Reviewerly/exHarmony huggingface dataset:
Challenging Artificial Intelligence With Multiopponent and Multimovement Prediction for the Card Game Big2

Big2 is one of the most popular card games in Chinese residential regions; however, there is lack of advanced computer players with challenging artificial intelligence.In this study, we propose the Big2 artificial intelligence (Big2AI) framework consisting of card superiority analysis, dynamic weight adjustment, game feature learning, and multi-opponent movement prediction based on Monte Carlo Tree Search (MCTS) and Information Set Monte Carlo Tree Search (ISMCTS).According to our review of relevant research, this is the first artificial intelligence framework that can perform self-playing with various computer players, improve win rates through historical game features, and predict multiple movements of multiple opponents in the card game Big2.An Android-based prototype of four-player Big2 game is implemented to verify the feasibility and superiority of Big2AI.Experimental results show that Big2AI outperforms existing artificial intelligence and can achieve the highest win rate and the least losing points against computer and human players in Big2 games.
Multifidelity regression of sparse plasma transport data available in disparate physical regimes

Physical data are typically generated by experiments and computations in limited parameter regimes. When datasets generated using such disparate methods are combined into one dataset, the resulting dataset is typically sparse, with dense "islands" in a potentially high-dimensional parameter space, and predictions must be interpolated among such islands. Using plasma transport data as our example, we propose a multifidelity Gaussian-process regression framework that incorporates physical data from multiple sources at multiple fidelities. The impact of the proposed framework varies from little improvement over simpler approaches to qualitatively changing the prediction with consistently increased confidence in regions lacking high-fidelity data. By varying low- and high-fidelity data sources, we demonstrate an approach for determining when multifidelity Gaussian-process regression adds value over single-fidelity regression and therefore when its additional computational costs are merited. We also examine the case in which the outputs of the low- and high-fidelity models correspond to different physical quantities, one of which may be intrinsically computationally cheaper to compute. We conclude by analyzing strategies for sampling high-fidelity data for use in this framework, and we develop a simple sampling approach for reducing regression error across large gaps in data.
Geometric quantum adiabatic methods for quantum chemistry

Existing quantum algorithms for quantum chemistry work well near the equilibrium geometry of molecules, but the results can become unstable when the chemical bonds are broken at large atomic distances. For any adiabatic approach, this usually leads to serious problems, such as level crossing and/or energy gap closing along the adiabatic evolution path. In this work, we propose a quantum algorithm based on adiabatic evolution to obtain molecular eigenstates and eigenenergies in quantum chemistry, which exploits a smooth geometric deformation by changing bond lengths and bond angles. Even with a simple uniform stretching of chemical bonds, this algorithm performs more stably and achieves better accuracy than our previous adiabatic method [Phys. Rev. Research 3, 013104 (2021)]. It solves the problems related to energy gap closing and level crossing along the adiabatic evolution path at large atomic distances. We demonstrate its utility in several examples, including H${}_2$O, CH${}_2$, and a chemical reaction of H${}_2$+D${}_2\rightarrow$ 2HD. Furthermore, our fidelity analysis demonstrates that even with finite bond length changes, our algorithm still achieves high fidelity with the ground state.
A Hybrid Framework for Efficient Detection of Fake Currency Notes

The monetary system of any country should be properly secured that make effective administration. The terrorists or outside underground riots may generate false notes that would appear like genuine notes that affect any country’s loss of economy. To provide more security to the country's currency, efficient methodologies are reviewed and are mixed in order to provide more accuracy and increase security in protecting the country's policies of currency notes. Although the characteristics are given by the concerned government, the determination of note is fake or genuine depends on the adapted double-check process in a very quick time. The two techniques (reinforcement learning + recurrent convolution neural network) along with the image processing technique that is used in determining the note’s transparency are applied one after the other. If any segment is missed according to the policy but passes through the first phase, that might be covered in the second phase technique. This way would help to the detection of the currency if fake or real.
A Comparative Analysis of Deep Fake Techniques

Recent developments and evolutions in machine learning algorithms have directed to the emergence of first-class altered pictures that create video frames and have a striking similarity to real-world pictures that create video frames. This can have a disastrous effect on how people perceive digitally available knowledge or facts. Recent developments in machine learning and camera function have allowed pictures that compose video frames and sounds to be convincingly altered. These deep-fake films differ from actual videos by replacing the soundtrack, the lip movement, or the location where the video was captured (background). Many relevant tools and automated systems for detecting such deep fake films have been developed. Researchers employ techniques such as pose estimation, facial artefacts, temporal pattern analysis, background comparison, eye blinking, and mesoscopic analysis. In a unique statistical research, we want to present a descriptive review of these traditional deep Fake detection approaches.
A Dependent Dependency Calculus

