Reviewerly/exHarmony huggingface dataset:
Olivaw: Mastering <i>Othello</i> Without Human Knowledge, nor a Fortune

In this article, we introduce <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Olivaw</small> , an artificial intelligence <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Othello</i> player adopting the design principles of the famous AlphaGo programs. The main motivation behind <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Olivaw</small> is to attain exceptional competence in a nontrivial board game at a tiny fraction of the cost of its illustrious predecessors. In this article, we show how the AlphaGo Zero’s paradigm can be successfully applied to the popular game of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Othello</i> using only commodity hardware and free cloud services. While being simpler than <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Chess</i> or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Go</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Othello</i> maintains a considerable search space and difficulty in evaluating board positions. To achieve this result, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Olivaw</small> implements some improvements inspired by recent works to accelerate the standard AlphaGo Zero learning process. The main modification implies doubling the positions collected per game during the training phase, by including also positions not played but largely explored by the agent. We tested the strength of <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Olivaw</small> in three different ways: by pitting it against Edax, considered by the strongest open-source <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Othello</i> engine, by playing anonymous games on the web platform OthelloQuest, and, finally, in two in-person matches against top-notch human players: a national champion and a former world champion.
Multi-Task Learning with Cross Attention for Keyword Spotting

Keyword spotting (KWS) is an important technique for speech applications, which enables users to activate devices by speaking a keyword phrase. Although a phoneme classifier can be used for KWS, exploiting a large amount of transcribed data for automatic speech recognition (ASR), there is a mismatch between the training criterion (phoneme recognition) and the target task (KWS). Recently, multi-task learning has been applied to KWS to exploit both ASR and KWS training data. In this approach, an output of an acoustic model is split into two branches for the two tasks, one for phoneme transcription trained with the ASR data and one for keyword classification trained with the KWS data. In this paper, we introduce a cross attention decoder in the multi-task learning framework. Unlike the conventional multi-task learning approach with the simple split of the output layer, the cross attention decoder summarizes information from a phonetic encoder by performing cross attention between the encoder outputs and a trainable query sequence to predict a confidence score for the KWS task. Experimental results on KWS tasks show that the proposed approach achieves a 12% relative reduction in the false reject ratios compared to the conventional multi-task learning with split branches and a bi-directional long short-team memory decoder.
Bionic Communication Network and Binary Pigeon-Inspired Optimization for Multiagent Cooperative Task Allocation

Toimprove the task execution efficiency of a multiagent system, an intelligent task allocation method combined with the bionic communication network and binary pigeon-inspired optimization (BPIO) is presented. This article first builds the bionic communication network model and introduces the visual attention mechanism with the threshold effect. Second, the version of BPIO based on fuzzy theory is put forward after reviewing the classical transfer functions. The comparison tests are conducted on the benchmark between the proposed one and other forms of BPIOs. Finally, the task assignment models of tasks, agents, and targets are established, where the bionic communication method is employed for information transfer, and the proposed BPIO is applied to generate the solutions of task allocation. The simulation results demonstrate that the proposed method is feasible and adaptable to deal with the multiple agents’ task allocation dynamically.
Injecting User Identity Into Pretrained Language Models for Document-Level Sentiment Classification

This paper mainly studies the combination of pre-trained language models and user identity information for document-level sentiment classification. In recent years, pre-trained language models (PLMs) such as BERT have achieved state-of-the-art results on many NLP applications, including document-level sentiment classification. On the other hand, a collection of works introduce additional information such as user identity for better text modeling. However, most of them inject user identity into traditional models, while few studies have been conducted to study the combination of pre-trained language models and user identity for even better performance. To address this issue, in this paper, we propose to unite user identity and PLMs and formulate User-enhanced Pre-trained Language Models (U-PLMs). Specifically, we demonstrate two simple yet effective attempts, i.e. embedding-based and attention-based personalization, which inject user identity into different parts of a pre-trained language model and provide personalization from different perspectives. Experiments in three datasets with two backbone PLMs show that our proposed methods outperform the best state-of-the-art baseline method with an absolute improvement of up to 3&#x0025;, 2.8&#x0025;, and 2.2&#x0025; on accuracy. In addition, our methods encode user identity with plugin modules, which are fully compatible with most auto-encoding pre-trained language models.
Continuous-time quantum walk based centrality testing on weighted graphs

