Reviewerly/exHarmony huggingface dataset:
A Multi-level Equilibrium Clustering Approach for Unsupervised Person Re-identification

Unsupervised person re-identification (re-ID) has not achieved desired results because learning a discriminative feature embedding without annotation is difficult. Fortunately, the special distribution of samples in this task provides critical priority information for addressing this problem. On the one hand, the distribution of samples belonging to the same identity is multi-centered. On the other hand, distribution is distinct for samples of different levels that cropped from the images. According to the first property, we propose the equilibrium criterion, which provides a suitable measurement of dissimilarity between samples around a center or that from different centers. According to the second property, we introduce multi-level labels guided learning to mine and utilize the complementary information among different levels. Extensive experiments demonstrate that our method is superior to the state-of-the-art unsupervised re-ID approaches in significant margins.
Entropic Proofs of Singleton Bounds for Quantum Error-Correcting Codes

We show that a relatively simple reasoning using von Neumann entropy inequalities yields a robust proof of the quantum Singleton bound for quantum error-correcting codes (QECC). For entanglement-assisted quantum error-correcting codes (EAQECC) and catalytic codes (CQECC), a type of generalized quantum Singleton bound [Brun et al., IEEE Trans. Inf. Theory 60(6):3073--3089 (2014)] was believed to hold for many years until recently one of us found a counterexample [MG, Phys. Rev. A 103, 020601 (2021)]. Here, we rectify this state of affairs by proving the correct generalized quantum Singleton bound, extending the above-mentioned proof method for QECC; we also prove information-theoretically tight bounds on the entanglement-communication tradeoff for EAQECC. All of the bounds relate block length $n$ and code length $k$ for given minimum distance $d$ and we show that they are robust, in the sense that they hold with small perturbations for codes which only correct most of the erasure errors of less than $d$ letters. In contrast to the classical case, the bounds take on qualitatively different forms depending on whether the minimum distance is smaller or larger than half the block length. We also provide a propagation rule: any pure QECC yields an EAQECC with the same distance and dimension, but of shorter block length.
Guided Attention Network for Object Detection and Counting on Drones

Object detection and counting are related but challenging problems, especially for drone based scenes with small objects and cluttered background. In this paper, we propose a new Guided Attention network (GAnet) to deal with both object detection and counting tasks based on the feature pyramid. Different from the previous methods relying on unsupervised attention modules, we fuse different scales of feature maps by using the proposed weakly-supervised Background Attention (BA) between the background and objects for more semantic feature representation. Then, the Foreground Attention (FA) module is developed to consider both global and local appearance of the object to facilitate accurate localization. Moreover, the new data argumentation strategy is designed to train a robust model in the drone based scenes with various illumination conditions. Extensive experiments on three challenging benchmarks (i.e., UAVDT, CARPK and PUCPR+) show the state-of-the-art detection and counting performance of the proposed method compared with existing methods. Code can be found at https://isrc.iscas.ac.cn/gitlab/research/ganet.
Revisiting Rubik’s Cube: Self-supervised Learning with Volume-Wise Transformation for 3D Medical Image Segmentation

Deep learning highly relies on the quantity of annotated data. However, the annotations for 3D volumetric medical data require experienced physicians to spend hours or even days for investigation. Self-supervised learning is a potential solution to get rid of the strong requirement of training data by deeply exploiting raw data information. In this paper, we propose a novel self-supervised learning framework for volumetric medical images. Specifically, we propose a context restoration task, i.e., Rubik’s cube++, to pre-train 3D neural networks. Different from the existing context-restoration-based approaches, we adopt a volume-wise transformation for context permutation, which encourages network to better exploit the inherent 3D anatomical information of organs. Compared to the strategy of training from scratch, fine-tuning from the Rubik’s cube++ pre-trained weight can achieve better performance in various tasks such as pancreas segmentation and brain tissue segmentation. The experimental results show that our self-supervised learning method can significantly improve the accuracy of 3D deep learning networks on volumetric medical datasets without the use of extra data.
Cluster-based information retrieval using pattern mining

Abstract This paper addresses the problem of responding to user queries by fetching the most relevant object from a clustered set of objects. It addresses the common drawbacks of cluster-based approaches and targets fast, high-quality information retrieval. For this purpose, a novel cluster-based information retrieval approach is proposed, named Cluster-based Retrieval using Pattern Mining (CRPM). This approach integrates various clustering and pattern mining algorithms. First, it generates clusters of objects that contain similar objects. Three clustering algorithms based on k-means, DBSCAN (Density-based spatial clustering of applications with noise), and Spectral are suggested to minimize the number of shared terms among the clusters of objects. Second, frequent and high-utility pattern mining algorithms are performed on each cluster to extract the pattern bases. Third, the clusters of objects are ranked for every query. In this context, two ranking strategies are proposed: i) Score Pattern Computing (SPC), which calculates a score representing the similarity between a user query and a cluster; and ii) Weighted Terms in Clusters (WTC), which calculates a weight for every term and uses the relevant terms to compute the score between a user query and each cluster. Irrelevant information derived from the pattern bases is also used to deal with unexpected user queries. To evaluate the proposed approach, extensive experiments were carried out on two use cases: the documents and tweets corpus. The results showed that the designed approach outperformed traditional and cluster-based information retrieval approaches in terms of the quality of the returned objects while being very competitive in terms of runtime.
Simulating building integrated photovoltaic facades: Comparison to experimental data and evaluation of modelling complexity

