Reviewerly/exHarmony huggingface dataset:
Performance Analysis of Dual Axis Solar Tracker

Solar energy has emerged as a viable source of electricity due to its environmental friendliness and ability to reduce greenhouse gas emissions throughout the world. Solar energy has the potential to meet the world's energy demands, but our ability to transform it into electrical energy in an efficient and cost-effective manner is the sole constraint. Weather fluctuations and how much radiation falls on the panel or reflector determine how much power is generated from a PV cell. Solar trackers are required to ensure that the PV panel receives the greatest amount of sunlight. This paper looks at the Dual Axis Solar Tracking (DAST) system and a Simulink model is developed with MATLAB software to compare the efficiency of fixed and DAST systems.
Transfer-Learning-Based SVM Method for Seismic Phase Picking With Insufficient Training Samples

Efficient seismic phase picking is fundamental to seismic signal processing. Phase picking methods based on neural networks show great potential in accurately picking signals with a low signal-to-noise ratio but require large training datasets. We present a transductive transfer-learning-based support vector machine (TTL-SVM) algorithm for seismic phase picking when the seismic dataset possesses insufficient training samples. An objective function of TTL-SVM, which is incorporated with a pretraining classification process in the source domain that possesses an adequate training dataset and quality labeling, is proposed for phase picking in the target domain with no quality labeling. Seismic compressional ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P$ </tex-math></inline-formula> -) and shear ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S$ </tex-math></inline-formula> -) phase picking is performed using two TTL-SVM processing steps: seismic phase and noise classification, and then <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P$ </tex-math></inline-formula> - and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S$ </tex-math></inline-formula> -phase classification from the picking phases. Experiments are performed to test the algorithm using a simulated dataset and two earthquake datasets from Jiuzaigou in China and New Zealand. The TTL-SVM results are remarkable compared with those obtained through traditional automatic and manual picking approaches. This algorithm provides an alternative approach for seismic phase picking when the dataset possesses insufficient training samples.
CRAFTS: Configurable REST APIs for Triple Stores

Massive amounts of Linked Open Data are readily available to anyone who wants to use them. Unfortunately, Semantic Web technologies such as SPARQL and RDF remain unfamiliar to the majority of web developers, more used to REST APIs. This paper addresses the challenge of accessing Linked Open Data through REST APIs. Configurable REST APIs For Triple Stores (CRAFTS) is the tool devised for this purpose. CRAFTS allows knowledge engineers to configure REST APIs over multiple triple stores. CRAFTS automatically handles the translation of API calls into SPARQL queries, delivering results in JSON format. Web developers can then use a CRAFTS API to read and write Linked Open Data. The API of CRAFTS is uniform, domain-independent, and described with the OpenAPI specification. A reference implementation of CRAFTS is published in a GitHub repository, and a live test site is readily available since February 2021. CRAFTS is currently employed in eight different applications, with more than 2.4K users, and more than 260K API calls.
Weakly Supervised Class-Agnostic Image Similarity Search Based on Convolutional Neural Network

For vision-based localization methods, the efficient and accurate image retrieval in a database with a large number of images is an important prerequisite and guarantee. The traditional content-based image retrieval method can retrieve database images that have similar details and features with the query image. However, there may be significant differences in semantics between them, which may result in mismatches. The method based on convolutional neural network can avoid such problems to a certain extent. In this paper, we propose an end-to-end weakly supervised class-agnostic image retrieval method based on convolutional neural networks. In the offline stage, the database images are preprocessed to separate the foregrounds and backgrounds, and the foregrounds are clustered for storage. The aim is to reduce the amount of data calculation in the online stage and avoid mismatches caused by backgrounds mixing. In the online stage, the same processing is performed on the query image, and the improved method based on the relational network is used to compare the similarity between the input objects to obtain the optimal retrieval result. The test results on Holidays, Paris 6k, Oxford 5k and three large-scale datasets show that the proposed method has better performance than existing image retrieval methods.
Recognising Flexible Intents and Multiple Domains in Extended Human-Robot Dialogues

We focus on extended multi-turn multi-topic human-robot dialogues, which can be more challenging than short question-answering interactions. In earlier work we developed two dialogue systems for Nao robots: WikiTalk supporting Wikipedia-based dialogues on open-domain topics, and CityTalk supporting task-based dialogues on restaurant and hotel domains. We used WikiTalk with ERICA at Kyoto University, and now wish to make our systems available on multiple robot platforms. To support this aim we use Rasa open-source conversational AI, which creates transformer-based dialogue models that aim to recognise flexible intents in multi-turn multi-domain dialogues. To improve CityTalk we use Rasa knowledgebase actions backed by Neo4j graph databases which support knowledge graphs for multiple domains. By adding taxonomies and other semantic context to the knowledge graphs we aim to give more intelligent dialogue responses. We also plan to use the large MultiWOZ multi-domain dialogue dataset to support additional domains.
Memory/Disk Operation Aware Lightweight VM Live Migration