Centrality measure is an essential tool in network analysis and widely used in the domain of computer science, biology and sociology. Taking advantage of the speedup offered by quantum computation, various quantum centrality measures have been proposed. However, few work of quantum centrality involves weighted graphs, while the weight of edges should be considered in certain real-world networks. In this work, we extend the centrality measure based on continuous-time quantum walk to weighted graphs. We testify the feasibility and reliability of this quantum centrality using an ensemble of 41,675 graphs with various topologies and comparing with the eigenvector centrality measure. The average Vigna's correlation index of all the tested graphs with all edge weights in [1, 10] is as high as 0.967, indicating the pretty good consistency of rankings by the continuous-time quantum walk centrality and the eigenvector centrality. The intuitive consistency of the top-ranked vertices given by this quantum centrality measure and classical centrality measures is also demonstrated on large-scale weighted graphs. Moreover, the range of the continuous-time quantum walk centrality values is much bigger than that of classical centralities, which exhibits better distinguishing ability to pick the important vertices from the ones with less importance. All these results show that the centrality measure based on continuous-time quantum walk still works well on weighted graphs.
Safe and Efficient Take-Off of VTOL UAV Swarms

Currently multicopters are facing a continuous growth in terms of recreational uses, and multiple companies focused on these aircrafts to facilitate certain tasks that were nearly inaccessible to humans, or otherwise involved a great cost. In this context, the drone swarm concept allows us to broaden and incorporate new, more refined applications in which various aircraft coordinate with each other to carry out large-scale tasks. When the number of UAVs involved becomes too high, guaranteeing that the take-off procedure is efficient and yet secure becomes quite complex. Hence, in this paper we propose and validate different algorithms to optimize the take-off time of drones belonging to a swarm, with the objective that there are no collisions between them. In particular, we propose algorithms for both trajectory analysis and batch generation for take-off. Based on a large number of experiments using the ArduSim simulator we prove that the proposed algorithms provide a robust solution within a reasonable time frame when testing with different aerial formations. In addition, we will assess how different UAV position assignment strategies impact our algorithm performance in terms of take-off time and number of batches required.
Imbalanced Classification in Diabetics Using Ensembled Machine Learning

Diabetics is one of the world's most common diseases which are caused by continued high levels of blood sugar.The risk of diabetics can be lowered if the diabetic is found at the early stage.In recent days, several machine learning models were developed to predict the diabetic presence at an early stage.In this paper, we propose an embedded-based machine learning model that combines the split-vote method and instance duplication to leverage an imbalanced dataset called PIMA Indian to increase the prediction of diabetics.The proposed method uses both the concept of over-sampling and under-sampling along with model weighting to increase the performance of classification.Different measures such as Accuracy, Precision, Recall, and F1-Score are used to evaluate the model.The results we obtained using K-Nearest Neighbor (kNN), Naïve Bayes (NB), Support Vector Machines (SVM), Random Forest (RF), Logistic Regression (LR), and Decision Trees (DT) were 89.32%, 91.44%, 95.78%, 89.3%, 81.76%, and 80.38% respectively.The SVM model is more efficient than other models which are 21.38% more than exiting machine learning-based works.
Observing interventions: a logic for thinking about experiments

Abstract This paper makes a first step towards a logic of learning from experiments. For this, we investigate formal frameworks for modeling the interaction of causal and (qualitative) epistemic reasoning. Crucial for our approach is the idea that the notion of an intervention can be used as a formal expression of a (real or hypothetical) experiment (Pearl, 2009, Causality. Models, Reasoning, and Inference, 2nd edn. Cambridge University Press, Cambridge; Woodward, 2003, Making Things Happen, vol. 114 of Oxford Studies in the Philosophy of Science. Oxford University Press). In a first step we extend a causal model (Briggs, 2012, Philosophical Studies, 160, 139–166; Galles and Pearl, 1998, An axiomatic characterisation of causal counterfactuals. Foundations of Science, 3, 151–182; Halpern, 2000, Axiomatizing causal reasoning. Journal of Artificial Intelligence Research, 12, 317–337; Pearl, 2009, Causality. Models, Reasoning, and Inference, 2nd edn. Cambridge University Press, Cambridge) with a simple Hintikka-style representation of the epistemic state of an agent. In the resulting setting, one can talk about the knowledge of an agent and information update. The resulting logic can model reasoning about thought experiments. However, it is unable to account for learning from experiments, which is clearly brought out by the fact that it validates the principle of no learning for interventions. Therefore, in a second step, we implement a more complex notion of knowledge (Nozick, 1981, Philosophical Explanations. Harvard University Press, Cambridge, Massachusetts) that allows an agent to observe (measure) certain variables when an experiment is carried out. This extended system does allow for learning from experiments. For all the proposed logics, we provide a sound and complete axiomatization.
Learning Adaptive Node Embeddings across Graphs

