Reviewerly/exHarmony huggingface dataset:
Evaluation of the union bound for the decoding error probability using characteristic functions

Introduction: Since the exact value of a decoding error probability cannot usually be calculated, an upper bounding technique is used. The standard approach for obtaining the upper bound on the maximum likelihood decoding error probability is based on the use of the union bound and the Chernoff bound, as well as its modifications. For many situations, this approach is not accurate enough. Purpose: Development of a method for exact calculation of the union bound for a decoding error probability, for a wide class of codes and memoryless channels. Methods: Use of characteristic functions of logarithm of the likelihood ratio for an arbitrary pair of codewords, trellis representation of codes and numerical integration. Results: The resulting exact union bound on the decoding error probability is based on a combination of the use of characteristic functions and the product of trellis diagrams for the code, which allows to obtain the final expression in an integral form convenient for numerical integration. An important feature of the proposed procedure is that it allows one to accurately calculate the union bound using an approach based on the use of transfer (generating) functions. With this approach, the edge labels in the product of trellis diagrams for the code are replaced by their corresponding characteristic functions. The final expression allows, using the standard methods of numerical integration, to calculate the values of the union bound on the decoding error probability with the required accuracy. Practical relevance: The results presented in this article make it possible to significantly improve the accuracy of the bound of the error decoding probability, and thereby increase the efficiency of technical solutions in the design of specific coding schemes for a wide class of communication channels.
Face Mask Detection Using Machine Learning

The purpose of the project “Face Mask Detection Using Machine Learning” is to create a tool that identifies the image of a human that can calculate the probability that he/she wearing a mask or not. Due to COVID, wearing a face mask is must in order to stay safe. As the country starts going through various stages of reopening, face masks have become an important element of our daily lives to stay safe. Wearing face masks will be required in order to socialize or conduct business. So, this application utilizes a camera to detect if a person is wearing a mask or not.
Angular‐flexible network for light field image super‐resolution

Light field (LF) cameras capture scenes from multiple views and provide additional angular information for image super-resolution (SR). Existing CNN-based LF image SR methods commonly develop specific models for different angular resolutions. However, since the angular resolution can vary significantly with different LF devices, these methods have limited flexibility for real-world applications. Here, an angular-flexible network to use a single model to super-resolve LF images of arbitrary angular resolution is proposed. In this method, spatial and angular feature extractors are designed to achieve angular-flexible feature extraction, and develop a decouple-and-fuse module for SR reconstruction. Moreover, a mixed-angular-resolution training strategy is proposed to further enhance the angular flexibility. Experimental results on five public datasets demonstrate the state-of-the-art performance of the method. Source codes are available at https://github.com/ZhengyuLiang24/LF-AFnet. Here, an angular-flexible network for light field image super-resolution is proposed. The network can handle LFs captured by different kinds of devices with arbitrary angular resolutions. Experimental results demonstrate the effectiveness and superior performance of the method.
RetroNLU: Retrieval Augmented Task-Oriented Semantic Parsing

While large pre-trained language models accumulate a lot of knowledge in their parameters, it has been demonstrated that augmenting it with non-parametric retrieval-based memory has a number of benefits ranging from improved accuracy to data efficiency for knowledgefocused tasks such as question answering.In this work, we apply retrieval-based modeling ideas to the challenging complex task of multi-domain task-oriented semantic parsing for conversational assistants.Our technique, RETRONLU, extends a sequence-to-sequence model architecture with a retrieval component, which is used to retrieve existing similar samples and present them as an additional context to the model.In particular, we analyze two settings, where we augment an input with (a) retrieved nearest neighbor utterances (utterancenn), and (b) ground-truth semantic parses of nearest neighbor utterances (semparse-nn).Our technique outperforms the baseline method by 1.5% absolute macro-F1, especially at the low resource setting, matching the baseline model accuracy with only 40% of the complete data.Furthermore, we analyse the quality, model sensitivity, and performance of the nearest neighbor retrieval component's for semantic parses of varied utterance complexity.
Weighted Conditional EL^bot Knowledge Bases with Integer Weights: an ASP Approach

Weighted knowledge bases for description logics with typicality have been recently considered under a "concept-wise" multipreference semantics (in both the two-valued and fuzzy case), as the basis of a logical semantics of Multilayer Perceptrons. In this paper we consider weighted conditional EL^bot knowledge bases in the two-valued case, and exploit ASP and asprin for encoding concept-wise multipreference entailment for weighted KBs with integer weights.
Relating Measurement Patterns to Circuits via Pauli Flow

