Reviewerly/exHarmony huggingface dataset:
Education 4.0 to Industry 4.0 Vision: Current Trends and Overview

As relevant to Education 4.0 as it depicts changes, Industry 4.0 is a response to the global call for an action as called by the World Economic Forum. In the lieu of Education 4.0, industries can expect skilled workforce, more employability and better composition of future with a great return. However, the emergence of Industry 4.0 in the current era has simultaneous effect on many other economic sectors, among them one is the education sector and though a new term has emerged which is known as Education 4.0. This research paper aims to highlight a perspective of the recent trend in the revolution of industries along with the education system, concentrated on the higher education system, in Indian context. This paper deals with the structured form of literature to investigate the ongoing issues regarding the given topic that also showcase the overview of Indian government and their laws, amendments and policies to enhance the scope of Industry 4.0 and Education 4.0.
Server-Aided Revocable Attribute-Based Encryption Revised: Multi-User Setting and Fully Secure

Attribute-based encryption (ABE) is a promising cryptographic primitive achieving fine-grained access control on encrypted data. However, efficient user revocation is always essential to keep the system dynamic and protect data privacy. Cui et al. (ESORICS 2016) proposed the first server-aided revocable attribute-based encryption (SR-ABE) scheme, in which an untrusted server manages all the long-term transform keys and update keys generated by key generation center (KGC) in order to achieve efficient user revocation. So, there's no need for any user to communicate with KGC to update his/her decryption key regularly. In addition, the most part of computational overhead of decryption is outsourced to the server and user keeps a small size of private key to decrypt the final ciphertext. Then, Qin et al.'s (CANS 2017) extended Cui et al.s' work to be decryption key exposure resistant (DKER).
Camera motion-induced systematic errors in stereo-DIC and speckle-based compensation method

Stereo-digital image correlation (stereo-DIC) is now a standard technique for determination of the mechanical properties of materials and structures. In stereo-DIC, cameras are assumed to be motionless after camera calibration, so the three-dimensional (3D) reconstruction can be implemented using pre-calibrated parameters. However, this assumption is not true in some situations, such as drop test, seismic shaking table test and non-laboratory environment. Due to the presence of ground shaking or wind blowing, it's almost impossible to avoid camera motion in these experiments even if mechanical fixing is adopted. Camera motion during the experiment can undoubtedly introduce significant errors on measured results. Generally, camera motion can be divided into pair motion and relative motion. The influence of these two kinds of motion on stereo-DIC measurement is different and worth of separate study. Keep this in mind, in this paper, the influence of camera motion on stereo-DIC is investigated and the effects of pair motion and relative motion are examined separately using precisely controllable simulated experiments. Specifically, by integrating into the stereo matching, the speckle-based compensation method for relative motion is proposed and the accuracy of the compensation methods is analyzed in detail. The reduction of camera motion-induced systematic errors will be helpful for the further applications of stereo-DIC in non-laboratory environments and engineering fields.
Toward the Ontological Modeling of Smart Contracts: A Solidity Use Case

Blockchain has become a pervasive technology in a wide number of sectors like industry, research, and academy. With the emergence of blockchain, new solutions with this technology to existing problems were devised, leading to the introduction of smart contracts. Smart contracts are similar to traditional contracts with the benefits provided by blockchain, such as immutability, privacy, and decentralisation. These contracts are usually defined based on a specific domain, and this domain knowledge can be represented through an ontology. Researches have explored the benefits of using domain ontologies with smart contracts, such as code generation, discovering other contracts in the network, or interaction with other contracts. Notwithstanding, the representation of smart contract languages themselves has not been studied. In this paper, we present an ontology for a well-known smart contract language, Solidity, defining all entities needed to cover the whole language and aligning it to other standardised ontologies such as EthOn, in a way to improve the knowledge of the ontology developed. Furthermore, the ontology has also been validated with already deployed contracts in the Ethereum blockchain. Thus, Solidity will be able to benefit from the advantages provided by ontologies, such as interoperability and the use of semantic web technologies.
Pupil-Contour-Based Gaze Estimation With Real Pupil Axes for Head-Mounted Eye Tracking

Accurate gaze estimation that frees from glints and the slippage problem is challenging. Pupil-contour-based gaze estimation methods can meet this challenge, except that the gaze accuracy is low due to neglecting the pupil’s corneal refraction This article proposes a refraction-aware gaze estimation approach using the real pupil axis, which is calculated from the virtual pupil image based on the derived function between the real pupil and the refracted virtual pupil. We present a 2-D gaze estimation method that regresses the real pupil normal’s spherical coordinates to the gaze point. The noise and outliers of calibration data are removed by aggregation filtering and random sample consensus, respectively. Moreover, we propose a 3-D gaze estimation method that transforms the real pupil axis to the gaze direction. Experimental results show that the proposed gaze estimation approach has comparable accuracy to state-of-the-art pupil-center-based gaze estimation methods, which suffer from the slippage problem.
Quantum Scheduling for Millimeter-Wave Observation Satellite Constellation

