Reviewerly/exHarmony huggingface dataset:
Assimilation of Pair-Wise Comparison Method to Decide Weights of Features Based on the Content of Image

Computer-based image recognition systems rely on training with an initial training set to recognize similar images. However, when such training set is not available, individual features within a given image can be used to identify and compare with congruent features in database to find similarity among images. An experiment is implemented for extracting similar images using this technique of computer-based image recognition. For this study multiple features color, shape and texture have been extracted from Corel 10k database images. After manual selection of individual feature weightage, all features with equal weightage have been used to identify similar images from database. After observing class wise average precision and average recall of Corel 10k database images, it has been found that equal weightage approach yields satisfactory results and also better as compared with individual feature approach in maximum number of classes. In some of the classes, results are not satisfactory with equal weightage approach. In these classes, results vary with weights of feature according to nature of image. The average precision and recall of entire classes are 96.62% based on all feature equal weight. Now, the problem is to decide weights of features. For this, this study proposes the automatic determination of the weightage for features using pair-wise comparison method. The problem is formulated as multiple criteria decision-making (MCMD) problem and solved through analytic hierarchical process (AHP). Graphical user interface (GUI) is also designed. The proposed approach determines color dominant, shape dominant and texture dominant based on importance on respective features. The average precision and recall of 95 classes are 100% in all dominant features. The average precision and recall of remaining five classes are 59%, 53% and 46% in color dominant, shape dominant and texture dominant, respectively. Performance measure shows that the proposed method archives better results as compared to manual assignment of weights of features.
A New Method to Determine Cluster Number Without Clustering for Every K Based on Ratio of Variance to Range in K-Means

In many clustering algorithms such as K-means and FCM, the cluster number K needs to be known beforehand. In this paper, we propose a new method to determine the cluster number without clustering for every K in K-means. We introduce a new statistics RVR (ratio of variance to range) and conduct Monte Carlo analysis of its characteristics. Based on the RVR, we propose an algorithm to determine the cluster number K and perform clustering utilizing it. We evaluate its effectiveness by performing a simulation test with different types of datasets; first, with real datasets, whose real number of clusters and components are known and second, with synthetic datasets. We observe a significant improvement in speed and quality of determining the cluster number and therefore clustering. Finally, we hope the proposed algorithm to be used efficiently and widely for clustering of multidimensional data.
<i> L <sup>2</sup> BEC <sup>2</sup> </i> : Local Lightweight Bidirectional Encoding and Channel Attention Cascade for Video Frame Interpolation

Video frame interpolation (VFI) is of great importance for many video applications, yet it is still challenging even in the era of deep learning. Some existing VFI models directly exploit existing lightweight network frameworks, thus making synthesized in-between frames blurry and creating artifacts due to imprecise motion representation. The other existing VFI models typically depend on heavy model architectures with a large number of parameters, preventing them from being deployed on small terminals. To address these issues, we propose a local lightweight VFI network ( L 2 BEC 2 ) that leverages bidirectional encoding structure with channel attention cascade. Specifically, we improve visual quality by introducing a forward and backward encoding structure with channel attention cascade to better characterize motion information. Furthermore, we introduce a local lightweight strategy into the state-of-the-art Adaptive Collaboration of Flows (AdaCoF) model to simplify its model parameters. Compared with the original AdaCoF model, the proposed L 2 BEC 2 obtains performance gain at the cost of only one-third of the number of parameters and performs favorably against the state-of-the-art works on public datasets. Our source code is available at https://github.com/Pumpkin123709/LBEC.git .
Agile methods in the German banking sector: some evidence on expectations, experiences and success factors

Abstract The importance of agile methods has increased in recent years, not only to manage IT projects but also to establish flexible and adaptive organisational structures, which are essential to deal with disruptive changes and build successful digital business strategies. This paper takes an industry-specific perspective by analysing the dissemination, objectives and relative popularity of agile frameworks in the German banking sector. The data provides insights into expectations and experiences associated with agile methods and indicates possible implementation hurdles and success factors. Our research provides the first comprehensive analysis of agile methods in the German banking sector. The comparison with a selected number of fintechs has revealed some differences between banks and fintechs. We found that almost all banks and fintechs apply agile methods in IT projects. However, fintechs have relatively more experience with agile methods than banks and use them more intensively. Scrum is the most relevant framework used in practice. Scaled agile frameworks are so far negligible in the German banking sector. Acceleration of projects is apparently the most important objective of deploying agile methods. In addition, agile methods can contribute to cost savings and lead to improved quality and innovation performance, though for banks it is evidently more challenging to reach their respective targets than for fintechs. Overall our findings suggest that German banks are still in a maturing process of becoming more agile and that there is room for an accelerated adoption of agile methods in general and scaled agile frameworks in particular.
Reversible Data Hiding of Digital Image Based on Pixel Combination Algorithm

