Reviewerly/exHarmony huggingface dataset:
Automatic Segmentation of Fluorescence Endoscopic Images Based on the Weighted Otsu Method

The multi-spectral fluorescence endoscope system uses a near-infrared camera system to image the fluorescence signal. By fusing the near-infrared image with the white light image of the same visual field, realizing the intraoperative fluorescence navigation. The basis of this system is the precise segmentation of the fluorescent region in the near-infrared image. In this paper we proposed an automatic segmentation method for the fluorescence region of near-infrared fluorescence endoscopy images. This method is based on the Otsu method, which uses the multi-step weighted Otsu method to segment the image. The segmentation quality is estimated by the misclassification error of the image. Compared with the traditional Otsu method, this method improves the efficiency of segmentation of fluorescent regions: the misclassification error of the proposed method is 4%, while Otsu is 16%.
Uses of Local Binary Pattern Codes for Enriching the Embedding Performance

Prediction error based single-layer reversible embedment schemes implant bits into two highest frequency errors. These schemes consider other errors as non-embeddable. The implantation rules change the values of embeddable errors by zero and one unit for implanting a bit value of 0 and 1, respectively. The modified errors then might be overlapped with non-embeddable errors. To overcome that issue, these schemes shift the non-embeddable errors by one unit. The non-embeddable errors are remarkably huge in number and thus, majorly contribute to image-distortions without accepting any bit. This research employs local binary pattern (LBP) to create an embeddable code for all pixels. The objective of the LBP used method is to implant a large volume of data. In the experiment, the LBP based method presents higher embedding capacity and stego image quality than the competing embedding schemes.
DP-<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:mi>k</mml:mi></mml:math>-modes: A self-tuning <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:mi>k</mml:mi></mml:math>-modes clustering algorithm

The k-modes clustering algorithm was proposed by Huang for handling datasets with categorical attributes, however, the dissimilarity measure used limits its applicability. Ng et al. improved on Huang’s k-modes algorithm by proposing a new dissimilarity measure between objects. Moreover, both k-modes algorithms require the initial seeds to be randomly chosen and the number of clusters be specified manually. To overcome the limitations of Huang’s and Ng’s k-modes clustering algorithms, we first extend the clustering algorithm published in Science in 2014 (“clustering by fast search and find of density peaks”). The optimal initial seeds and the number of clusters of a dataset are determined simultaneously by taking the standard deviation as the self-tuning cutoff distance and the simple match dissimilarity as the distance measurement in the definition of the density of a point. A new dissimilarity measure is proposed to calculate the dissimilarities between objects to improve on that of Ng’s k-modes algorithm. The performance of our resulting self-tuning k-modes clustering algorithm was tested on nine datasets (three being relatively large) from the UCI (University of California in Irvine) machine learning repository. The clustering results were compared to those produced by Huang’s and Ng’s algorithms. Statistical tests of three k-modes algorithms were undertaken to determine whether or not there is significant difference between our self-tuning k-modes algorithm and Huang’s and Ng’s k-modes algorithms. All these experimental results demonstrate that our proposed k-modes clustering algorithm is superior to Hang’s and Ng’s k-modes algorithms in terms of clustering accuracy (ACC) and the well-known Adjusted Rand Index (ARI) metric. Our self-tuning k-modes algorithm is significantly different from both Huang’s and Ng’s k-modes algorithms, and there is no statistically significant difference between Ng’s and Huang’s k-modes algorithms.
Hierarchical Multi-label Text Classification based on a Matrix Factorization and Recursive-Attention Approach

Hierarchical Multi-label Text Classification (HMTC) is an important and challenging task in the field of natural language processing (NLP). For example, the automatic classification of complaint texts in customer service of communication operators is a typical HMTC task. These complaint texts are assigned to multiple categories stored in a hierarchical structure and categories at different levels are related to each other. Most existing HMTC methods, however, either process classification tasks with all levels simultaneously or utilize multiple classifiers for each level separately, which ignore the dependencies among different levels in the hierarchical structure. In this paper, we proposed a Matrix Factorization (MF) and Recursive-Attention (RA) Approach, which is called MF-RA, to handle HMTC tasks. By capturing the associations among labels from different levels, MF-RA can raise the reliability of HMTC. Based on the real-world complaint data of customer service from the communication operator, experiments demonstrate that the Top-1 F1 score of our proposed MF-RA method achieves a maximum increase of 21.0% compared with the commonly used machine learning algorithms (Hierarchical SVM and Clus HMC) and a maximum increase of 5.6% compared with deep learning algorithms (GRU, TextCNN, BERT) in this scene, respectively.
Defuzzification: Maxima methods with improved efficiency and their performance evaluation

