Reviewerly/exHarmony huggingface dataset:
A Framework to Generate Smart Contracts On the Fly

Blockchain is a disruptive technology that offers a secure decentralized network, and allows for direct transactions between distributed entities, without the need for a trusted third party. Smart contracts are executable codes hosted on the blockchain and enable the implementation of several decentralized applications, in different domains. However, due to the complexity of contract construction, it is necessary to seek ways to facilitate and make this innovative development process robust. This paper presents Gifflar, a framework for generating smart contract code on the fly, in such a way that it allows a system to write, compile and implement blockchain contracts with a decentralized application still running. Gifflar is structured in components that implement design patterns to better define the responsibility of each one of the parts and exports a simple interaction module for contract modeling in JSON and automatic code writing using the TypeScript language. As far as we know, this framework is one of the first to generate contracts dynamically at run time. Thus, the work contributes to the state of the art by addressing a new paradigm, where systems can act as smart contract developers.
CHAD for expressive total languages

Abstract We show how to apply forward and reverse mode Combinatory Homomorphic Automatic Differentiation (CHAD) (Vákár (2021). ESOP , 607–634; Vákár and Smeding (2022). ACM Transactions on Programming Languages and Systems 44 (3) 20:1–20:49.) to total functional programming languages with expressive type systems featuring the combination of • tuple types; • sum types; • inductive types; • coinductive types; • function types. We achieve this by analyzing the categorical semantics of such types in $\Sigma$ -types (Grothendieck constructions) of suitable categories. Using a novel categorical logical relations technique for such expressive type systems, we give a correctness proof of CHAD in this setting by showing that it computes the usual mathematical derivative of the function that the original program implements. The result is a principled, purely functional and provably correct method for performing forward- and reverse-mode automatic differentiation (AD) on total functional programming languages with expressive type systems.
Knowledge-Based Management of Virtual Training Scenarios

Virtual reality (VR) gains increasing attention as a method of implementing training systems in different domains, in particular, when real training is potentially dangerous for the trainees or the environment, or requires expensive equipment. The essential element of professional training is domain-specific knowledge, which can be represented using the semantic web approach. It enables reasoning as well as complex queries against the representation of training scenarios, which can be valuable for teaching purposes. However, the available methods and tools for creating VR training systems do not use semantic knowledge representation. Currently, the creation, modification, and management of training scenarios require skills in programming and computer graphics. Hence, they are unavailable to domain experts without expertise in IT. In this paper, we propose an ontology-based representation and a method of modeling VR training scenarios. In our approach, trainees’ activities, potential mistakes as well as equipment and its possible errors are represented using domain knowledge understandable to domain experts. We illustrate the approach by modeling VR training scenarios for electrical operators of high-voltage installations.
Role of Artificial Intelligence in COVID-19 Prediction Based on Statistical Methods

Coronavirus disease 2019 (COVID-19) is a respiratory ailment that can spread from individual to individual, first recognized during an outbreak in Wuhan, China. Possibility of getting COVID-19 is greater for people who are in contact of someone known to have COVID-19, for example clinical professional, or family member. Peoples at higher peril for sickness are the those who live in or have starting late been in a zone with advancing spread of COVID-19. A few patients have pneumonia in the two lungs, multi-organ failure and sometimes death. In this work we are predicting the impact of COVID-19 cases in India based on time series analysis, correlation analysis, Granger Test, and Group Method of Data Handling (GMDH). We have compared the prediction of four algorithms namely combinatorial (quick), stepwise forward, stepwise mixed, and GMDH neural network (GMDH-NN) for predicting the future of India. Out of that stepwise mixed method gives good prediction for confirmed cases but GMDH-NN gives better prediction in case of death and recovered cases. As this disease is declared as an epidemic, the present study will help researchers to understand the impact of this outbreak. We have used combinatorial (quick), stepwise forward selection, stepwise mixed selection, and GMDH neural network to predict the spread of disease in India.
On Wireless Channel Classification Based on CP-OFDM System