In beyond 5G and 6G network scenarios, the use of satellites has been actively discussed for extending target monitoring areas, even for extreme circumstances, where the monitoring functionalities can be realized due to the usage of millimeter-wave wireless links. This paper designs an efficient scheduling algorithm which minimizes overlapping monitoring areas among observation satellite constellation. In order to achieve this objective, a quantum optimization based algorithm is used because the overlapping can be mathematically modelled via a max-weight independent set (MWIS) problem which is one of well-known NP-hard problems.
Sync-Switch: Hybrid Parameter Synchronization for Distributed Deep Learning

Stochastic Gradient Descent (SGD) has become the de facto way to train deep neural networks in distributed clusters. A critical factor in determining the training throughput and model accuracy is the choice of the parameter synchronization protocol. For example, while Bulk Synchronous Parallel (BSP) often achieves better converged accuracy, the corresponding training throughput can be negatively impacted by stragglers. In contrast, Asynchronous Parallel (ASP) can have higher throughput, but its convergence and accuracy can be impacted by stale gradients. To improve the performance of synchronization protocol, recent work often focuses on designing new protocols with a heavy reliance on hard-to-tune hyper-parameters. In this paper, we design a hybrid synchronization approach that exploits the benefits of both BSP and ASP, i.e., reducing training time while simultaneously maintaining the converged accuracy. Based on extensive empirical profiling, we devise a collection of adaptive policies that determine how and when to switch between synchronization protocols. Our policies include both offline ones that target recurring jobs and online ones for handling transient stragglers. We implement the proposed policies in a prototype system, called Sync-Switch, on top of TensorFlow, and evaluate the training performance with popular deep learning models and datasets. Our experiments show that Sync-Switch can achieve ASP level training speedup while maintaining similar converged accuracy when comparing to BSP. Moreover, Sync-Switch's elastic-based policy can adequately mitigate the impact from transient stragglers.
A fuzzy approach for sarcasm detection in social networks

The great success of social networks is due to their ability to offer Internet users a space of free expression where they can produce a large amount of information which provides every day with a new challenge for data analysts. The ease of use of social media encourages users to increasingly express their opinions either by using simple words expressing feelings or by using irony and sarcasm. The new challenges are to extract and analyze this mass of information which can then be used in different applications such as sentiment analysis and sarcasm detection. Sarcasm detection is a subarea of sentiment analysis, opinion mining, and emotion mining which are all representing the process of automatic identification of people’s orientation or sentiment toward individuals, products, services, issues, and events. Sarcasm detection, which is the fact of deciding if a text is ironic or not, could be used, for example, to improve the precision of the sentiment analysis. In most of the existing approaches, sarcasm detection is a binary classification; each text is classified as sarcastic or non-sarcastic; however, since tweets are generally written by humans and humans are by default fuzzy in their emotions and expressions, we can’t 100% confirm that a text is sarcastic or not. In addition, tweets are expressed in natural language which is full of ambiguity and non-precision, which motivates us more to adopt fuzzy logic, not just to detect sarcasm but to give it a score. In this manuscript, we propose a fuzzy sarcasm detection approach using social information such as replies, historical tweets and likes, etc multiplying each by a degree of importance. The evaluation shows that the use of fuzzy logic has led us to improve the precision metric of the classification and to improve the accuracy of our approach. Using degrees of importance gave us the best values for recall, precision, and accuracy measures compared to existing approaches.
Variational Topic Inference for Chest X-Ray Report Generation

Automating report generation for medical imaging promises to reduce workload and assist diagnosis in clinical practice. Recent work has shown that deep learning models can successfully caption natural images. However, learning from medical data is challenging due to the diversity and uncertainty inherent in the reports written by different radiologists with discrepant expertise and experience. To tackle these challenges, we propose variational topic inference for automatic report generation. Specifically, we introduce a set of topics as latent variables to guide sentence generation by aligning image and language modalities in a latent space. The topics are inferred in a conditional variational inference framework, with each topic governing the generation of a sentence in the report. Further, we adopt a visual attention module that enables the model to attend to different locations in the image and generate more informative descriptions. We conduct extensive experiments on two benchmarks, namely Indiana U. Chest X-rays and MIMIC-CXR. The results demonstrate that our proposed variational topic inference method can generate novel reports rather than mere copies of reports used in training, while still achieving comparable performance to state-of-the-art methods in terms of standard language generation criteria.
The Role of Blockchain in Improving the Processes and Workflows in Construction Projects

