Reviewerly/exHarmony huggingface dataset:
Visual Representation Learning with Self-Supervised Attention for Low-Label High-data Regime

Self-supervision has shown outstanding results for natural language processing, and more recently, for image recognition. Simultaneously, vision transformers and its variants have emerged as a promising and scalable alternative to convolutions on various computer vision tasks. In this paper, we are the first to question if self-supervised vision transformers (SSL-ViTs) can be adapted to two important computer vision tasks in the low-label, high-data regime: few-shot image classification and zero-shot image retrieval. The motivation is to reduce the number of manual annotations required to train a visual embedder, and to produce generalizable and semantically meaningful embeddings. For few-shot image classification we train SSL-ViTs without any supervision, on external data, and use this trained embedder to adapt quickly to novel classes with limited number of labels. For zero-shot image retrieval, we use SSL-ViTs pre-trained on a large dataset without any labels and fine-tune them with several metric learning objectives. Our self-supervised attention representations outperforms the state-of-the-art on several public benchmarks for both tasks, namely miniImageNet and CUB200 for few-shot image classification by up-to 6%-10%, and Stanford Online Products, Cars196 and CUB200 for zero-shot image retrieval by up-to 4%-11%. Code is available at \url{https://github.com/AutoVision-cloud/SSL-ViT-lowlabel-highdata}.
Combining Evidence

The problem of combining the evidence concerning an unknown, contained in each of $k$ Bayesian inference bases, is discussed. This can be considered as a generalization of the problem of pooling $k$ priors to determine a consensus prior. The linear opinion pool of Stone (1961) is seen to have the most appropriate properties for this role. In particular, linear pooling preserves a consensus with respect to the evidence and other rules do not. While linear pooling does not preserve prior independence, it is shown that it still behaves appropriately with respect to the expression of evidence in such a context. For the general problem of combining evidence, Jeffrey conditionalization plays a key role.
A Study of Transducer based End-to-End ASR with ESPnet: Architecture, Auxiliary Loss and Decoding Strategies

In this study, we present recent developments of models trained with the RNN-T loss in ESPnet. It involves the use of various architectures such as recently proposed Conformer, multi-task learning with different auxiliary criteria and multiple decoding strategies, including our own proposition. Through experiments and benchmarks, we show that our proposed systems can be competitive against other state-of-art systems on well-known datasets such as LibriSpeech and AISHELL-1. Additionally, we demonstrate that these models are promising against other already implemented systems in ESPnet in regards to both performance and decoding speed, enabling the possibility to have powerful systems for a streaming task. With these additions, we hope to expand the usefulness of the ESPnet toolkit for the research community and also give tools for the ASR industry to deploy our systems in realistic and production environments.
Database Search Results Disambiguation for Task-Oriented Dialog Systems

As task-oriented dialog systems are becoming increasingly popular in our lives, more realistic tasks have been proposed and explored. However, new practical challenges arise. For instance, current dialog systems cannot effectively handle multiple search results when querying a database, due to the lack of such scenarios in existing public datasets. In this paper, we propose Database Search Result (DSR) Disambiguation, a novel task that focuses on disambiguating database search results, which enhances user experience by allowing them to choose from multiple options instead of just one. To study this task, we augment the popular task-oriented dialog datasets (MultiWOZ and SGD) with turns that resolve ambiguities by (a) synthetically generating turns through a pre-defined grammar, and (b) collecting human paraphrases for a subset. We find that training on our augmented dialog data improves the model's ability to deal with ambiguous scenarios, without sacrificing performance on unmodified turns. Furthermore, pre-fine tuning and multi-task learning help our model to improve performance on DSR-disambiguation even in the absence of in-domain data, suggesting that it can be learned as a universal dialog skill. Our data and code will be made publicly available.
Relative Topological Complexity and Configuration Spaces

Given a space $X$, the topological complexity of $X$, denoted by $TC(X)$, can be viewed as the minimum number of "continuous rules" needed to describe how to move between any two points in $X$. Given subspaces $Y_1$ and $Y_2$ of $X$, there is a "relative" version of topological complexity, denoted by $TC_X(Y_1\times Y_2)$, in which one only considers paths starting at a point $y_1\in Y_1$ and ending at a point $y_2\in Y_2$, but the path from $y_1$ to $y_2$ can pass through any point in $X$. We discuss general results that provide relative analogues of well-known results concerning $TC(X)$ before focusing on the case in which we have $Y_1=Y_2=C^n(Y)$, the configuration space of $n$ points in some space $Y$, and $X=C^n(Y\times I)$, the configuration space of $n$ points in $Y\times I$, where $I$ denotes the interval $[0,1]$. Our main result shows $TC_{C^n(Y\times I)}(C^n(Y)\times C^n(Y))$ is bounded above by $TC(Y^n)$ and under certain hypotheses is bounded below by $TC(Y)$.
Quantum non-demolition measurements of moving target states

