Reviewerly/exHarmony huggingface dataset:
An introduction to variational quantum algorithms on gate-based quantum computing for combinatorial optimization problems

Noisy intermediate-scale quantum computers (NISQ computers) are now readily available, motivating many researchers to experiment with Variational Quantum Algorithms (VQAs). Among them, the Quantum Approximate Optimization Algorithm (QAOA) is one of the most popular one studied by the combinatorial optimization community. In this tutorial, we provide a mathematical description of the class of Variational Quantum Algorithms, assuming no previous knowledge of quantum physics from the readers. We introduce precisely the key aspects of these hybrid algorithms on the quantum side (parametrized quantum circuit) and the classical side (guiding function, optimizer). We devote a particular attention to QAOA, detailing the quantum circuits involved in that algorithm, as well as the properties satisfied by its possible guiding functions. Finally, we discuss the recent literature on QAOA, highlighting several research trends.
Some recent advances in reasoning based on analogical proportions

Analogical proportions compare pairs of items (a, b) and (c, d) in terms of their differences and similarities. They play a key role in the formalization of analogical inference. The paper first discusses how to improve analogical inference in terms of accuracy and in terms of computational cost. Then it indicates the potential of analogical proportions for explanation. Finally, it highlights the close relationship between analogical proportions and multi-valued dependencies, which reveals an unsuspected aspect of the former.
Review of the Dictionary “Ecology of Human Existence. Part II. Media Ecology”

The subject of this review is the second part of the information-military dictionary “Ecology of human existence: media ecology”. The dictionary presents the theme of media ecology or ecology of communications through a number of terms in the aspect of the ecohumanist approach. The authors of the vocabulary articles developed and used it as a new methodology in the study and understanding of the capabilities of modern digital technologies that form the information environment for the existence of a contemporary human. The editors and authors of the vocabulary set a goal of carrying out a philosophical interpretation of the technical and technological conditions of human ‘in-der-Welt-Sein’, in the aspect of information environment (media space), and propose a new, namely, eco-humanistic approach for solving the main problems of the relationship “human – technical and technological world”. The texts of vocabulary articles substantiate the thesis that the capabilities and power of modern digital technologies actualize the need for an eco-humanistic measurement of the relationship “human – technical and technological world”, which does not pre-refuse digital technologies, but means the optimal use of all technological tools, taking into account the detection and preservation of their humanistic potential. The review pays attention to the specifics of the theoretical development of the eco-humanistic approach and its main principles: integrity, optimality, coordination, and eco-systemity. As a result of the analysis the reviewer concludes that the eco-humanistic approach proposed and used by the authors will allow to study and explore the processes and phenomena of contemporary culture in a multidimensional manner, to receive a more accurate interpretation of the ways of human existence and eco-cultural practices that ensure the effect of the staneity of the “ecosystems” in the situations of any intensive technological transformations.
Renormalization in the neural network-quantum field theory correspondence

A statistical ensemble of neural networks can be described in terms of a quantum field theory (NN-QFT correspondence). The infinite-width limit is mapped to a free field theory, while finite N corrections are mapped to interactions. After reviewing the correspondence, we will describe how to implement renormalization in this context and discuss preliminary numerical results for translation-invariant kernels. A major outcome is that changing the standard deviation of the neural network weight distribution corresponds to a renormalization flow in the space of networks.
Multilingual News Location Detection using an Entity-Based Siamese Network with Semi-Supervised Contrastive Learning and Knowledge Base

Early detection of relevant locations in a piece of news is especially important in extreme events such as environmental disasters, war conflicts, disease outbreaks, or political turmoils. Additionally, this detection also helps recommender systems to promote relevant news based on user locations. Note that, when the relevant locations are not mentioned explicitly in the text, state-of-the-art methods typically fail to recognize them because these methods rely on syntactic recognition. In contrast, by incorporating a knowledge base and connecting entities with their locations, our system successfully infers the relevant locations even when they are not mentioned explicitly in the text. To evaluate the effectiveness of our approach, and due to the lack of datasets in this area, we also contribute to the research community with a gold-standard multilingual news-location dataset, NewsLOC. It contains the annotation of the relevant locations (and their WikiData IDs) of 600+ Wikinews articles in five different languages: English, French, German, Italian, and Spanish. Through experimental evaluations, we show that our proposed system outperforms the baselines and the fine-tuned version of the model using semi-supervised data that increases the classification rate. The source code and the NewsLOC dataset are publicly available for being used by the research community at https://github.com/vsuarezpaniagua/NewsLocation.
The Quantum Path Kernel: a Generalized Quantum Neural Tangent Kernel for Deep Quantum Machine Learning

