Reviewerly/exHarmony huggingface dataset:
Using Social Tag Embedding in a Collaborative Filtering Approach for Recommender Systems

Nowadays, the use of social information is extending to more and more application domains. In the field of recommender systems, this information has been exploited in different ways to address some problems, especially associated with collaborative filtering methods, and thus achieve more reliable recommendations. Specifically, social tagging is used in this area mainly to characterize the items that are the subject of the recommendations. In this work, a user-based collaborative filtering approach is presented, where tags processed by word embedding techniques are used to characterize users. User similarities based on both tag embedding and ratings are combined to generate the recommendations. In the study conducted on two popular datasets, the reliability of this approach for rating prediction and top-N recommendations was tested, showing the best performance against the most widely used collaborative filtering methods.
Pix2Pt Map for Transfer-Based Few-Shot Learning

Following the rapid increase of vehicles in the streets, it has become a daunting task to automatically identify tire patterns to aid with traffic accident management. Hence, this paper aims to provide a high accuracy tire classification system using Meta-Learning. We use a transfer-based method known as “Few-Shot learning with leveraged feature distribution method” (PT + MAP), upgrading it with an image segmentation model, pix2pix (‘unet128’), to pre-process the image before feeding it into the PT+MAP.The current approach utilizes three steps to build the method:1)Pre-process Tire patterns image as a Mask (Image preprocessing).2)Using a pre-trained backbone to obtain a Gaussian-like distribution by pre-processing the feature vector extracted3)Using the optimal transport-inspired algorithm to leverage the pre-processing and perform prediction.Our upgraded architecture results in high accuracy for tire classification and hence has shown our system’s applicability as an effective automatic tire identification system.
An Experimental Framework of Bangla Text Classification for Analyzing Sentiment Applying CNN &amp; BiLSTM

Analysis of sentiment is the utilization of NLP, which works with data to analyze emotions or thoughts that may be useful in many aspects. Sentiment analysis with Bangla text has been a challenging task as only several research on it. As a decision-maker, emotion extrication catches customer perceptions and helps in social behavior. In this paper, we have explained a new way of analyzing sentiment applying text classification on the Bangla dataset utilizing the CNN-BiLSTM framework. This classification model presents the text as either positive or negative. In our model, we have used a dataset on Bangla news comments, consisting of Bangla posts for developing a deep learning model. To achieve accurate analytical results, we have prepossessed our dataset involving language translation, stop word elimination, tokenization, and high-frequency word extraction. Then to generate the sentiment analysis model, layers of CNN and LSTM are designed in such a way that we have successfully achieved our high accuracy.
Make your own

Interacting with chatbots has become ubiquitous nowadays. Nevertheless, conversational agents often remain unable to reliably succeed in social contexts, which negatively influences users’ experience and prevents them from exploiting the technology's full potential. To improve user experience and subsequent trust-formation in chatbots only very little attention has been paid to the active involvement of the user and with that to customization options. Employing a preregistered experimental 1x2 between-subjects study design (N = 171) this study explores an alternative approach to the typical one-chatbot-fits-all solution and investigates the potential of active user-based chatbot customization for the development of trust in chatbots. While customization had no direct effect on trust, anthropomorphism was identified as a significant mediator. The chatbot's interpersonal communicational competence was not affected by customization, yet it did predict trust. Exploratory analyses of participants’ feedback point towards the importance of individual differences between users and generally show a positive impact of customization on the overall chatbot experience.
Boundary-Aware Arbitrary-Shaped Scene Text Detector With Learnable Embedding Network

Benefiting from the popularity of deep learning theory, scene text detection algorithms have developed rapidly in recent years. Methods representing text region by text segmentation map are proved to capture arbitrary-shaped text in a more flexible and accurate way. However, such segmentation-based methods are prone to be disturbed by the text-like background patterns (like the fence, grass, etc.), which generally suffer from imprecise boundary detail problem. In this paper, LEMNet is proposed to handle the imprecise boundary problem by guiding the generation of text boundary based on a priori constraint. In the training stage, Boundary Segmentation Branch is firstly constructed to predict coarse boundary mask for each text instance. Then, through mapping pixels into an embedding space, the proposed Pixel Embedding Branch makes the embedding representation of boundary points learn to be more similar, meanwhile enlarging the characteristic distance between background points and boundary points. During inference, noise in the coarse boundary segmentation map can be effectively suppressed by a Noisy Point Suppression Algorithm among pixel embedding vectors. In this way, LEMNet can generate a more precise boundary description of text regions. To further enhance the distinguishability of boundary features, we propose a Context Enhancement Module to capture feature interactions in different representation subspaces, in which features are parallelly performed attention and concatenated to generate enhanced features. Extensive experiments are conducted over four challenging datasets, which demonstrate the effectiveness of LEMNet. Specifically, LEMNet achieves F-measure of 85.2&#x0025;, 87.6&#x0025; and 85.2&#x0025; on CTW1500, Total-Text and MSRA-TD500 respectively, which is the latest SOTA.
Dissecting the High-Frequency Bias in Convolutional Neural Networks