Live virtual machine migration technique allows migrating an entire OS with running applications from one physical host to another, while keeping all services available without interruption. It provides a flexible and powerful way to balance system load, save power, and tolerate faults in data centers. Meanwhile, with the stringent requirements of latency, scalability, and availability, an increasing number of applications are deployed across distributed data-centers. However, existing live migration approaches still suffer from long downtime and serious performance degradation in cross data-center scenes due to the mass of dirty retransmission, which limits the ability of cross data-center scheduling. In this paper, we propose a system named Memory/disk operation aware Lightweight VM Live Migration across data-centers with low performance impact (MLLM). It significantly improves the cross data-center migration performance by reducing the amount of dirty data in the migration process. In MLLM, we predict disk read workingset (i.e., more frequently read contents) and memory write workingset (i.e., more frequently write contents) based on the access sequence traces. And then we adjust the migration models and data transfer sequence by the workingset information. We further proposed an improved algorithm for workingset estimation. Moreover, we discussed the potential use of machine learning (ML) to enhance the performance of the VM migration and also propose a two-level hierarchical network model to make the ML-based prediction more efficient. We implement MLLM and its improved versions on the QEMU/KVM platform and conduct several experiments. The experimental results show that 1) MLLM averagely reduces 62.9% of total migration time and 36.0% service downtime over existing methods; 2) The improved workingset estimation algorithm reduces 9.32% memory pre-copy time on average over the original algorithm.
Detecting and Mitigating Test-time Failure Risks via Model-agnostic Uncertainty Learning

Reliably predicting potential failure risks of machine learning (ML) systems when deployed with production data is a crucial aspect of trustworthy AI. This paper introduces Risk Advisor, a novel post-hoc meta-learner for estimating failure risks and predictive uncertainties of any already-trained black-box classification model. In addition to providing a risk score, the Risk Advisor decomposes the uncertainty estimates into aleatoric and epistemic uncertainty components, thus giving informative insights into the sources of uncertainty inducing the failures. Consequently, Risk Advisor can distinguish between failures caused by data variability, data shifts and model limitations and advise on mitigation actions (e.g., collecting more data to counter data shift). Extensive experiments on real-world datasets covering a variety of ML failure scenarios show that the Risk Advisor reliably predicts deployment-time failure risks in all the scenarios, and outperforms strong baselines.
Recent Query Reformulation Approaches for Information Retrieval System - A Survey

Abstract: Around trillions of data are uploaded to the internet every year. Extracting useful information using only a few keywords has become a major challenge. The field of Query Reformulation (QR) is focused on the efficient retrieval of information to overcome this. It is widely used in the domain of information retrieval (IR) and related fields such as search engines, multimedia IR, cross-language IR, recommender systems, and so on. Query reformulation techniques incur extra computational costs. Due to this reason, the use of query reformulation techniques is sometimes prohibited in internet searches as searching over the internet requires a fast response time. But due to the success of NLP (Natural Language Processing) using machine learning/deep learning in recent years, there has been a boom of study in this area. In this literature, a variety of term selection, term extraction, and query reformulation strategies based on recent technologies used by researchers have been presented, necessitating a wide survey to focus research in this promising area. Recent QR approaches and the datasets, techniques, and evaluation metrics used in this paper will help researchers understand and focus more on research in this promising area so that a better solution will be proposed. From the survey, it may be observed that one of the hottest subjects in the field of IR right now is applying deep learning to IR systems for query reformulation.
Transforming Ontology from CSV file and Relational Databases: A Methodology

Today every organization creates and uses relational database (RDatabase) to manage the requirements of its users. The two ways to retrieve data from these databases may include 1. SQL and 2. Through interface provided to user. The vision of semantic web is possible in true sense by transforming Ontology from RDatabase. First a detailed comparison is elaborated between RDatabase and Ontology. Secondly, different ways to create ontology are discussed. The rules used for transforming Databases to ontologies are based on resemblance among constructs of RDatabase and OWL 2. This resemblance is summarized next in the subsequent text and a method is proposed to define ontology by constructing RDatabase from csv file and using it to construct domain Ontology which can be used for personalised recommendation.
Boosting Accuracy of Differentially Private Federated Learning in Industrial IoT With Sparse Responses