Building a quantum analog of classical deep neural networks represents a fundamental challenge in quantum computing. A key issue is how to address the inherent non-linearity of classical deep learning, a problem in the quantum domain due to the fact that the composition of an arbitrary number of quantum gates, consisting of a series of sequential unitary transformations, is intrinsically linear. This problem has been variously approached in the literature, principally via the introduction of measurements between layers of unitary transformations. In this paper, we introduce the Quantum Path Kernel, a formulation of quantum machine learning capable of replicating those aspects of deep machine learning typically associated with superior generalization performance in the classical domain, specifically, hierarchical feature learning. Our approach generalizes the notion of Quantum Neural Tangent Kernel, which has been used to study the dynamics of classical and quantum machine learning models. The Quantum Path Kernel exploits the parameter trajectory, i.e. the curve delineated by model parameters as they evolve during training, enabling the representation of differential layer-wise convergence behaviors, or the formation of hierarchical parametric dependencies, in terms of their manifestation in the gradient space of the predictor function. We evaluate our approach with respect to variants of the classification of Gaussian XOR mixtures - an artificial but emblematic problem that intrinsically requires multilevel learning in order to achieve optimal class separation.
Xgression The Dynamic and Explainable Regression/Classification Solution

Abstract Xgression is the solution to find relationships between data to an equation by constructing computational tree that represent the steps of calculation from inputs to an output of algorithm then modify the tree with various operator to find the optimal solution. As the result Xgression can perform many forms of regression without setting initial equations or parameters
HMM-based data augmentation for E2E systems for building conversational speech synthesis systems

This paper proposes an approach to build a high-quality text-to-speech (TTS) system for technical domains using data augmentation. An end-to-end (E2E) system is trained on hidden Markov model (HMM) based synthesized speech and further fine-tuned with studio-recorded TTS data to improve the timbre of the synthesized voice. The motivation behind the work is that issues of word skips and repetitions are usually absent in HMM systems due to their ability to model the duration distribution of phonemes accurately. Context-dependent pentaphone modeling, along with tree-based clustering and state-tying, takes care of unseen context and out-of-vocabulary words. A language model is also employed to reduce synthesis errors further. Subjective evaluations indicate that speech produced using the proposed system is superior to the baseline E2E synthesis approach in terms of intelligibility when combining complementing attributes from HMM and E2E frameworks. The further analysis highlights the proposed approach's efficacy in low-resource scenarios.
On the Energy and Communication Efficiency Tradeoffs in Federated and Multi-Task Learning

Recent advances in Federated Learning (FL) have paved the way towards the design of novel strategies for solving multiple learning tasks simultaneously, by leveraging cooperation among networked devices. Multi-Task Learning (MTL) exploits relevant commonalities across tasks to improve efficiency compared with traditional transfer learning approaches. By learning multiple tasks jointly, significant reduction in terms of energy footprints can be obtained. This article provides a first look into the energy costs of MTL processes driven by the Model-Agnostic Meta-Learning (MAML) paradigm and implemented in distributed wireless networks. The paper targets a clustered multi-task network setup where autonomous agents learn different but related tasks. The MTL process is carried out in two stages: the optimization of a meta-model that can be quickly adapted to learn new tasks, and a task-specific model adaptation stage where the learned meta-model is transferred to agents and tailored for a specific task. This work analyzes the main factors that influence the MTL energy balance by considering a multi-task Reinforcement Learning (RL) setup in a robotized environment. Results show that the MAML method can reduce the energy bill by at least 2 × compared with traditional approaches without inductive transfer. Moreover, it is shown that the optimal energy balance in wireless networks depends on uplink/downlink and sidelink communication efficiencies.
Conceptualising Innovation and Creativity in Knowledge Based Society

This paper raises a discussion on innovation, nature of knowledge and the growing significance of skills in contemporary society. We propose the argument that knowledge is commonly perceived in terms of skills only, as a result of an applicability logic rule in knowledge based social orders. Moreover, innovation is recognized as yet another central issue of our times that is strongly interrelated to technological developments and the realm od science. Creativity as a specific human feature, on the other hand, is widely accepted as a critical precondition for generating innovative products and ideas. The essential problem lies in the oversight of the role humanities and arts play in nurturing humanistic values, whereas means are commonly mistaken for an end.
Revocable Certificateless Public Key Encryption with Equality Test