For convolutional neural networks (CNNs), a common hypothesis that explains both their generalization capability and their characteristic brittleness is that these models are implicitly regularized to rely on imperceptible high-frequency patterns, more than humans would do. This hypothesis has seen some empirical validation, but most works do not rigorously divide the image frequency spectrum. We present a model to divide the spectrum in disjointed discs based on the distribution of energy and apply simple feature importance procedures to test whether high-frequencies are more important than lower ones. We find evidence that mid or high-level frequencies are disproportionately important for CNNs. The evidence is robust across different datasets and networks. Moreover, we find the diverse effects of the network’s attributes, such as architecture and depth, on frequency bias and robustness in general. Code for reproducing our experiments is available at: https://github.com/Abello966/FrequencyBiasExperiments
An Overview and Current Status of Blockchain Simulators

Since the introduction of Bitcoin, blockchain has attracted tremendous interest from both academia and industry. During the last decade, various large-scale blockchain systems were developed. However, the complexity of large-scale distributed systems makes the performance evaluation process challenging and costly. Here, blockchain simulators give the possibility to repeat complex real-world processes at a low cost. Simulators are easily extensible and can test distributed ledger performance using different settings and parameter variations. This work reviews and summarizes the current status of the state-of-the-art blockchain simulators.
DFI: The Data Flow Interface for High-Speed Networks

In this paper, we propose the Data Flow Interface (DFI) as a way to make it easier for data processing systems to exploit high-speed networks without the need to deal with the complexity of RDMA. By lifting the level of abstraction, DFI factors out much of the complexity of network communication and makes it easier for developers to declaratively express how data should be efficiently routed to accomplish a given distributed data processing task. As we show in our experiments, DFI is able to support a wide variety of data-centric applications with high performance at a low complexity for the applications.
Adaptive hypergraph learning with multi-stage optimizations for image and tag recommendation

An efficient learning scheme is proposed for hypergraph ranking based on multiple optimizations. This scheme dynamically optimizes the hypergraph structure through the incidence matrix and employs adaptive hyperedge weight estimation based on gradient descent method. By doing so, optimized hypergraph ranking vectors are derived, which recommend images of places of interest (POIs) worth visiting or tags to images associated to POIs. Experiments are conducted on a dataset of Greek POIs crawled from Flickr and the NUS-WIDE-LITE dataset. The proposed method offers higher quality recommendations than state-of-the-art methods, employing either a static hypergraph structure, a hypergraph structure learning, or a hyperedge weight update with respect to precision.
Web Page Classification Based on Graph Neural Network

Web page, a kind of semi-structured document, includes a lot of additional attribute content besides text information. Traditional web page classification technology is mostly based on text classification methods. They ignore the additional attribute information of web page text. We propose WEB-GNN, an approach for Web page classification. There are two major contributions to this work. First, we propose a web page graph representation method called W2G that reconstructs text nodes into graph representation based on text visual association relationship and DOM-tree hierarchy relationship and realizes the efficient integration of web page content and structure. Our second contribution is to propose a web page classification method based on graph convolutional neural network. It takes the web page graph representation as to the input, integrates text features and structure features through graph convolution layer, and generates the advanced webpage feature representation. Experimental results on the Web-black dataset suggest that the proposed method significantly outperforms text-only method.
Dynamic Games for Social Model Training Service Market via Federated Learning Approach

In recent years, an increasing amount of new social applications have been emerging and developing with the profound success of deep learning technologies, which have been significantly reshaping our daily life, e.g., interactive games and virtual reality. Deep learning applications are generally driven by a huge amount of training samples collected from the users’ participation, e.g., smartphones and watches. However, the users’ data privacy and security issues have been one of the main restrictions for a broader distribution of these applications. In order to preserve privacy while utilizing deep learning applications, federated learning becomes one of the most promising solutions, which gains growing attention from both academia and industry. It can provide high-quality model training by distributing the training tasks to individual users, relying on on-device local data. To this end, we model the users’ participation in social model training as a training service market. The market consists of model owners (MOs) as consumers (e.g., social applications) who purchase the training service and a large number of mobile device groups (MDGs) as service providers who contribute local data in federated learning. A two-layer hierarchical dynamic game is formulated to analyze the dynamics of this market. The service selection processes of MOs are modeled as a lower level evolutionary game, while the pricing strategies of MDGs are modeled as a higher level differential game. The uniqueness and stability of the equilibrium are analyzed theoretically and verified via extensive numerical evaluations.
Various Swarm Optimization Algorithms: Review, Challenges, and Opportunities

