Reviewerly/exHarmony huggingface dataset:
Multi-Source Image Registration Based on Slic Superpixel Segmentation and Autoencoder

Abstract A new Harris-AE feature matching algorithm based on image edge information combined with SLIC superpixel segmentation method is proposed to achieve high-precision registration of visible and infrared images. First, perform edge enhancement and histogram equalization processing on visible light and infrared images, superpixel segmentation technology is used to eliminate sub-images with low information entropy, and then by constructing a multi-scale Gaussian pyramid to detect the Harris corners of the image edge information, using autoencoder neural network to generate feature descriptors corresponding to feature points, and match the feature points of two images through the fast nearest-neighbor algorithm with bidirectional matching strategy. Finally, the RANSAC algorithm is used to purify the matching points, and the spatial geometric change parameters between the two images are estimated by the least-squares solution to complete the registration of visible light and infrared images. Experiments have proved that this algorithm improves the accuracy of matching while shortening the registration time.
Active recursive Bayesian inference using Rényi information measures

Recursive Bayesian inference (RBI) framework provides optimal Bayesian latent variable estimates in real-time settings with streaming noisy observations. Active RBI attempts to effectively select queries that lead to more informative observations to reduce uncertainty until the process is stopped at a certain confidence level. However, the mismatch between querying objective and stopping criterion creates the conundrum of improving the performance of one objective at the expense of deteriorating the other. Moreover, conventional active querying methods stagger in the presence of misleading prior information. Inspired by information theoretic approaches, we propose an active RBI framework where query and stopping criterion are jointly selected through a unified objective based on Rényi information measures. The proposed unified formulation enables us to jointly enhance speed and accuracy in the RBI process. We theoretically demonstrate that the proposed objective encourages exploration in the presence of misleading prior. Furthermore, we motivate our framework by proving a geometrical representation for active querying and decision making on a probability simplex. We provide empirical and experimental studies on two applications including restaurant recommendation and brain-computer interface (BCI) typing systems and demonstrate that our method outperforms comparable approaches by improving both speed and accuracy.
Quantum Information Processing Fundamentals

This chapter is devoted to the basic concepts of quantum information processing (QIP). After describing the elementary QIP features, the chapter introduces the superposition principle and quantum parallelism concept as well as QIP basics. It continues with formulation and proof of no-cloning theorem. We further formulate and prove an important theorem, which claims that it is impossible unambiguously to distinguish nonorthogonal quantum states. The next section is devoted to the various aspects of entanglement, including the Schmidt decomposition theorem, purification, superdense coding, and entanglement swapping. How entanglement can be used in detecting and correcting quantum errors is described as well. Various measures of entanglement are introduced, including the entanglement of formation, concurrence, and entanglement fidelity. Furthermore, we discuss the operator-sum representation of a quantum operation and apply this concept to describe the quantum errors and decoherence. The bit-flip, phase-flip, phase-damping, depolarizing, and amplitude-damping channels are described as well. Then we describe different quantum computing libraries, including Qiskit, Cirq, Forest, and Quantum Development Kit. After summarizing the chapter, a set of problems is provided for better understanding of QIP concepts.
Hard Deterministic Particle Swarm Optimisation for Certain Result Solution

Stochastic optimisation's performance is globally acknowledged for its flexibility, robustness and performance – the result of optimisation is usually outstanding and acceptable. However, stochastic methods benefit from random variance to produce feasible results. However, there is one significant disadvantage when it comes to the certainty of results. The stochastic method may give an unsatisfied result. Even though it is an unlikely possibility, it can happen. Therefore, we propose an algorithm that eliminates this problem by changing the calculation core to a deterministic base. In this paper, we select Particle Swarm Optimisation (PSO) as the prototype algorithm and modify the particle moving method to generate an inevitable result. The results show that our process can produce a certain result solution, and the output from our deterministic algorithm is also acceptable, like the original algorithm.
A False Bank Accounts Detection Method Based on Naive Bayesian Network

Abstract At present, more and more cases of economic fraud are based on false bank accounts. Due to the limitation of the bank’s technology and ability to verify the true identity of customers, the online verification system of banks can only check the authenticity of the ID card, while the consistent verification of people and cards can only be completed manually by bank tellers. It is easy to produce errors if the consistency is judged by the teller’s manual naked eye. In this paper was proposed a method of constructing a naive Bayesian network classification model by automatically detect the authenticity of bank accounts to avoid the problem of economic fraud of false bank accounts, and verified the feasibility of this method through specific examples. The experimental results show that the method which has certain theoretical and practical significance, and provides reference for the detection of other false accounts.
Enhanced the Quality of Telemedicine Real-Time Video Transmission and Distortion Minimization in Wireless Network