Recently, learning embeddings of nodes in graphs has attracted increasing research attention. There are two main kinds of graph embedding methods, i.e., transductive embedding methods and inductive embedding methods. The former focuses on directly optimizing the embedding vectors, and the latter tries to learn a mapping function for the given nodes and features. However, little work has focused on applying the learned model from one graph to another, which is a pervasive idea in Computer Vision or Natural Language Processing. Although some of the graph neural networks (GNNs) present a similar motivation, none of them considers graph biases between graphs. In this paper, we present a novel graph embedding problem called Adaptive Task (AT), and propose a unified framework for the adaptive task, which introduces two types of alignment to learn adaptive node embeddings across graphs. Then, based on the proposed framework, a novel Graph Adaptive Embedding network (GraphAE) is designed to address the adaptive task. Furthermore, we extend GraphAE to a multi-graph version to consider a more complex adaptive situation. The extensive experimental results demonstrate that our model significantly outperforms the state-of-the-art methods, and also show that our framework can make a great improvement over a number of existing GNNs.
Hierarchical Dynamic Programming Module for Human Pose Refinement

We observed that remarkable and impressive performance on image-based human pose estimation have been achieved by deep Convolutional Neural Networks (CNN). Nevertheless, directly applying these image-based models on videos is not only computionally intensive, but also may cause jitter and loss. The main reason is that the image-based models purely focus on the local features of individual frames and totally ignore the temporal information among adjacent frames. Some existing methods are proposed to address the temporal coherency issue. However, these methods need to be designed carefully and cannot be combined with existing image-based methods. In this paper, we propose a simple yet effective module to refine the estimated pose by exploiting the temporal coherency among the heatmaps of adjacent frames, which can be easily inserted into image-based networks as a plug-in. We show that the temporal coherency issue among the heatmap frames could be re-formulated as a graph path selection optimization problem. Moreover, to speed up the refinement process, we propose a hierarchical graph optimization to achieve the refinement from coarse to fine. Experimental results on two large-scale video pose estimation benchmarks show that our module can improve the performance with little speed loss when combined with image-based methods as an efficient plug-in.
Image Captioning Model Using Part-of-Speech Guidance Module for Description With Diverse Vocabulary

Image captions aim to generate human-like sentences that describe the image’s content. Recent developments in deep learning (DL) have made it possible to caption images for accurate descriptions and detailed expressions. However, since DL learns the relationship between images and captions, it constructs sentences based on commonly frequented words in the dataset. Although these generated sentences are highly accurate, they have low lexical diversity, unlike humans due to limited vocabulary. Therefore, in this paper, we propose a Part-Of-Speech (POS) guidance module and a multimodal-based image captioning model that determines the intensity of images and word sequences and generates sentences through POS to enhance the lexical diversity of DL. The proposed POS guidance module enables rich expression by controlling the information of images and sequences based on the predicted POS guidance to predict words. Then, the POS multimodal layer adds POS and output vector of Bi-LSTM using the multimodal layer to predict the next caption, considering the grammatical structure. We trained and tested the proposed model on the Flicker 30K and MS COCO datasets and compared them with current state-of-the-art studies. Also, we analyzed the lexical diversity of the caption model through the Type-Token Ratio (TTR) and confirmed that the proposed model generates sentences using several words.
A Sequential Pattern Mining Based Approach to Adaptively Detect Anomalous Paths in Floating Vehicle Trajectories

The detection of anomalous paths from floating vehicle trajectories plays an increasingly important role in dynamic path planning because of its ability to identify fraudulent transport behaviors, unexpected traffic accidents, and traffic restriction areas. Existing studies mostly emphasize the significant deviations based on global shape metrics and cannot manage trajectories with missing segments. To address these problems, this study proposes a new sequential pattern mining based method for detecting anomalous paths (SPDAP) in floating vehicle trajectories. First, discrete trajectory points were transformed into consecutive road segment sequences using map matching and essential empirical information. Focusing on these segment sequences, this method constructs a directed graph and calculates the conditional occurrence probabilities of multi-order sub-paths on the graph. On this basis, we designed a sequential pattern mining algorithm by estimating kernel density distributions to adaptively extract multi-order frequent sub-paths. Finally, these anomalous paths can be identified by matching the operations with frequent paths. Comparative experiments on simulated trajectories demonstrated that SPDAP could accurately, adequately, and adaptively detect anomalous paths. Additionally, using real-life taxi trajectories, we conducted semantic analysis by considering travel time and charges to further derive four patterns from the detected anomalous paths. These patterns are expected to support the identification of fraudulent taxi trips and the discovery of candidate driving routes with a preference for saving time or charge. In the future, we will focus on tracing the intrinsic causes of anomalous paths by introducing multisource information and performing real-time anomalous path detection using adaptive time intervals.
Searchable encryption : A survey