The one-way model of Measurement-Based Quantum Computing and the gate-based circuit model give two different presentations of how quantum computation can be performed. There are known methods for converting any gate-based quantum circuit into a one-way computation, whereas the reverse is only efficient given some constraints on the structure of the measurement pattern. Causal flow and generalised flow have already been shown as sufficient, with efficient algorithms for identifying these properties and performing the circuit extraction. Pauli flow is a weaker set of conditions that extends generalised flow to use the knowledge that some vertices are measured in a Pauli basis. In this paper, we show that Pauli flow can similarly be identified efficiently and that any measurement pattern whose underlying graph admits a Pauli flow can be efficiently transformed into a gate-based circuit without using ancilla qubits. We then use this relationship to derive simulation results for the effects of graph-theoretic rewrites in the ZX-calculus using a more circuit-like data structure we call the Pauli Dependency DAG.
FMSNet: Underwater Image Restoration by Learning from a Synthesized Dataset

Underwater images suffer from various degradation, which can significantly lower the visual quality and the accuracy of subsequent applications. Moreover, the artificial light source tends to invalidate many image restoration algorithms. In this paper, an underwater image restoration (UIR) method using a novel Convolutional Neural Network (CNN) architecture and a synthesized underwater dataset is proposed. We discuss the reason for the over enhancement that exists in current UIR methods and revise the underwater image formation model (IFM) to alleviate the problem. With the revised IFM, we proposed an underwater image synthesizing method that can create a realistic underwater dataset. In order to effectively conduct end-to-end supervised learning, we design a network based on the characteristics of image restoration tasks, namely FMSNet. Different from existing networks, the decomposition and fusion operation in FMSNet can process the feature maps more efficiently and improve the contrast more prominently. The UIR method built by FMSNet can directly recover the degraded underwater images without the need of any pre-processing and post-processing. The experimental results indicate that FMSNet performs favorably against the widely used network architectures and our UIR method can outperform the state-of-the-art methods on both qualitative and quantitative evaluations. Comparing with the original underwater images, the experiments carried out by subsequent mission shows that 285% more feature points can be detected in the restored images by using our method.
“Library 2030: Building the Future Today”: Results of the All-Russian Library Congress

On 16—20 May 2021, the All-Russian Library Congress: XXV Anniversary Annual Conference of the Russian Library Association (RLA) was held in the Library Capital of Russia for 2020/2021, Petrozavodsk (Republic of Karelia), with the support of the Ministry of Culture of the Russian Federation and the Government of the Republic of Karelia. The theme of the Congress was “Library 2030: Building the Future Today”. For the first time, this forum was held in a hybrid format, i.e., both offline and online, while all the events were streamed online, which made it possible to expand the audience reach. The strategic goals of the Congress were to identify the main trends in the development of librarianship and to form the image of the library of the future. Consequently, many speakers focused on the pivotal document for the library field “The strategy for the development of librarianship in the Russian Federation until 2030”, approved in 2021. In addition, the participants deliberated the draft of the new professional standard “Specialist in library and information activities”. The plenary and sectional sessions involved the discussions on the development of libraries of various types, the library profession in the modern world, etc. The Congress was accompanied by the XXI Exhibition of publishing products, new information technologies, goods and services, which was also organised in a hybrid format. At the final plenary meeting, the results of the elections of the President of the RLA and the members of the RLA Board for 2021—2024 were announced. M.D. Afanasyev was elected President of the RLA for a second term. One of the important outcomes of the Congress was the adoption at the final plenary session of the RLA normative and advisory act “Methodological recommendations for the formation of basic standards for the provision of public libraries services in the constituent entities of the Russian Federation”. Nizhny Novgorod received the status of the Library Capital of Russia for 2022, and it is going to host the XXVI All-Russian Library Congress next year.
Forecasting and Monitoring Smart Buildings with the Internet of Things, Digital Twins and Blockchain

Smart buildings have grown in numbers and capabilities worldwide. Smart buildings rely on the sensor networks and Internet of Things (IoT), which allows the development and optimization performance through various methods, including efficient energy management solutions and predictive technologies for future decisions. Smart buildings generate data in real-time, and human-based choices are often taken in real-time. IoT provides connectivity for the various sensors. Smart buildings must ensure the proper operation with minimum time and energy with regards to dynamic human demands and performance in real-time. Blockchain technology can ensure data integrity and regulated autonomous flow of information between users who have a varying degree of trust. Therefore, the combination of Blockchain and the IoT can achieve data integrity between IoT devices installed in the smart buildings where multiple humans are involved and have different non-overlapping expectations from the building’s environment and response. In this paper, a set of predictive technologies are analyzed, and their common parameters are shown to affect the possible prediction and visualization of the IoT data for users. A digital twin based on Blockchain is proposed where individual users owing some sensors can use and visualize processed data based on rules set by the owners in the Blockchain.
Arabic Topic Identification: A Decade Scoping Review

