Reviewerly/exHarmony huggingface dataset:
A Derivative-Persistence Method for Real Time Photovoltaic Power Forecasting

This paper focuses on very-short-term photovoltaic power forecasting based solely on power measurement. The approach proposed in the paper is tailored for real time operation of smart grids and microgrids, that deals with energy management and control of the various resources of the grid and, particularly, on those based on renewable energy sources. These resources are characterized by variable power production, thus constituting a challenge for the effectiveness of strategies and tools of management and control. This paper refers to photovoltaic power production and presents a new real time forecasting method which improves the classical persistence model. The proposed method is based on the idea of conveniently weighting information on past data by imposing continuity of the function, of its first derivative and of the second derivative so obtaining three estimates of the function in the forecast interval which are then opportunely weighted to provide the forecast. The effectiveness of the proposed approach is shown in the numerical application based on actual measured data. The accuracy of the proposed approach is also tested through comparisons with two models based on the persistence and auto regressive moving average models.
Computational Intelligence Approach for Municipal Council Elections Using Blockchain

Blockchain is an innovative technology that disrupts different industries and offers decentralized, secure, and immutable platforms.Its first appearance is connected with monetary cryptocurrency transactions, followed by adaptation in several domains.We believe that blockchain can provide a reliable environment by utilizing its unique characteristics to offer a more secure, costless, and robust mechanism suitable for a voting application.Although the technology has captured the interest of governments worldwide, blockchain as a service is still limited due to lack of application development experience, technology complexity, and absence of standardized design, architecture, and best practices.Therefore, this study aims to build an imperial example for a blockchain electronic voting (e-voting) application using digital identity management for fulfilling immutable, transparent, and secure distributed blockchain features.The paper reviews the current types of e-voting systems and discusses the standard processes.We propose a conceptual design for a blockchain providing a digital identity management service to secure the e-voting application results.The blockchain development process implemented in this study follows the Proof of Concept to verify the e-voting application's function for illustrating the architecture and description of the application's business process model.The development is based on the Ethereum platform, which allows the implementation of the Proof of Work consensus algorithm.The developed e-voting application saves time, requires fewer processes, and results in higher accuracy, more transparency, considerable voters' privacy, and accountable system management.We expect that the e-voting blockchain application will impact governmental processes during the election, reduce spending, support digital transformation, and ensure fairness of results.
Facilitated and Enhanced Human Activity Recognition via Semi-supervised LightGBM

With the fast development of mobile computing and huge market of health industry, health monitoring based on wearable sensors or smartphones in a ubiquitous way has become emerging in recent years. For health monitoring, human activity recognition has played an important role, especially for recognizing the daily indoor activities. A traditional approach of human activity recognition relying on supervised learning requires a great amount of labeled data to train a model. However, data collection of these labeled data is time consuming and labor intensive. Hence, in this work, we target on an efficient solution for human activity recognition based on a semi-supervised learning approach which can automatically enrich the data set based on a large amount of unlabeled data. Moreover, based on the enriched data set, we apply Light GBM as a robust classifier to improve the recognition accuracy and robustness. Using a data set collected from 20 subjects, we investigate the performance of the proposed semi-supervised Light GBM and compare it to some commonly used supervised and semi-supervised learning approaches. The experimental results show that this semi-supervised learning can well enrich the data set with high accuracy and the proposed semi-supervised Light GBM provides the most robust and accurate solution.
X-ray image denoising for Cargo Dual Energy Inspection System

The current instruments for cargo inspection systems use dual energy system e.g. 9/6 MeV or 6/3 MeV for material discrimination, the low energy transmission system introduces a special kind of noise, a random vertical lines noise, this noise may cause severe problems in material discrimination. We propose a novel convolutional neural network-based technique for 3 MeV x-ray image denoising and we also introduce a mathematical model to artificially generate this kind of noise, due to the lack of a large x-ray cargo images. We trained our model on artificially generated images with the simulated random vertical noise, while we tested the model on both of artificially generated images and a real image generated by an x-ray dual energy system. The system showed a competitive peak-to-noise ratio PSNR of 41.2 and a structure similarity index of 0.956.
A mobile technology‐based cooperative learning platform for undergraduate biology courses in common college classrooms