In various research areas there is a requirement for optimized results in various research-oriented parameters. In steganography, the important task is to find out the areas, where we can hide our secret image. This search can be done through optimization techniques. Various other parameters related to image processing research areas like peak signal to noise ratio, compression ratio, etc. can be optimized using various optimization techniques. Optimization techniques are parts of artificial intelligence. This paper provides a brief review of various optimization algorithms with implementations of the salp swarm optimization algorithm and particle swarm optimization algorithm. Swarm intelligence (SI) is a technique that gives various optimization techniques. These techniques give the optimized results of any research area. Many types of swarm optimization algorithms have evolved, like artificial bee colony algorithm, glowworm swarm optimization, particle swarm optimization, ant colony optimization, and salp swarm optimization algorithm. This paper also gives advantages and disadvantages. The future scope of the swarm optimization algorithm is also discussed.
Banking Sector Intermediation Development and Economic Growth: Evidence from Nigeria

In developing economies, banks act as a conduit for the efficient mobilization of financial resources from the surplus sectors for effective allocation to the deficit sectors for productive investment that will in turn lead to economic growth. Thus, the study is aimed at evaluating whether development in the banking sector intermediation process in the form of an increase in the number of branches, credit to private sectors, intermediation efficiency and total assets stimulated economic growth in Nigeria during the period 1987⎯2018. The study employed the Johansen cointegration test, dynamic ordinary least square regression and error correction model in determining the relationship between the variables. The results of the cointegration test confirmed the existence of a long-run relationship between banking sector development indicators and economic growth in Nigeria; whereas in the short-run only number of bank branches and bank’s total assets have a positive and significant impact on economic growth, signifying that much of Nigeria’s superior growth performance is attributed to an increase in the number of bank branches and growth in the bank’s total assets. Going by the above findings, the study recommends that the Central Bank of Nigeria should monitor the activities of Commercial Banks in terms of branch distribution to ensure a wider spread and even distribution and also review the asset base of banks from time to time so as to measure up to international standards in order to increase investment and economic growth.
Topography of Violence: Considerations for Ethical and Collaborative Visualization Design

Abstract Based on a collaborative visualization design process involving sensitive historical data and historiographical expertise, we investigate the relevance of ethical principles in visualization design. While fundamental ethical norms like truthfulness and accuracy are already well‐described and common goals in visualization design, datasets that are accompanied by specific ethical concerns need to be processed and visualized with an additional level of carefulness and thought. There has been little research on adequate visualization design incorporating such considerations. To address this gap we present insights from Topography of Violence, a visualization project with the Jewish Museum Berlin that focuses on a dataset of more than 4,500 acts of violence against Jews in Germany between 1930 and 1938. Drawing from the joint project, we develop an approach to the visualization of sensitive data, which features both conceptual and procedural considerations for visualization design. Our findings provide value for both visualization researchers and practitioners by highlighting challenges and opportunities for ethical data visualization.
Task Scheduling and Load Balancing Techniques Using Genetic Algorithm in Cloud Computing

Task scheduling and load balancing are challenging problems in cloud computing, as the demand of cloud computing resources is escalating everywhere anytime. The load on cloud nodes is very high with the increase in client request for cloud services. This condition needs load balancing techniques to uniformly distribute the client request among existing resources so that underutilization and overutilization of resources can be minimized. An impoverished scheduling strategy may over burdensome virtual machines while remaining virtual machines are idle. In this paper, load balance-aware task scheduling policy is proposed using genetic algorithm. The performance of the proposed technique is compared with existing schemes. The simulation output represents that the proposed algorithm outperforms than the current algorithms.
Quadrotor UAV 3D Path Planning with Optical-Flow-based Obstacle Avoidance

A real-time waypoint-based 3D local path planning algorithm is proposed for obstacle avoidance using the optical flow obtained by a frontal monocular camera mounted on a quadrotor UAV. The algorithm accounts for vertical and horizontal obstacle avoidance, as well as for avoidance of frontally approaching obstacles. Implementation and testing are carried out in the ROS environment and the algorithm effectiveness is demonstrated via Gazebo simulations. Realtime algorithm performance is also assessed through software profiling and in terms of worst case execution time using the NVIDIA Jetson TX1 and RaspberryPi 4 for hardware-in-the-loop (HIL) tests.
Adoption of Blockchain Technology