Fuzzy logic offers a broad range of applications in almost every field. The world is being reshaped by fuzzy systems. Defuzzification is the last phase in the fuzzy inference process, and it is responsible for converting the fuzzy output into a single numerical result. This paper proposes two new maxima methods- Best of Maxima and Proportionate Maxima. Proposed methods are evaluated on major properties- Accuracy (based on average % difference), time efficiency, continuity as well as other set of essential properties. An innovative way to analyze or test defuzzification method is introduced in which 100 setups of fuzzy sets with variations in asymmetries, amount of overlapping, values of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\alpha$</tex> -cut and type of membership functions are generated and tested. Detailed comparative results are presented which will be helpful for new researchers.
Control of a Semi-autonomous Powered Wheelchair

Abstract A model reference controller for a powered wheelchair is described. The chair is fitted with sensor systems to assist a disabled user with steering their chair. The controller can cope with varying circumstances and situations. Non-linear terms are compensated for using an adaptive and automatic scheme. Consistent and dependable veer-controll is considered and the system was able to deal with uncertainties, for example changing surfaces, different shifting weights of users, hills, bumps, slopes and differences in tires and wheels. The controller has a quasi-linear closed-loop behaviour and that means that extra outer control loops can be appended later, for example path-following algorithms. An assistive agent was also created so that sperate wheelchairs will be able to communicate with each other in the future.
Features of Semantic Similarity Assessment

Semantic similarity is a fundamental concept in computational linguistics. The models used for the representation of text have a major role in similarity computation. The text with multilingual and multimodal components shows the need for computing similarity based on different characteristics of text. This chapter studies various aspects of semantic similarity of linguistic units, cross-level similarity, semantic models, and similarity measures. One of the main motivations of this chapter is to analyze semantic similarity models such as geometric models, feature-based models, graph-based models, vector space models, and formal concept analysis models. In addition, a composite summary score based on words and hashtags is applied for the tweet summarization task which is effective when compared with other measures.
Genre-Controllable Story Generation via Supervised Contrastive Learning

While controllable text generation has received attention due to the recent advances in large-scale pre-trained language models, there is a lack of research that focuses on story-specific controllability. To address this, we present Story Control via Supervised Contrastive learning model (SCSC), to create a story conditioned on genre. For this, we design a supervised contrastive objective combined with log-likelihood objective, to capture the intrinsic differences among the stories in different genres. The results of our automated evaluation and user study demonstrate that the proposed method is effective in genre-controlled story generation.
CoSimHeat: An Effective Heat Kernel Similarity Measure Based on Billion-Scale Network Topology✱

Myriads of web applications in the Big Data era demand an effective measure of similarity based on billion-scale network structures, e.g., collaborative filtering. Recently, CoSimRank has been devised as a promising graph-theoretic similarity model, which iteratively captures the notion that “two distinct nodes are evaluated as similar if they are connected with similar nodes”. However, the existing CoSimRank model for assessing similarities may either yield unsatisfactory results or rather cost-inhibitive, rendering it impractical in massive graphs. In this paper, we propose CoSimHeat, a novel scalable graph-theoretic similarity model based on heat diffusion. Specifically, we first formulate CoSimHeat model by taking advantage of heat diffusion to emulate the activities of similarity propagations on the Web. Then, we show that the similarities produced by CoSimHeat are more satisfactory than those from CoSimRank families since CoSimHeat fulfils four axioms that an ideal similarity model should satisfy while circumventing the “dead-loop” problem of CoSimRank. Next, we propose a fast algorithm to substantially accelerate CoSimHeat computations on billion-sized graphs, with guarantees of accuracy. Our experiments on various datasets validate that CoSimHeat achieves higher accuracy and is order-of-magnitude faster than state-of-the-art competitors.
ExpScore: Learning Metrics for Recommendation Explanation

Many information access and machine learning systems, including recommender systems, lack transparency and accountability. High-quality recommendation explanations are of great significance to enhance the transparency and interpretability of such systems. However, evaluating the quality of recommendation explanations is still challenging due to the lack of human-annotated data and benchmarks. In this paper, we present a large explanation dataset named RecoExp, which contains thousands of crowdsourced ratings of perceived quality in explaining recommendations. To measure explainability in a comprehensive and interpretable manner, we propose ExpScore, a novel machine learning-based metric that incorporates the definition of explainability from various perspectives (e.g., relevance, readability, subjectivity, and sentiment polarity). Experiments demonstrate that ExpScore not only vastly outperforms existing metrics and but also keeps itself explainable. Both the RecoExp dataset and open-source implementation of ExpScore will be released for the whole community. These resources and our findings can serve as forces of public good for scholars as well as recommender systems users.
Topological Transduction for Hybrid Few-shot Learning

