Reviewerly/exHarmony huggingface dataset:
Blockchain, a Sustainable Solution for Cybersecurity Using Cryptocurrency for Financial Transactions in Smart Grids

Power systems are experiencing evolutionary changes. Future grids will be smarter and with a higher level of autonomy. In addition, the penetration of demand-side small-scale distributed generation is mounting. The proliferation of electric vehicles (EVs) conveys a promising and brilliant future for this technology, particularly for vehicles with V2G capability, that will result in a boom in the pervasiveness of EVs in the next decades. Digitalization and modernization of power systems, as well as ever-growing technological advances in information and communications technology (ICT) and the internet of things (IoT), will procure a smart platform that facilitates peer-to-peer (P2P) communication of power systems’ elements. Thus, future power systems will be immensely complicated and interconnected in terms of data transfer and data processing. Hence, in addition to the employment of big data techniques, some structural changes are required to deal with such a massive body of data. Therefore, the security of data must be ensured specifically for financial transactions. Furthermore, in the future grids, millions of microsources will sell their generating power to the local loads in demand-side, and a load may be served by several microsources. Furthermore, flexible loads can use this platform to trade interchangeably with inelastic loads or the main grid. In this respect, myriad transactions must be recorded, which cannot be handled with the current banking system. This matter necessitates the deployment of blockchain-based cryptocurrencies for handling these microtransactions without needing to supervisory and authority. Hence, a proper platform is indispensable to connect the vendors and purchasers easily and executes the transaction after authentication, validation, and verification of both sides. This study delves into the necessity of employment of blockchain in power systems. Besides, the conceptual background of blockchain and cryptocurrency are explained.
Deep Signature Transforms

The signature is an infinite graded sequence of statistics known to characterise a stream of data up to a negligible equivalence class. It is a transform which has previously been treated as a fixed feature transformation, on top of which a model may be built. We propose a novel approach which combines the advantages of the signature transform with modern deep learning frameworks. By learning an augmentation of the stream prior to the signature transform, the terms of the signature may be selected in a data-dependent way. More generally, we describe how the signature transform may be used as a layer anywhere within a neural network. In this context it may be interpreted as a pooling operation. We present the results of empirical experiments to back up the theoretical justification. Code available at \texttt{github.com/patrick-kidger/Deep-Signature-Transforms}.
Disagreement-Regularized Imitation Learning

We present a simple and effective algorithm designed to address the covariate shift problem in imitation learning. It operates by training an ensemble of policies on the expert demonstration data, and using the variance of their predictions as a cost which is minimized with RL together with a supervised behavioral cloning cost. Unlike adversarial imitation methods, it uses a fixed reward function which is easy to optimize. We prove a regret bound for the algorithm in the tabular setting which is linear in the time horizon multiplied by a coefficient which we show to be low for certain problems in which behavioral cloning fails. We evaluate our algorithm empirically across multiple pixel-based Atari environments and continuous control tasks, and show that it matches or significantly outperforms behavioral cloning and generative adversarial imitation learning.
Ontology engineering: Current state, challenges, and future directions

In the last decade, ontologies have become widely adopted in a variety of fields ranging from biomedicine, to finance, engineering, law, and cultural heritage. The ontology engineering field has been strengthened by the adoption of several standards pertaining to ontologies, by the development or e xtension of ontology building tools, and by a wider recognition of the importance of standardized vocabularies and formalized semantics. Research into ontology engineering has also produced methods and tools that are used more and more in production settings. Despite all these advancements, ontology engineering is still a difficult process, and many challenges still remain to be solved. This paper gives an overview of how the ontology engineering field has evolved in the last decade and discusses some of the unsolved issues and opportunities for future research.
FLNet: Landmark Driven Fetching and Learning Network for Faithful Talking Facial Animation Synthesis

Talking face synthesis has been widely studied in either appearance-based or warping-based methods. Previous works mostly utilize single face image as a source, and generate novel facial animations by merging other person's facial features. However, some facial regions like eyes or teeth, which may be hidden in the source image, can not be synthesized faithfully and stably. In this paper, We present a landmark driven two-stream network to generate faithful talking facial animation, in which more facial details are created, preserved and transferred from multiple source images instead of a single one. Specifically, we propose a network consisting of a learning and fetching stream. The fetching sub-net directly learns to attentively warp and merge facial regions from five source images of distinctive landmarks, while the learning pipeline renders facial organs from the training face space to compensate. Compared to baseline algorithms, extensive experiments demonstrate that the proposed method achieves a higher performance both quantitatively and qualitatively. Codes are at https://github.com/kgu3/FLNet_AAAI2020.
Fuzzy Swing Up Control and Optimal State Feedback Stabilization for Self-Erecting Inverted Pendulum