We present a protocol for probing the state of a quantum system by its resonant coupling and entanglement with a meter system. By continuous measurement of a time evolving meter observable, we infer the evolution of the entangled systems and, ultimately, the state and dynamics of the system of interest. The photon number in a cavity field is thus resolved by simulated monitoring of the time dependent excited state population of a resonantly coupled two-level system, and we propose to regard this as an extension of quantum non-demolition measurements with potential applications in quantum metrology and quantum computing.
Geometric properties of evolutionary graph states and their detection on a quantum computer

Geometric properties of evolutionary graph states of spin systems generated by the operator of evolution with Ising Hamiltonian are examined, using their relationship with fluctuations of energy. We find that the geometric characteristics of the graph states depend on properties of the corresponding graphs. Namely, it is obtained that the fluctuations of energy in graph states and therefore the velocity of quantum evolution, the curvature and the torsion of the states are related with the total number of edges, triangles and squares in the corresponding graphs. The obtained results give a possibility to quantify the number of edges, triangles and squares in a graph on a quantum devise and achieve quantum supremacy in solving this problem with the development of a multi-qubit quantum computer. Geometric characteristics of graph states corresponding to a chain, a triangle, and a square are detected on the basis of calculations on IBM's quantum computer ibmq-manila.
Action Candidate Driven Clipped Double Q-learning for Discrete and Continuous Action Tasks

Double Q-learning is a popular reinforcement learning algorithm in Markov decision process (MDP) problems. Clipped Double Q-learning, as an effective variant of Double Q-learning, employs the clipped double estimator to approximate the maximum expected action value. Due to the underestimation bias of the clipped double estimator, the performance of clipped Double Q-learning may be degraded in some stochastic environments. In this paper, in order to reduce the underestimation bias, we propose an action candidate-based clipped double estimator for Double Q-learning. Specifically, we first select a set of elite action candidates with high action values from one set of estimators. Then, among these candidates, we choose the highest valued action from the other set of estimators. Finally, we use the maximum value in the second set of estimators to clip the action value of the chosen action in the first set of estimators and the clipped value is used for approximating the maximum expected action value. Theoretically, the underestimation bias in our clipped Double Q-learning decays monotonically as the number of action candidates decreases. Moreover, the number of action candidates controls the trade-off between the overestimation and underestimation biases. In addition, we also extend our clipped Double Q-learning to continuous action tasks via approximating the elite continuous action candidates. We empirically verify that our algorithm can more accurately estimate the maximum expected action value on some toy environments and yield good performance on several benchmark problems.
Pretraining with Artificial Language: Studying Transferable Knowledge in Language Models

We investigate what kind of structural knowledge learned in neural network encoders is transferable to processing natural language. We design artificial languages with structural properties that mimic natural language, pretrain encoders on the data, and see how much performance the encoder exhibits on downstream tasks in natural language. Our experimental results show that pretraining with an artificial language with a nesting dependency structure provides some knowledge transferable to natural language. A follow-up probing analysis indicates that its success in the transfer is related to the amount of encoded contextual information and what is transferred is the knowledge of position-aware context dependence of language. Our results provide insights into how neural network encoders process human languages and the source of cross-lingual transferability of recent multilingual language models.
IMPLEMENTING CONTINUAL LEARNING SCENARIOS: REVIEW OF A ML TECHNQIUE

One of the biggest traps "AI" (artificial intelligence) projects fall into is demanding an entire solution fit into the ML (machine learning) paradigm. One of the continual learning techniques in ML to overcome this challenge is the so called 'Learning to Prompt for Continual Learning (L2P)' that can be applied to practical continual learning scenarios without known task identity or boundaries. L2P uses a single frozen backbone model and learns a prompt pool to conditionally instruct the model. After discussing main categories of recent continual learning algorithms, this paper provides an overview of LSP by discussing its layers.
Person re-identification based on attention of fine-grained features

In people re-identification tasks, the most intuitive method for selecting people representation features is to directly extract a global feature map of the people. However, relying on global features alone often fails to accurately identify people in the presence of occlusion, misalignment and background interference. In addition, due to interference from factors such as low camera resolution and illumination, some key local features (e.g. carried objects, body parts such as the face or limbs) are not clearly observed. To this end, we are inspired by fine-grained image classification tasks and propose an attentionbased fine-grained feature network model (AFGF) for people re-identification tasks, which will fully consider the relationship between global and local features and incorporate attention mechanisms to effectively extract fine features of people and improve their discrimination ability. The effectiveness of our model is validated on the Market-1501 and DukeMTMC-reID datasets, and the experimental results show that the method improves significantly on both the supervised baseline and unsupervised baselines.
Triples-to-Text Generation with Reinforcement Learning Based Graph-augmented Neural Networks