Achieving good quality and minimum distortion of the video frames is one of the most challenging requirements in the telemedicine system. Transmission process for a real-time video over the wireless network is due to various real-time restrictions, such as encoding mechanism, noise, and bandwidth fluctuations. The restrictions introduce distortions and delay, hence adversely affect the reliability and quality of the video transmission system. This study aims to propose a new system which can fine-tune the encoding process dynamically. The proposed system consists of an Enhanced Video Quality and Distortion Minimization (EVQDM) algorithm to achieve guaranteed quality, minimum distortion, and the minimum delay in the transmission of the video. This system guarantees the video quality by using the adaptive video encoding technique and minimizes the distortion by considering the truncating distortion in the enhanced distortion minimization algorithm. The results of applying the proposed EVQDM algorithm and the state-of-the-art solutions are compared, and it was shown that the proposed algorithm improved the state-of-the-art solution. The video quality has been increased from 47.2 dB to 50.13 dB, the video distortion has been minimized from 0.6802 to 0.3509 and the end-to-end delay has been reduced from 123.58 ms to 112.57 ms. The proposed solution focuses on truncating distortion, to minimize the total distortion of the video. For summarization, this solution addressed the issues of achieving minimum distortion and delay while providing the guaranteed video quality within the boundaries of the real-time constraints that are imposed on the system.
Mutually Connected Neural Networks

This chapter focuses on the mutually connected neural networks. Section 4.1 describes the Hopfield network, and Sect. 4.2 the Boltzmann machine.
Interactive Visualization of 3D CNN Relevance Maps to Aid Model Comprehensibility

Relevance maps derived from convolutional neural networks (CNN) indicate the influence of a particular image region on the decision of the CNN model. Individual maps are obtained for each single input 3D MRI image and various visualization options need to be adjusted to improve information content. In the use case of model prototyping and comparison, the common approach to save the 3D relevance maps to disk is impractical given the large number of combinations. Therefore, we developed a web application to aid interactive inspection of CNN relevance maps. For the requirements analysis, we interviewed several people from different stakeholder groups (model/visualization developers, radiology/neurology staff) following a participatory design approach. The visualization software was conceptually designed in a Model–View–Controller paradigm and implemented using the Python visualization library Bokeh. This framework allowed a Python server back-end directly executing the CNN model and related code, and a HTML/Javascript front-end running in any web browser. Slice-based 2D views were realized for each axis, accompanied by several visual guides to improve usability and quick navigation to image areas with high relevance. The interactive visualization tool greatly improved model inspection and comparison for developers. Owing to the well-structured implementation, it can be easily adapted to other CNN models and types of input data.
Using Stories to Develop EFL Students’ Vocabulary at SMK 17 Agustus 1945

Story reading has long been recognized as an effective educational strategy. Current researches strengthened the ideas that reading stories provides a powerful languageinputs for EFL learners in the attempt to develop language skills and vocabulary. This study was an action research aimed to improve tenth graders’ vocabulary mastery byusing stories at SMK 17 Agustus 1945. The study was carried out in two cycles. Throughout the study, quantitative data was collected by using tests and qualitative data was obtained through a questionnaire. The results showed that the use of story reading technique was successful to develop the vocabulary mastery. It did not only enhance the students’ vocabulary test scores but also changed their views from a negative one to a positive one concerning the study of English in general, the importance of vocabularydevelopment, and the use of story reading to develop vocabulary. Based on the results, teachers are suggested to use story reading technique to improve students’ mastery ofEnglish vocabulary.
Intelligent Data Analytics for Global Solar Radiation Forecasting for Solar Power Production Using Deep Learning Neural Network (DLNN)

This chapter proposes DLNN (deep learning neural network) based intelligent data analytics forecasting approach for multi-step ahead (MSA) global solar radiation (GSR). In the proposed approach, per minute recorded data of 3 years (during 2015–17) was first collected from meteorological department of India, then data was preprocessed, which include cleaning the data, missing value filling, and spikes removal. The MSA forecasting is achieved recursively by utilizing the first forecasted data as an input to generate the next forecasting data and the process is achieved upto level four. In order to achieve accuracy of the proposed approach, the evaluated results are compared with other method (artificial neural network-ANN), which represents that proposed DLNN based forecasting approach has excellent performance and can be used for online forecasting applications.
Framing the Challenge of Social Interaction Modelling

