Reviewerly/exHarmony huggingface dataset:
Procedural Video Game Scene Generation by Genetic and Neutrosophic WASPAS Algorithms

The demand for automated game development assistance tools can be fulfilled by computational creativity algorithms. The procedural generation is one of the topics for creative content development. The main procedural generation challenge for game level layout is how to create a diverse set of levels that could match a human-crafted game scene. Our game scene layouts are created randomly and then sculpted using a genetic algorithm. To address the issue of fitness calculation with conflicting criteria, we use weighted aggregated sum product assessment (WASPAS) in a single-valued neutrosophic set environment (SVNS) that models the indeterminacy with truth, intermediacy, and falsehood memberships. Results are presented as an encoded game object grid where each game object type has a specific function. The algorithm creates a diverse set of game scene layouts by combining game rules validation and aesthetic principles. It successfully creates functional aesthetic patterns without specifically defining the shapes of the combination of games’ objects.
Quantum Fourier transform to estimate drive cycles

Drive cycles in vehicle systems are important determinants for energy consumption, emissions, and safety. Estimating the frequency of the drive cycle quickly is important for control applications related to fuel efficiency, emission reduction and improving safety. Quantum computing has established the computational efficiency that can be gained. A drive cycle frequency estimation algorithm based on the quantum Fourier transform is exponentially faster than the classical Fourier transform. The algorithm is applied on real world data set. We evaluate the method using a quantum computing simulator, demonstrating remarkable consistency with the results from the classical Fourier transform. Current quantum computers are noisy, a simple method is proposed to mitigate the impact of the noise. The method is evaluated on a 15 qubit IBM-q quantum computer. The proposed method for a noisy quantum computer is still faster than the classical Fourier transform.
Gradualizing the Calculus of Inductive Constructions

We investigate gradual variations on the Calculus of Inductive Construction (CIC) for swifter prototyping with imprecise types and terms. We observe, with a no-go theorem, a crucial tradeoff between graduality and the key properties of normalization and closure of universes under dependent product that CIC enjoys. Beyond this Fire Triangle of Graduality, we explore the gradualization of CIC with three different compromises, each relaxing one edge of the Fire Triangle. We develop a parametrized presentation of Gradual CIC (GCIC) that encompasses all three variations, and develop their metatheory. We first present a bidirectional elaboration of GCIC to a dependently-typed cast calculus, CastCIC, which elucidates the interrelation between typing, conversion, and the gradual guarantees. We use a syntactic model of CastCIC to inform the design of a safe, confluent reduction, and establish, when applicable, normalization. We study the static and dynamic gradual guarantees as well as the stronger notion of graduality with embedding-projection pairs formulated by New and Ahmed, using appropriate semantic model constructions. This work informs and paves the way towards the development of malleable proof assistants and dependently-typed programming languages.
Learning Compact Metrics for MT

Recent developments in machine translation and multilingual text generation have led researchers to adopt trained metrics such as COMET or BLEURT, which treat evaluation as a regression problem and use representations from multilingual pre-trained models such as XLM-RoBERTa or mBERT.Yet studies on related tasks suggest that these models are most efficient when they are large, which is costly and impractical for evaluation.We investigate the trade-off between multilinguality and model capacity with RemBERT, a stateof-the-art multilingual language model, using data from the WMT Metrics Shared Task.We present a series of experiments which show that model size is indeed a bottleneck for cross-lingual transfer, then demonstrate how distillation can help addressing this bottleneck, by leveraging synthetic data generation and transferring knowledge from one teacher to multiple students trained on related languages.Our method yields up to 10.5% improvement over vanilla fine-tuning and reaches 92.6% of RemBERT's performance using only a third of its parameters.
Symmetry-aware reservoir computing

We demonstrate that matching the symmetry properties of a reservoir computer (RC) to the data being processed dramatically increases its processing power. We apply our method to the parity task, a challenging benchmark problem that highlights inversion and permutation symmetries, and to a chaotic system inference task that presents an inversion symmetry rule. For the parity task, our symmetry-aware RC obtains zero error using an exponentially reduced neural network and training data, greatly speeding up the time to result and outperforming artificial neural networks. When both symmetries are respected, we find that the network size N necessary to obtain zero error for 50 different RC instances scales linearly with the parity-order n. Moreover, some symmetry-aware RC instances perform a zero error classification with only N=1 for n≤7. Furthermore, we show that a symmetry-aware RC only needs a training data set with size on the order of (n+n/2) to obtain such a performance, an exponential reduction in comparison to a regular RC which requires a training data set with size on the order of n2^{n} to contain all 2^{n} possible n-bit-long sequences. For the inference task, we show that a symmetry-aware RC presents a normalized root-mean-square error three orders-of-magnitude smaller than regular RCs. For both tasks, our RC approach respects the symmetries by adjusting only the input and the output layers, and not by problem-based modifications to the neural network. We anticipate that the generalizations of our procedure can be applied in information processing for problems with known symmetries.
Neural Collaborative with Sentence BERT for News Recommender System