Abstract Over twenty years ago, Abadi et al. established the Dependency Core Calculus (DCC) as a general purpose framework for analyzing dependency in typed programming languages. Since then, dependency analysis has shown many practical benefits to language design: its results can help users and compilers enforce security constraints, eliminate dead code, among other applications. In this work, we present a Dependent Dependency Calculus (DDC), which extends this general idea to the setting of a dependently-typed language. We use this calculus to track both run-time and compile-time irrelevance, enabling faster type-checking and program execution.
Design of a Blockchain Architecture and Use of Smart Contracts to Improve Processes in Notary Office

The present investigation proposes the use of the blockchain as a replacement mechanism for the current notarial system, the use of smart contracts is applied for this purpose, in which all the terms or clauses of a transaction are detailed, as in a traditional contract, but with new capabilities and security mechanisms specific to blockchain due to its native encryption algorithms, without the intervention of third parties, the proposed architecture establishes the processes of consensus and distribution of the general ledger in each of the nodes, distributed in the different certification entities or organizations, as well as each of the components that intervene during the process of generating a new transaction that will begin with the agreement between a seller and a buyer on a good, whether a vehicle, a house, a land, etc., also seeking to eliminate the intervention of several state entities in the notarial processes, in addition to complying go fully all the clauses of the contracts generated.
Resource-Aware Discretization of Accelerated Optimization Flows: The Heavy-Ball Dynamics Case

This article proposes a methodology for discretizing accelerated optimization flows while retaining their convergence properties. Inspired by the success of resource-aware control in developing efficient closed-loop feedback implementations on digital systems, we view the last sampled state of the system as the resource to be aware of. We illustrate our design methodology for discretization on a newly introduced continuous-time dynamics, the heavy-ball dynamics with displaced gradient. Our algorithm design employs techniques from resource-aware control that, in this context, have interesting parallelisms with the discrete-time implementation of optimization algorithms. These include derivative- and performance-based triggers to monitor the evolution of the Lyapunov function as a way of determining the stepsize, exploiting sampled information to enhance performance, and employing high-order holds using more accurate integrators of the original dynamics. Our approach gives rise to variable-stepsize discrete-time algorithms that retain by design the monotonically decreasing properties of the Lyapunov certificate of the continuous-time heavy-ball dynamics with displaced gradient.
Domain Adaptation in Robotics: A Study Case on Kitchen Utensil Recognition

Recognition methods based on Deep Learning (DL) currently represent the state of the art in a number of robot-related tasks as, for instance, computer vision for autonomous guidance or object manipulation. Nevertheless, the large requirements of annotated data constitute one of their main drawbacks, at least when considering supervised frameworks. In this context, the Domain Adaptation (DA) paradigm stands as a promising, yet largely unexplored, framework for tackling such cases avoiding the need for manually data annotation. This work presents a case of study of unsupervised DA algorithms for robot-based kitchenware manipulation. The influence of combining several source or target domains is also analyzed as a proposal to improve the adaptation process. For that, we evaluate three representative state-of-the-art techniques (DANN, PixelDA, and DeepCORAL) and assess them using four corpora of kitchen household images, being three of them specifically developed for this work. The results obtained show that the DeepCORAL strategy generally outperforms the rest of the cases. Moreover, the various scenarios posed suggest that the combination of source sets enhances the adaptation to novel target domains. Finally, the experimentation state the relevance of DA methods and their usefulness in robotic applications.
Graphfire: Synergizing Fetch, Insertion, and Replacement Policies for Graph Analytics

Despite their ubiquity in many important big-data applications, graph analytic kernels continue to challenge modern memory hierarchies due to their frequent, long-latency, pointer indirect accesses to vertex property data. Such accesses exhibit poor locality and variable reuse that trouble cache replacement policies, and consequently increase memory bandwidth pressure. Specialized graph-tailored prefetching mechanisms, processor designs, and memory hierarchy engines have been developed to tolerate the long latencies of such accesses. However, these approaches are either too bandwidth-intensive, require invasive hardware changes that inhibit general-purpose computation flexibility, or rely on software preprocessing that limits true speedup. This work introduces Graphfire, a flexible memory hierarchy approach that learns different access patterns in graph processing and exploits the synergy of specialized fetch, insertion, and replacement optimizations for problematic indirect accesses without relying on software or ISA support. More specifically, Graphfire identifies when these irregular accesses occur and employs tailored access granularities, data-aware insertion, and frequency-based replacement accordingly. It achieves up to a 1.79× speedup (geomean 1.3×) and these improvements scale due to bandwidth efficiency; with 64 cores, Graphfire yields up to a 71.33× speedup (geomean 63.32×) over a single baseline core and allows memory-bound graph analytic codes to scale far beyond prior work.
Experimental Proof Manifest Bilateral Blur as Outstanding Blurring Technique for CNN Based SR Models to Converge Quickly