The level of social cognition in artificial agents depends on its ability to identify and interpret the world surrounding it [11]. Therefore, one design objective when creating artificial systems, such as a social robot, is to give it the ability to identify and interpret the transaction of social signals during social interactions. This is, however, an open research problem, often seen as a wicked problem[4]. One of the key difficulties is to properly frame the problem, as social interactions are complex, highly dynamic, and not easily formalised.In this short article, we present one situation (extracted from the small dataset of social interactions that we recorded) that illustrates in one snapshot the complexity of social situation assessment and might help frame appropriately the problem.
The Reform and Construction of Computer Essential Courses in New Liberal Arts Aiming at Improving Data Literacy

In the era of Big Data, all countries put innovation at the core of national development. The Higher Education Department of the Ministry of Education of the People’s Republic of China calls for the comprehensive promotion of the construction of new liberal arts, with the aim of introducing the latest information technology into the traditional liberal arts teaching. As a normal university, East China Normal University is an important cradle to promote the ‘‘CS for ALL’’ talents. Therefore, in the computer reform of Shanghai colleges and universities, it takes the lead in the reform of computer basic teaching of New Liberal Arts. The fundamental goal of this reform is to cultivate students’ Data Thinking and improve their Data Literacy. Good results are achieved in the aspects of target achievement degree and result satisfaction degree.
Expanded Basis Sets for the Manipulation of Random Forests

Random Forests is considered one of the best off-the-shelf algorithms for data mining. However, it suffers from poor interpretability and an opaque decision structure. In this paper, we develop a method for generating an “expanded basis set” for a Random Forest model that captures every possible decision rule and vastly improves the transparency of the classifier. The expanded basis set allows the structure of a Random Forest model to be algebraically manipulated and facilitates a number of operations, including inverse mapping from outputs to the domain of inputs, systematic identification of every decision boundary, and comparison of Random Forest models. The expanded basis set facilitates visualization of the global behaviour of a Random Forest classifier and a data set by combining parallel coordinates with a non-linear binning transformation. The global visualization allows classifier performance to be compared against domain expertise, and areas of underfitting and overfitting to be readily identified. Additionally, the expanded basis set underpins the generation of counterfactuals and anchors—combinations of variables that control the local outputs of a Random Forest model. The basis states can also be used to place bounds on the model stability in response to single or multi-feature perturbations. These stability bounds are especially useful when the model inputs may be uncertain or subject to variation over time.
Rethinking Pixel-Wise Loss for Face Super-Resolution

Face super-resolution has become much attractive in terms of its applications, i.e., video surveillance, and face identification system. However, it is difficult to reconstruct super-resolved images from low-resolution images due to the lack of information in missing pixels. To achieve it, most state-of-the-art methods have used deep neural networks to compensate for missing parts in the reconstruction process by optimizing pixel-wise loss functions. In these methods, various basic pixel-wise loss functions were utilized in image-level and feature-level, but there did not exist the analysis of pixel-wise functions. In this paper, we analyze pixel-wise loss functions in different image domains and feature-level to explore the effects of the loss function in face super-resolution. Furthermore, we provide extensive experiments on CelebA to rethink existing pixel-wise loss and make the best combination of existing loss functions.
A Comparative Analysis of Query-Constrained Video Summarization Techniques

This paper is an analysis of the literature available in the field of query-constrained video summarization. For the sake of concision, five of the more recent, prominent research works have been explored. This research work will dive deep into the datasets used by each of the models, concerning their preparation methods, scale, description, and comparison. Next, the methodologies associated with each of these papers covering their models used, metrics employed, and draw parallels between them are described. The ablation analyses among model variants are discussed to gauge the importance of specific features in each paper. Finally, a description of each model's metric results has been provided for a quantitative comparison among the models, built on some standard metrics described by each of these papers.
Indifferentiability of SKINNY-HASH Internal Functions

