Reviewerly/exHarmony huggingface dataset:
A Travelling Recommendation System based on Knowledge Graph

In recent years, the improvement of people&#x2019;s live standard lead to an increasing demand for travelling, but the information on scenic spots on the Internet is ponderous and the accuracy of scenic spot recommendations is not high. This study aims to solve these problems to realize a more accurate scenic spot recommendation for self-drivings and provide a better visualization of recommendation results. A recommendation model based on the Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation (MKR) algorithm is designed in this study. Then, based on this model, an intelligent knowledge graph based recommendation system for scenic spots is constructed. Rich unstructured text data of scenic spots in major scenic spot websites are crawled using Selenium and are stored and managed by the neo4j graph database. Through experimental simulation, the recommendation accuracy can reach over 84%. Compared to the Propagating User Preferences on the Knowledge Graph for Recommender Systems (RippleNet), the accuracy rate is enhanced by 6.2%.
Enhanced Dynamic Sign Language Recognition using SlowFast Networks

In this paper, we use the SlowFast Networks developed by the Facebook research team to enhance the accuracy of dynamic sign language recognition. Firstly, we prepared the Word-Level American Sign Language (WLASL) dataset so each sign can be considered an action. We used the pre-trained SLOWFAST_8×8_R50 model provided on the official PySlowFast Github repository to initialize the weights of our model and fine-tune using the WLASL dataset and performed a param-eter sweeping to fit the Dynamic Sign Language Recognition task. Through this transfer learning approach, we managed to introduce a new state-of-the-art accuracy on the WLASL300 (300 words e.g., 300 classes) dataset with an improvement of 23.2 % top-1 accuracy compared to the previous state-of-the-art introduced in the WLASL paper using an I3D model. The top-1 accuracy was improved from 56.14% to 79.34% and the top-5 accuracy from 79.94% to 90.31 %.
A modified FCN-based method for Left Ventricle endocardium and epicardium segmentation with new block modules

Cardiac segmentation of medical magnetic resonance images has been crucial nowadays owing to its necessity for cardiac problems diagnosis. In the increasing demand of advanced procedures for cardiac disease diagnosis and inspired by the structure of receptive field block; in this paper, we propose a new block module then further assembling into a deep fully convolutional neural network to deal with automated left ventricle segmentation. With only one learning stage, our proposed model is trained end-to-end, pixels-to-pixels and validated on two popular cardiac MRI benchmarks, ACDC and SunnyBrook datasets. Several experiments have proved that our new model architecture has a better performance than previous segmentation methods with enhanced feature discriminability and robustness, despite having much less training parameters.
Modeling of solar photovoltaic power using a two-stage forecasting system with operation and weather parameters

The integration of solar photovoltaic (SPV) system to the grid has introduced a new source of intermittency in the grid, and the grid has to react smartly to the changes that occur in the penetration of SPV power. Accurate modeling of weather-dependent SPV power will be helpful in forecasting the penetration of SPV power into the grid. An SPV power output forecasting model has been developed based on artificial neural network (ANN) approach. Two forecasters, namely ANN forecaster and two-stage hybrid-ANN forecaster, are developed with operational and weather parameters. The historical data of SPV power (P), hours of operation of SPV system (to), daily global solar radiation (H), and ambient temperature(T) are used as modeling parameters. The combination of modeling parameters {P, H, T, to} is identified as the best combination that influences the forecasting of day-ahead power output. A relative root mean square error (RRMSE) of 5.74% was obtained with the combination of {P, H, T, to}. An RRMSE of 6.04% was observed with the combination of {P, H, T} as inputs, and the hours of operation of the SPV plant could be ignored in the model. The historical power data of the SPV plant is identified as the crucial parameter in the SPV power forecast model and has given an RRMSE of 7.25%. The models developed with temperature and radiation as modeling parameters have resulted in good forecasting accuracy, which could be best suitable for feasibility studies of SPV plant at a particular location. Solar radiation prediction models are used in the development of hybrid-ANN forecaster. It has produced an RRMSE of 7.35% with four inputs. The hybrid-ANN forecaster with predicted radiations as modeling input will eliminate the need of a costly pyranometer. The models developed in the present study have utilized readily available parameters as modeling parameters, thereby cost of the forecasting system has been decreased. The developed models will be useful for energy scheduling and energy management in the smart grid.Abbreviations: GSR, Global Solar Radiation; ANN, Artificial Neural networks; SPV, Solar Photovoltaics; MAPE, Mean Absolute Percentage Error; RRMSE, Relative Root Mean Square Error; MSE, Mean Square Error.
Durable Subgraph Matching on Temporal Graphs