Construction is prone to disputes similar to other business dealings. Construction delays are the most common causes of disputes in construction projects. Most of the time, construction projects are delayed not only because of a lack of funding, but also because of disputes between the contractor and the client. Blockchain Technologies (BCT) have the potential to make the construction industry more efficient, inexpensive, or completely automated. In this paper, an extensive literature review is conducted to locate authoritative sources about the most common applications of blockchain technology in the construction industry. The study aims to identify the potential, benefits, challenges, and uses of blockchain in the construction industry. Furthermore, this paper explores the roles of BCTs in improving and enhancing the workflow efficiency of construction projects. In addition, this paper identifies the major causes of disputes and delays in the construction industry. These causes were used as the baseline for identifying the most relevant solutions provided by blockchain technologies to develop a matching model to find applicable solutions from the literature cases to resolve the identified causes of disputes and delays. The research findings indicated that the common cases of BCT including administrative purposes, smart contracts for transactions, permanent transaction records, and permanent ownership records contribute to solving the causes of disputes and delays in construction projects. The findings of this paper recommend that both public and private stakeholders in the construction sector adopt blockchain technologies and blockchain-based Building Information Models (BIMs) to improve construction workflows and processes.
Measuring Asset Composability as a Proxy for DeFi Integration

Decentralized financial (DeFi) applications on the Ethereum blockchain are highly interoperable because they share a single state in a deterministic computational environment. Stakeholders can deposit claims on assets, referred to as ‘liquidity shares’, across applications producing effects equivalent to rehypothecation in traditional financial systems. We seek to understand the degree to which this practice may contribute to financial integration on Ethereum by examining transactions in ‘composed’ derivatives for the assets DAI, USDC, USDT, ETH and tokenized BTC for the full set of 344.8 million Ethereum transactions computed in 2020. We identify a salient trend for ‘composing’ assets in multiple sequential generations of derivatives and comment on potential systemic implications for the Ethereum network.
Improving graph neural network via complex-network-based anchor structure

The technique of graph/network embedding in artificial intelligence, which embeds graph data into a low-dimensional vector space in the form of machine learning, can help computer to efficiently process and analyze the complex graph data by using the vector operations. Graph Neural Network (GNN) and neural network, an end-to-end graph embedding technique based on Graph Signal Processing (GSP), which aggregates the topological information of the neighborhoods of each node in a graph, has attracted wide attention. However, most of the existing GNN models are limited to local structure information, and the location differences of nodes in the global topology are not sufficiently considered. This leads to that many nodes with the similar local topology are very difficult to distinguish. To address this problem, we propose an anchor-structure-aware GNN (AS-GNN) model to implement more accurate node distinguishment by capturing the global topology information based on the characteristics of complex networks. Anchor structure is defined as a key sub-graph composed of key nodes and edges in a graph. Taking it as a location reference, we can get the location information of each node in the global topology of graph and carry it into the embedding of nodes. By this way, the node vectors including richer topology information of graph are produced by GNN, and thus the nodes with similar local topologies can be distinguished well. To evaluate the performance of AS-GNN, we compare AS-GNN with some existing baseline models by the experiments of the classic GNN application tasks of link prediction and pairwise node classification on five real-world datasets. The experimental results have confirmed the above claims.
Divide and Conquer for Single-frame Temporal Action Localization

Single-frame temporal action localization (STAL) aims to localize actions in untrimmed videos with only one timestamp annotation for each action instance. Existing methods adopt the one-stage framework but couple the counting goal and the localization goal. This paper proposes a novel two-stage framework for the STAL task with the spirit of divide and conquer. The instance counting stage leverages the location supervision to determine the number of action instances and divide a whole video into multiple video clips, so that each video clip contains only one complete action instance; and the location estimation stage leverages the category supervision to localize the action instance in each video clip. To efficiently represent the action instance in each video clip, we introduce the proposal-based representation, and design a novel differentiable mask generator to enable the end-to-end training supervised by category labels. On THUMOS14, GTEA, and BEOID datasets, our method outperforms state-of-the-art methods by 3.5%, 2.7%, 4.8% mAP on average. And extensive experiments verify the effectiveness of our method.
Propagating Visual Designs to Numerous Plots and Dashboards