Empowered by 5G, it has been extensively explored by existing works on the deployment of differentially private federated learning (DPFL) in the Industrial Internet of Things (IIoT). Through federated learning, decentralized IIoT devices can collaboratively train a machine learning model by merely exchanging model gradients with a parameter server (PS) for multiple global iterations. Differentially private (DP) mechanisms will be incorporated by IIoT devices (also called clients) to prevent the leakage of privacy due to the exposure of gradients because original gradients will be distorted DP noises. Yet, learning with distorted gradients can seriously deteriorate model accuracy, making DPFL unusable in reality. To address this problem, we propose a novel DPFL with sparse responses (DPFL-SR) algorithm, which applies the sparse vector technique to reduce the privacy budget consumption in each global iteration. Specifically, DPFL-SR evaluates the value of each gradient, and only distorts and uploads significant gradients to the PS because significant gradients are more essential for model training. Since insignificant gradients are not disclosed, the reserved privacy budget can be used to return significant gradients for more iterations so that DPFL-SR can achieve higher model accuracy without lowering the privacy protection level. Extensive experiments are conducted with the MNIST and Fashion-MNIST datasets to demonstrate the practicability and superiority of DPFL-SR in IIoT systems.
Attendance Monitoring System Design Based on Face Segmentation and Recognition

Aim: The proposed work aim was to monitor real-time attendance using face recognition in every institutional sector. It is one of the key concerns in every organization. Background: Nowadays, most organizations spend a lot of time marking attendance for a large number of individuals manually. Many technologies like Radio Frequency Identification (RFID) and biometric systems are introduced to overcome the manual attendance system. when using these RFID and biometric people need to stand in queue to make their presence. Objective: The main objective of the system is to provide an automated attendance system with the help of face recognition owing to the difficulty in the manual as well as other traditional attendance systems. Methods: The proposed work was done through face recognition using Machine Learning. Face recognition is a part of biometric characteristic of a human. It was easy to process than other biometric measurements like fingerprint, iris scan, hand scan, retina scan. The Haarcascade classifier will detect a face, and the LBPH algorithm will recognize the face. The experiment performs on the creation of real-time face data. Results: Using the web camera connected to the computer, face detection and recognition are performed, and recognized faces mark as attended. Here, the admin module and teacher modules are implemented with different functionalities to monitor attendance. Conclusion: Experiment results get 94.5% accuracy in face detection and 98.5% accuracy in face recognition by using the Haarcascade classifier and LBPH algorithm. This application system will be simple to implement, accurate, and efficient in monitoring attendance in real-time.
An Efficient Signcryption Scheme Using Near-Ring Hybrid Approach for an IoT-Based System

Signcryption is a cryptographic primitive which combines both the functions of digital signature and public key encryption logically in a single step, with a computational cost significantly less than the traditional signature-then-encryption approach. Signcryption is another approach to accomplish secrecy and validation simultaneously across Internet of Things (IoT). Numerous signcryption schemes have been created and executed , but it uses different security attributes and computational cost. In this paper, introducing a new signcryption scheme whose security relies on the intractability of combination of the Twisted Root Extraction Problem (TREP) and Conjugacy Search Problem (CSP) which can utilised for IoT based system for secure transferring of data. It is applied over an IoT to upgrade the security of information and its confidentiality. Experiment shows that modified signcryption algorithm outperforms than existing approaches.
VPN and Non-VPN Network Traffic Classification Using Time-Related Features

The continual growth of the use of technological appliances during the COVID-19 pandemic has resulted in a massive volume of data flow on the Internet, as many employees have transitioned to working from home.Furthermore, with the increase in the adoption of encrypted data transmission by many people who tend to use a Virtual Private Network (VPN) or Tor Browser (dark web) to keep their data privacy and hidden, network traffic encryption is rapidly becoming a universal approach.This affects and complicates the quality of service (QoS), traffic monitoring, and network security provided by Internet Service Providers (ISPs), particularly for analysis and anomaly detection approaches based on the network traffic's nature.The method of categorizing encrypted traffic is one of the most challenging issues introduced by a VPN as a way to bypass censorship as well as gain access to geo-locked services.Therefore, an efficient approach is especially needed that enables the identification of encrypted network traffic data to extract and select valuable features which improve the quality of service and network management as well as to oversee the overall performance.In this paper, the classification of network traffic data in terms of VPN and non-VPN traffic is studied based on the efficiency of time-based features extracted from network packets.Therefore, this paper suggests two machine learning models that categorize network traffic into encrypted and non-encrypted traffic.The proposed models utilize statistical features (SF), Pearson Correlation (PC), and a Genetic Algorithm (GA), preprocessing the traffic samples into net flow traffic to accomplish the experiment's objectives.The GA-based method utilizes a stochastic method based on natural genetics and biological evolution to extract essential features.The PC-based method performs well in removing different features of network traffic.With a microsecond perpacket prediction time, the best model achieved an accuracy of more than 95.02 percent in the most demanding traffic classification task, a drop in accuracy of only 2.37 percent in comparison to the entire statistical-based machine learning approach.This is extremely promising for the development of real-time traffic analyzers.3092
Biodigital Philosophy, Technological Convergence, and Postdigital Knowledge Ecologies