The channel feature identification plays a key role in booting the transmission efficiency through dynamically adjusting the transceiver parameters. In this paper, we propose a novel machine learning-based channel identification method for cyclic-prefix orthogonal frequency division multiplexing (CP-OFDM) systems. We first extract the features of the channel state information (CSI) in the time domain. On this basis, learning based classification methods, including K-nearest neighbor (KNN), support vector machine (SVM) and neural network (NN), are used to classify the different channel types. To further reduce the computational complexity, we leverage data extraction and data truncation. Our simulation results show that the proposed method can achieve a favorable performance in terms of classification accuracy (CA). For example, the KNN algorithm can achieve CA <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\geq 90\%$</tex> when SNR is −10dB, and KNN and SVM algorithm can achieve CA <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\geq 90\%$</tex> when SNR is −8dB. In addition, we also introduce the multi-frame gain combination method to further improve the classification performance in the low SNR regions.
Research on Intelligent Calibration of English Long Sentence Translation Based on Corpus

With the continuous promotion and development of the new curriculum reform, English teaching is becoming more practical and comprehensive. As an indispensable part of daily English topics, the use rate and scope of English attributive clauses are extensive. Moreover, due to English attributive provisions, the length of the whole English sentence will inevitably increase; therefore, we can accurately understand and translate sentences by mastering the translation and understanding details of attributive clauses. In addition, there are noticeable differences between English and Chinese in attributive clauses. Chinese will not add a variety of modifiers like English but will directly put them in front of them as attributives, so we should pay attention to this in translation. This process increases the difficulty of the English translation. Therefore, this paper proposes a Corpus-based intelligent calibration of English long sentence translation. Based on the construction of the English long sentence Corpus, an intelligent calibration algorithm for English long sentence translation is designed, and experiments verify the effectiveness of this method.
Detecting deepfake videos by visual-audio synchronism

Different to traditional works on frame-level features and temporal characteristics, we propose a deepfake video detection method based on visual-audio synchronism, which compares the audio stream and the visual stream by an improved siamese neural network. We combine the audio stream and visual stream as a 2-channel input and design a 2-branches network to achieve the visual-audio synchronism detection. Preliminary experiments demonstrate the efficiency of the proposed method, which can achieve the highest accuracy compared with other existing methods.
A FORMALIZED ALGORITHM FOR OPTIMIZING THE DECISION-MAKING PROCESS IN CONDITIONS OF STOCHASTIC UNCERTAINTY

One of the main tasks of the decision support theory is the study of methods and tools for solving the problem of minimizing the negative consequences and risks in choosing strategic directions for the development of the studied system - the object of management. The formal algorithm of the optimization in conditions of the decision-making process stochastic uncertainty, and realization of steady states in system is investigated. The purpose of the algorithm model is to provide the predicted dynamics, compensation of structural, parametric uncertainty of the control system. The ambiguity of the choice the alternative solutions and as a consequence - the inadequacy of the mathematical model, due to the significant amount of stochastic and functional relationships, different ways of presenting input data, the impossibility formalizing the studied processes. Solutions in conditions of partial or complete uncertainty can be found by searching for elements of a set the alternatives, each of which with some probability may be the optimal solution. If statistical observations of the studied object or management process are incomplete, insufficiently formalized, or impossible at all, then the uncertainty of the decision to predict the directions of their possible development is clear. The decision-making process in conditions of uncertainty is proposed to be divided into stages: specification and formalization of the decision-making model; choice methods and algorithms for constructing alternatives taking into account the peculiarities of the chosen decision-making model. Parametric uncertainty is described as an interval estimate of possible values of the studied parameter. The interval can be strictly limited by numerical values, or with not clear limits - descriptive qualitative variables. Modeling of the control process in conditions of stochastic uncertainty is based on the definition of the object under study as a complex system. A promising area of research on this topic is a mathematical description of the value distribution function within the interval, which can be formalized on the basis of expert estimates, or as a heuristic probability distribution function of unpredictable events.
Cartoon Figure Recognition with The Deep Residual Network

Because of the wide application of deep learning, there are more neural network structures in image recognition technology nowadays, but there are various differences in the accuracy of image recognition because of the various differences in network structures. For this reason, it is especially important to use different neural network structures for different forms of image data. This paper focuses on exploring the differences between LSTM networks, residual networks, and CNN networks in terms of the accuracy of cartoon character recognition. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</sup> Firstly, the web crawler acquires 14 different cartoon character images and manually screens the original data to remove the duplicate images and obtain the preliminary data. Then data enhancement was performed on the preliminary data, and the form of rotating the images was selected to complete the pre-processing of the data, which solved the problem of using different code forms for different forms of data importing into the neural network; the LSTM network, CNN network and CNN network with added residual function were used to recognize the pre-processed data. The experiments show that the CNN network structure with residual function can achieve higher accuracy compared to LSTM, with the final result of 76.08%.
Surveillance Through Semi-Autonomous Bot