Image super-resolution is utilized in a variety of applications, including medical imaging, video surveillance, satelite photography and astronomy, fraud detection, real-time scene monitoring for security and others. The most recent literature encompasses a number of image low- resolution methodologies. Image processing based on CNN deep learning’s super-resolution techniques are the simplest and most resource-efficient. In contrast, the original SRCNN architecture incorporated Gaussian blur, which takes a long time to converge. This not only increases the computational cost, but it also extends the training time. As a result, we retrained SRCNN using widely available blurring and discovered that the bilateral filter outperforms the Gaussian filter. Our objective is to keep the original SRCNN functionalities while shortening training time and computational requirements. To the best of our knowledge, this is the first research of its sort, and no other study has applied this alternative blurring training on SRCNN and ranked it as the best for image super-resolution.
Facial expression recognition system based on variational mode decomposition and whale optimized KELM

In this paper, a methodology for the Facial Expression Recognition (FER) system is proposed using the Variational Mode Decomposition (VMD) and Whale Optimization (WO) with Kernel Extreme Learning Machine (KELM) classifier. A non-stationary, adaptive, and variational signal analysis technique called VMD is adopted in this work, which depends on the signal's frequency information content. The VMD decomposed the input image into four modes, and the 4th mode of the VMD decomposition is considered for feature representation, a high-frequency band. This VMD mode preserves the edge and shape features from the face image efficiently. The high dimensional features are reduced using the Principal Component Analysis + Linear Discriminant Analysis (PCA + LDA) method, which minimizes the feature dimension and retains the high variance among emotion classes. A hybrid classifier with better scalability and faster learning speed than SVM and Least Squares SVM (LS-SVM), namely WO-KELM, is proposed to discriminate facial expressions accurately. The WO algorithm is employed for optimal parameter tuning of the KELM with the RBF kernel. The performance of the proposed framework are compared with state-of-the-art methods. Extensive experiments are assessed on the two benchmark datasets, namely Japanese Female Facial Expression (JAFFE) and the Extended Cohn-Kanade (CK +). Experimental results founded on 5-fold Stratified Cross-Validation (SCV) test reveal the superiority of the proposed method over state-of-the-art systems.
Development of AR Application to Promote the Historical Past of the Native Land

The article demonstrates the possibilities of using augmented reality technology to create a software application in the field of local lore “Monuments of the city of Zhytomyr”. The AR program “Monuments of the City of Zhytomyr” was implemented, the main task of which is to simplify the submission of information about people whose monuments are located in the city of Zhytomyr. To do this, nine 3D models were created, for each of which information was selected about the person to whom the monument is dedicated. Photographs of the sites were also taken for further use as triggers. Audio help is recorded for each model. The proposed development can be used to promote tourism and the history of the city. Augmented reality technology in the educational process has only just begun to develop and be increasingly used. We believe that this software application can be used in the educational process for such disciplines as “Local History”, “Culturology”, etc.
Sarcasm Detection Using Stacked Bi-Directional LSTM Model

Sarcasm is one of the sentiments which is being used to communicate a negative opinion utilizing positive words. The world is full of social media and many kinds of the web-based portal and this media stores a huge amount of textual data which contains sentiments, and sarcasm is one of the sentiments which is being used nowadays in many of this platform, using sarcasm someone can communicate their negative words in a positive way which is we can call a sarcastic way of communication. In opinion mining, the field of natural language processing detection of sarcasm from a given data is an important task. It is a binary classification task for which model proposed a system which classifies whether a given set of word is sarcastic or not-sarcastic. In this research work, we proposed the work based on the Stacked Bi-Directional Long Short-Term Memory (Stk-BLSTM) network which enhances the overall result in terms of performance matrix.
Named Entity Recognition Based on Transformer Encoder in the Medical Field