In order to improve the security effect of image information, this paper studies the reversible information hiding of the digital images combined with the pixel combination algorithm and proposes an improved simulated annealing algorithm using the incremental calculation method of statistical functions. Moreover, according to the gray gradient information contained in the image, this paper proposes an automatic threshold determination algorithm suitable for the unimodal gray distribution images and uses this algorithm to complete the threshold determination and binary segmentation of all images. In addition, this paper optimizes the model by transforming the isolated pores inside the model and removing the isolated matrix area. Through comparative experiments, it can be seen that the reversible information hiding method for digital images based on the pixel combination algorithm proposed in this paper has a good image information confidentiality effect.
Formation of professional readiness of future informatics teachers for using artificial intelligence technologies

The article presents the organizational and pedagogical conditions for training future informatics teachers for using modern artificial intelligence technologies. Their implementation made it possible to modernize the process of mastering artificial intelligence technologies by students through changing the content of the academic discipline, aimed at developing the skills and ideas of the future informatics teacher in the field of artificial intelligence, ensuring the inclusion in the educational process of creating individual learning trajectories. Criteria as well as evaluation and diagnostic tools for determining the level of professional readiness are given. The main results of experimental work on approbation of organizational and pedagogical conditions through the implementation of the author’s experimental practice-oriented course “Artificial intelligence technologies”, introduced into the educational process of Novosibirsk State Pedagogical University, are presented. The experiment involved 127 bachelor students of the 3rd and 4th years of the directions 44.03.01 “Pedagogical education” and 44.03.05 “Pedagogical education (with two training profiles)”, profiles: “Informatics and Information and Communication Technologies”, “Informatics and Information and Communication Technologies and Economic Education”, “Mathematics and Informatics”, “Physics and Informatics”. The experiment was conducted from 2018 to 2021 (4 academic years) and showed the effectiveness of using the identified organizational and pedagogical conditions for the professional training of future informatics teachers in the use of artificial intelligence technologies. The results of the study may be of interest to university teachers who provide professional training for students of non-core specialties in the field of artificial intelligence, and researchers studying the issues of professional training of teachers in the application of artificial intelligence technologies in education.
Finding relevance in smartphone advertising

Abstract Smartphone ads compete for the user’s attention, which is initially intended to focus on other areas of the small screen of the device. Despite this competition, smartphone advertisements aim to produce as much cognitive reward as possible in exchange for the mental effort expended in their processing, that is, they aim at the audience’s relevance, as claimed by relevance theory ( Sperber and Wilson 1995 ), a theory in which cyberpragmatics ( Yus 2011 ) is rooted. This paper addresses several key qualities of effective smartphone advertising from a cyberpragmatics perspective that focuses on possible sources of relevance of online communication, and now applied to smartphone ads. Furthermore, it is claimed that today’s smartphone-based advertising cannot be accounted for pragmatically without the incorporation of key terms such as contextual constraint and non-propositional effect , which add to more traditional pragmatic accounts of online communication ( Yus 2017a , 2021a ).
A Machine Learning and Deep Learning Approach for Recognizing Handwritten Digits

Optical character recognition (OCR) can be a subcategory of graphic design that involves extracting text from images or scanned documents. We have chosen to make unique handwritten digits available on the Modified National Institute of Standards and Technology website for this project. The Machine Learning and Depp Learning algorithms are used in this project to measure the accuracy of handwritten displays of letters and numbers. Also, we show the classification accuracy comparison between them. The results showed that the CNN classifier achieved the highest classification accuracy of 98.83%.
Referring Expression Comprehension Via Enhanced Cross-modal Graph Attention Networks