Traditional public key cryptography requires certificates as a link between each user’s identity and her/his public key. Typically, public key infrastructures (PKI) are used to manage and maintain certificates. However, it takes a lot of resources to build PKI which includes many roles and complex policies. The concept of certificateless public key encryption (CL-PKC) was introduced to eliminate the need for certificates. Based on this concept, a mechanism called certificateless public key encryption with equality test (CL-PKEET) was proposed to ensure the confidentiality of private data and provide an equality test of different ciphertexts. The mechanism is suitable for cloud applications where users cannot only protect personal private data but also enjoy cloud services which test the equality of different ciphertexts. More specifically, any two ciphertexts can be tested to determine whether they are encrypted from the same plaintext. Indeed, any practical system needs to provide a solution to revoke compromised users. However, these existing CL-PKEET schemes do not address the revocation problem, and the related research is scant. Therefore, the aim of this article is to propose the first revocable CL-PKEET scheme called RCL-PKEET which can effectively remove illegal users from the system while maintaining the effectiveness of existing CL-PKEET schemes in encryption, decryption, and equality testing processes. Additionally, we formally demonstrate the security of the proposed scheme under the bilinear Diffie-Hellman assumption.
Accuracy evaluation of convolutional neural network classification algorithms for building identification in rural and urban areas from very-high-resolution satellite imagery in Jambi, Indonesia

Accurate land cover data are essential to a reliable decision-making process; therefore, researchers have turned to novel land cover classification algorithms employing machine learning on high-resolution satellite imagery to improve classification accuracy. The experiment presented in this paper aims to assess the accuracy performance of three patch-based, convolutional neural network architectures (LeNet, VGGNet, and XCeption) in classifying building footprints in rural and urban areas from satellite imagery data, with conventional, pixel-based classification algorithms as a benchmark. The experiment concluded that the CNN classification algorithms consistently outperformed pixel-based algorithms in the accuracy of the resulting building-footprint classification raster. It was also demonstrated that larger image patch size does not always improve classification accuracy in all CNN architectures. This study also revealed that the XCeption architecture performed best among the three CNN architectures assessed, with a 72-pixel patch size having the best accuracy.
Wave Function Realization of a Thermal Collision Model

An efficient algorithm to simulate dynamics of open quantum system is presented. The method describes the dynamics by unraveling stochastic wave functions converging to a density operator description. The stochastic techniques are based on the quantum collision model. Modeling systems dynamics with wave functions and modeling the interaction with the environment with a collision sequence reduces the scale of the complexity significantly. The algorithm developed can be implemented on quantum computers. We introduce stochastic methods that exploit statistical characteristics of the model such as Markovianity, Brownian motion, and binary distribution. The central limit theorem is employed to study the convergence of distributions of stochastic dynamics of pure quantum states represented by wave vectors. By averaging a sample of functions in the distribution we prove and demonstrate the convergence of the dynamics to the mixed quantum state described by a density operator.
Parallelly Running and Privacy-Preserving k-Nearest Neighbor Classification in Outsourced Cloud Computing Environments

Classification is used in various areas where k-nearest neighbor classification is the most popular as it produces efficient results. Cloud computing with powerful resources is one reliable option for handling large-scale data efficiently, but many companies are reluctant to outsource data due to privacy concerns. This paper aims to implement a privacy-preserving k-nearest neighbor classification (PkNC) in an outsourced environment. Existing work proposed a secure protocol (SkLE/SkSE) to compute k data with the largest/smallest value privately, but this work discloses information. Moreover, SkLE/SkSE requires a secure comparison protocol, and the existing protocols also contain information disclosure problems. In this paper, we propose a new secure comparison and SkLE/SkSE protocols to solve the abovementioned information disclosure problems and implement PkNC with these novel protocols. Our proposed protocols disclose no information and we prove the security formally. Then, through extensive experiments, we demonstrate that the PkNC applying the proposed protocols is also efficient. Especially, the PkNC is suitable for big data analysis to handle large amounts of data, since our SkLE/SkSE is executed for each dataset in parallel. Although the proposed protocols do require efficiency sacrifices to improve security, the running time of our PkNC is still significantly more efficient compared with previously proposed PkNCs.
IMAGE ORIENTATION BY EMBEDDING IN A GAN LATENT SPACE