With the large-scale popularization of electronic medical record system (electronic medical record system EMR), EMR contains lots of consultation, examination and other information about patient. So how to make full use of these data to provide support for follow-up medical research and medical practice has developed as a research focus in the current medical field. Named entity recognition in medical texts is the basic task among them. The existing medical named entity recognition models are mostly founded on the BiLSTM neural network. Because the BiLSTM network is difficult to parallel calculations, and there is a risk of gradient disappearance or explosion, this paper designs a Transformer Encoder medical named entity recognition model combined with BERT. The mechanism for extracting features enables the network to parallel calculations and at the same time improve the recognition effect.
Pre-Training-Based Grammatical Error Correction Model for the Written Language of Chinese Hearing Impaired Students

Grammatical error correction has been considered as an application closely related to daily life and an important shared task in many prestigious competitions and workshops.The neural machine translation with an encoder-decoder architecture containing language models has been the fundamental solution for the grammatical error correction.Whereas Grammatical error correction task on texts of hearing impaired people or its solution has not been seen yet, and common Grammatical error correction tasks are suffering several challenges, such as insufficient training data, insufficient accuracy due to the unsatisfactory capacity of extracting semantic and grammatical patterns.Under these circumstances, we proposed a novel encoder-decoder architecture based on multi-head self-attention along with multiple strategies, which excels at extracting deep representations from the corrupted sentences of hearing impaired students and further reconstructing the sentences into grammatical ones.Via the re-ranking strategy, our model can correct various kinds of errors including spelling and complex syntax errors.The ablation experiments prove that the semantic extracting of self-attention mechanism excluding the position encoding with the word order shuffle operation can significantly learn the hearing impaired students' sentence patterns whose word order is quite different from the ones of hearing people and improve the correction scores.The pre-training can enhance the restoring efficiency of sentence structure in the decoding process.The comparison experiments with baseline models show that our model obtains superior performance either in the hearing impaired students' grammatical error correction or in a common grammatical error correction shared task.
A Systematic Literature Review: The Taxonomy of Hybrid Cryptography Models

The COVID-19 pandemic altered the way millions of citizens around the world live and work, establishing a so-called new norm in social standards. Also, the pandemic has created a variety of unique circumstances that affect society and business due to cybercrime. Because of the increased anxiety caused by the pandemic, people were more likely to launch successful cyberattacks and this increased the variety and number of cyberattacks. We present a two-dimensional classification of existing research on Hybrid Cryptography Models based on processing phases and applications domain. As a result, we were able comparison study has been made between other state-of-the-art existed ones to aid hybrid models' performance enhancement in the aftermath of this pandemic.
Cognitive decisions based on a rule-based fuzzy system

We develop an agent-based artificial general intelligent system that can be implemented in compact and power-efficient electronic hardware. The hardware under development is called the Street Engine, which is a hardware-based cognitive architecture for implementing agent-based artificial intelligence. In this paper, we introduce an agent-based system to replicate simple cognitive behaviours. In the processes of this system, numerical data are converted into fuzzy symbolic representations of the surrounding environment, and reasoning rules are included in a modified Fuzzy Inference System to support the cognitive decision-making. We use a case study example, the homing behaviour of the honey bee, to demonstrate constructing production rules and implementing the cognitive and reasoning capabilities of agents. The low level cognitive behaviour is converted into a rule-based fuzzy system, and hardware-based experiments have been conducted to verify the effectiveness of the proposed technique.
NPT-Loss: Demystifying Face Recognition Losses With Nearest Proxies Triplet

Face recognition (FR) using deep convolutional neural networks (DCNNs) has seen remarkable success in recent years. One key ingredient of DCNN-based FR is the design of a loss function that ensures discrimination between various identities. The state-of-the-art (SOTA) solutions utilise normalised Softmax loss with additive and/or multiplicative margins. Despite being popular and effective, these losses are justified only intuitively with little theoretical explanations. In this work, we show that under the LogSumExp (LSE) approximation, the SOTA Softmax losses become equivalent to a proxy-triplet loss that focuses on nearest-neighbour negative proxies only. This motivates us to propose a variant of the proxy-triplet loss, entitled Nearest Proxies Triplet (NPT) loss, which unlike SOTA solutions, converges for a wider range of hyper-parameters and offers flexibility in proxy selection and thus outperforms SOTA techniques. We generalise many SOTA losses into a single framework and give theoretical justifications for the assertion that minimising the proposed loss ensures a minimum separability between all identities. We also show that the proposed loss has an implicit mechanism of hard-sample mining. We conduct extensive experiments using various DCNN architectures on a number of FR benchmarks to demonstrate the efficacy of the proposed scheme over SOTA methods.