In today's world, society has become totally digitalized where technology is playing a very important role in everyone's life. Blockchain is a method that is useful in recording information and makes its difficult to change, hack, or fraud system. A blockchain is fundamentally a digital ledger of volume of transactions that is distributed across the overall network of IT system on the blockchain. This technology acts as a reliable layer in the evolution of e-commerce. Walmart has been working with IBM on a food safety blockchain solution to add transparency to the decentralized food supply ecosystem by digitizing the food supply chain process. This chapter will cover the concept and origin/emergence of blockchain technology and implications of blockchain technology in supply chain (Walmart), the significance and role of blockchain technology for the users and the stakeholders, the implications for the users, challenges faced by Walmart during the adoption of blockchain technology, and the approachability of Walmart to IBM for the implication of blockchain to their organization.
Deep Learning Object Detection Techniques for Thin Objects in Computer Vision: An Experimental Investigation

Efficient detection of thin objects, from stationary or moving images, is significant in a variety of research areas. These research areas include but are not limited to electric power line detection systems, sperm tail detection for clinical sperm research, mooring lines detection, road-lane line detection for autonomous vehicles, and cracks detection for the integrity assessment of building structures. However, the detection of thin objects is a challenging computer vision task owing to the slimmer and less compact nature of these objects. Moreover, the complexity present in certain images, such as the background clutter, further adds to this problem of accurately detecting thin objects. In this work, we investigate a series of state-of-the-art deep learning detectors for thin objects’ detection. The detectors examined in this work were: EfficientDet, YOLOv5 and U-Net. The experimental results of this study reveal that generic state-of-the-art deep detectors are not suitable for detecting thin objects due to their reliance on coarse bounding boxes and/or excessive pixel-level computations while the application-specific detectors possess poor generalization capabilities and do not work accurately outside their domains. These empirical findings indicate the necessity of the identification of critical factors affecting thin objects detection and the subsequent design of a generic thin objects’ detector.
Performance Analysis of Network Pruning for Deep Learning based Age-Gender Estimation

With the advance of visual AI technology, age-gender estimation plays a fundamental role in identifying individuals. As deep learning technologies are emerging, identification schemes show significant progress and can handle many challenges of unconstrained imaging conditions. Research on age-gender estimation has begun applying deep convolutional neural networks (CNN) as a framework. However, due to large memory footprints and computational workloads, deep neural networks are hard to apply to on-device training and inference for embedded devices which have limited hardware resources. To solve this issue, network model pruning has been proposed as an efficient approach to reduce the model redundancy without significant degradation of the performance. In this paper, we modeled and characterized several pre-training models with architecture variations on baseline age-gender estimation before applying pruning schemes. For each of the models, three types of pruning comprised of weight, layer and filter pruning are applied and the pruning results are analyzed in terms of complexity and accuracy to find optimal pruning conditions. Combined schemes of pre-training models and network pruning techniques are discussed, and their results are compared with the original model's. Based on our experiments, the actual size of a fully trained prediction model can be reduced by as much as 90% with an accuracy loss of 2%~9%.
EDENet: Elaborate density estimation network for crowd counting

For the CNN-based density estimation approaches in the field of crowd counting, how to generate a high-quality density map with accurate counting performance and detailed spatial description is still an open question. In this paper, to tackle the aforementioned contradiction, we propose an end-to-end trainable architecture called Elaborate Density Estimation Network for Crowd Counting (EDENet), which can gradually generate high-quality density estimation maps based on distributed supervision. Specifically, EDENet is composed of Feature Extraction Network (FEN), Feature Fusion Network (FFN), Double-Head Network (DHN) and Adaptive Density Fusion Network (ADFN). The FEN adopts VGG as the backbone network and employs Spatial Adaptive Pooling (SAP) to extract coarse-grained features. The FFN can effectively fuse contextual information and localization information for enhancing the spatial description ability of fine-grained features. In the DHN, the Density Attention Module (DAM) can provide attention masks of foreground-background, thereby urging the Density Regression Module (DRM) to focus on the pixels around the head annotations to regress density maps with different resolutions. The ADFN constructed on the basis of the adaptive weighting mechanism can directly introduce coarse-grained density representation into high-resolution density maps to strengthen the commonality and dependency among density maps. Extensive experiments on four benchmark crowd datasets (the ShanghaiTech, the UCF-QNRF, the JHU-CRWORD++ and the NWPU-Crowd) indicate that EDENet can achieve state-of-the-art recognition performance and high robustness. Not only that, the density map with the highest Peak Signal to Noise Ratio (PSNR) can be considered to be of high quality.