This paper consists of the design and implementation of the semi-autonomous surveillance bot. The idea presented in the paper is to monitor a surrounding or unknown area. This paper carries the different phases of realization such as the motion of the robot, obstacle avoidance, and video capturing and streaming. After the video has been captured the recorded clip is streamed to a remote server which assesses the surrounding. For the implementation of the idea, different tools were required for the execution of the precursor. For building the model, the hardware required was Raspberry Pi, infrared sensors, L293D motor driver. Raspberry Pi consisting of a Raspbian operating system running on python language for video capturing and obstacle sensing was used.
Advancing OpenMP Offload Debugging Capabilities in LLVM

Debugging an application is famously twice as hard as writing the application in the first place. While this sentiment predates modern GPU programming by decades, it is all the more true when the application has to manage computation and memory across different architectures, memory spaces, and execution modes. Any subtle error, whether in the application, the compiler, or runtime system, can lead to unexpected behavior that is hard to understand from the program output alone. While some tooling solutions for GPU debugging exist, their maturity and usefulness varies gravely between vendors. Furthermore, as OpenMP offloading puts an abstraction layer between the programmer and the underlying hardware, the information from a native GPU driver (debugging tool) is not always transferable to the OpenMP programming model. As the OpenMP Tooling [12] (OMPT) and Debug [4] (OMPD) interfaces are still not ready to debug OpenMP offloading code in production, developers have a hard time to comprehend the implementation state, error sources, and interplay of the OpenMP world with the foreign device runtimes, e.g., CUDA.
Transforming CRM Through Artificial Intelligence

The present times are disrupting times for every kind of business and every aspect of a business. It is not about contactlessness; it is about seamlessness. The auto manufacturers have already started “Amazoning” dealerships. Brands are developing customer-specific platforms like jaguar.rockar.com, where one can explore the range, check the price, select dealer, search inventory, and schedule test drives. The brand Cadillac creates virtual reality experiences in Google Search, wherein a car appears in a living room through a phone call. One can see how it looks, walk around it, open the doors, and get a sense of the interior. This chapter explores the transformation of CRM through artificial intelligence.
Synthesizing VERDICT Maps from Standard DWI Data Using GANs

VERDICT maps have shown promising results in clinical settings discriminating normal from malignant tissue and identifying specific Gleason grades non-invasively. However, the quantitative estimation of VERDICT maps requires a specific diffusion-weighed imaging (DWI) acquisition. In this study we investigate the feasibility of synthesizing VERDICT maps from standard DWI data from multi-parametric (mp)-MRI by employing conditional generative adversarial networks (GANs). We use data from 67 patients who underwent both standard DWI-MRI and VERDICT MRI and rely on correlation analysis and mean squared error to quantitatively evaluate the quality of the synthetic VERDICT maps. Quantitative results show that the mean values of tumour areas in the synthetic and the real VERDICT maps were strongly correlated while qualitative results indicate that our method can generate realistic VERDICT maps that could supplement mp-MRI assessment for better diagnosis.
Overview of Speckle Noise Suppression Methods in SAR Image Based on NLM

Due to the diffuse reflection of the ground in SAR image and its serious speckle noise, the technology of noise suppression is an important research content. In this paper, the technology of noise suppression and various improved methods based on non-local means (NLM) are introduced. Besides, the experimental comparison of each NLM filter method is carried out. Finally, the possible research directions of NLM in future are considered and discussed.
Designing in Complex Security Contexts: Enabling Frame Awareness through Sharks, Dollar Signs, and Police Badges

Over the last decade, the emergence of military design theorists, educators, and practitioners has coagulated into a community of practice internationally and across multiple armed forces. This new group of military academics and professionals demonstrates a keen interest in developing and expanding the notion of “designing for war.” For military design theory, there are unexpected design contributions from postmodern philosophy, non-Western concepts, and other highly subjective and alternative fields and practices that provide insight and novel thought for human opportunities within the dynamic and complex conflict contexts of the 21st century. Many of these novel constructs are not available in mainstream commercial design applications, methods, or theory—they were developed for military design contexts and are largely unknown outside of the community of practice for security contexts. Part of this lack of exposure is inevitable, given the classification requirements of many design efforts. This article presents one of the most popular military design techniques used by the Joint Special Operations University (JSOU), a military education platform in Tampa, Florida. JSOU is part of the US Special Operations Command (USSOCOM) and provides tailored education to Special Operations Forces (SOF) worldwide.
Machine Learning based approach to Image Description for the Visually Impaired