Arabic topic identification is a part of text classification that aims to assign a given text a set of pre-defined classes (i.e., topics) based on its content and extracted features. This task can be performed using rule-based methods or data-driven approaches. These latter gained more popularity since they require much less human effort to accurately classify a large number of documents. Due to the tremendous growth of Web contents primarily in news websites and social media, topic identification had received a great deal of attention over the last years, and has become a cornerstone of both search engines and information retrieval. The Arabic language is the fourth most used language on the web and records the highest growth in the last two decades (2000–2020). Based on these facts currently available, it seems fair to look closer at the advancements in the Arabic topic identification in the last decade. To this end, we performed the first of its kind scoping review that addresses recent studies in the field of Arabic topic identification that follows the PRISMA-ScR guidelines. This review is based on various online bibliographic databases (e.g., Springer, ScienceDirect, and IEEE Xplore) and datasets search engines (e.g., Google Dataset Search).
Robust quantum classifier with minimal overhead

To witness quantum advantages in practical settings, substantial efforts are required not only at the hardware level but also on theoretical research to reduce the computational cost of a given protocol. Quantum computation has the potential to significantly enhance existing classical machine learning methods, and several quantum algorithms for binary classification based on the kernel method have been proposed. These algorithms rely on estimating an expectation value, which in turn requires an expensive quantum data encoding procedure to be repeated many times. In this work, we calculate explicitly the number of repetition necessary for acquiring a fixed success probability and show that the Hadamard-test and the swap-test circuits achieve the optimal variance in terms of the quantum circuit parameters. The variance, and hence the number of repetition, can be further reduced only via optimization over data-related parameters. We also show that the kernel-based binary classification can be performed with a single-qubit measurement regardless of the number and the dimension of the data. Finally, we show that for a number of relevant noise models the classification can be performed reliably without quantum error correction. Our findings are useful for designing quantum classification experiments under limited resources, which is the common challenge in the noisy intermediate-scale quantum era.
Noisy-to-Noisy Voice Conversion Framework with Denoising Model

In a conventional voice conversion (VC) framework, a VC model is often trained with a clean dataset consisting of speech data carefully recorded and selected by minimizing background interference. However, collecting such a high-quality dataset is expensive and time-consuming. Leveraging crowd-sourced speech data in training is more economical. Moreover, for some real-world VC scenarios such as VC in video and VC-based data augmentation for speech recognition systems, the background sounds themselves are also informative and need to be maintained. In this paper, to explore VC with the flexibility of handling background sounds, we propose a noisy-to-noisy (N2N) VC framework composed of a denoising module and a VC module. With the proposed framework, we can convert the speaker's identity while preserving the background sounds. Both objective and subjective evaluations are conducted, and the results reveal the effectiveness of the proposed framework.
ASSESSMENT OF MULTI-OBJECTIVE AND COEVOLUTIONARY GENETIC PROGRAMMING FOR PREDICTING THE STOKES FLOW AROUND A SPHERE

Genetic Programming (GP) has been used in a variety of fields to learn the relationships between physical measurements of real-world problems.In this article, we combine different techniques from the area of evolutionary optimization and, particularly, GP to solve a fluid-dynamics problem: the Stokes flow around a rigid sphere.This serves as the starting point to explore the potential of applying different GP techniques to such complex physical problems.From the definition of the considered fluid-dynamics problem, six benchmark instances with different lengths and complexities are derived.We use single-and multi-objective GP methods and compare their performance using different objective functions.More precisely, we study how model complexity, correlation and the consistency with physical laws (i.e.physical units of measurement) can be included as different objective functions, and whether their inclusion is beneficial to the overall search process.In addition, we include the concept of Cooperative Coevolution, which maintains multiple independent populations of solutions, into our GP implementations and explore the capabilities and limitations of such coevolution-based optimization.We further propose a novel multi-phase approach, which alternates in the GP process between phases of traditional optimization and a mutation-only phase to reduce the model complexity.The results indicate that using multi-objective optimization is beneficial to the search process and can help finding numerically correct solutions to the problems, especially when including a transformed Spearman correlation as an additional optimization goal.Furthermore, the inclusion of the physical units of measurement also helps guide the GP toward numerically correct and physically meaningful equations.While the concept of coevolution did not lead to a superior performance in all cases, the precomputation of additional features, and the resulting reduction of the function set of the GP, leads to a drastic increase in performance and enables the algorithms to solve even the most complex of our benchmark instances.In the future, we aim to extend this research to more complex flows with multiple spheres and at higher Reynolds numbers, which involve a large number of input features.
Sistem Deteksi Senjata Otomatis Menggunakan Deep Learning Berbasis CCTV Cerdas