Considering a collection of RDF triples, the RDF-to-text generation task aims to generate a text description. Most previous methods solve this task using a sequence-to-sequence model or using a graph-based model to encode RDF triples and to generate a text sequence. Nevertheless, these approaches fail to clearly model the local and global structural information between and within RDF triples. Moreover, the previous methods also face the non-negligible problem of low faithfulness of the generated text, which seriously affects the overall performance of these models. To solve these problems, we propose a model combining two new graph-augmented structural neural encoders to jointly learn both local and global structural information in the input RDF triples. To further improve text faithfulness, we innovatively introduce a reinforcement learning (RL) reward based on information extraction (IE). We first extract triples from the generated text using a pretrained IE model and regard the correct number of the extracted triples as the additional RL reward. Experimental results on two benchmark datasets demonstrate that our proposed model outperforms the state-of-the-art baselines, and the additional reinforcement learning reward does help to improve the faithfulness of the generated text.
18. Co-presence and beyond: Spatial configurations of communication in virtual environments

Users of electronic communication tools in the form of stationary computers, laptops, mobile phones or similar devices interact both in physical and in virtual spaces, and the two worlds are often not clearly separated.The electronic device is part of the physical surrounding, and the physical surrounding impacts in multiple ways on the communicative activities on these devices.The virtual communicative spaces themselves are multi-layered and provide levels of constructed (interactional) spaces.In this contribution, we survey a large range of research that has been carried out on spatial configurations of communication in virtual environments with a special focus on immersive virtual worlds such as World of Warcraft, Minecraft or Second Life.On the basis of the existing research, we develop both a model for the spatial configurations of virtual game worlds displayed on a computer screen that is situated in a physical environment and a model that accounts for the interactive doing of space.Users sitting in front of their (physical) computers interact with each other via characters and their avatars in virtual worlds.This poses specific problems for establishing co-presence through co-orientation, which is a prerequisite of co-ordination and co-operation in the game world.As an illustrative case study, we analyze a Minecraft UHC teamplayer game as an example of a collaborative video game play in which players have to co-ordinate their actions.They use their avatars and virtual objects within the spatial configuration of the shared virtual environment to establish a quasi-physical and virtual co-presence, which allows them to co-operate successfully and to jointly perform the tasks set by the game.
Maximum Entropy of Random Permutation Set

Recently, a new type of set, named as random permutation set (RPS), is proposed by considering all the permutations of elements in a certain set. For measuring the uncertainty of RPS, the entropy of RPS is presented. However, the maximum entropy principle of RPS entropy has not been discussed. To address this issue, in this paper, the maximum entropy of RPS is presented. The analytical solution for maximum entropy of RPS and its corresponding PMF condition are respectively proofed and discussed. Numerical examples are used to illustrate the maximum entropy RPS. The results show that the maximum entropy RPS is compatible with the maximum Deng entropy and the maximum Shannon entropy. When the order of the element in the permutation event is ignored, the maximum entropy of RPS will degenerate into the maximum Deng entropy. When each permutation event is limited to containing just one element, the maximum entropy of RPS will degenerate into the maximum Shannon entropy.
Collaborative Art Workshop in Virtual Environment

The proposed workshop will bring together VR artists in a collaborative workshop in visual arts, taking place exclusively in virtual environments. It will investigate the potential of the virtual tools in collaboration in a creative exercise with a non-firmly structured goal and the prospects of such platforms employing art in learning processes in public set-ups.
The Subjective Condition of Human Communication, a Synthesis

This article synthetically develops the bases of a subjective model of human communication.The text tries to answer the question: what is subjective in human communication, and if the subjective can be understood as a condition of existence and occurrence of communication.
Analisis Persepsi Mahasiswa Akuntansi Terhadap Etika Profesi Akuntan: Studi Empiris pada Sekolah Tinggi Ilmu Ekonomi GICI

The purpose of this research is to know the difference between the perceptions of early student accounting and final level students, and the difference in perception among unemployed accounting students and accounting students who are working.The research method used is research using research in the form of data collection using questionnaires. This research uses a comparative method that is used to know whether between two variables or more there is a difference in an aspect. The sample selection is done by Acidental Sampling. The sample amounted to 211 respondents, using the calculation formula Slovin.The results of the T test show that only variables of early-level accounting students and final-level accounting students occur differences in perception of the profession ethics of accountants with the value of Thitung (2.353) &gt; This (1.998) with the result of 0.03. As for the variable students accounting has not worked and accounting students are working no difference in perception against the ethics of the profession of accountants with the value of Thitung (0.237) &lt; This (1.998) with a result of 0.815.&#x0D; Keywords: accounting perceptionevel accounting, profession accountants
A YOLO-X Deep Learning Approach to Detect Traffic Targets from UAV Video with On-Board Vehicle Data Validation

