Reviewerly/exHarmony huggingface dataset:
360-Degree Video Bandwidth Reduction: Technique and Approaches Comprehensive Review

Recently, the usage of 360-degree videos has prevailed in various sectors such as education, real estate, medical, entertainment and more. The development of the Virtual World “Metaverse” demanded a Virtual Reality (VR) environment with high immersion and a smooth user experience. However, various challenges are faced to provide real-time streaming due to the nature of high-resolution 360-degree videos such as high bandwidth requirement, high computing power and low delay tolerance. To overcome these challenges, streaming methods such as Dynamic Adaptive Streaming over HTTP (DASH), Tiling, Viewport-Adaptive and Machine Learning (ML) are discussed. Moreover, the superiorities of the development of 5G and 6G networks, Mobile Edge Computing (MEC) and Caching and the Information-Centric Network (ICN) approaches to optimize the 360-degree video streaming are elaborated. All of these methods strike to improve the Quality of Experience (QoE) and Quality of Service (QoS) of VR services. Next, the challenges faced in QoE modeling and the existing objective and subjective QoE assessment methods of 360-degree video are presented. Lastly, potential future research that utilizes and further improves the existing methods substantially is discussed. With the efforts of various research studies and industries and the gradual development of the network in recent years, a deep fake virtual world, “Metaverse” with high immersion and conducive for daily life working, learning and socializing are around the corner.
Exploring Public Attitude Towards Children by Leveraging Emoji to Track Out Sentiment Using Distil-BERT a Fine-Tuned Model

Sentiment analysis is a computational method that extracts emotional keywords from different texts through initial emotion analysis (e.g., Happy, Sad, Positive, Negative & Neutral). A recent study by a human rights organization found that 30% of children in Bangladesh are being abused on online in the COVID-19 epidemic by various obscene comments. The main goal of our research is to collect textual data from social media and classify the way children are harassed by various abusive comments online through the use of emoji in a text-mining method and to expose to society the risks that children face online. Another goal of this study is to set a precedent through a detailed study of child abuse and neglect in the big data age. To make the work effective, 3373 child abusive comments are collected manually from online (e.g. Facebook, Newspapers and various Blogs). At present, there is still a very limited number of Bengali child sentiment analysis studies. Fine-tuned general purpose language representation models, such as the BERT family model (BERT, Distil-BERT), and glove word embedding based CNN and Fast-Text models have been used to successfully complete the study. We show that Distil-BERT defeated BERT, Fast-Text, and CNN by 96.09% (relative) accuracy, while Bert, Fast-Text and CNN have 93.66%, 95.73%, and 95.05%, respectively. But observations show that the accuracy of the Distil-BERT does not differ much from the rest of the models. From our analysis, it can be said that the pre-trained models performed outstanding and in addition, child sentiment analysis can serve as a potential motivator for the government to formulate child protection policies and build child welfare systems.
Restricted Boltzmann Machine with Multivalued Hidden Variables

Generalization is one of the most important issues in machine learning problems. In this study, we consider generalization in restricted Boltzmann machines (RBMs). We propose an RBM with multivalued hidden variables, which is a simple extension of conventional RBMs. We demonstrate that the proposed model is better than the conventional model via numerical experiments for contrastive divergence learning with artificial data and a classification problem with MNIST.
Multiple Parallel Federated Learning via Over-the-Air Computation

This paper investigates multiple parallel federated learning in cellular networks, where a base station schedules several FL tasks in parallel and each task has a group of devices involved. To reduce the communication overhead, over-the-air computation is introduced by utilizing the superposition property of multiple access channels (MAC) to accomplish the aggregation step. Since all devices use the same radio resource to transfer their local updates to the BS, in order to separate the received signals of different tasks, we use the zero-forcing receiver combiner to mitigate the mutual interference across different groups. Besides, we analyze the impact of receiver combiner and device selection on the convergence of our multiple parallel FL framework. Also, we formulate an optimization problem that jointly considers receiver combiner vector design and device selection for improving FL performance. We address the problem by decoupling it into two sub-problems and solve them alternatively, adopting successive convex approximation (SCA) to derive the receiver combiner vector, and then solve the device scheduling problem with a greedy algorithm. Simulation results demonstrate that the proposed framework can effectively solve the straggler issue in FL and achieve a near-optimal performance on all tasks.
Machine Vision-Based Human Action Recognition Using Spatio-Temporal Motion Features (STMF) with Difference Intensity Distance Group Pattern (DIDGP)