Durable subgraph matching on a temporal graph finds all subgraphs in the temporal graph that not only match the given query graph but also have duration longer than a user-specified duration threshold. The state-of-the-art algorithm (Semertzidis and Pitoura, 2016, 2019) for solving this problem requires lots of memory when the input temporal graph is large. In this paper, a new algorithm is proposed to solve this problem. Many effective techniques are developed to improve the performance of this algorithm, including the DFS-based query decomposition method, the TD-tree index structure, the sort-based vertex matching order, and the time instance set compaction method. The experimental results show that the proposed algorithm is an order of magnitude faster and requires significantly less memory than the state-of-the-art algorithm.
Certificate-Based Signature Scheme for Industrial Internet of Things Using Hyperelliptic Curve Cryptography

The Industrial Internet of Things (IIoT) is a technology that uses the Internet of Things (IoT) infrastructure to sense, process, and communicate real-time events in the industrial system to cut down on unnecessary operating costs and to speed up industrial automation of internal and external working processes. Since the IIoT system inherits the same cyber-physical vulnerabilities that the IoT system already encounters, it requires additional work to address security concerns owing to its heterogeneous nature. As a result, an efficient security mechanism is essential to protect against various and unknown cyber-attacks. In this article, we propose a certificate-based signature scheme based on hyperelliptic curve cryptography (HECC), with the aim of improving security while reducing computational and communication costs in the IIoT environment. The proposed scheme outperforms existing schemes in terms of both computational and communication costs, as well as offering better security.
Design and Development of Healthcare System using Block chain and Artificial Intelligence

The healthcare industry faces a broad spectrum of challenges. These challenges might range from unavailability of resources, Affordability to slight human errors or even accessibility. All of them resulting in the loss of money or lives for the ordinary person. While some can&#x2019;t afford the expensive treatments or pay the enormous medical bills, others face a lack of availability of doctors. This project aims to minimize and overcome these challenges in the health care system, thereby making the services affordable and available at the same time. The goal is to provide a design to the existing healthcare system to make it more flexible, affordable, and accessible. This new design is discussed in this paper with one developed system to test the end-to-end life cycle of the healthcare services/systems.
Recycling Hashes from Reversible Bitcoin Mining to Seed Pseudorandom Number Generators

We analyzed the Bitcoin difficulty data and noticed that the difficulty has been around the level of $$10^{13}$$ for three years (H2 2018–H1 2021). Our calculation showed about $$10^{28}$$ hashes have been generated during bitcoin mining around the world for securing the addition of 703,364 blocks to the Bitcoin blockchain. We introduced a concept of Recycling Hashes in the hope to (a) jump-start bespoke silicon (customized silicon) for reversible computing, (b) open up the possibility of Bitcoin's Proof-of-Work to be less energy-consuming in the future, (c) provide scientific value or new services, in the form of entropy pool or random numbers, to Internet users while still achieving the security level of Bitcoin of today, (d) decrease the old mining hardware e-waste by using them to recycle hashes to the entropy pool, and (e) solve the problem of low mining rewards. We found that the bit rates of the current irreversible bitcoin miners are millions of times as high as the existing Internet connections, so it would be difficult to send all the hashes generated in real-time via the Internet. Even if only 0.000000355% of the hashes can be recycled, it would still mean that $$355\cdot 10^{18}$$ hashes (355 EH) would have been recycled since the beginning of Bitcoin. Storing all the hashes, so far, would need storage of $$2.560\cdot 10^{30}$$ bits, and it is not currently possible to keep all of them. Our simulation of 10,000 bitcoin hashes showed that the occurrences of zeros and ones in bitcoin hashes are almost 50% and 50%, so it is an encouraging finding for seeding the Pseudorandom Number Generators. We also proposed a second coin for the Bitcoin blockchain, an inflationary coin with a different currency unit (BTCi), to motivate the entropy providers to keep the old mining hardware online. The proposed second coin might keep Bitcoin's security model safe in the future when the deflationary bitcoin (BTC or BTCd) block reward is becoming too low.
Monitoring system architecture for the multi-scale blockchain-based logistic network