The unmanned aerial vehicle (UAV) monitoring platform has the advantages of flexibility, traceability, convenient storage and wide-view, but the challenge is to extract the traffic parameters from the UAV-videos accurately. This paper proposes a YOLO-X deep learning approach to detect vehicles using the DJI Phantom 4RTK UAV and vehicle on-board unit. The UAV is used to monitor the traffic situation at various altitudes, and the vehicle on-board unit is used to record the accurate speed of the experiment vehicle. The YOLO-X deep learning framework and the video frame-based detection algorithm are incorporated and a field experiment was conducted. The model was trained based on collected and aerial data processed by the deep learning neural network, and compared with the ground vehicle data. Results show the accuracy of target detection is 92.58%, and the target vehicle speed detection is 94%. This demonstrates the proposed approach is promising for traffic target detection.
Query Embedding on Hyper-relational Knowledge Graphs

Multi-hop logical reasoning is an established problem in the field of representation learning on knowledge graphs (KGs). It subsumes both one-hop link prediction as well as other more complex types of logical queries. Existing algorithms operate only on classical, triple-based graphs, whereas modern KGs often employ a hyper-relational modeling paradigm. In this paradigm, typed edges may have several key-value pairs known as qualifiers that provide fine-grained context for facts. In queries, this context modifies the meaning of relations, and usually reduces the answer set. Hyper-relational queries are often observed in real-world KG applications, and existing approaches for approximate query answering cannot make use of qualifier pairs. In this work, we bridge this gap and extend the multi-hop reasoning problem to hyper-relational KGs allowing to tackle this new type of complex queries. Building upon recent advancements in Graph Neural Networks and query embedding techniques, we study how to embed and answer hyper-relational conjunctive queries. Besides that, we propose a method to answer such queries and demonstrate in our experiments that qualifiers improve query answering on a diverse set of query patterns.
Student Surpasses Teacher: Imitation Attack for Black-Box NLP APIs

Machine-learning-as-a-service (MLaaS) has attracted millions of users to their splendid large-scale models. Although published as black-box APIs, the valuable models behind these services are still vulnerable to imitation attacks. Recently, a series of works have demonstrated that attackers manage to steal or extract the victim models. Nonetheless, none of the previous stolen models can outperform the original black-box APIs. In this work, we conduct unsupervised domain adaptation and multi-victim ensemble to showing that attackers could potentially surpass victims, which is beyond previous understanding of model extraction. Extensive experiments on both benchmark datasets and real-world APIs validate that the imitators can succeed in outperforming the original black-box models on transferred domains. We consider our work as a milestone in the research of imitation attack, especially on NLP APIs, as the superior performance could influence the defense or even publishing strategy of API providers.
Least-Squares Linear Dilation-Erosion Regressor Trained using a Convex-Concave Procedure

This paper presents a hybrid morphological neural network for regression tasks called linear dilation-erosion regressor ($\ell$-DER). An $\ell$-DER is given by a convex combination of the composition of linear and morphological operators. They yield continuous piecewise linear functions and, thus, are universal approximators. Besides introducing the $\ell$-DER model, we formulate their training as a difference of convex (DC) programming problem. Precisely, an $\ell$-DER is trained by minimizing the least-squares using the convex-concave procedure (CCP). Computational experiments using several regression tasks confirm the efficacy of the proposed regressor, outperforming other hybrid morphological models and state-of-the-art approaches such as the multilayer perceptron network and the radial-basis support vector regressor.
LDP-FPMiner: FP-Tree Based Frequent Itemset Mining with Local Differential Privacy

Data aggregation in the setting of local differential privacy (LDP) guarantees strong privacy by providing plausible deniability of sensitive data. Existing works on this issue mostly focused on discovering heavy hitters, leaving the task of frequent itemset mining (FIM) as an open problem. To the best of our knowledge, the-state-of-the-art LDP solution to FIM is the SVSM protocol proposed recently. The SVSM protocol is mainly based on the padding and sampling based frequency oracle (PSFO) protocol, and regarded an itemset as an independent item without considering the frequency consistency among itemsets. In this paper, we propose a novel LDP approach to FIM called LDP-FPMiner based on frequent pattern tree (FP-tree). Our proposal exploits frequency consistency among itemsets by constructing and optimizing a noisy FP-tree with LDP. Specifically, it works as follows. First, the most frequent items are identified, and the item domain is cut down accordingly. Second, the maximum level of the FP-tree is estimated. Third, a noisy FP-tree is constructed and optimized by using itemset frequency consistency, and then mined to obtain the k most frequent itemsets. Experimental results show that the LDP-FPMiner significantly improves over the state-of-the-art approach, SVSM, especially in the case of a high privacy level.