In recent years, human action recognition is modeled as a spatial-temporal video volume. Such aspects have recently expanded greatly due to their explosively evolving real-world uses, such as visual surveillance, autonomous driving, and entertainment. Specifically, the spatio-temporal interest points (STIPs) approach has been widely and efficiently used in action representation for recognition. In this work, a novel approach based on the STIPs is proposed for action descriptors i.e., Two Dimensional-Difference Intensity Distance Group Pattern (2D-DIDGP) and Three Dimensional-Difference Intensity Distance Group Pattern (3D-DIDGP) for representing and recognizing the human actions in video sequences. Initially, this approach captures the local motion in a video that is invariant to size and shape changes. This approach extends further to build unique and discriminative feature description methods to enhance the action recognition rate. The transformation methods, such as DCT (Discrete cosine transform), DWT (Discrete wavelet transforms), and hybrid DWT+DCT, are utilized. The proposed approach is validated on the UT-Interaction dataset that has been extensively studied by past researchers. Then, the classification methods, such as Support Vector Machines (SVM) and Random Forest (RF) classifiers, are exploited. From the observed results, it is perceived that the proposed descriptors especially the DIDGP based descriptor yield promising results on action recognition. Notably, the 3D-DIDGP outperforms the state-of-the-art algorithm predominantly.
Information and communication technology adoption and unemployment in West Africa Monetary Zone

This study examined the impact of information and communication technology (ICT) on unemployment among the countries in West Africa Monetary Zones (WAMZ) for the period 2005–2020. The study engaged the data sourced from World Bank Development Indicators (WDI), International labour organisation (ILO) and International Telecommunication Union (ITU). The study estimated the fixed and random effect models and pooled mean group estimators. The results indicate a negative and significant effect of ICT adoption on unemployment among countries of WAMZ. On the other hand, the magnitude of the effects differs, depending on the indicator of ICT being examined.
Combining attention with spectrum to handle missing values on time series data without imputation

In the development of predictive models, the problem of missing data is a critical issue that traditionally requires a two-step analysis. Data scientists analyze the patterns of missing values, select variables, impute missing values on the basis of domain knowledge, and then train a model. Models typically have their input sizes hardcoded, and have limitations in handling data with high missing rates or changes in available variables. We propose an attention-based neural network combined with a novel real number representation, which requires little work on manually selecting variables, and in which missing data can be overlooked, making imputation unnecessary. In this proposed model, data analysis can be one step, omitting the first step of imputing missing values. The study included data on 32,709 intensive care unit (ICU) admissions and 60 healthcare variables from the Medical Information Mart for Intensive Care (MIMIC)-IV. The proposed algorithm yielded an area under the receiver operating characteristic curve (AUC) of 0.842 (95% CIs: 0.828–0.856) when predicting prolonged length of stay in the ICU, outperforming current approaches using imputation methods. The proposed algorithm can be applied to a range of problems in data science, as it addresses the issue of incomplete data with automatic variable selection.
Web 3.0 and Decentralized Applications

The web we use today has seen many iterations over the years since the original concept of the World Wide Web was introduced in early 1990s.The first emergence of the web was called the static web or Web 1.0, which was read-only. A further iteration of the web then came along and was called the Social Web or Web 2.0, which was interactive in nature and the users could do more than read static pages. This was readable and writable, and saw the emergence of numerous social platforms. Web 3.0 offers an unmediated read&ndash;write web, or, to put it another way, a decentralized Internet. This paper provides a brief idea of how the journey of the web has so far transitioned from Web 1.0 through Web 2.0 and now onto Web 3.0, and what all lies ahead in future with emerging technologies and Web 3.0.
Institutional quality, macroeconomic uncertainty and efficiency of financial institutions in Sub-Saharan Africa

Purpose This paper evaluates how institutions of governance and macroeconomic uncertainty influence efficiency of financial institutions in the subregion of Sub-Saharan Africa (SSA). Data for the empirical inquiry were compiled from relevant sources for 33 countries in the subregion from 2002 to 2019. Empirical estimates verifying hypothesized relationships were carried out using the continuous updating estimator (CUE) by Hansen et al. (1996). Design/methodology/approach The purpose of this paper is to evaluates how institutions of governance and macroeconomic uncertainty influence efficiency of financial institutions in the subregion of Sub-Saharan Africa (SSA). Data for the empirical inquiry were compiled from relevant sources for 33 countries in the subregion from 2002 to 2019. Empirical estimates verifying hypothesized relationships were carried out using the continuous updating estimator (CUE) by Hansen et al. (1996). Findings The results suggest that institutional quality has significant positive effect on financial institution efficiency, supporting the view that improved and supportive structures of governance tend to promote operational efficiency among financial institutions among economies in SSA. In addition, improvement in individual governance indicators such as corruption control, government effectiveness, regulatory quality and rule of law was also found to support or enhance efficiency of financial institutions among economies in the subregion. Macroeconomic uncertainty on the other hand is found to impede efficiency of financial institutions; the same condition (macroeconomic uncertainty) is further found to negate any positive impact corruption control, government effectiveness, regulatory quality and rule of law have on operational efficiency among financial institutions in the subregion. Originality/value Unlike most of related studies, this study adopts a different approach on the dynamics of financial institutions. Approach pursued in this empirical inquiry examines how the regulatory environment within which financial institutions operate, the form of governance and the quality of government institutions influence efficiency of financial institutions among emerging economies in Sub-Sahara. Empirical analysis conducted examines effects of variables that are unique to this study; these variables are either constructed or econometrically derived specifically for various interactions verified in the study. For instance, institutional quality variable is an index constructed specifically for this study using principal component analysis approach.
It is not (only) about privacy: How multi-party computation redefines control, trust, and risk in data sharing