The number of news produced every day is as much as 3 million per day, making readers have many choices in choosing news according to each reader's topic and category preferences. The recommendation system can make it easier for users to choose the news to read. The method that can be used in providing recommendations from the same user is collaborative filtering. Neural collaborative filtering is usually being used for recommendation systems by combining collaborative filtering with neural networks. However, this method has the disadvantage of recommending the similarity of news content such as news titles and content to users. This research wants to develop neural collaborative filtering using sentences BERT. Sentence BERT is applied to news titles and news contents that are converted into sentence embedding. The results of this sentence embedding are used in neural collaboration with item id, user id, and news category. We use a Microsoft news dataset of 50,000 users and 51,282 news, with 5,475,542 interactions between users and news. The evaluation carried out in this study uses precision, recall, and ROC curves to predict news clicks by the user. Another evaluation uses a hit ratio with the leave one out method. The evaluation results obtained a precision value of 99.14%, recall of 92.48%, f1-score of 95.69%, and ROC score of 98%. Evaluation measurement using the hit ratio@10 produces a hit ratio of 74% at fiftieth epochs for neural collaborative with sentence BERT which is better than neural collaborative filtering (NCF) and NCF with news category.
Revisiting the Impact of Dependency Network Metrics on Software Defect Prediction

Software dependency network metrics extracted from the dependency graph of the software modules by the application of Social Network Analysis (SNA metrics) have been shown to improve the performance of the Software Defect prediction (SDP) models. However, the relative effectiveness of these SNA metrics over code metrics in improving the performance of the SDP models has been widely debated with no clear consensus. Furthermore, some of the common SDP scenarios like predicting the number of defects in a module (Defect-count) in Cross-version and Cross-project SDP contexts remain unexplored. Such lack of clear directive on the effectiveness of SNA metrics when compared to the widely used code metrics prevents us from potentially building better performing SDP models. Therefore, through a case study of 9 open source software projects across 30 versions, we study the relative effectiveness of SNA metrics when compared to code metrics across 3 commonly used SDP contexts (Within-project, Cross-version and Cross-project) and scenarios (Defect-count, Defect-classification (classifying if a module is defective) and Effort-aware (ranking the defective modules w.r.t to the involved effort)). We find the SNA metrics by themselves or along with code metrics improve the performance of SDP models over just using code metrics on 5 out of the 9 studied SDP scenarios (three SDP scenarios across three SDP contexts). However, we note that in some cases the improvements afforded by considering SNA metrics over or alongside code metrics might only be marginal, whereas in other cases the improvements could be potentially large. Based on these findings we suggest that the future work should: consider SNA metrics alongside code metrics in their SDP models; as well as consider Ego metrics and Global metrics, the two different types of the SNA metrics separately when training SDP models as they behave differently.
A Liquid Democracy Enabled Blockchain-Based Electronic Voting System

Developing an electronic voting system that meets the practical needs of administrators has been a difficult task for a long time. Now, blockchain technologies solve this problem by providing a distributed ledger with immutable, encrypted, and secure transactions. Distributed ledger technologies are an interesting technological leap in the field of data innovation, transparency, and trustability. In public blockchain, distributed ledger technology is widely used. The blockchain technology can be used in an almost infinite number of ways to benefit from sharing economies. The purpose of this study is to assess how blockchain may be utilized to build electronic voting systems that can be used as a service. The purpose of electronic voting systems is explained in this article, as are the technological and legal limitations of employing blockchain as a service. Then, using blockchain as a foundation, we propose a new electronic voting system that fixes the flaws we observed. In general, this paper evaluates the capabilities of distributed ledger technologies by depicting a contextual investigation in order to fine-tune the process of political election decisions and employing a blockchain-based application that improves security and lowers the cost of conducting nationwide elections.
Effect of Kinematics and Fluency in Adversarial Synthetic Data Generation for ASL Recognition With RF Sensors