Referring expression comprehension aims to localize a specific object in an image according to a given language description. It is still challenging to comprehend and mitigate the gap between various types of information in the visual and textual domains. Generally, it needs to extract the salient features from a given expression and match the features of expression to an image. One challenge in referring expression comprehension is the number of region proposals generated by object detection methods is far more than the number of entities in the corresponding language description. Remarkably, the candidate regions without described by the expression will bring a severe impact on referring expression comprehension. To tackle this problem, we first propose a novel Enhanced Cross-modal Graph Attention Networks (ECMGANs) that boosts the matching between the expression and the entity position of an image. Then, an effective strategy named Graph Node Erase (GNE) is proposed to assist ECMGANs in eliminating the effect of irrelevant objects on the target object. Experiments on three public referring expression comprehension datasets show unambiguously that our ECMGANs framework achieves better performance than other state-of-the-art methods. Moreover, GNE is able to obtain higher accuracies of visual-expression matching effectively.
Verifiable (2, n) Image Secret Sharing Scheme Using Sudoku Matrix

As Internet technology continues to profoundly impact our lives, techniques for information protection have become increasingly advanced and become a common discussion topic. With the aim to protect private images, this paper splits a secret image into n individual shares using a Sudoku matrix with authentication features. Later, the shares can be compiled to completely reconstruct the secret image. The shares are meaningful ones in order to avoid detection and suspicion among malicious users. Our proposed matrix is unique because the embedding rate of the secret data is very high, while the visual quality of the shares can be well guaranteed. In addition, the embedded authentication codes can be retrieved to authenticate the integrity of the secret image. Experimental results prove the advantages of our approach in terms of visual quality and authentication ability.
Visually Explaining Uncertain Price Predictions in Agrifood: A User-Centred Case-Study

The rise of ‘big data’ in agrifood has increased the need for decision support systems that harvest the power of artificial intelligence. While many such systems have been proposed, their uptake is limited, for example because they often lack uncertainty representations and are rarely designed in a user-centred way. We present a prototypical visual decision support system that incorporates price prediction, uncertainty, and visual analytics techniques. We evaluated our prototype with 10 participants who are active in different parts of agrifood. Through semi-structured interviews and questionnaires, we collected quantitative and qualitative data about four metrics: usability, usefulness and needs, model understanding, and trust. Our results reveal that the first three metrics can directly and indirectly affect appropriate trust, and that perception differences exist between people with diverging experience levels in predictive modelling. Overall, this suggests that user-centred approaches are key for increasing uptake of visual decision support systems in agrifood.
A Study on Attention Attracting Elements of 360-Degree Videos Based on VR Eye-Tracking System

In 360-degree virtual reality (VR) videos, users possess increased freedom in terms of gaze movement. As a result, the users’ attention may not move according to the narrative intended by the director and miss out on important parts of the narrative of the 360-degree video. Therefore, it is necessary to study a directing technique that can attract user attention in 360-degree VR videos. In this study, we analyzed the directing elements that can attract users’ attention in a 360-degree VR video and developed a 360 VR eye-tracking system to investigate the effect of the attention-attracting elements on the user. Elements that can attract user attention were classified into five categories: object movement, hand gesture, GUI insertion, camera movement, and gaze angle variation. We developed a 360 VR eye-tracking system to analyze whether five attention-attracting elements influence the user’s attention. Based on the eye tracking system, an experiment was conducted to analyze whether the user’s attention moves according to the five attention-attracting elements. Based on the experimental results, it can be seen that ‘hand gesture’ attracted the second most attention shift of the subjects, and ‘GUI insertion’ induced the smallest shift of attention of the subjects.
RCA-LF: Dense Light Field Reconstruction Using Residual Channel Attention Networks

Dense multi-view image reconstruction has played an active role in research for a long time and interest has recently increased. Multi-view images can solve many problems and enhance the efficiency of many applications. This paper presents a more specific solution for reconstructing high-density light field (LF) images. We present this solution for images captured by Lytro Illum cameras to solve the implicit problem related to the discrepancy between angular and spatial resolution resulting from poor sensor resolution. We introduce the residual channel attention light field (RCA-LF) structure to solve different LF reconstruction tasks. In our approach, view images are grouped in one stack where epipolar information is available. We use 2D convolution layers to process and extract features from the stacked view images. Our method adopts the channel attention mechanism to learn the relation between different views and give higher weight to the most important features, restoring more texture details. Finally, experimental results indicate that the proposed model outperforms earlier state-of-the-art methods for visual and numerical evaluation.
Local Defogging Algorithm for the First Frame Image of Unmanned Surface Vehicles Based on a Radar-Photoelectric System