Abstract Firms are often reluctant to share data because of mistrust, concerns over control, and other risks. Multi-party computation (MPC) is a new technique to compute meaningful insights without having to transfer data. This paper investigates if MPC affects known antecedents for data sharing decisions: control, trust, and risks. Through 23 qualitative interviews in the automotive industry, we find that MPC (1) enables new ways of technology-based control, (2) reduces the need for inter-organizational trust, and (3) prevents losing competitive advantage due to data leakage. However, MPC also creates the need to trust technology and introduces new risks of data misuse. These impacts arise if firms perceive benefits from sharing data, have high organizational readiness, and perceive data as non-sensitive. Our findings show that known antecedents of data sharing should be specified differently with MPC in place. Furthermore, we suggest reframing MPC as a data collaboration technology beyond enhancing privacy.
Photon blockade and single-photon generation with multiple quantum emitters

Generation and manipulation of single photons are at the heart of photon-based quantum information processing. An ensemble of quantum emitters (QEs) as a versatile light-matter interface are very promising for quantum technology such as quantum storage and repeater. However, it is commonly believed that many QEs are unable to create photon blockade and generate single photons with high purity due to the multiexcitation effect. Here, we show efficient photon blockade and single-photon generation with a few to ten QEs by suppressing multiexcitation with a detuned auxiliary cavity. Because the auxiliary cavity induces a strong dipole-dipole interaction among the QEs, these QEs can be treated as a two-level superatom. By coupling this superatom to another collecting cavity, efficient photon blockade with a high transmission is obtained. With the same setup, single-photon emission can also be achieved with yield and purity larger than $90%$ simultaneously even in the bad-cavity limit. Our method may open up a simple route to generate and manipulate single photons on demand.
Explaining One-Dimensional Convolutional Models in Human Activity Recognition and Biometric Identification Tasks

Due to wearables' popularity, human activity recognition (HAR) plays a significant role in people's routines. Many deep learning (DL) approaches have studied HAR to classify human activities. Previous studies employ two HAR validation approaches: subject-dependent (SD) and subject-independent (SI). Using accelerometer data, this paper shows how to generate visual explanations about the trained models' decision making on both HAR and biometric user identification (BUI) tasks and the correlation between them. We adapted gradient-weighted class activation mapping (grad-CAM) to one-dimensional convolutional neural networks (CNN) architectures to produce visual explanations of HAR and BUI models. Our proposed networks achieved 0.978 and 0.755 accuracy, employing both SD and SI. The proposed BUI network achieved 0.937 average accuracy. We demonstrate that HAR's high performance with SD comes not only from physical activity learning but also from learning an individual's signature, as in BUI models. Our experiments show that CNN focuses on larger signal sections in BUI, while HAR focuses on smaller signal segments. We also use the grad-CAM technique to identify database bias problems, such as signal discontinuities. Combining explainable techniques with deep learning can help models design, avoid results overestimation, find bias problems, and improve generalization capability.
Incidents1M: a Large-Scale Dataset of Images With Natural Disasters, Damage, and Incidents

Natural disasters, such as floods, tornadoes, or wildfires, are increasingly pervasive as the Earth undergoes global warming. It is difficult to predict when and where an incident will occur, so timely emergency response is critical to saving the lives of those endangered by destructive events. Fortunately, technology can play a role in these situations. Social media posts can be used as a low-latency data source to understand the progression and aftermath of a disaster, yet parsing this data is tedious without automated methods. Prior work has mostly focused on text-based filtering, yet image and video-based filtering remains largely unexplored. In this work, we present the Incidents1M Dataset, a large-scale multi-label dataset which contains 977,088 images, with 43 incident and 49 place categories. We provide details of the dataset construction, statistics and potential biases; introduce and train a model for incident detection; and perform image-filtering experiments on millions of images on Flickr and Twitter. We also present some applications on incident analysis to encourage and enable future work in computer vision for humanitarian aid. Code, data, and models are available at http://incidentsdataset.csail.mit.edu.
Visual Analytics of Contact Tracing Policy Simulations During an Emergency Response