Contemporary control processes and methods in multi-scale, cyber-physical systems require precise data collection at various levels, timely transmission, and analysis involving large number of computing and storage elements connected within high-performance permissioned consensus networks. For example, in transport networks, resources tend to form multi-scale dynamical systems with diverse operational requirements, including data exchange policies and consensus protocols. Apart from designing complete topology, chaincodes and consensus logic, effective monitoring of the applications and infrastructure of such complex systems remains a research challenge. In this paper, we discuss important aspects of the data-intensive applications monitoring investigated in the frames of the ADAPT project. We present highlights on the toolsets, architectures and details on possible optimization approaches for monitoring data collection. We introduce a dynamic multi-scale monitoring system architecture with preliminary workflow model. It allows obtaining effective low-latency publish-subscribe matching of the dynamically varying monitoring tasks and executing machines.
Classification of Rastrelliger kanagurta and Rastrelliger brachysoma using Convulational Neural Network (CNN)

Abstract Mackerel is an important commercial caught fish for local fishermen, including Rastrelliger kanagurta and Rastrelliger brachysoma . However, to distinguish these two species is rather difficult because of their similar appearance. Convolutional Neural Network (CNN) is a deep learning method that can be used to classify images. One of parameters contributing to the level of accuracy is the layers number applied in CNN architecture. This study aims to classify those two species using CNN with a range of two to five convolutional layers architectures i.e CNN1, CNN2, CNN3 and CNN4, respectively. In this study, 434 images were used as a training group with 217 images for each class. The validation group consisted of 21 images for each class and the test group consisted of 19 images. The results showed that the CNN3 provided the best training and validation accuracy of respectively 100% and 92.6%. The lowest value of training loss and validation loss of 0.000057 and 0.49. The accuracy values of the CNN models using different testing images reached 94.7%.
An Inter and Intra Transformer for Hate Speech Detection

Hate speech is prevalent in the Internet environment, under circumstances of anonymity and physical distance on the Web. Hate speech detection has also had an increasing trend of development. In recent years, successful hate speech detection models have been developed, such as using CNN, LSTM, but they lack a sufficient amount of discriminative power. In order to tackle the task of hate speech detection, this paper introduces the use of center loss &#x2013; known for its applications in image understanding &#x2013; on top of the basis of the Transformer architecture. Our model can become more discriminative with the help of the center loss application. We performed experiments to validate the performance of our proposal using a hate speech detection dataset of manually labeled Wikipedia comments. Our experiments show that this method can outperform previous architectures for hate speech detection with an accuracy of 94.08% and an F1-score of 68.46% on the hate speech detection dataset.
A Comprehensive Study and Detailed Review On Hate Speech Classification : A Systematic Analysis

Hate speech is about making insults, threats, or stereotypes towards people or a group of people because of its characteristics such as origin, race, gender, religion, disabilities, and more. Modern society uses social networking websites for sharing thoughts and emotions. However, sometimes it can lead to hate speech. Hate speech is a severe issue as it may lead to repulsive outcomes. Hate speech is a critical element of social media, and there are various types of hate speech due to the ambiguity of the sentences. The traditional state-of-art methods give significant accuracy for classifying hate speech. Therefore, in recent years, the state-of art-methods have been succeeded in detecting and recognizing hate speech. In this paper, we have investigated the various research work that has been accomplished so far for hate speech classification.
Agrichain: A Blockchain-Based Food Supply Chain Management System

The food supply chain is extremely complex owing to the presence of multiple entities and the perishable nature of goods. In India, the infrastructure facilitating collaboration among stakeholders is very weak which leads to participants working in silos. The food supply chain needs sophisticated ways to gather, integrate and track data published by various stakeholders so as to function effectively and efficiently. Blockchain as a distributed digital ledger technology ensures transparency, traceability, and security, and has shown promise for easing food supply chain management problems. In this paper, we propose Agrichain: a Blockchain-based, low-cost Distributed Ledger Technology solution for the Indian food supply chain ecosystem with detailed design and specifications. The proposed system will enable various stakeholders to register for their specific roles in the supply chain and publish relevant information to the blockchain. This information will then be used to track and trace perishable goods as they move forward in the chain.
“Palisade”—A Student Friendly Social Media Website

In this application, we have developed a website where users can sign up and login and be able to share their thoughts and events at their colleges or companies and experience at interviews or etc. and also like, comment on respective posts, and also user can able to follow or unfollow a user to get updates from different users. We feel very difficult to find blood donors at difficult times, this website provides a list of blood donors with their details such that it will be very helpful in finding blood donors. It is very helpful to people who require blood urgently. There is a good number of donors but we find it difficult to find them. This website solves this issue. We can create chat rooms or join chat rooms and discuss things with people around the world in this application.
Order-Constrained Representation Learning for Instructional Video Prediction

