Reviewerly/exHarmony huggingface dataset:
Comparison of Shapley-Shubik and Banzhaf-Coleman power indices applied to aggregation of predictions obtained based on dispersed data by k-nearest neighbors classifiers

In this paper a new method of fusion predictions obtained based on dispersed data is proposed. In the method a power index is used. This approach allows to calculate the real power of prediction vectors generated based on local data with using the k-nearest neighbors classifier. The use of two power indices: Shapley-Shubik and Banzhaf-Coleman power index is analyzed. The influence of k-parameter value and the value of quota in simple game on the classification accuracy is also studied. The obtained results are compared with the approach in which the power index was not used. It was found that the proposed method of using the power index improves the classification accuracy. Moreover, both analyzed power indices generate comparable results.
Unpacking Cultural Perceptions of Future Elder Care Through Design Fiction

We present a case using Design Fiction to unpack cultural perceptions of future elder care rooted in the Asian context of Singapore. We created two design fictions, addressing the tensions between filial piety and automated care and the controversy of integrating elder care facilities into residential communities. The design fictions took the visual forms of a shopping web page and a petition site and the public were invited to make fictional decisions. Received in total 109 responses, we identify the key tensions and value conflicts and illustrate them through visual narratives. Further, implied by the Asian perspective of familial relations in care, we propose the positioning of social relationships as the protagonist in creating elder care design fiction.
ACADEMIC INTEGRITY – A GUARANTEE OF TRAINING HIGHLY QUALIFIED PROFESSIONALS

The article considers the issue of academic integrity at the university. The origins of dishonesty starting from the school desk and ending with higher education institutions are analyzed. It is established that excessive workload of students pushes them to use the Internet, not only as a source of knowledge, but also as a source of finished work, which is published as their own work. It is stated that the basic principles of academic integrity are honesty, trust, justice, respect, responsibility, courage, which must be followed by all participants in the educational process. The main violations of academic integrity are: in the field of educational activities – extortion or provocation of money, gifts, personal services in various forms of student assessment, lack of fair evaluation of plagiarized educational and qualification work, performance for payment of such work to order, forced purchase of educational and methodical literature, underestimation of grades and coercion to additional educational services and consultations, updating of equipment at the expense of students or purchase by students of various consumables, stationery; in the field of scientific research – preparation and publication of scientific, educational and methodical texts with the help of plagiarism; falsification of experimental data underlying scientific publications; inclusion in the list of authors of persons who did not make a proper contribution to the results; approval of research or dissertations on topics that have no scientific, practical, social and other significance; providing positive reviews and feedback on works that, according to their scientific results, do not deserve it; obtaining unjustified preferences in the provision of scientific and educational grants; misuse of his official position. The rights and responsibilities of each participant in the educational process in the field of compliance with the requirements of academic integrity are given. Virtue in the modern civilized world is the cornerstone that forms the foundation of the inner harmony of man, the stability of his character and the consistency of the moral image. Higher education through teaching and research actually binds this foundation with elements of academic integrity. Academic integrity must be formed from the beginning of university studies and adhere to its principles throughout the period of study. For this purpose, the documents adopted by the KhNTUSG – the Code of Honor and the Regulations on Academic Integrity of Participants in the Educational Process, and the Ethics and Professional Commission and the Commission on Academic Integrity are created. This will not only train a highly qualified specialist, but also form a highly conscious member of society. Keywords: academic integrity, university, University honor code, professional activity, highly qualified specialists.
Robust Federated Learning With Noisy Labeled Data Through Loss Function Correction

Federated learning (FL) is a communication-efficient machine learning paradigm to leverage distributed data at the network edge. Nevertheless, FL usually fails to train a high-quality model from the networks, where the edge nodes collect noisy labeled data. To tackle this challenge, this paper focuses on developing an innovative robust FL. We consider two kinds of networks with different data distribution. Firstly, we design a reweighted FL under a full-data network, where all edge nodes are equipped with both numerous noisy labeled dataset and small clean dataset. The key idea is that edge devices learn to assign the local weights of loss functions in noisy labeled dataset, and cooperate with central server to update global weights. Secondly, we consider a part-data network where some edge nodes exclude clean dataset, and can not compute the weights locally. The broadcasting of the global weights is added to help those edge nodes without clean dataset to reweight their noisy loss functions. Both designs have a convergence rate of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {O}(1/T^{2})$</tex-math></inline-formula> . Simulation results illustrate that the both proposed training processes improve the prediction accuracy due to the proper weights assignments of noisy loss function.
Learning Purified Feature Representations from Task-irrelevant Labels