The paper, supported with a review of the literature on image description technologies, introduces an alternative approach that can automatically generate audio description of images, which can greatly support the visually impaired. The need for the paper has arisen from the reality that, the interaction points for visually impaired is getting constrained in an increasingly digitised environment and accessing digital medium through an image describer can be an enabler for the visually impaired. The images which are unseen by the visually impaired are processed, suitable description are generated and converted to a voice output. As against the standard methods like Computer Vision and Convolutional Neural Networks (CNN), the paper makes use of Inception Resnet - V2 model as the feature extractor and decoder (GRU-RNN) along with Bahdanau attention model to generate the text description of the image which is finally converted to an audio using Google Text-to-Speech converter. The results found to be more accurate and accordingly can be supportive in accessing the digital medium for the visually impaired.
Joint Multitarget Detection and Tracking in Multipath Environment Using Expectation Maximization Algorithm

Joint detection and tracking is fundamentally important in signal processing, navigation, and radar applications.Especially, multitarget detection and tracking in multipath environments is a promising issue and broadening range for joint detection and tracking.However, its wide adoption in realworld systems is challenged by the unknown data association, complex and changeable motion environment, and data mutual coupling.To address those problems, we propose a joint detection and tracking scheme based on expectation maximization (EM) algorithm.By alternately computing the complete likelihood function and optimizing it with respect to target existence state (detection) and target kinematic state (tracking), the proposed scheme iteratively optimizes estimation and data association.Furthermore, we provide a hybrid forward and backward algorithm during the M-step to deal with the coupling of the target existence state and target kinematic state.We also provide a convergence speed-up algorithm based on K-means method and stochastic initialization method to accelerate convergence speed.Finally, we verify the effectiveness of the proposed joint detection and tracking scheme by conducting extensive simulations with different tracking scenarios.The simulation results show that the proposed algorithm not only improves the detection and tracking performance but also stays stable performance in complex and changeable motion environment and high noise background.
Novel Augmented Reality System for Oral and Maxillofacial Surgery

Recently, in the field of surgical visualization, augmented reality technology has shown incomparable advantages in oral and maxillofacial surgery. However, the current augmented reality methods need to develop personalized occlusal splints and perform secondary Computed Tomography (CT) scanning. These unnecessary preparations lead to high cost and extend the time of preoperative preparation. In this paper, we propose an augmented reality surgery guidance system based on 3D scanning. The system innovatively designs a universal occlusal splint for all patients and reconstructs the virtual model of patients with occlusal splints through 3D scanning. During the surgery, the pose relationship between the virtual model and the markers is computed through the marker on the occlusal splint. The proposed method can replace the wearing of occlusal splints for the secondary CT scanning during surgery. Experimental results show that the average target registration error of the proposed method is \(\text{1.38}\pm \text{0.43 mm}\), which is comparable to the accuracy of the secondary CT scanning method. This result suggests the great application potential and value of the proposed method in oral and maxillofacial surgery.
Improving Safety in Deep Reinforcement Learning using Unsupervised Action Planning

One of the key challenges to deep reinforcement learning (deep RL) is to ensure safety at both training and testing phases. In this work, we propose a novel technique of unsupervised action planning to improve the safety of on-policy reinforcement learning algorithms, such as trust region policy optimization (TRPO) or proximal policy optimization (PPO). We design our safety-aware reinforcement learning by storing all the history of “recovery” actions that rescue the agent from dangerous situations into a separate “safety” buffer and finding the best recovery action when the agent encounters similar states. Because this functionality requires the algorithm to query similar states, we implement the proposed safety mechanism using an unsupervised learning algorithm, k-means clustering. We evaluate the proposed algorithm on six robotic control tasks that cover navigation and manipulation. Our results show that the proposed safe RL algorithm can achieve higher rewards compared with multiple baselines in both discrete and continuous control problems. The supplemental video can be found at: https://youtu.be/AFTeWSohILo.