RF sensors have been recently proposed as a new modality for sign language processing technology. They are non-contact, effective in the dark, and acquire a direct measurement of signing kinematic via exploitation of the micro-Doppler effect. First, this work provides an in depth, comparative examination of the kinematic properties of signing as measured by RF sensors for both fluent ASL users and hearing imitation signers. Second, as ASL recognition techniques utilizing deep learning requires a large amount of training data, this work examines the effect of signing kinematics and subject fluency on adversarial learning techniques for data synthesis. Two different approaches for the synthetic training data generation are proposed: 1) adversarial domain adaptation to minimize the differences between imitation signing and fluent signing data, and 2) kinematically-constrained generative adversarial networks for accurate synthesis of RF signing signatures. The results show that the kinematic discrepancies between imitation signing and fluent signing are so significant that training on data directly synthesized from fluent RF signers offers greater performance (93% top-5 accuracy) than that produced by adaptation of imitation signing (88% top-5 accuracy) when classifying 100 ASL signs.
Refocusing on Relevance: Personalization in NLG

Many NLG tasks such as summarization, dialogue response, or open domain question answering focus primarily on a source text in order to generate a target response. This standard approach falls short, however, when a user's intent or context of work is not easily recoverable based solely on that source text-a scenario that we argue is more of the rule than the exception. In this work, we argue that NLG systems in general should place a much higher level of emphasis on making use of additional context, and suggest that relevance (as used in Information Retrieval) be thought of as a crucial tool for designing user-oriented text-generating tasks. We further discuss possible harms and hazards around such personalization, and argue that value-sensitive design represents a crucial path forward through these challenges.
Blockchain-Based Energy Trading in Electric Vehicles Using an Auctioning and Reputation Scheme

The massive adoption of electric vehicles (EVs) has caused an increasing demand for electric energy to charge the vehicles.Efficiently managing energy trading between energy providers and energy consumers can lead to meet the high demand for charging EVs while reducing its cost compared to traditional power provided by the utility company.However, a large portion of the existing systems leveraged for trading energy between EVs are centralized and fall short in providing transparency, reliability, audit, security, and trustworthy features.In this paper, we propose blockchain-based energy trading using an auctioning and reputation scheme.We develop Ethereum smart contracts which enable owners of EVs to automatically request electricity to charge their vehicles in a reliable, cost-effective, secure, and trustworthy manner.The proposed approach ensures the lowest rate available by implementing a reverse auctioning scheme for fair competition between providers to provide the requested service at the lowest cost.The proposed solution enforces high quality of service through a reputation-based approach that quantifies the performance of the service providers and gives an advantage to more reputable providers.We present the implementation details of the deployed system on a test Ethereum blockchain platform.We perform system testing and evaluation to validate and assess the functionality and performance of the proposed solution.Furthermore, we present security and cost analyses to show the affordability, robustness, and practicality of the proposed approach.
Rˆ3Net:Relation-embedded Representation Reconstruction Network for Change Captioning

Change captioning is to use a natural language sentence to describe the fine-grained disagreement between two similar images.Viewpoint change is the most typical distractor in this task, because it changes the scale and location of the objects and overwhelms the representation of real change.In this paper, we propose a Relation-embedded Representation Reconstruction Network (R 3 Net) to explicitly distinguish the real change from the large amount of clutter and irrelevant changes.Specifically, a relation-embedded module is first devised to explore potential changed objects in the large amount of clutter.Then, based on the semantic similarities of corresponding locations in the two images, a representation reconstruction module (RRM) is designed to learn the reconstruction representation and further model the difference representation.Besides, we introduce a syntactic skeleton predictor (SSP) to enhance the semantic interaction between change localization and caption generation.Extensive experiments show that the proposed method achieves the state-of-the-art results on two public datasets 1 .
Heisenberg-limited estimation robust to photon losses in a Mach-Zehnder network with squeezed light

We propose a quantum metrological protocol based on a Mach-Zehnder interferometer with a squeezed vacuum input state and an antisqueezing operation at one of its output channels. A simple and intuitive geometrical picture of the state evolution is provided by the marginal Wigner functions of the state at each interferometer output channel. The protocol allows us to detect the values of the sum $\ensuremath{\beta}=\frac{1}{2}({\ensuremath{\varphi}}_{1}+{\ensuremath{\varphi}}_{2})+{\ensuremath{\theta}}_{\mathrm{in}}\ensuremath{-}{\ensuremath{\theta}}_{\mathrm{out}}$, of the relative phase ${\ensuremath{\theta}}_{\mathrm{in}}\ensuremath{-}{\ensuremath{\theta}}_{\mathrm{out}}$ between the two squeezers, and of the average of the phase delays ${\ensuremath{\varphi}}_{1}$ and ${\ensuremath{\varphi}}_{2}$ in the two arms of the interferometer. The detection sensitivity scales at the Heisenberg limit and, remarkably, is robust not only to detector inefficiencies but also to any photon losses occurring before the antisqueezing operation. Interestingly, we demonstrate that in the latter case an increase of sensitivity can even occur by increasing the losses in a suitable range.
Simple Multiuser Twin-Field Quantum Key Distribution Network