Abstract As a high‐impact educational practice, cooperative learning uses a structured group study to promote students' active learning. Currently, it lacks economical yet effective tools to facilitate the interactive nature of structured cooperative learning in regular classrooms. Here, we have established a mobile technology‐based cooperative learning (MBCL) platform that comprises the 2018 iPad, Apple Pencil, LiveBoard, Google Forms, and Google Drive. We tested the MBCL platform in multiple undergraduate biology courses. During semester‐long MBCL studies, the students engaged in cooperative learning to discuss a real‐life issue or chapter‐based contents. With the MBCL platform, the students' group study processes were shown on shared, visible electronic whiteboards that were updated in real‐time, generating visible thinking and instant, interactive communication. The instructor was able to guide the students promptly to conduct knowledge integration and knowledge synthesis using tables and diagrams. The deep learning outcome was evident in the examples and quantitative analyses of students' whiteboard study results and team presentations. Thus, integrating innovative mobile technologies into high‐impact teaching practices, exemplified by the MBCL platform, promotes deep learning in higher education.
Interpretable Sentiment Analysis based on Deep Learning: An overview

Sentiment analysis (SA) or emotion AI or opinion mining uses natural language processing (NLP). Sentiment Analysis identify, study, quantify, obtain, tacit states and subject related information. Broad spectrum of areas influenced due to Sentiment Analysis such as policy making by the government, finding mental health of individuals, finding misuse of drugs in healthcare, fraud detection in the financial sector, covid-19 awareness and impact, Cyber-crime etc. As the amplitude of social media data increases day by day, there is a need to automatically address sentiment analysis. Deep learning handles it very well. It gives very good accuracy but incomprehensibility in decision strategy. For better decision-making trust, believe, fairness, reliability, and unbiasing is important. This paper explores the work done in this area along with popular techniques to address interpretability in sentiment analysis and its evaluation criteria.
Random Error in Strain Calculation using Regularized Polynomial Smoothing (RPS) and Point-wise Least Squares (PLS) in Digital Image Correlation

The strain error analysis is greatly concerned recently as digital image correlation (DIC) is used to measure the heterogeneous deformation. This paper focuses on the estimation of random error and under-matched error caused by two strain calculation methods, i.e. the point-wise least squares (PLS) and the regularized polynomial smoothing method (RPS). Two assumptions are put forward on the noise error of the calculated displacement that are: a) it is pure random error without bias and b) in each strain window, it is the independent Gaussian white noise with zero-mean. Based on the assumptions, the random error of displacement and strain is estimated, and the under-matched error of displacement and strain is theoretically analyzed by the aid of Laplacian operator. These two error solutions are verified by some stimulated experiments. Then for the typical kernel function of 3rd order polynomial, a self-adaptive algorithm minimizing the total error is proposed to choose the optimal parameters, i.e. window size and parameter λ. Experiments show that when the original displacement noise conforms to the assumptions strictly, 1) the estimated random error and under-matched error agrees very well with the experimental value, 2) the self-adaptive algorithm can give the optimal parameters in restoring the displacement and strain field, and 3) the estimation of random error and under-matched error is affected by DIC noise greatly, and it is better to use low-pass Gaussian filter before utilizing self-adaptive algorithm.
Enhanced Symmetric Cryptography for IoT using Novel Random Secret Key Approach

The deployment of IoT devices in different domains enables technical innovations and value-added services to users but also creates multiple requirements in terms of effective communication and security. IoT devices are constrained by less computing resources and limited battery power. Generally, the TLS/SSL protocol is used to provide communication security on IoT and the protocol utilizes important encryption algorithms like RSA, Elliptic Curve Cryptography, and AES. However, these conventional encryption algorithms are computationally and economically expensive to implement in IoT devices. Lightweight Cryptography (LWC) algorithms were introduced recently for IoT and the aim of the algorithms is to provide the same level security with a minimal amount of computing resources and power. This paper proposes a novel Random Secret Key (RSK) technique to provide an additional security layer for symmetric LWC algorithms for IoT applications. In RSK, IoT devices do not transmit keys over the network; they share a random matrix, calculate their own RSK, encrypt, and transmit the cipher text. When a random matrix lifetime expires new matrix published and RSK resets. Regular change in the RSK makes the IoT networks resistant to brute-force/dictionary attacks. The RSK added one more simple and effective secure layer to strengthen the security of the original secret key and is successfully implemented in a smart greenhouse environment. The outcomes of the experiments prove that the RSK provides enhanced and efficient protection for symmetric LWC algorithms in any IoT systems, consume a minimum amount of resources and more resistant to key-based attacks.
Lightweight Hybrid Encryption System with FPGA Design Proposal