Learning an empirically effective model with generalization using limited data is a challenging task for deep neural networks. In this paper, we propose a novel learning framework called Purified Learning to exploit task-irrelevant features extracted from task-irrelevant labels when training models on small-scale datasets. Particularly, we purify feature representations by using the expression of task-irrelevant information, thus facilitating the learning process of classification. Our work is built on solid theoretical analysis and extensive experiments, which demonstrate the effectiveness of Purified Learning. According to the theory we proved, Purified Learning is model-agnostic and doesn't have any restrictions on the model needed, so it can be combined with any existing deep neural networks with ease to achieve better performance.
DiSSECT: Distinguisher of Standard and Simulated Elliptic Curves via Traits

It can be tricky to trust elliptic curves standardized in a non-transparent way. To rectify this, we propose a systematic methodology for analyzing curves and statistically comparing them to the expected values of a large number of generic curves with the aim of identifying any deviations in the standard curves. For this purpose, we put together the largest publicly available database of standard curves. To identify unexpected properties of standard generation methods and curves, we simulate over 250 000 curves by mimicking the generation process of four standards. We compute 22 different properties of curves and analyze them with automated methods to pinpoint deviations in standard curves, pointing to possible weaknesses.
WTB-LLL: A Watercraft Tracking Benchmark Derived by Low-Light-Level Camera

Object tracking under the low illumination condition draws insufficient attention so far. Low-light-level camera is a sophisticated sensor used for nighttime video surveillance. In this paper, we propose the first object tracking dataset derived by low-light-level camera, called Low-light-level Watercraft Tracking Benchmark (WTB-LLL). WTB-LLL consist of 57853 annotated frames with 7 challenge attributes. Since WTB-LLL is more challenging than common daytime benchmark, we extensively evaluate 14 state-of-the-art trackers and conduct two validation experiments to understand how the attributes of the dataset impact the trackers. We observed several instructive findings from the experiments. Briefly, Siamese trackers maintain the stablest performance when confronted with thermal noise. Reasonable assistance of transformer structure is helpful for the most severe challenge, background clutter, of WTB-LLL. However, the discriminative appearance modules are proved to be the best paradigms for our low-light-level dataset thanks to the powerful ability of feature representation and the strong discriminative capacity obtained by the online learning process. By proposing WTB-LLL, we expect to introduce a new prospect for low illumination object tracking and facilitate future research.
Cross-level Attention and Ratio Consistency Network for Ship Detection

In ship detection task, target objects with extreme aspect ratios are common in practical applications. However, existing ship detection methods seldom make efforts to tackle this issue. In this paper, we present a novel Cross-level Attention and Ratio Consistency (CARC) Network for ship detection. First, we propose a Cross-Level Attention (CLA) module to generate attention signals by integrating information from both higher and lower level features. Specifically, for each feature, we calculate its similarity with features from adjacent levels. These similarities are utilized as weights to enhance the channels that consist of different-level information. By fusing multi-level information, a channel-wise attention vector is generated to enhance the learned representations in the base feature. Second, we propose a Ratio Consistency loss that promotes the networks to localize the ships with more accurate aspect ratios. Existing ship detection methods have different sensitiveness to width and height predictions, significantly increasing the learning difficulty for localizing target ships. We append an auxiliary supervision signal to the detection head in our method. This supervision signal measures the error between the predicted and the ground truth aspect ratios of target ships. Experiment results show that our model achieves significant performance gains compared to existing methods.
A Study on the Back and Forth Manzai of Milkboy by Focusing on Embodied Motions and Actions for Liven-Up

Manzai is a form of comedy, and there have been reported attempts to make robots perform manzai. On the other hand, it captures the essence of human communication, such as interactions in live performances. In this study, we extracted the basic units that make up manzai and analyzed the changes in these units to elucidate the laughing mechanism of the characteristic story progression called the “back and forth manzai” of Milkboy. As a result, it was revealed that the story itself is the object of tsukkomi; the comedy is generated by clever story progression; and the comedy is enlivened by embodied motions and actions.
Chinese Sentence Matching with Multiple Alignments and Feature Augmentation