Abstract. Estimation of an image exterior orientation is required in multiple tasks including 3D reconstruction, autonomous driving and navigation, and single-photo 3D reconstruction. The problem can be easily solved if some reference points (keypoints) with known coordinates in the reference frame are detected in the image. While multiple robust keypoint detectors were developed, estimation of an image exterior orientation from a single image remains challenging in many cases. For example, repeating structures in the scene or absence of textures can reduce the performance of keypoint detectors. In this paper, we propose an algorithm for estimation of an image exterior orientation that leverages the latent space of Generative Adversarial Network (GAN). We propose a modification of the StyleGAN2 model that we term ExteriorGAN. Unlike the StyleGAN2 that generates random images from a random noise vector z, we aim training a mapping from a random vector z and a given image exterior orientation p G : (z,p) → A. Our model generates random images for a constant exterior orientation p and random z that have constant geometry but differs in the scene appearance (e.g. different light direction or intensity). We perform embedding of the image A into the latent space w and reconstruct the input noise vector z and exterior orientation parameters w using a stochastic gradient descent. We developed a dedicated dataset with 50k images and corresponding orientation parameters to train and validate our ExteriorGAN model. The results of evaluation demonstrate that our algorithm allows estimation of the exterior orientation of an image with respect to a known 3D scene. The accuracy of the exterior orientation is comparable with modern state-of-the-art methods. The camera pose can be recovered with a mean error of 50 mm for a working space of 5 by 5 meters.
Towards fairness and QoE-based edge allocation for multiplayer virtual reality applications in edge computing

Edge computing has emerged as the next big thing in distributed computing, by extending the cloud paradigm and offering efficient ways to engage with latency-intolerant applications, such as Virtual Reality (VR) multiplayer games. In edge computing, the service providers can benefit from existing cellular infrastructure to deploy services on edge servers that reside in close proximity to the users. Given the limited available budget for edge resource investment, one fundamental problem that manifests is the discovery of a prudent edge allocation strategy, that will efficiently prescribe which users are assigned to which edge servers, in order to tackle application-specific requirements, like minimizing system deployment costs. In this paper, considering the frequent interactions and view inconsistencies occurring among multiple users immersed in the same VR game, we address the problem from the users' perspective, focusing on improving their edge admission rate, resource provisioning and overall fairness, in order to subsequently maximize the average Quality of Experience (QoE). We call this the "Fairness and QoE-Based Edge Allocation" (FQEA) problem, formally formulating its properties and theoretically proving its complexity. However, discovering optimal solutions to the NP-hard FQEA in large-scale VR scenarios is challenging. Hence, we propose FQEA-H, a heuristic algorithm to generate allocation strategies in reasonable time. Comprehensive simulations, conducted on a real-world topological trace, demonstrate how FQEA-H can tackle the problem effectively, generally outperforming both the baseline and state-of-the-art alternatives.
Sanal Gerçeklik Deneylerinde Algılanan Bulunma Hissinin İncelenmesi

Expressed as a set of tools and techniques that create an emotional and psychological experience for users, virtual reality technology depends on the principle that users can feel themselves in the presented universe and as a part of the presented scenario. The concept called "presence" in the literature is accepted as one of the key elements that make virtual reality technology unique and become the focus of virtual reality-based studies. In this study, it is aimed to examine whether the level of sense of presence felt by users in virtual reality environments differs according to the demographic characteristics of individuals. In this direction, the virtual holiday experiment, designed using the Unity program, has been experienced by 50 participants between the ages of 18-55, selected through quota sampling, and the data obtained as a result of the experiments were analyzed via the SPSS 21.0 program. As a result of the study, all null hypotheses were supported, and it was determined that there was no significant relationship or difference between the demographic characteristics of the participants and their sense of presence in virtual reality environments. Keywords: Virtual Reality, Sence of Presence, Experiment Method, Virtual Reality Experience JEL Codes: C91, M31
Rising Bilingual Children in Indonesian Context: Review of The Scientific Advantages

Bilingual education has become very popular among Indonesian children nowadays. Other than learning it from school, they are also exposed to the second language in their homes. Although some misconceptions persist in society, some parents continue to do so because they believe that bilingualism benefits their children's development, whether in the language or cognitive areas. This paper focuses on the common misconceptions spreading among parents. This research was specifically done based on literature review. Here, the five common myths about bilingualism on early childhood were answered based on scientific explanations given by experts. The finding indicates that being bilingual helps children have strong language development and executive functioning at the same time. Results are interpreted in the context of relevant research and explained based on the theories of some experts.
Integrating Collaborative Filtering Technique Using Rating Approach to Ascertain Similarity Between the Users

The recommender system handles the plethora of data by filtering the most crucial information based on the dataset provided by a user and other criterion that are taken into account.(i.e., user's choice and interest). It determines whether a user and an item are compatible and then assumes that they are similar in order to make recommendations. Recommendation system uses Singular value decomposition method as collaborative filtering technique. The objective of this research paper is to propose the recommendation system that has an ability to recommend products to users based on ratings. We collect essential information like ratings given by the users from e-commerce that are required for recommendation, Initially the dataset that are gathered are sparse dataset, cosine similarity is used to find the similarity between the users. Subsequently, we collect non-sparse data and use Euclidian distance and Manhattan distance method to measure the distance between users and the graph is plotted, this ensures the similar liking and preferences between them. This method of making recommendations are more reliable and attainable.