New technological ability is leading postdigital science, where biology as digital information, and digital information as biology, are now dialectically interconnected. In this chapter we firstly explore a philosophy of biodigitalism as a new paradigm closely linked to bioinformationalism. Both involve the mutual interaction and integration of information and biology, which leads us into discussion of biodigital convergence. As a unified ecosystem, this allows us to resolve problems that isolated disciplinary capabilities cannot, creating new knowledge ecologies within a constellation of technoscience. To illustrate our arrival at this historical flash point via several major epistemological shifts in the post-war period, we venture a tentative typology. The convergence between biology and information reconfigures all levels of theory and practice, and even critical reason itself now requires a biodigital interpretation oriented towards ecosystems and coordinated Earth systems. In this understanding, neither the digital humanities, the biohumanities, nor the posthumanities sit outside of biodigitalism. Instead, posthumanism is but one form of biodigitalism that mediates the biohumanities and the digital humanities, no longer preoccupied with the tradition of the subject, but with the constellation of forces shaping the future of human ontologies. This heralds a new biopolitics which brings the philosophy of race, class, gender, and intelligence, into a compelling dialog with genomics and information.
Towards Sustainable Smart City via Resilient Internet of Things

Every day, 2.5 quintillion bytes of data are generated which is an unimaginable figure to human beings and even machine. To achieve the global smart city vision, automation, resilience, and sustainable development are crucial elements. This chapter focuses on resilient Internet of Things that links individuals and sensing devices which forms the foundation of data collection and provides ground truth of information. With the tremendous growth of primary data volumes and diversity in every domain, they have played an ever more crucial role in enabling researchers and enterprises to formulate processing and analysis methods to extract latent information from multiple data resources and to leverage a broad range of data management and analytics platforms. We have been witnessed the successful technology story of artificial intelligence in various applications. However, resilient and sustainable development has not yet fully integrated into artificial intelligence applications. It requires automated update and improvement of trained machine learning model with the ever-increasing data. This chapter is organised as follows. Firstly, a systematic review of the existing works of resilience and sustainability for smart city is presented. This is followed by a comparison of IoT solutions in software and hardware perspective. Various future research directions and conceptual study of smart city application are discussed.
A Survey of Verification, Validation and Testing Solutions for Smart Contracts

Smart contracts are programs stored on a blockchain that run when predetermined conditions are met. However, designing and implementing a smart contract is not trivial since upon deployment on a blockchain, it is no longer possible to modify it (neither for improving nor for bug fixing). It is only possible by deploying a new version of the smart contract which is costly (deployment cost for the new contract and destruction cost for the old contract). To this end, there are many solutions for testing the smart contracts before their deployment. Since realizing bug-free smart contracts increase the reliability, as well as reduce the cost, testing is an essential activity. In this paper, we group the existing solutions that attempt to tackle smart contract testing into following categories: public test networks, security analysis tools, blockchain emulators and blockchain simulators. Then, we analyze these solutions, categorize them and show what their pros and cons are.
EasyARPiano: Piano Teaching Mobile App with Augmented Reality

Music has evolved into a fascinating component of human life and a vital role player. Piano is one of the key fields of music, and music venues all over the world welcome a huge number of people who are interested in learning it. Despite this high demand, music institutions charge students a high price for piano lessons. The conducted studies related to this topic didn't solve the issue of learning costs since they required expensive devices to setup. The purpose of this study is to create an affordable, simple, easy-to-set-up, and accessible piano teaching mobile application that everybody can use with their basic augmented reality devices, such as smartphones, which have become an essential part of our lives in the last decades. This cross-platform app can be installed on Android or iOS smartphones. The player sits in front of a genuine MIDI keyboard and launches the app, selecting a music track and the proper mode to play in, whether it is beginner, medium, or expert. The app uses the smartphone's screen to provide hints by displaying falling notes on the piano keys that must be touched at the right time. It also uses the smartphone's camera to track the player's hand movements. Each song has a weighted score that the player should achieve. As the player continues playing, the earned score is displayed on the screen. After the game is completed, the final score is displayed on the screen as shared feedback on the player's performance.