updated in real-time according to scene changes like displaced objects and illumination changes. In order to better compare the results of the color transfer algorithms, we created a data set of 3D point clouds consisting of reconstructions of an indoor scene under different lighting conditions, and applied two comparison methods for an objective evaluation, namely histogram comparison and voxel comparison, which will be described in detail in this paper.
Parameter space study of optimal scale-dependent weights in TV image denoising

We investigate the choice of finite-dimensional parameter spaces within a bilevel optimization framework for selecting scale-dependent weights in total variation image denoising with non-uniform noise. Due to the pointwise box constraints on the parameter function, we prove existence of Lagrange multipliers in low regularity spaces, and derive a first-order optimality system. To cope with the difficulties related to the lack of regularity, a Moreau-Yosida regularization is introduced and convergence of the regularized optimal parameters towards the optimal weight for the original problem is verified. For each regularized bilevel problem a second-order quasi-Newton algorithm is proposed, together with a semismooth Newton scheme for solving the lower-level problem. Finally, several numerical tests are carried out to compare the different parameter space choices and draw some conclusions.
The importance of signal pre-processing for machine learning: The influence of Data scaling in a driver identity classification

Machine Learning (ML) and Deep Learning (DL) algorithms have overtaken the attention of the scientific community for their important capabilities and their over the top results. However, the excessive focus on hyperparameters and the model’s architectures made the pre-processing step often neglected. In spite of its importance, it represented a weak point for most of the machine learning applications as well as a blind spot in many research studies. In this paper, we will demonstrate through a CAN-Bus vehicle data-based driver identification case study, the importance of testing the use of different methods of data scaling and normalization while demonstrating their role in improving the performance of several Machine Learning algorithms.
Image Processing Based Vehicle Detection and Tracking: A Comparative Study

With the increasing economical-developments and urban population, the number of vehicles on road is increasing as well and hence the traffic. There comes the need to lower the congestion of roads caused due to vehicular traffic. Out of numerous vehicle detection and tracking techniques, this paper deals with Image- processing-based methods simulated using MATLAB Simulink. Few such methods are Background subtraction with gaussian and Kalman Filter, Blob analysis, Horn- Schunck, Particle Filter and Monte Carlo method. Background subtraction is the most familiar one these days followed by the Morphological operations. It depends on certain parameters like accuracy, time for processing, segmenting, and complexity. The various traffic parameters like the speed of the car, count, and its tracking are calculated using its threshold values in some of the detection methods. The work proposed is done in real-time taking the challenging examples. The results mentioned throw light on the lustiness of the study proposed.
Transformation of Educational Space in the System of Digitalization of the Economy

The current state of the economy and the globalization processes affecting it require significant changes in all spheres of society, including the system of highly qualified personnel training. A list of professions has been identified by now as well as the priorities for their development for the next five years. The work aims to study new trends in education development based on the analysis of specific methods and tools that allow not only to change significantly the situation, but also to introduce new levers, tools, approaches and methods, to form a new paradigm for solving certain problems. The implementation of new tools and methods requires a significant change in the educational space itself. A new challenge today is to adapt to the digital environment that has become part of the education system. Based on a comparison of various aspects of the system of offline and online learning, conclusions about the formation of an independent direction of online education with specific content and specific environment were made herein. The views of different researchers regarding the impact of digitalization on educational content, which has become more structured, have been studied. It is concluded that it is important to understand the fundamentals that form the existing dependencies, the need to use creative approaches is considered. As a novelty, it is proposed to consider the use of new digital educational technologies as an additional impetus for the transformation of the educational space, allowing to meet the interests of students, business and the labor market.
Online Gaussian Process State-space Model: Learning and Planning for Partially Observable Dynamical Systems