In the process of developing an infrastructure for providing visualization and visual analytics (VIS) tools to epidemiologists and modeling scientists, we encountered a technical challenge for applying a number of visual designs to numerous datasets rapidly and reliably with limited development resources. In this paper, we present a technical solution to address this challenge. Operationally, we separate the tasks of data management, visual designs, and plots and dashboard deployment in order to streamline the development workflow. Technically, we utilize: an ontology to bring datasets, visual designs, and deployable plots and dashboards under the same management framework; multi-criteria search and ranking algorithms for discovering potential datasets that match a visual design; and a purposely-design user interface for propagating each visual design to appropriate datasets (often in tens and hundreds) and quality-assuring the propagation before the deployment. This technical solution has been used in the development of the RAMPVIS infrastructure for supporting a consortium of epidemiologists and modeling scientists through visualization.
Deep Shape-Aware Person Re-Identification for Overcoming Moderate Clothing Changes

Although person re-identification (person re-id) has advanced substantially in recent years, most methods are based on the assumption that the identities would not change clothes. This assumption might not hold in practice considering criminals who intentionally change clothes. In this work, we attempt to solve person re-id under moderate clothing change. Since the human body shape is considered as relatively more invariant under moderate clothing changes, we propose to learn a reliable shape-aware feature representation by mutually learning both colorful images and contour images. Instead of directly extracting shape features from contour images, we utilize contour feature learning as regularization and excavate more effective shape-aware feature representations from colorful images. We propose a multi-scale appearance and contour deep infomax (MAC-DIM) to maximize mutual information between colorful appearance features and contour shape features, and in this way, the extracted appearance features are constrained to be shape-aware in terms of both low-level visual properties and high-level semantics. To better model the long-range human body shape and explicitly capture contour segment relations, we introduce hierarchical graph modeling as aggregation headers, propagating structural context through graph convolutional networks (GCNs). The extensive results on benchmarks under clothing changes demonstrate the effectiveness of our shape-aware feature learning scheme.
Improving De-raining Generalization via Neural Reorganization

Most existing image de-raining networks could only learn fixed mapping rules between paired rainy/clean images on single synthetic dataset and then stay static for lifetime. However, since single synthetic dataset merely provides a partial view for the distribution of rain streaks, deep models well trained on an individual synthetic dataset tend to overfit on this biased distribution. This leads to the inability of these methods to well generalize to complex and changeable real-world rainy scenes, thus limiting their practical applications. In this paper, we try for the first time to accumulate the de-raining knowledge from multiple synthetic datasets on a single network parameter set to improve the de-raining generalization of deep networks. To achieve this goal, we explore Neural Reorganization (NR) to allow the de-raining network to keep a subtle stability-plasticity trade-off rather than naive stabilization after training phase. Specifically, we design our NR algorithm by borrowing the synaptic consolidation mechanism in the biological brain and knowledge distillation. Equipped with our NR algorithm, the deep model can be trained on a list of synthetic rainy datasets by overcoming catastrophic forgetting, making it a general-version de-raining network. Extensive experimental validation shows that due to the successful accumulation of de-raining knowledge, our proposed method can not only process multiple synthetic datasets consistently, but also achieve state-of-the-art results when dealing with real-world rainy images.
Text Mining and Sentiment Analysis of Newspaper Headlines

Text analytics are well-known in the modern era for extracting information and patterns from text. However, no study has attempted to illustrate the pattern and priorities of newspaper headlines in Bangladesh using a combination of text analytics techniques. The purpose of this paper is to examine the pattern of words that appeared on the front page of a well-known daily English newspaper in Bangladesh, The Daily Star, in 2018 and 2019. The elucidation of that era’s possible social and political context was also attempted using word patterns. The study employs three widely used and contemporary text mining techniques: word clouds, sentiment analysis, and cluster analysis. The word cloud reveals that election, kill, cricket, and Rohingya-related terms appeared more than 60 times in 2018, whereas BNP, poll, kill, AL, and Khaleda appeared more than 80 times in 2019. These indicated the country’s passion for cricket, political turmoil, and Rohingya-related issues. Furthermore, sentiment analysis reveals that words of fear and negative emotions appeared more than 600 times, whereas anger, anticipation, sadness, trust, and positive-type emotions came up more than 400 times in both years. Finally, the clustering method demonstrates that election, politics, deaths, digital security act, Rohingya, and cricket-related words exhibit similarity and belong to a similar group in 2019, whereas rape, deaths, road, and fire-related words clustered in 2018 alongside a similar-appearing group. In general, this analysis demonstrates how vividly the text mining approach depicts Bangladesh’s social, political, and law-and-order situation, particularly during election season and the country’s cricket craze, and also validates the significance of the text mining approach to understanding the overall view of a country during a particular time in an efficient manner.