This paper presents the realisation of self-erecting inverted pendulum controls via two switched control approaches, a rule based fuzzy control for swing up inverted pendulum rod to pose upright position from downright position and an optimal state feedback control for stabilization as pendulum on upright position close to its equilibrium vertical line. The aim of this study is to solve two important problems on self-erecting inverted pendulum; swing up and stability in its upright balance position. Simulation and experimental results showed that control methods enabled the inverted pendulum swinging up and reaching its stable attitude in upright position even though small impulse and pulse disturbances were given.
ResLap: Generating High-Resolution Climate Prediction Through Image Super-Resolution

In recent years, many models based on the convolutional neural network have achieved highquality reconstruction for single image super-resolution.Meanwhile, many researches on image superresolution have been applied to various fields.However, only a few research works have been applied to climate prediction.In this paper, we present ResLap to achieve high-resolution climate prediction.ResLap is a spatial downscaling method that converts low spatial resolution climate data into high-resolution regional climate forecasts.This method mainly introduces a novel residual dense block (RDB) into the Laplacian pyramid super-resolution network (LapSRN).Among them, we use LapSRN to achieve upsampling image reconstruction, and adopt RDB to fully extract the hierarchical features from all the convolutional layers.Extensive experimental results on benchmark climate datasets show that our new proposed model performs better than many super-resolution methods.Besides, the climate data are more complicated than the general image, because of its dynamic and chaotic nature.To facilitate model training, we integrate original climate data provided by the China Meteorological Administration, then convert it into trainable climate images.We also publish some climate image datasets online for research.Finally, we avoid the checkerboard artifacts in the generated high-resolution climate images.
Cellphone tracking could help stem the spread of coronavirus. Is privacy the price?

“IT IS POSSIBLE TO STOP THE EPIDEMIC ” That’s the message splashed atop a website built by a University of Oxford team this week to share new research on the spread of the novel coronavirus Below that hopeful statement comes a big caveat: To stop the virus’ spread, health officials need to find and isolate the contacts of infected people—lots of them—and fast Such contact tracing is a mainstay of infectious disease control But the Oxford team is one of several now advocating for a new approach: tapping into cellphone location data to track the spread of infection and warn people who may have been exposed
Error Reconciliation in Quantum Key Distribution Protocols

Quantum Key Distribution (QKD) protocols allow the establishment of symmetric cryptographic keys up to a limited distance at limited rates. Due to optical misalignment, noise in quantum detectors, disturbance of the quantum channel or eavesdropping, an error key reconciliation technique is required to eliminate errors. This chapter analyses different key reconciliation techniques with a focus on communication and computing performance. We also briefly describe a new approach to key reconciliation techniques based on artificial neural networks.
JumpVR: Jump-Based Locomotion Augmentation for Virtual Reality

One of the great benefits of virtual reality (VR) is the implementation of features that go beyond realism. Common "unrealistic" locomotion techniques (like teleportation) can avoid spatial limitation of tracking, but minimize potential benefits of more realistic techniques (e.g. walking). As an alternative that combines realistic physical movement with hyper-realistic virtual outcome, we present JumpVR, a jump-based locomotion augmentation technique that virtually scales users' physical jumps. In a user study (N=28), we show that jumping in VR (regardless of scaling) can significantly increase presence, motivation and immersion compared to teleportation, while largely not increasing simulator sickness. Further, participants reported higher immersion and motivation for most scaled jumping variants than forward-jumping. Our work shows the feasibility and benefits of jumping in VR and explores suitable parameters for its hyper-realistic scaling. We discuss design implications for VR experiences and research.
Hand Gesture Recognition of Static Letters American Sign Language (ASL) Using Deep Learning

An American Sign Language (ASL) is a complex language. It is depending on the special gesture stander of marks. These marks are represented by hands with assistance by facial expression and body posture. ASL is the main communication language of deaf and people who have hard hearing from North America and other parts of the world. In this paper, Gesture recognition is proposed of static ASL using Deep Learning. The contribution consists of two solutions to the problem. The first one is resized with Bicubic static ASL binary images. Besides that, good recognition results in of detection the boundary hand using the Robert edge detection method. The second solution is to classify the 24 alphabets static characters of ASL using Convolution Neural Network (CNN) and Deep Learning. The classification accuracy equals to 99.3 % and the error of loss function is 0.0002. According to 36 minutes with 15 seconds of elapsed time result and 100 iterations. The training is fast and gives the very good results, in comparison with other related works of CNN, SVM, and ANN for training
Sentiment analysis for customer relationship management: an incremental learning approach