Digging informative knowledge and analyzing contents from the internet is a challenging task as web data may contain new concepts that are lack of sufficient labeled data as well as could be multimodal. Few-shot learning (FSL) has attracted significant research attention for dealing with scarcely labeled concepts. However, existing FSL algorithms have assumed a uniform task setting such that all samples in a few-shot task share a common feature space. Yet in the real web applications, it is usually the case that a task may involve multiple input feature spaces due to the heterogeneity of source data, that is, the few labeled samples in a task may be further divided and belong to different feature spaces, namely hybrid few-shot learning (hFSL). The hFSL setting results in a hybrid number of shots per class in each space and aggravates the data scarcity challenge as the number of training samples per class in each space is reduced. To alleviate these challenges, we propose the Task-adaptive Topological Transduction Network, namely TopoNet, which trains a heterogeneous graph-based transductive meta-learner that can combine information from both labeled and unlabeled data to enrich the knowledge about the task-specific data distribution and multi-space relationships. Specifically, we model the underlying data relationships of the few-shot task in a node-heterogeneous multi-relation graph, and then the meta-learner adapts to each task’s multi-space relationships as well as its inter- and intra-class data relationships, through an edge-enhanced heterogeneous graph neural network. Our experiments compared with existing approaches demonstrate the effectiveness of our method.
Element-guided Temporal Graph Representation Learning for Temporal Sets Prediction

Given a sequence of sets with timestamps, where each set includes an arbitrary number of elements, temporal sets prediction aims to predict elements in the consecutive set. Indeed, predicting temporal sets is much more complicated than the conventional predictions of time series and temporal events. Recent studies on temporal sets prediction follow the same pipeline that only learns from each user’s own sequence, which fails to discover the collaborative signals among the sequences of different users. In this paper, we propose a novel element-guided temporal graph neural network to tackle the above issue in temporal sets prediction. Specifically, we first connect sequences of different users via a temporal graph, where nodes contain users and elements, and edges represent user-element interactions with time information. Then, we devise a new message aggregation mechanism to improve the model expressive ability via adaptively learning element-specific representations for each user with the guidance of elements. By performing the element-guided message aggregation among multiple hops, collaborative signals latent in high-order user-element interactions are explicitly encoded. Finally, we present a temporal information utilization module to capture both the semantic and periodic patterns in user sequential behaviors. Experiments on real-world datasets demonstrate that our approach could not only outperform the existing methods with a significant margin but also capture the collaborative signals. Codes and datasets are available at https://github.com/yule-BUAA/ETGNN.
Boolean-Based (k, n, m) Multi-Secret Image Sharing

Multi-secret image sharing technology shares multiple secret images among shared images for participants and the recovery requires enough shared images for fulfilling the security property. Simultaneously, the adopted Boolean method preserves high performance. The proposed (k, n, m) multi-secret image sharing scheme shares m secret images among n participants and gathering k participants’ shared images perfectly recovers these m secret images. The proposed scheme exhibits high performance because of the usage of Boolean-based processing. Moreover, the usage of a sharing matrix based strategy acquires perfect reconstruction of the secret images under (k, n) thresholds. Two proposed schemes, the source-random-mixed (SRM) and the source-random-separate (SRS) methods, are presented in this study from different strategies of sharing the random image mixed with the secret images or separated to the secret images, respectively. Moreover, the proposed SRS method exhibits the better sharing capacity. Experimental results show that the proposed schemes perform well on sharing efficiency and security.
Generative Compression for Face Video: A Hybrid Scheme

As the latest video coding standard, versatile video coding (VVC) has shown its ability in retaining pixel quality. To excavate more compression potential for video conference scenarios under ultra-low bitrate, this paper proposes a bitrate-adjustable hybrid compression scheme for face video. This hybrid scheme combines the pixel-level precise recovery capability of traditional coding with the generation capability of deep learning based on abridged information, where Pixel-wise Bi-Prediction, Low-Bitrate-FOM and Lossless Keypoint Encoder collaborate to achieve PSNR up to 36.23 dB at a low bitrate of 1.47 KB/s. Without introducing any additional bi-trate, our method has a clear advantage over VVC under a completely fair comparative experiment, which proves the effectiveness of our proposed scheme. Moreover, our scheme can adapt to any existing encoder/configuration to deal with different encoding requirements, and the bitrate can be dynamically adjusted according to the network condition.
Context-Aware Multi-Scale Aggregation Network for Congested Crowd Counting