Abstract Security and privacy issues of limited resources communications such as the internet of things (IoT) applications have caught significant attention. Traditional security solutions are not optimal for limited resources devices due to resource constraints in terms of computing capacity and memory and energy. Elliptic curves cryptosystems (ECC) is efficient for limited resources devices more than other public-key cryptosystems as it provides the same security level with reduced key sizes compared with other security techniques. In this paper, we introduce a lightweight hybrid encryption system that uses a key exchange algorithm based on the Elliptic Curve Diffie-Hellman (ECDH) protocol. In addition, a lightweight version of Advanced Encryption Standard (AES) as a block cipher is proposed in order to use the exchanged key for data encryption. The simulation is done using SageMath Tool and the results show that the proposed lightweight AES provide an accepted security level with lower computing capacity. Finally, we proposed a hardware top level design for FPGA implementation.
Disaster Detector on Twitter Using Bidirectional Encoder Representation from Transformers with Keyword Position Information

Deep learning, as one of the most currently remarkable machine learning techniques, has achieved great success in many applications such as image analysis, speech recognition, and text understanding. This work aims to make use of the bidirectional encoder representation from transformers (BERT) model to a particular problem in our real life, detecting disaster on Twitter. Specifically, we first validate the practicability of BERT in disaster detection, and then improve the model practicability by extracting keyword information to classify the text. The experimental results show that our method achieves a more accurate prediction of texts announcing a disaster compared to the models without keyword position information. The result of this research will be helpful in monitoring and tracking social media content and in discovering how people's descriptions of disasters are like.
DeepSegment: Segmentation of motion capture data using deep convolutional neural network

In this paper, we propose a novel framework to segment 3D human motion capture data into distinct behaviors. First, in preprocessing, we build a normalized pose space by eliminating translation and orientation from the 3D poses. We then transform these normalized 3D poses into 2D RGB images, and as a result, we simplify the task of motion segmentation as image classification and recognition. Furthermore, we identify the most significant joints of the skeleton that contribute substantially to executing a motion and get benefits from them by assigning them more weights. The weight allocation to the specific joint has been done purely based on its deviation capability. Finally, each motion is encoded into compact visual representation by exploiting RGB images with weighted joints. We adopt a transfer learning approach to extract a fixed-size feature vector using off-the-shelf deep Convolutional Neural Network (CNN), Alexnet, after fine-tuning. We develop a Kd-tree on these highly descriptive feature vectors to retrieve the nearest neighbors. Based on a similarity measure, we classify the motion segments and ultimately place the cuts on the ongoing motion sequences. We perform extensive experiments to evaluate our proposed approach on popular Motion Capture (MoCap) datasets, CMU and HDM05. Our approach almost outperforms all other state-of-the-art methods, and the results highlight the capabilities of our proposed scheme for effective segmentation.
A Taxonomy and Survey on Container Migration Techniques in Cloud Computing

The virtualization of containers is a technique to run multiple processes in an isolated manner. Container gained popularity in improved application management and deployment because of its lightweight environment, flexible deployment, and fine-grained sharing of resources. Organizations are using containers extensively to deploy their increasingly complex workloads resulting from new technologies including online infrastructure, big data, and the Internet of things in controlled clusters or data centers in the private and public cloud. It opens the possibility of saving a container’s entire state and restarting it later. Checkpointing is used to perform a live migration of containers, and it allows the state of a running container to be saved and restarted on the same or a separate host later on. Multiple dumps will handle this checkpointing and restart. It is transparent for running applications and network connections. In this paper, we present a taxonomy for container migration. Further, the survey is carried out on the basis of the proposed taxonomy helping to identify the sustainable solution. Future directions are identified to facilitate the researchers in this field.
Algorithm for choosing the best frame in a video stream in the task of identity document recognition