Unmanned surface vehicles frequently encounter foggy weather when performing surface object tracking tasks, resulting in low optical image quality and object recognition accuracy. Traditional defogging algorithms are time consuming and do not meet real-time requirements. In addition, there are problems with oversaturated colors, low brightness, and overexposed areas in the sky. In order to solve the problems mentioned above, this paper proposes a defogging algorithm for the first frame image of unmanned surface vehicles based on a radar-photoelectric system. The algorithm involves the following steps. The first is the fog detection algorithm for sea surface image, which determines the presence of fog. The second is the sea-sky line extraction algorithm which realizes the extraction of the sea-sky line in the first frame image. The third is the object detection algorithm based on the sea-sky line, which extracts the target area near the sea-sky line. The fourth is the local defogging algorithm, which defogs the extracted area to obtain higher quality images. This paper effectively solves the problems above in the sea test and dramatically reduces the calculation time of the defogging algorithm by 86.7%, compared with the dark channel prior algorithm.
2D Self-organized ONN model for Handwritten Text Recognition

Deep Convolutional Neural Networks (CNNs) have recently reached state-of-the-art Handwritten Text Recognition (HTR) performance. However, recent research has shown that typical CNNs’ learning performance is limited since they are homogeneous networks with a simple (linear) neuron model. With their heterogeneous network structure incorporating non-linear neurons, Operational Neural Networks (ONNs) have recently been proposed to address this drawback. Self-ONNs are self-organized variations of ONNs with the generative neuron model that can generate any non-linear function using the Taylor approximation. In this study, in order to improve the state-of-the-art performance level in HTR, the 2D Self-organized ONNs (Self-ONNs) in the core of a novel network model are proposed. Moreover, deformable convolutions, which have recently been demonstrated to tackle variations in the writing styles better, are utilized in this study. The results over the IAM English dataset and HADARA80P Arabic dataset show that the proposed model with the operational layers of Self-ONNs significantly improves Character Error Rate (CER) and Word Error Rate (WER). Compared with its counterpart CNNs, Self-ONNs reduce CER and WER by 1.2% and 3.4 % in the HADARA80P and 0.199% and 1.244% in the IAM dataset. The results over the benchmark IAM demonstrate that the proposed model with the operational layers of Self-ONNs outperforms recent deep CNN models by a significant margin while the use of Self-ONNs with deformable convolutions demonstrates exceptional results.
Interaction and Expressivity in Collective Decision-Making

Collective decisions among human and artificial agents can be enhanced by allowing for more interaction among decision-makers and by letting them express more information about their preferences. In this paper I present ongoing research on two settings: iterative voting, which repeatedly applies a voting rule until decision-makers converge to an outcome, and delegative voting on multiple issues.
Prototypical networks relation classification model based on entity convolution

Sentence-level relation classification is a technique for classifying the relation between the head entity and the tail entity in a sentence. Currently, it is popularly used to realize relation classification based on deep learning methods. However, these methods rely heavily on large-scale annotated data, and the role of head and tail entities’ information is not fully explored. In response to the above problems, we propose a prototypical networks model based on entity convolution for relation classification, which deforms the head entity and tail entity vectors encoded by BERT into multiple different convolution kernels and then performs convolution operations on the original sentence. Thus we can extract the features related to the entities in the sentence and classify the extracted features by using prototypical networks to realize relation classification. Experimental results strongly demonstrate that our model achieves state-of-the-art results compared with baseline models.
Weighted forward looking adaptive coding

Huffman coding is known to be optimal under certain constraints, yet its dynamic version, which constantly alters the Huffman tree as a function of the already processed characters, may be even more efficient in practice. A new forward looking variant of Huffman compression has been proposed recently, that provably always performs better than static Huffman coding by at least m−1 bits, where m denotes the size of the alphabet, and has a better worst case size than the standard dynamic Huffman coding. This paper introduces a new generic coding method, extending the known static and dynamic variants and including them as special cases. In fact, the generalization is applicable to all statistical methods, including arithmetic coding. This leads then to the formalization of a new double-pass coding method that is adaptive in the sense that it uses changing statistics depending on the current position within the processed file, yet it behaves like static coding, as it assumes the knowledge of the distribution in the entire file; this is contrary to online variants that rely only on the text seen so far and adapt the model dynamically. We call the new method positional coding, and its compression performance, using global statistics, is provably always at least as good as that of the best dynamic variants known to date. Moreover, we present empirical results that show improvements by positional coding and its extensions over static and dynamic Huffman and arithmetic coding, even when the encoded file includes the model description.