In this paper, we propose a context-aware multi-scale aggregation network named CMSNet for dense crowd counting, which effectively uses contextual information and multi-scale information to conduct crowd density estimation. To achieve this, a context-aware multi-scale aggregation module (CMSM) is designed. Specifically, CMSM consists of a multi-scale aggregation module (MSAM) and a context-aware module (CAM). The MSAM is used to obtain multi-scale crowd features. The CAM is used to enhance the extracted multi-scale crowd feature with more context information to efficiently recognize crowds. We conduct extensive experiments on three challenging datasets, i.e., ShanghaiTech, UCF_CC_50, and UCF-QNRF, and the results showed that our model yielded compelling performance against the other state-of-the-art methods, which demonstrate the effectiveness of our method for congested crowd counting.
Using ANPR data to create an anonymized linked open dataset on urban bustle

Abstract ANPR cameras allow the automatic detection of vehicle license plates and are increasingly used for law enforcement. However, also statistical data generated by ANPR cameras are a potential source of urban insights. In order for this data to reach its full potential for policy-making, we research how this data can be shared in digital twins, with researchers, for a diverse set of machine learning models, and even Open Data portals. This article’s key objective is to find a way to anonymize and aggregate ANPR data in a way that it still can provide useful visualizations for local decision making. We introduce an approach to aggregate the data with geotemporal binning and publish it by combining nine existing data specifications. We implemented the approach for the city of Kortrijk (Belgium) with 43 ANPR cameras, developed the ANPR Metrics tool to generate the statistical data and dashboards on top of the data, and tested whether mobility experts from the city could deduct valuable insights. We present a couple of insights that were found as a result, as a proof that anonymized ANPR data complements their currently used traffic analysis tools, providing a valuable source for data-driven policy-making.
Meta-Learning Helps Personalized Product Search

Personalized product search that provides users with customized search services is an important task for e-commerce platforms. This task remains a challenge when inferring users’ preferences from few records or even no records, which is also known as the few-shot or zero-shot learning problem. In this paper, we propose a Bayesian Online Meta-Learning Model (BOML), which transfers meta-knowledge, from the inference for other users’ preferences, to help to infer the current user’s interest behind her/his few or even no historical records. To extract meta-knowledge from various inference patterns, our model constructs a mixture of meta-knowledge and transfers the corresponding meta-knowledge to the specific user according to her/his records. Based on the meta-knowledge learned from other similar inferences, our proposed model searches a ranked list of products to meet users’ personalized query intents for those with few search records (i.e., few-shot learning problem) or even no search records (i.e., zero-shot learning problem). Under the records arriving sequentially setting, we propose an online variational inference algorithm to update meta-knowledge over time. Experimental results demonstrate that our proposed BOML outperforms state-of-the-art algorithms.
Interpretability and Unification

In a recent reply to our article, “What is Interpretability?,” Prasetya argues against our position that artificial neural networks are explainable. It is claimed that our indefeasibility thesis—that adding complexity to an explanation of a phenomenon does not make the phenomenon any less explainable—is false. More precisely, Prasetya argues that unificationist explanations are defeasible to increasing complexity, and thus, we may not be able to provide such explanations of highly complex AI models. The reply highlights an important lacuna in our original paper, the omission of the unificationist account of explanation, and affords us the opportunity to respond. Here, we argue that artificial neural networks are explainable in a way that should satisfy unificationists and that interpretability methods present ways in which ML theories can achieve unification.
Multi-dimensional Probabilistic Regression over Imprecise Data Streams

In applications of Web of Things or Web of Events, a massive volume of multi-dimensional streaming data are automatically and continuously generated from different sources, such as GPS, sensors, and other measurement devices, which are essentially imprecise (inaccurate and/or uncertain). It is challenging to monitor and get insights over imprecise and low-level streaming data, in order to capture potentially important data changing trends and to initiate prompt responses. In this work, we investigate solutions for conducting multi-dimensional and multi-granularity probabilistic regression for the imprecise streaming data. The probabilistic nature of streaming data poses big computational challenges to the regression and its aggregation. In this paper, we study a series of techniques on multi-dimensional probabilistic regression, including aggregation, sketching, popular path materialization, and exception-driven querying. Extensive experiments on real and synthetic datasets show the efficiency and scalability of our proposals.
Fast Increased Fidelity Samplers for Approximate Bayesian Gaussian Process Regression

Gaussian processes (GPs) are common components in Bayesian non-parametric models having a rich methodological literature and strong theoretical grounding. The use of exact GPs in Bayesian models is limited to problems containing several thousand observations due to their prohibitive computational demands. We develop a posterior sampling algorithm using H -matrix approximations that scales at O(nlog2n) . We show that this approximation's Kullback-Leibler divergence to the true posterior can be made arbitrarily small. Though multidimensional GPs could be used with our algorithm, d-dimensional surfaces are modeled as tensor products of univariate GPs to minimize the cost of matrix construction and maximize computational efficiency. We illustrate the performance of this fast increased fidelity approximate GP, FIFA-GP, using both simulated and non-synthetic data sets.