Nowadays, security and safety are big concerns in this modern and cyberwar era. Many countries invest some safety infrastructure to ensure their inhabitants for keeping their lives safely. Indonesia is the country with many problems because of urbanization and other challenges. This problem should be solved with smart city solution and it must be able to face the challenge of ensuring the safety and improving the quality of life regarding network centric warfare era. This problem also should be tackled with CCTV analytics with the ability to implement an automatic weapon detection system. It also can provide the early detection of potentially violent situations that is of paramount importance for citizens security. This paper is using deep Learning techniques based on Convolutional Neural Networks (CNN) can be trained to detect this type of object with YOLOv4 model and it proposes to implement CCTV analytics as a platform to process real-time data for monitoring weapon detection into knowledge displayed in a dashboard with accuracy 0.89, precision 0.82, recall 0.96 dan F1 Score 0.90 result on weapon detection with a real time speed of processing with NVIDIA 2080 Ti around of 35 FPS. It will send an early warning notification if the system detects the weapon detection such as a knife, gun etc.
Generators and Relations for Un(Z[1/2,i])

Consider the universal gate set for quantum computing consisting of the gates X, CX, CCX, omega^dagger H, and S. All of these gates have matrix entries in the ring Z[1/2,i], the smallest subring of the complex numbers containing 1/2 and i. Amy, Glaudell, and Ross proved the converse, i.e., any unitary matrix with entries in Z[1/2,i] can be realized by a quantum circuit over the above gate set using at most one ancilla. In this paper, we give a finite presentation by generators and relations of U_n(Z[1/2,i]), the group of unitary nxn-matrices with entries in Z[1/2,i].
Role of Classical Chinese Philosophy in Creating the Chinese Piano Music of the 1980s

The purpose of the research is to consider the influence of Chinese thinkers’ philosophical ideas on the professional piano art of China during the 1980s. The paper is novel in that it is the first to examine the interdisciplinary connection between music and philosophy in relation to the period of active professional piano art development in China. The paper for the first time notes the role that the main tenets of Chinese philosophy play in the formation of image-bearing characteristics of programme music for piano. The example of the correspondence between the structural organisations of the philosophical treatise “Zhou Yi” and Zhao Xiaosheng’s piano work “Tai Chi” at the level of compositional technique is introduced into the field of music and Chinese studies. The research is the first to trace the influence of classical Chinese philosophy on professional piano art development in China during the 1980s.
Deep Reinforcement Learning for Job Scheduling on Cluster

Job scheduling is a key function of cluster computing. Efficient job scheduling can improve hardware resource utilization and promote the execution efficiency of jobs. Conventional scheduling work is dominated by heuristic algorithms. The scheduling efficiency of the heuristic algorithm is not optimal. In this paper, we improved the deep reinforcement learning algorithm for the cluster scheduling, which named DeepCM. Test results on the simulation data shows that the DeepCM is capable of improving the performance for job scheduling on the cluster. The slowdown could be improved from 2.248 to 2.235 in a environment of 3 machines. The fusion of internal baseline and external baseline could reduce the variations of the performance on different jobsets. The experimental results demonstrate that the deep reinforcement learning get improved scheduling efficiency in cluster computing. The performance advantage is more obvious when the load gets heavier.
A Case-Based Approach to Data-to-Text Generation

Traditional Data-to-Text Generation (D2T) systems utilise carefully crafted domain specific rules and templates to generate high quality accurate texts. More recent approaches use neural systems to learn domain rules from the training data to produce very fluent and diverse texts. However, there is a trade-off with rule-based systems producing accurate text but that may lack variation, while learning-based systems produce more diverse texts but often with poorer accuracy. In this paper, we propose a Case-Based approach for D2T that mitigates the impact of this trade-off by dynamically selecting templates from the training corpora. In our approach we develop a novel case-alignment based, feature weighing method that is used to build an effective similarity measure. Extensive experimentation is performed on a sports domain dataset. Through Extractive Evaluation metrics, we demonstrate the benefit of the CBR system over a rule-based baseline and a neural benchmark.