In this paper, we propose a weakly-supervised approach called Order-Constrained Representation Learning (OCRL) to predict future actions from instructional videos by observing incomplete steps of actions. Most conventional methods focus on predicting actions based on partially observed video frames, which mainly study low-level semantics such as motion consistency. Unlike performing a single action, completing a task in an instructional video usually requires several steps of action and longer periods. Motivated by the fact that the order of action steps is key to learning task semantics, we develop a new frame of contrastive loss, called StepNCE, to integrate the shared semantic information between step order and task semantics under the framework of the memory bank-based momentum-updating algorithm. Specifically, we learn the video representations from step order-rearranged trimmed video clips based on the proposed task-consistency rule and order-consistency rule. Our StepNCE loss can be used to pre-train a video feature encoder, which is then fine-tuned to carry out the instructional video prediction task. Our approach digs deeper into the sequential logic between different action steps with respect to a certain task, which is able to promote the video understanding methods to a new semantic level. We evaluate our method on five popular instructional video and action prediction datasets: COIN, CrossTask, UT-Interaction, BIT-Interaction, and ActivityNet v1.2, and the results show that our approach gains improvements from conventional prediction methods.
Empirical evaluation of an architectural technical debt index in the context of the Apache and ONAP ecosystems

Architectural Technical Debt (ATD) in a software-intensive system denotes architectural design choices which, while being suitable or even optimal when adopted, lower the maintainability and evolvability of the system in the long term, hindering future development activities. Despite the growing research interest in ATD, how to gain an informative and encompassing viewpoint of the ATD present in a software-intensive system is still an open problem.In this study, we evaluate ATDx, a data-driven approach providing an overview of the ATD present in a software-intensive system. The approach, based on the analysis of a software portfolio, calculates severity levels of architectural rule violations via a clustering algorithm, and aggregates results into different ATD dimensions.To evaluate ATDx, we implement an instance of the approach based on SonarQube, and run the analysis on the Apache and ONAP ecosystems. The analysis results are then shared with the portfolio contributors, who are invited to participate in an online survey designed to evaluate the representativeness and actionability of the approach.The survey results confirm the representativeness of the ATDx, in terms of both the ATDx analysis results and the used architectural technical debt dimensions. Results also showed the actionability of the approach, although to a lower extent when compared to the ATDx representativeness, with usage scenarios including refactoring, code review, communication, and ATD evolution analysis.With ATDx, we strive for the establishment of a sound, comprehensive, and intuitive architectural view of the ATD identifiable via source code analysis. The collected results are promising, and display both the representativeness and actionability of the approach. As future work, we plan to consolidate the approach via further empirical experimentation, by considering other development contexts (e.g., proprietary portfolios and other source code analysis tools), and enhancing the ATDx report capabilities.
Semantics and Anomaly Preserving Sampling Strategy for Large-Scale Time Series Data

We propose PASS , a O ( n ) algorithm for data reduction that is specifically aimed at preserving the semantics of time series data visualization in the form of line chart. Visualization of large trend line data is a challenge and current sampling approaches do produce reduction but result in loss of semantics and anomalous behavior. We have evaluated PASS using seven large and well-vetted datasets (Taxi, Temperature, DEBS challenge 2012-2014 dataset, New York Stock Exchange data, and Integrated Surface Data) and found that it has several benefits when compared to existing state-of-the-art time series data reduction techniques. First, it can preserve the semantics of the trend. Second, the visualization quality using the reduced data from PASS is very close to the original visualization. Third, the anomalous behavior is preserved and can be well observed from the visualizations created using the reduced data. We have conducted two user surveys collecting 3,000+ users’ responses for visual preference as well as perceptual effectiveness and found that the users prefer PASS over other techniques for different datasets. We also compare PASS using visualization metrics where it outperforms other techniques in five out of the seven datasets.
Hierarchical Task and Motion Planning through Deep Reinforcement Learning

Task and motion planning (TAMP) integrates the generation of high-level tasks in a discrete space and the execution of low-level actions in a continuous space. Such planning integration is susceptible to uncertainties and computationally challenging as low-level actions should be verified for high-level tasks. Therefore, this paper presents a hierarchical task and motion planning method under uncertainties. We utilize Markov Decision Processes (MDPs) to model task and motion planning in a stochastic environment. The motion planner handles motion uncertainty and leverages physical constraints to synthesize an optimal low-level control policy for a single robot to generate motions in continuous action and state spaces. Given the optimal control policy for multiple homogeneous robots, the task planner synthesizes an optimal high-level tasking policy in discrete task and state spaces addressing both task and motion uncertainties. Our optimal tasking and control policies are synthesized through deep reinforcement learning algorithms. The performance of our method is validated in realistic physics-based simulations with two quadrotors transporting objects in a warehouse setting.