Chinese sentence matching is a critical and yet challenging task in natural language processing. Recent work on modeling sentence semantic relations with deep neural models has shown its great potential in improving the performance of sentence matching. However, existing sentence matching methods usually focus on generating word-level sentence representation, which neglects the character-level information and leads to weak semantic representations. Also, they usually capture the interactive features with an attention-based alignment, which are typically implemented on sentence level and neglect the interactions among characters, words and sentences. This paper proposes a novel Chinese sentence matching model with Multiple Alignments and Feature Augmentation (MAFA). Specifically, the model first employs the multi-level embedding layer to accept the character and word sequences of sentences, and introduces the multiple alignment layer to capture the interactions among characters, words and sentences in turn. Then, the feature augmentation layer is applied to combine the interactive features to generate the final semantic matching representations. Finally, the prediction layer is utilized to judge the matching degree of the input sentences. Substantial and extensive experiments are conducted on two real-world data sets to show that MAFA significantly outperforms the competing methods and achieve comnarable nerformance with BERT-based methods.
Lifelong Fine-Grained Image Retrieval

Fine-grained image retrieval has been extensively explored in a zero-shot manner. A deep model is trained on the seen part and then evaluated the generalization performance on the unseen part. However, this setting is infeasible for many real-world applications since (1) the retrieval dataset can be non-fixed so that new data are added constantly, and (2) data samples of the seen categories are also common in practice and are important for evaluation. In this paper, we explore lifelong fine-grained image retrieval (LFGIR), which learns continuously on a sequence of new tasks with data from different datasets. We first use knowledge distillation to minimize catastrophic forgetting on old tasks. Training continuously on different datasets causes large domain shifts between the old and new tasks while image retrieval is sensitive to even small shifts in the features. This tends to weaken the effectiveness of knowledge distillation by the frozen teacher. To mitigate the impact of domain shifts, we use the network inversion method to generate images of the old tasks. In addition, we design an on-the-fly teacher which transfers knowledge captured on a new task to the student to improve better generalization performance, thereby achieving a better balance between old and new tasks in the end. We name the whole framework as Dual Knowledge Distillation (DKD), whose efficacy is demonstrated by extensive experimental results on sequential tasks including 7 datasets.
Graph Wavelet Convolutional Network with Graph Clustering

In this paper, we present a novel Graph Wavelet Convolutional Network (GWCN) approach with a graph clus-tering algorithm such as METIS. GWCN is a graph wavelet transform-based method. It has better locality than Graph Convolutional Network (GCN) using the graph Fourier transform, and results higher classification accuracy. In this work, the graph clustering algorithm is applied to GWCN for providing a mechanism to the mini-batch selection in deep learning, which has an effective impact on learning.
Research and Application of a New Lion Swarm Algorithm

Aiming at the defects of fast convergence speed of lion swarm algorithm and easy to fall into local optimum, a variable-speed elastic collision lion swarm algorithm (VELSO) is proposed.Firstly, the flexibility of lioness search is increased according to the variable spiral search strategy, and then the two lionesses learn the learning strategies of teaching and learning algorithms to enhance the interactive behavior of lioness hunting.Then, the improved refraction reverse learning strategy is used to increase the diversity of the population and make the individual quality of the population better.Finally, the variablespeed elastic collision strategy is used to increase the probability of the algorithm jumping out of the local optimum and improve the ability of the algorithm to obtain the optimal solution.In order to verify the effectiveness of the proposed algorithm, 16 test functions were used to test the proposed algorithm, and compared with other algorithms, which proved that the proposed algorithm is very effective.Finally, the proposed algorithm is applied to DV-Hop positioning, which verifies the feasibility and practicability of the proposed algorithm.
Enhanced Mask Interaction Network for SAR Ship Instance Segmentation

We propose an enhanced mask interaction network (EMIN) for ship instance segmentation from synthetic aperture radar (SAR) images. EMIN adopts three techniques to improve SAR ship instance segmentation performance — 1) an atrous spatial pyramid pooling (ASPP) to enable multi-resolution feature responses, 2) a non-local block (NLB) to capture long-range spatial dependencies, and 3) a concatenation shuffle attention (CSA) to boost mask interaction benefits. Results on the public SAR ship detection dataset (SSDD) show that — 1) the above each technique can offer an observable accuracy gain, and 2) EMIN surpasses the original MIN by 2.1% detection AP and 2.4% mask AP on SSDD.
Multi-label Feature Extraction With Distance-Based Graph Attention Network

Feature extraction deals with information redundancy in data with a large number of features. Existing feature extraction approaches to multi-label data usually consider label correlations, while rarely consider sample correlations. In this paper, we propose a multi-label feature extraction with the distance-based graph attention network (DBGAT) algorithm. First, to easily extract the neighbors of the sample later, we construct an adjacency matrix according to the distance between samples and the number of neighbors specified by the user. Second, to obtain the importance of neighbor features to instances, we get the weight coefficients of each instance and its neighbors through the attention network. Third, a new representation for each instance is obtained by weighted summation of neighboring instances. The difference in the weight coefficient reflects the degree of influence of different neighbors on the new feature. We tested the new algorithm and eight other popular algorithms on twelve datasets. Experiments show that this method improves the accuracy of multi-label classification.
Evaluation Of Performance Of Artificial Intelligence System During Voice Recognition In Social Conversations using NLP