This paper proposes an online learning method of Gaussian process state-space model (GP-SSM). GP-SSM is a probabilistic representation learning scheme that represents unknown state transition and/or measurement models as Gaussian processes (GPs). While the majority of prior literature on learning of GP-SSM are focused on processing a given set of time series data, data may arrive and accumulate sequentially over time in most dynamical systems. Storing all such sequential data and updating the model over entire data incur large amount of computational resources in space and time. To overcome this difficulty, we propose a practical method, termed onlineGPSSM, that incorporates stochastic variational inference (VI) and online VI with novel formulation. The proposed method mitigates the computational complexity without catastrophic forgetting and also support adaptation to changes in a system and/or real environments. Furthermore, we present application of onlineGPSSM into the reinforcement learning (RL) of partially observable dynamical systems by integrating onlineGPSSM with Bayesian filtering and trajectory optimization algorithms. Numerical examples are presented to demonstrate applicability of the proposed method.
Restart of Accelerated First-Order Methods With Linear Convergence Under a Quadratic Functional Growth Condition

Accelerated first order methods, also called fast gradient methods, are popular optimization methods in the field of convex optimization. However, they are prone to suffer from oscillatory behaviour that slows their convergence when medium to high accuracy is desired. In order to address this, restart schemes have been proposed in the literature, which seek to improve the practical convergence by suppressing the oscillatory behaviour. This paper presents a restart scheme for accelerated first order methods for which we show linear convergence under the satisfaction of a quadratic functional growth condition, thus encompassing a broad class of non-necessarily strongly convex optimization problems. Moreover, the worst-case convergence rate is comparable to the one obtained using a (generally non-implementable) optimal fixed-rate restart strategy. We compare the proposed algorithm with other restart schemes by applying them to a model predictive control case study.
A Novel Bio-Inspired Path Planning for Autonomous Underwater Vehicle for Search and Tracing of Underwater Target

Underwater search and tracing is a complex engineering problem, due to various factors, such as unpredicted oceanic environment, poor communications, underwater navigations. Therefore, to accomplish a challenging mission an autonomous underwater vehicle (AUV) cooperation is needed. This study focuses on the search and tracing of an underwater target, based on an improved cooperative path planning model. To achieve the possible results, the mission is divided into search and tracing phases aiming to maximize the search space thus reducing terminal error respectively. The developed method is inspired by bio-inspired Improved Whale optimization Algorithm (IWOA) based method, hence modeling improved path for autonomous underwater vehicle followed by simulations of various parameters for experimentation, such as maneuverability and communication ranges between autonomous vehicles, in a distributed or centralized scenarios. For the above process, the IWOA is compared with the improved particle swarm optimization (IPSO) algorithm for different parameters for the search and tracing of the underwater target. The simulated results exhibit that the proposed method presents superior results for the search and tracing phase, which also increases the computational performance together with well-achieved optimization on large-scale complex problems for cooperative path planning.
A Dynamic Attention and Multi-Strategy-Matching Neural Network Based on Bert for Chinese Rice-Related Answer Selection

To allow the intelligent detection of correct answers in the rice-related question-and-answer (Q&amp;A) communities of the “China Agricultural Technology Extension Information Platform”, we propose an answer selection model with dynamic attention and multi-strategy matching (DAMM). According to the characteristics of the rice-related dataset, the twelve-layer Chinese Bert pre-training model was employed to vectorize the text data and was compared with Word2vec, GloVe, and TF-IDF (Term Frequency–Inverse Document Frequency) methods. It was concluded that Bert could effectively solve the agricultural text’s high dimensionality and sparsity problems. As well as the problem of polysemy having different meanings in different contexts, dynamic attention with two different filtering strategies was used in the attention layer to effectively remove the sentence’s noise. The sentence representation of question-and-answer sentences was obtained. Secondly, two matching strategies (Full matching and Attentive matching) were introduced in the matching layer to complete the interaction between sentence vectors. Thirdly, a bi-directional gated recurrent unit (BiGRU) network spliced the sentence vectors obtained from the matching layer. Finally, a classifier was employed to calculate the similarity of splicing vectors, and the semantic correlation between question-and-answer sentences was acquired. The experimental results showed that DAMM had the best performance in the rice-related answer selection dataset compared with the other six answer selection models, of which MAP (Mean Average Precision) and MRR (Mean Reciprocal Rank) of DAMM gained 85.7% and 88.9%, respectively. Compared with the other six kinds of answer selection models, we present a new state-of-the-art method with the rice-related answer selection dataset.
Analysis of Comparison-Based Sorting Algorithms