Abstract In recent years there has been a significant rethinking of corporate management, which is increasingly based on customer orientation principles. As a matter of fact, customer relationship management processes and systems are ever more popular and crucial to facing today’s business challenges. However, the large number of available customer communication stimuli coming from different (direct and indirect) channels, require automatic language processing techniques to help filter and qualify such stimuli, determine priorities, facilitate the routing of requests and reduce the response times. In this scenario, sentiment analysis plays an important role in measuring customer satisfaction, tracking consumer opinion, interacting with consumers and building customer loyalty. The research described in this paper proposes an approach based on Hierarchical Attention Networks for detecting the sentiment polarity of customer communications. Unlike other existing approaches, after initial training, the defined model can improve over time during system operation using the feedback provided by CRM operators thanks to an integrated incremental learning mechanism. The paper also describes the developed prototype as well as the dataset used for training the model which includes over 30.000 annotated items. The results of two experiments aimed at measuring classifier performance and validating the retraining mechanism are also presented and discussed. In particular, the classifier accuracy turned out to be better than that of other algorithms for the supported languages (macro-averaged f1-score of 0.89 and 0.79 for Italian and English respectively) and the retraining mechanism was able to improve the classification accuracy on new samples without degrading the overall system performance.
RC-chain: Reputation-based crowdsourcing blockchain for vehicular networks

As the commercial use of 5G technologies has grown more prevalent, smart vehicles have become an efficient platform for delivering a wide array of services directly to customers. The vehicular crowdsourcing service (VCS), for example, can provide immediate and timely feedback to the user regarding real-time transportation information. However, different sources can generate spurious information towards a specific service request in the pursuit of profit. Distinguishing trusted information from numerous sources is the key to a reliable VCS platform. This paper proposes a solution to this problem called “RC-chain”, a reputation-based crowdsourcing framework built on a blockchain platform (Hyperledger Fabric). We first establish the blockchain-based platform to support the management of crowdsourcing trading and user-reputation evaluating activities. A reputation model, the Trust Propagation & Feedback Similarity (TPFS), then calculates the reputation values of participants and reveals any malicious behavior accordingly. Finally, queueing theory is used to evaluate the blockchain-based platform and optimize the system performance. The proposed framework was deployed on the IBM Hyperledger Fabric platform to observe its real-world running time, effectiveness, and overall performance.
An Imbalanced-Data Processing Algorithm for the Prediction of Heart Attack in Stroke Patients

Early predicting heart attack out of stroke patients in a view of data analysis is an approach to reduce a high mortality rate. Stroke-patient data in Intensive Care Unit are imbalanced due to that stroke patients with heart attack are in the minority of stroke patients. How to predict heart attack in the stroke-patient data becomes a challenge. For processing the imbalanced data, this paper designs an algorithm by leveraging random undersampling, clustering and oversampling techniques, which is called undersampling-clustering-oversampling algorithm (shortly, UCO algorithm). The UCO algorithm generates nearly balanced data which are utilized to train machine-learning models for predicting heart attack. Over the database of Medical Information Mart for Intensive Care III, extensive experiments are conducted to evaluate the UCO algorithm. A setting of undersampling number of 120 in the algorithm UCO, denoted UCO(120), shows good performance in helping machine-learning classifiers extract features. Five classifiers are separately deployed to predict heart attack based on outputs of the UCO(120). Our results show that random forest classifier achieves the best predicting performance with an accuracy of 70.29%, and precision of 70.05%. It could be well-predicted using UCO(120) and random forest that whether a stroke patient will have heart attack or not.
Artificial intelligence translation under the influence of multimedia teaching to study English learning mode