Discourse acknowledgment, otherwise called mechanized discourse acknowledgment (ASR), PC voice acknowledgment, or discourse to-message, is the capacity of a product to decipher communicated in words into composed language. Regardless of being sometimes mistook for voice acknowledgment, discourse acknowledgment focuses on changing discourse from a verbal to a composed organization while voice acknowledgment in particular tries to recognize someone in particular’s voice. Human correspondence’s intricacy has made advancement testing. Since it blends dialects, math, what’s more, insights, it is viewed as quite possibly of the most testing areas of software engineering. Voice recognizers are worked of a few parts, including the discourse input, highlight extraction, include vectors, a decoder, and a word yield.Despite the fact that it isn’t generally a particular method utilized in voice acknowledgment, NLP is a discipline of computerized reasoning that spotlights on the association among people and machines through language through discourse and text. Speech recognition is a feature that many mobile devices’ operating systems have to enable voice search (like Siri) and improve messaging accessibility.. The main motive of the paper is to evaluate the artificial intelligence algorithms and do a comparison analysis based on different metrics of accuracy.
Study of Error Propagation and Generation in Harrow-Hassidim-Lloyd (HHL) Quantum Algorithm

In this paper, we study the error propagation and generation in the Harrow-Hassidim-Lloyd (HHL) quantum algorithm runs on IBM-Q hardware with the help of a MATLAB simulator. HHL is a quantum algorithm that can provide exponential speedup over the fastest classical algorithm (conjugate gradient method) in solving systems of linear equations (SLE). However, without error correction, it cannot give correct results even in a 2-variable system due to its complexity. In this study, an HHL quantum circuit for a 2-variable SLE is implemented in IBM-Q and the error is extracted after each stage of the circuit and compared to a MATLAB simulator. We identified three major sources of errors, namely single-qubit flipping, gate infidelity, and error propagation. We also found that at the ancillary bit rotation stage, the error becomes large but the encoded solution still has high fidelity. However, the solution is mostly lost after the inverse quantum phase estimation which is necessary to extract the solution efficiently. Therefore, it is suggested that error correction resources, if limited, should be added to the second half of the circuit.
Research and Implementation of Interaction Design on Dunhuang Culture

Interaction can not only capture users’ attention, but also improve the efficiency of users’ acquisition of information, which is today’s important way of communication. Dunhuang culture as an important traditional Chinese culture has returned to people’s field of vision in recent years, but people are also faced with challenges of integrating interaction into Dunhuang culture for better understanding and transmission. This paper explored a brand new interaction design on Dunhuang culture by developing an interactive game themed on Dunhuang culture based on Unity Engine. The game is positioned as an interactive storybook to cater to the development direction of cultural products and meet the actual interaction needs of the audience while transmitting Dunhuang culture.
Improving Actor–Critic Reinforcement Learning via Hamiltonian Monte Carlo Method

The actor–critic reinforcement learning (RL) is widely used in various robotic control tasks. By viewing the actor–critic RL from the perspective of variational inference (VI), the policy network is trained to obtain the approximate posterior of actions, given the optimality criteria. However, in practice, the actor–critic RL may yield suboptimal policy estimates due to the amortization gap and insufficient exploration. In this work, inspired by the previous use of Hamiltonian Monte Carlo (HMC) in VI, we propose to integrate the policy network of actor–critic RL with HMC, which is termed as Hamiltonian policy. As such we propose to evolve actions from the base policy according to HMC, and our proposed method has many benefits. First, HMC can improve the policy distribution to better approximate the posterior and hence reduce the amortization gap. Second, HMC can also guide the exploration more to the regions of action spaces with higher <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> values, enhancing the exploration efficiency. Further, instead of directly applying HMC into RL, we propose a new leapfrog operator to simulate the Hamiltonian dynamics. Finally, in safe RL problems, we find that the proposed method can not only improve the achieved return, but also reduce safety constraint violations by discarding potentially unsafe actions. With comprehensive empirical experiments on continuous control baselines, including MuJoCo and PyBullet Roboschool, we show that the proposed approach is a data-efficient and easy-to-implement improvement over previous actor–critic methods.