With the expansion of computing, random and unorder data on the internet is also increasing. The searching of useful information from this scattered data has become a critical problem. To solve this problem, researchers has devoted a lot of efforts by developing new approaches and algorithms. They are still working on it to improve the results to be more efficient, fast, and reliable. Sorting is an approach used to organize unordered data. There are many sorting algorithms that helps to extract meaningful information and use it for problem solving. In this paper, the functioning of six sorting algorithms is explained with the help of various factors: algorithm, stability, adaptability, time complexity, and their pros and cons.
Spam Detection in Twitter Data

Among all of an existing social media sites Twitter has grown to be the most admired by the internet users as it has changed the way of information exchange in recent years. But, as its popularity has spiked, spammers have emerged as one of twitter’s biggest limitations finding it easily accessible for attacking the trending topics to spoil useful content, generate traffic and revenue. They increase privacy concerns of the users by attacking their personal information. According to Twitter it mostly uses detection and blocking of spam creating accounts as a mechanism for dealing with spam issues. However, spammers always have the option of creating new accounts. So, there is a need of spam detection rather than detecting the accounts. Twitter is currently using Google Safe Browsing for the purpose of detecting and blocking of spam links but they are not proving to be much useful. In this paper we introduce bodywork that considers user based, tweet-based features and tweet text features. Then we use different machine learning algorithms. Our framework gives 95.695% accuracy with Naive Bayes algorithm.
Semantic analysis of Arab leaders on social media

Social media has led to the creation of complex networks of political and social leaders, and has allowed for instantaneous interaction with broad audiences. Semantic analysis of these interactions is essential for policy-makers and various experts, such as in the anti-terrorism community. However, comprehensive quantitative analysis of the attitudes of Arab political and religious leaders is currently limited. To handle this problem, we first create a novel dataset consisting of 1,145,525 tweets posted in 2009–2018 from 449 political, social, and religious leaders from 12 Arab countries. Next, to semantically process various Arabic dialects in their informal expressions on social media, we develop a Latent Semantic Analysis algorithm that is then used to identify the individuals with the highest semantic matches to our pre-specified categories of interest. Finally, we construct an original Temporal Semantic Similarity (TSS) measure that allows the tracking of those expressions over time. Experimental results demonstrate that the proposed semantic approach can effectively and efficiently process documents in each category. We have made all the data, code and results publicly available on the project homepage: https://github.com/ArabLeader.
Effect of Feature selection on the performance of Liver Tumor classification using Neural Networks

Neural network designs have pushed the boundaries of clinical picture analysis achieving remarkable performance in responsibilities together with tissue category and segmentation as well as prediction of numerous scientific consequences. There are different kinds of cancers happening in the liver. Various growths have diverse pictorial appearances and this changes after infusion of the differentiation medium. Hence, finding the location of liver malignancies is a difficult undertaking. In this paper, we are going to discuss liver tumor classification using neural networks. Here we have used a Pattern recognition network, with a training function as Scaled Conjugate Gradient, hidden neurons are 10, performance function as cross-entropy. Here we have collected 67 CT image which includes both Liver abscess and Hepatocellular carcinoma. 93 features were extracted from these images for training, validation, and test of the network. By including GLRLM features, the mean square error is reduced by 28.31%.
Explainable Artificial Intelligence: A New Era of Artificial Intelligence

Recently, Artificial Intelligence (AI) has emerged as an emerging with advanced methodologies and innovative applications. With the rapid advancement of AI concepts and technologies, there has been a recent trend to add interpretability and explainability to the paradigm. With the increasing complexity of AI applications, their a relationship with data analytics, and the ubiquity of demanding applications in a variety of critical applications such as medicine, defense, justice and autonomous vehicles , there is an increasing need to associate the results with sound explanations to domain experts. All of these elements have contributed to Explainable Artificial Intelligence (XAI).