Meet Alice, Bob, and David. Establishing secure encryption keys between any two of these users is important for a long-range communication network, but with dubious Charlie also around, it becomes tricky. Twin-field quantum key distribution (TFQKD), while promising for this application, has been limited so far to two users, because of its requirement for phase stability of optical signals. In this work, the authors demonstrate a TFQKD network by connecting three users in a Sagnac fiber ring, a configuration with inherent phase stability. This experiment suggests that the Sagnac TFQKD system is an effective, practical approach to implementing a long-range secure communication network.
Audio Watermarking for Security and Non-Security Applications

The digitization of audiovisual data is significantly increasing.Thus, to guarantee the protection of the intellectual properties of this digital content, watermarking has appeared as a solution.Watermarking can be used in reality in several types of applications that target two different contexts: the first for security applications and the second for non-security ones.In this paper, we carry a big interest in studying these two types of applications.Moreover, we propose a first digital watermarking scheme for security copyright protection applications, where we have involved neural network architecture in the insertion and detection processes, and integrated some masking phenomena of the human psychoacoustic model with linear predictive coding spectral envelope estimation of the audio file.Experiments proved the efficiency of exploiting perceptual masking with spectral envelope consideration in terms of imperceptibility and robustness results.In addition, we suggest a second audio watermarking technique for non-security content characterization applications based on a deep learning classification architecture.In this scheme, the extracted watermark advises about the audio class: music or speech, speaker gender, and emotion.The reported results indicated that the suggested scheme achieved a higher performance at the classification level, as well as at the watermarking properties.
Drug Sentiment Analysis using Machine Learning Classifiers

In recent times, one of the most emerging subdimensions of natural language processing is sentiment analysis which refers to analyzing opinion on a particular subject from plain text.Drug sentiment analysis has become very significant in present times as classifying medicines based on their effectiveness through analyzing reviews from users can assist potential future consumers in gaining knowledge and making better decisions about a particular drug.The objective of this proposed research is to measure the effectiveness level of a particular drug.Currently most of the text mining researches are based on unsupervised machine learning methods to cluster data.When supervised learning methods are used for text mining, the usual primary concern is to classify the data into two classes.Lack of technical terms in similar datasets make the categorization even more challenging.The proposed research focuses on finding out the keywords through tokenization and lemmatization so that better accuracy can be achieved for categorizing the drugs based on their effectiveness using different algorithms.Such categorization can be instrumental for treating illness as well as improve one's health and well-being.Four machine learning algorithms have been applied for binary classification and one for multiclass classification on the drug review dataset acquired from the UCI machine learning repository.The machine learning algorithms used for binary classification are naive Bayes classifier, random forest, support vector classifier (SVC), and multilayer perceptron; among these machine learning algorithms, linear SVC was used for multiclass classification.Results obtained from these four classifier algorithms have been analyzed to evaluate their performances.The random forest has been proven to have the best performance among these four algorithms.However, multiclass classification was found to have low performance when applied to natural language processing.On the contrary, the applied linear SVC algorithm performed better for class 2 with AUC 0.82 in this research.
Blockchain in the Quantum World

Blockchain is one of the most discussed and highly accepted technologies, primarily due to its application in almost every field where third parties are needed for trust.Blockchain technology relies on distributed consensus for trust, which is accomplished using hash functions and public-key cryptography.Most of the cryptographic algorithms in use today are vulnerable to quantum attacks.In this work, a systematic literature review is done so that it can be repeated, starting with identifying the research questions.Focusing on these research questions, literature is analysed to find the answers to these questions.The survey is completed by answering the research questions and identification of the research gaps.It is found in the literature that 30% of the research solutions are applicable for the data layer, 24% for the application and presentation layer, 23% for the network layer, 16% for the consensus layer and only 1% for hardware and infrastructure layer.We also found that 6% of the solutions are not blockchain-based but present different distributed ledger technology.