With the gradual advancement of education reform, English learning has become more and more important, and efficient and fast English learning has become a concern of people. In this study, an online translation platform based on artificial intelligence was selected for exploration. Use SQLite software database server as text data source. When the user performs English translation, the query word will be input into the translation platform, and at the same time, the artificial intelligence Google API translation technology will translate the text. Based on the collected actual user service usage records, build edge computing solutions and analyze user data input records. Use word2vec to construct the feature vector of the word, and use LSTM to construct the word ranking of the text. The word ranking method is used to predict the user's service usage, select the corresponding edge server, and combine the relevant probability model to preload the service. Combined with the edge algorithm compression data processing technology of the Internet of Things, the data is synchronized and tracked. After the data is compressed, the translated text is displayed on the application interface in the form of voice and words. The research results show that the use of edge computing of the Internet of Things increases the preservation rate of historical query records of intelligent translation by 30%, and the intelligent translation platform predicts that the matching degree of users' translation queries is 90%. The use of edge algorithm compression technology of the Internet of Things can greatly save the network traffic and bandwidth of the server. The optimized query algorithm can improve query efficiency, save query time, and increase users' enthusiasm for learning English.
Deep Learning Models Combining for Breast Cancer Histopathology Image Classification

A C TBreast cancer is one of the foremost reasons of death among women in the world.It has the largest mortality rate compared to the types of cancer accounting for 1.9 million per year in 2020.An early diagnosis may increase the survival rates.To this end, automating the analysis and the diagnosis allows to improve the accuracy and to reduce processing time.However, analyzing breast imagery's is non-trivial and may lead to experts' disagreements.In this research, we focus on breast cancer histopathological images acquired using the microscopic scan of breast tissues.We present combined two deep convolutional neural networks (DCNNs) to extract distinguished image features using transfer learning.The pre-trained Inception and the Xceptions models are used in parallel.Then, the feature maps are combined and reduced by dropout before being fed to the last fully connected layers for classification.We follow a sub-image classification then a whole image classification based on majority vote and maximum probability rules.Four tissue malignancy levels are considered: normal, benign, in situ carcinoma, and invasive carcinoma.The experimentations are performed to the Breast Cancer Histology (BACH) dataset.The overall accuracy for the sub-image classification is 97.29% and for the carcinoma cases the sensitivity achieved 99.58%.The whole image classification overall accuracy reaches 100% by majority vote and 95% by maximum probability fusion decision.The numerical results showed that our proposed approach outperforms the previous methods in terms of accuracy and sensitivity.The proposed design allows an extension to whole-slide histology images classification.
Efficient Spatio-Temporal Modeling Methods for Real-Time Violence Recognition

Violence recognition is challenging since recognition must be performed on videos acquired by a lot of surveillance cameras at any time or place. It should make reliable detections in real time and inform surveillance personnel promptly when violent crimes take place. Therefore, we focus on efficient violence recognition for real-time and on-device operation, for easy expansion into a surveillance system with numerous cameras. In this paper, we propose a novel violence detection pipeline that can be combined with the conventional 2-dimensional Convolutional Neural Networks (2D CNNs). In particular, frame-grouping is proposed to give the 2D CNNs the ability to learn spatio-temporal representations in videos. It is a simple processing method to average the channels of input frames and group three consecutive channel-averaged frames as an input of the 2D CNNs. Furthermore, we present spatial and temporal attention modules that are lightweight but consistently improve the performance of violence recognition. The spatial attention module named Motion Saliency Map (MSM) can capture salient regions of feature maps derived from the motion boundaries using the difference between consecutive frames. The temporal attention module called Temporal Squeeze-and-Excitation (T-SE) block can inherently highlight the time periods that are correlated with a target event. Our proposed pipeline brings significant performance improvements compared to the 2D CNNs followed by the Long Short-Term Memory (LSTM) and much less computational complexity than existing 3D-CNN-based methods. In particular, MobileNetV3 and EfficientNet-B0 with our proposed modules achieved state-of-the-art performance on six different violence datasets. Our codes are available at https://github.com/ahstarwab/Violence_Detection.
Efficient Dynamic S-Box Generation Using Linear Trigonometric Transformation for Security Applications

Protection of data transmitted over the network from illegal access is one of the major challenges being posed by exponential growth of data in online digital communication. Modern cryptosystems assist in data sanctuary by utilizing substitution-boxes (S-boxes). This paper presents a modest and novel technique to erect dynamic and key dependent S-boxes with the help of a novel linear trigonometric transformation. A new optimization plan is also suggested to improvise the nonlinearity characteristic of the preliminary S-box generated through trigonometric transformation. The proposed technique has the competence to create significant quantity of cryptographic strong S-boxes with the help of projected scheme. A specimen S-box is procreated, and standard performance criteria is applied to appraise the cryptographic strength of the resultant S-box and other known S-boxes available in the literature. Comparative performance analyses validate the noteworthy contribution of the proposed scheme for the generation of dynamic and secure S-boxes. An image privacy preserving scheme based on the proposed S-box is also suggested to validate the fact that it holds strong candidature for modern cryptosystems to protect multimedia data.