Abstract Epidemiologists use individual‐based models to (a) simulate disease spread over dynamic contact networks and (b) to investigate strategies to control the outbreak. These model simulations generate complex ‘infection maps’ of time‐varying transmission trees and patterns of spread. Conventional statistical analysis of outputs offers only limited interpretation. This paper presents a novel visual analytics approach for the inspection of infection maps along with their associated metadata, developed collaboratively over 16 months in an evolving emergency response situation. We introduce the concept of representative trees that summarize the many components of a time‐varying infection map while preserving the epidemiological characteristics of each individual transmission tree. We also present interactive visualization techniques for the quick assessment of different control policies. Through a series of case studies and a qualitative evaluation by epidemiologists, we demonstrate how our visualizations can help improve the development of epidemiological models and help interpret complex transmission patterns.
A Typology of Guidance Tasks in Mixed‐Initiative Visual Analytics Environments

Abstract Guidance has been proposed as a conceptual framework to understand how mixed‐initiative visual analytics approaches can actively support users as they solve analytical tasks. While user tasks received a fair share of attention, it is still not completely clear how they could be supported with guidance and how such support could influence the progress of the task itself. Our observation is that there is a research gap in understanding the effect of guidance on the analytical discourse, in particular, for the knowledge generation in mixed‐initiative approaches. As a consequence, guidance in a visual analytics environment is usually indistinguishable from common visualization features, making user responses challenging to predict and measure. To address these issues, we take a system perspective to propose the notion of guidance tasks and we present it as a typology closely aligned to established user task typologies. We derived the proposed typology directly from a model of guidance in the knowledge generation process and illustrate its implications for guidance design. By discussing three case studies, we show how our typology can be applied to analyze existing guidance systems. We argue that without a clear consideration of the system perspective, the analysis of tasks in mixed‐initiative approaches is incomplete. Finally, by analyzing matchings of user and guidance tasks, we describe how guidance tasks could either help the user conclude the analysis or change its course.
Blockchain implementation for agricultural food supply chain using hyperledger fabric

Agricultural Food Supply chain management has arisen as an area of day-to-day importance for the agricultural food sector, since stakeholders involved in the execution of decision-making processes. Quality less agricultural products are added to the market in day-to-day life which leads to usage of chemicals in the production process. These leads to the major issues that gives the impact on agricultural product’s quality as well as overall well-being of the consumers. Devices are needed to identify the quality of the food products which are highly demanded due to the lack of transparency in the recent processes. Henceforth Blockchain technology is evolving as a decentralized and secure infrastructure which could replace involvement of a third party to verify the transactions inside the system. The purpose of the proposed work is to implement a Blockchain based solution i.e. constructing a Decentralized Application (DApp) using Hyperledger Fabric framework to verify the food quality and the cause of the agricultural supply chain. A private permissioned Blockchain concept is chosen instead of a public Blockchain in the proposed work to ensure transparency and secure transaction by consenting any person to access the network. Smart contract chain code was instantiated for the deployment of Blockchain network. All the performers who are involved in the supply chain must be able to interact with the system to achieve the transparency. Transaction and queries related to a food product are validated by peers of the Blockchain network. A Barcode &amp; QR code-based scanning mechanism is used to indicate the customer’s satisfaction with their products. Transactions without third party gives Farmer’s reputation for their products. A unique DApp mechanism is used to identify each product within the food supply chain. Thus, the proposed system has been implemented as a prototype and validated using smart contracts.
Multi-granularity Association Learning for On-the-fly Fine-grained Sketch-based Image Retrieval

Fine-grained sketch-based image retrieval (FG-SBIR) addresses the problem of retrieving a specific photo from a given query sketch. However, its widespread applicability is limited because it is difficult for most people to draw a complete sketch, and the drawing process is often time consuming. In this study, we aim to retrieve the target photo from an partial sketch with the least number of strokes possible; the method is referred to as on-the-fly FG-SBIR (Bhunia et al., 2020), in which the retrieval begins after each stroke of the drawing. We consider that a significant correlation exists between these incomplete sketches in the sketch-drawing episodes of each photo. We propose a multi-granularity-association-learning method that further optimizes the embedding space of all incomplete sketches to learn an efficient joint-embedding space. Specifically, based on the integrity of the sketch, a complete sketch episode can be divided into several stages, each of which corresponds to a simple linear-mapping layer. Furthermore, our framework guides the vector space representation of the current sketch to approximate that with its later sketches. In this manner, the retrieval performance of a sketch with fewer strokes can approach that of a sketch with more strokes. We conducted experiments that included more realistic challenges, and our method achieved superior early-retrieval efficiency over the state-of-the-art methods and alternative baselines on two publicly available fine-grained sketch-retrieval datasets.