We explore the field of searchable encryption (SE) and present a comprehensive survey of relevant literature. Since the existing survey articles are primarily written for the security experts, we present the discussion to assist the general practitioners (not from security background) in identifying an appropriate SE scheme for their application of interest. We initiate with the brief overview of SE along with its application-oriented criteria. By analyzing various SE schemes, we derive five significant characteristics – key structure, search structure, search functionality, support to reader/writers, and reader's capability. Based on these characteristics, we categorize the existing SE schemes and showcase the significant features offered by each scheme. We explore numerous schemes based on symmetric/asymmetric key structures, simple/inverted search structure, single/multi-keyword search functionality, single/multiple reader/writer support, and verification functionality owned by data reader. A most promising part of the survey is the comparative analysis of the existing schemes under specific category in terms of tables showing efficiency and security. We hope that this survey is indeed beneficial for the general practitioners to pick an appropriate SE scheme better suited to the selected application.
Deep Learning for Object Detection and Segmentation in Videos: Toward an Integration With Domain Knowledge

Deep learning has enabled the rapid expansion of computer vision tasks from image frames to video segments. This paper focuses on the review of the latest research in the field of computer vision tasks in general and on object localization and identification of their associated pixels in video frames in particular. After performing a systematic analysis of the existing methods, the challenges related to computer vision tasks are presented. In order to address the existing challenges, a hybrid framework is proposed, where deep learning methods are coupled with domain knowledge. An additional feature of this survey is that a review of the currently existing approaches integrating domain knowledge with deep learning techniques is presented. Finally, some conclusions on the implementation of hybrid architectures to perform computer vision tasks are discussed.
Learning to Reduce Information Bottleneck for Object Detection in Aerial Images

Object detection in aerial images is a fundamental research topic in the geoscience and remote sensing domain. However, the advanced approaches on this topic mainly focus on designing the elaborate backbones or head networks but ignore neck networks. In this letter, we first underline the importance of the neck network in object detection from the perspective of information bottleneck. Then, to alleviate the information deficiency problem in the current approaches, we propose a global semantic network (GSNet), which acts as a bridge from the backbone network to the head network in a bidirectional global pattern. Compared to the existing approaches, our model can capture the rich and enhanced image features with less computational costs. Besides, we further propose a feature fusion refinement module (FRM) for different levels of features, which are suffering from the problem of semantic gap in feature fusion. To demonstrate the effectiveness and efficiency of our approach, experiments are carried out on two challenging and representative aerial image datasets (i.e., DOTA and HRSC2016). Experimental results in terms of accuracy and complexity validate the superiority of our method. The code has been open-sourced at GSNet.
A Novel Subpixel Industrial Chip Detection Method Based on the Dual-Edge Model for Surface Mount Equipment

Vision-based location of industrial chips is crucial for high-speed and high-precision mounting in surface mount technology (SMT) applications. The conventional general location method requires numerous details of the chips’ physical characteristics, making it unsuitable for unknown chip locations and offline learning. In this article, we propose a general chip detection algorithm based on subpixel features from accelerated segment test (FAST) points that offers strong applicability, high precision, and high speed. The core part of our method is the extraction of subpixel boundary FAST points. The conventional subpixel calculation method uses a single-edge model that results in large deviations between calculated and actual positions when applied to actual FAST points. We propose a dual-edge subpixel model containing two groups of edges to reduce this error. Compared with the iterative calculation method in OpenCV, this method has a closing solution and faster performance. We determine model parameters using spatial moments, and present the relationship between the model and subpixel positions. Our experiments on the SMT hardware platform demonstrate that our method is robust to noise, illumination, position, and chip type, and is faster, more accurate, and more reliable than the Hanwha SM481-Plus placement machine and point registration method.