During the process of document recognition in a video stream using a mobile device camera, the image quality of the document varies greatly from frame to frame. Sometimes recognition system is required not only to recognize all the specified attributes of the document, but also to select final document image of the best quality. This is necessary, for example, for archiving or providing various services; in some countries it can be required by law. In this case, recognition system needs to assess the quality of frames in the video stream and choose the “best” frame. In this paper we considered the solution to such a problem where the “best” frame means the presence of all specified attributes in a readable form in the document image. The method was set up on a private dataset, and then tested on documents from the open MIDV-2019 dataset. A practically applicable result was obtained for use in recognition systems.
Generative Pre-training for Paraphrase Generation by Representing and Predicting Spans in Exemplars

Paraphrase generation is a long-standing problem and serves an essential role in many natural language processing problems. Despite some encouraging results, recent methods either confront the problem of favoring generic utterance or need to retrain the model from scratch for each new dataset. This paper presents a novel approach to paraphrasing sentences, extended from the GPT-2 model. We develop a template masking technique, named first-order masking, to masked out irrelevant words in exemplars utilizing POS taggers. So that, the paraphrasing task is changed to predicting spans in masked templates. Our proposed approach outperforms competitive baselines, especially in the semantic preservation aspect. To prevent the model from being biased towards a given template, we introduce a technique, referred to as second-order masking, which utilizes Bernoulli distribution to control the visibility of the first-order-masked template's tokens. Moreover, this technique allows the model to provide various paraphrased sentences in testing by adjusting the second-order-masking level. For scale-up objectives, we compare the performance of two alternatives template-selection methods, which shows that they were equivalent in preserving semantic information.
Conversational Question Answering Over Knowledge Base using Chat-Bot Framework

Conversational Question Answering aims at answering natural language questions via well-structured relation information between entities stored in knowledge base. Knowledge Base Question Answering is one of the promising approaches for extracting substantial information from the Knowledge Bases. Existing Question Answering systems answer each Question independently, adding redundancy to repeat the entity even if the current Question is a follow-up one to the previous one. In this paper, we propose a Robust-Answer-Driven-Assistant (RADA) using the chatbot framework to overcome this problem. It consists of an ensemble of Entity Recognition, Entity Prediction, Question Answering models, and dialogue system. We conduct quantitative experiments, including comparisons with the state-of-the-art on the Web-Question dataset. Our experiments suggest the effectiveness of RADA in comparison with other methods under the F1-score metric.
Effects of network topology on the performance of consensus and distributed learning of SVMs using ADMM

The Alternating Direction Method of Multipliers (ADMM) is a popular and promising distributed framework for solving large-scale machine learning problems. We consider decentralized consensus-based ADMM in which nodes may only communicate with one-hop neighbors. This may cause slow convergence. We investigate the impact of network topology on the performance of an ADMM-based learning of Support Vector Machine using expander, and mean-degree graphs, and additionally some of the common modern network topologies. In particular, we investigate to which degree the expansion property of the network influences the convergence in terms of iterations, training and communication time. We furthermore suggest which topology is preferable. Additionally, we provide an implementation that makes these theoretical advances easily available. The results show that the performance of decentralized ADMM-based learning of SVMs in terms of convergence is improved using graphs with large spectral gaps, higher and homogeneous degrees.
Feature-based Vehicle Tracking at Roundabouts in Visual Sensor Networks

During the last decade, visual monitoring has gained an increasing interest especially in the field of smart cities and intelligent transportation systems. This paper describes a feature-based vehicle tracking system in visual sensor networks at roundabouts. In the proposed system, the tasks for vehicle tracking are divided among smart camera nodes and the receiver. Smart camera nodes are embedded devices equipped with camera sensor, multiprocessors for video processing and computer vision tasks, and an RF transceiver for wireless communication. They are responsible for the vehicle detection and classification as well as features extraction. In order to satisfy the constraints of the visual sensor network in terms of power and communication resources, we assume that only the features are transmitted to the sink node. The received features will then be used to retrieve the vehicle trajectories. In order to train the vehicle classifier used at the camera nodes and to test the proposed tracking approach, a training database was built by placing cameras around a realworld roundabout. The system is shown to be capable of tracking vehicles even in partial occlusion cases.