We provide a formal proof for the indifferentiability of SKINNY-HASH internal function from a random oracle. SKINNY-HASH is a family of sponge-based hash functions that use functions (instead of permutations) as primitives, and it was selected as one of the second round candidates of the NIST lightweight cryptography competition. Its internal function is constructed from the tweakable block cipher SKINNY. The construction of the internal function is very simple and the designers claim n-bit security, where n is the block length of SKINNY. However, a formal security proof of this claim is not given in the original specification of SKINNY-HASH. In this paper, we formally prove that the internal function of SKINNY-HASH has n-bit security, i.e., it is indifferentiable from a random oracle up to O(2n) queries, substantiating the security claim of the designers.
Education quality from the viewpoint of educational process participants and the parties concerned

The relevance of the study of issues related to improving the quality of education in current realities is not in doubt. One may note a wide range of opinions regarding the content and essence of this concept, its structure, ensuring the quality of the educational process, in particular, in educational institutions of higher education, and understanding of the quality of existing education by the educational process participants (teachers and students) as well as parties concerned (employers, parents, etc.). Improving the quality of education is a multifaceted and continuous process that requires careful attention from the scientific community. The authors consider the main definitions of the concept of quality of education and uncover its content. To study different views on the quality of contemporary education, several surveys of the main participants in the educational process, i.e. teachers and students, were conducted. The authors revealed the opinions of employers and graduates regarding the quality of domestic higher education and, based on these qualities, created a profile of contemporary graduates (young professionals) who have received a high-quality education. As a result of the conducted research, the conclusion is confirmed that quality is understood by each person according to their accumulated experience and the subjective idea of this category, and changes according to the requirements that are dictated by society and the state in a particular historical period, as well as depending on the conditions of the surrounding reality (a clear example is a current situation with coronavirus which shows that the main skills for employees are the skills and abilities to perform work remotely, which also become key ones when making a decision by the employer about hiring an employee).
Matching Ontologies Through Evolutionary Algorithm with Context-Based Reasoning

Ontology matching can solve the heterogeneity problem between two ontologies, and EA represents a state-of-the-art technique for matching ontologies. However, there are two defects concerning the EA-based ontology matching technique: (1) a reference alignment between two ontologies to be matched is required in advance; (2) the confidence of entity similarity measure is low computational complexity of measuring the similarity value is high. To overcome these drawbacks, in this paper, an Evolutionary Algorithm with Context-based Reasoning method (EA-CR) is proposed, where: (1) an approximate metric without the reference alignment is utilized for evaluating the alignment’s quality; (2) a Context-based Reasoning method is presented to distinguish the heterogeneous entities. The experimental results show that the proposed approach is effective.
A Deep Learning Approach to Outbreak related Tweet Detection

Due to the popularity of social media around the world, people use to report and discuss real-world events, personal health complications, and disaster situations through these platforms. These social media data streams can be used to track and detect different types of outbreaks. A mechanism is needed to identify outbreak-related tweets to predict the outbreak in advance. In this paper, we propose a deep learning model that can detect tweets related to different outbreaks Epidemics, Public Disorders, and Disasters. GloVe (Global Vectors for Word Representation) embeddings are used as the feature extraction technique as it can capture the semantic meanings of the tweets. Long Short-term Memory (LSTM) which is a specialized Recurrent Neural Network architecture is used as the classification algorithm. In the process, first, outbreak-related tweets were manually collected and curated. Pretrained GloVe word embeddings of 100 dimensions were then used to represent the words of the tweets. As the next step, a Deep Learning Model was trained by using LSTM technique on the curated dataset. Finally, the performance of the model was evaluated using a different dataset. With the results, it can be concluded that the proposed deep learning model is an accurate approach for outbreak-related tweet detection.
Efficient Large Scale Semi-Supervised Learning for CTC Based Acoustic Models

Semi-supervised learning (SSL) is an active area of research which aims to utilize unlabeled data to improve the accuracy of speech recognition systems. While the previous studies have established the efficacy of various SSL methods on varying amounts of data, this paper presents largest ASR SSL experiment ever conducted till date where 75K hours of labeled and 1.2 million hours of unlabeled data is used for model training. In addition, the paper introduces couple of novel techniques to facilitate such a large scale experiment: 1) a simple scalable Teacher-Student based SSL method for connectionist temporal classification (CTC) objective and 2) effective data selection mechanisms for leveraging massive amounts of unlabeled data to boost the performance of student models. Further, we apply SSL in all stages of the acoustic model training, including final stage sequence discriminative training. Our experiments indicate encouraging word error rate (WER) gains up to 14% in such a large transcribed data regime due to the SSL training.