Building integrated photovoltaic (BIPV) systems provide an opportunity for renewable energy generation in the built environment. In order to quantify the BIPV potential, numerical models of varying levels of complexity have been developed. This paper investigates how the complexity of BIPV models affects their predictions. The study starts with a detailed multi-physics BIPV model that combines a high-resolution one-diode model with physics-based thermal and airflow models. Next, simplifications are introduced into the model. The model predictions are compared to experimental data from a BIPV curtain wall installed in a test building in Leuven, Belgium. The results show that the detailed BIPV model is capable of estimating the BIPV daily energy yield with an average difference of 6.2 % (2.0 % for clear sky days) and the back-of-module temperature with an average difference of 1.74 °C. The use of a linear power model instead of a high-resolution one-diode model affects the average differences, but not significantly: 8.7 % for daily energy yield predictions (4.5 % for clear sky days) and 1.71 °C for temperature predictions. The use of two different empirical temperature correlations instead of a physics-based approach increases the average temperature difference to 3.5 and 4.4 °C. The average difference in daily energy yield increases to 10.2 and 10.4 %, respectively (5.9 and 5.5 % for clear sky days). These findings indicate that the detailed version of multi-physics BIPV model provides the best agreement with experimental data, but it is still possible to reduce the model complexity with acceptable accuracy.
Ensembled CTR Prediction via Knowledge Distillation

Recently, deep learning-based models have been widely studied for click-through rate (CTR) prediction and lead to improved prediction accuracy in many industrial applications. However, current research focuses primarily on building complex network architectures to better capture sophisticated feature interactions and dynamic user behaviors. The increased model complexity may slow down online inference and hinder its adoption in real-time applications. Instead, our work targets at a new model training strategy based on knowledge distillation (KD). KD is a teacher-student learning framework to transfer knowledge learned from a teacher model to a student model. The KD strategy not only allows us to simplify the student model as a vanilla DNN model but also achieves significant accuracy improvements over the state-of-the-art teacher models. The benefits thus motivate us to further explore the use of a powerful ensemble of teachers for more accurate student model training. We also propose some novel techniques to facilitate ensembled CTR prediction, including teacher gating and early stopping by distillation loss. We conduct comprehensive experiments against 12 existing models and across three industrial datasets. Both offline and online A/B testing results show the effectiveness of our KD-based training strategy.
Capability-based IoT access control using blockchain

Internet of Things (IoT) devices facilitate intelligent service delivery in a broad range of settings, such as smart offices, homes and cities. However, the existing IoT access control solutions are mainly based on conventional identity management schemes and use centralized architectures. There are known security and privacy limitations with such schemes and architectures, such as the single-point failure or surveillance (e.g., device tracking). Hence, in this paper, we present an architecture for capability-based IoT access control utilizing the blockchain and decentralized identifiers to manage the identity and access control for IoT devices. Then, we propose a protocol to provide a systematic view of system interactions, to improve security. We also implement a proof-of-concept prototype of the proposed approach and evaluate the prototype using a real-world use case. Our evaluation results show that the proposed solution is feasible, secure, and scalable.
Deep neural network for water/fat separation: Supervised training, unsupervised training, and no training

To use a deep neural network (DNN) for solving the optimization problem of water/fat separation and to compare supervised and unsupervised training.The current T2∗ -IDEAL algorithm for solving water/fat separation is dependent on initialization. Recently, DNN has been proposed to solve water/fat separation without the need for suitable initialization. However, this approach requires supervised training of DNN using the reference water/fat separation images. Here we propose 2 novel DNN water/fat separation methods: 1) unsupervised training of DNN (UTD) using the physical forward problem as the cost function during training, and 2) no training of DNN using physical cost and backpropagation to directly reconstruct a single dataset. The supervised training of DNN, unsupervised training of DNN, and no training of DNN methods were compared with the reference T2∗ -IDEAL.All DNN methods generated consistent water/fat separation results that agreed well with T2∗ -IDEAL under proper initialization.The water/fat separation problem can be solved using unsupervised deep neural networks.
Continuous auditing and threat detection in multi-cloud infrastructure

Efficient change control and configuration management is imperative for addressing the emerging security threats in cloud infrastructure. These threats majorly exploit misconfiguration vulnerabilities e.g. excessive permissions, disabled logging features and publicly accessible cloud storage buckets. Traditional security tools and mechanisms are unable to effectively and continuously track changes in cloud infrastructure owing to transience and unpredictability of cloud events. Therefore, novel tools that are proactive, agile and continuous are imperative. This article proposes CSBAuditor, a novel cloud security system that continuously monitors cloud infrastructure, to detect malicious activities and unauthorized changes. CSBAuditor leverages two concepts: state transition analysis and reconciler pattern to overcome the aforementioned security issues. Furthermore, security metrics are used to compute severity scores for detected vulnerabilities using a novel scoring system: Cloud Security Scoring System. CSBAuditor has been evaluated using various strategies including security chaos engineering (fault injection) strategies on Amazon Web Services and Google Cloud Platform. CSBAuditor effectively detects misconfigurations in real-time with a detection rate of over 98%. Also, the performance overhead is within acceptable limits.
Rotate3D: Representing Relations as Rotations in Three-Dimensional Space for Knowledge Graph Embedding

Knowledge graph embedding, which aims to learn low-dimensional embeddings of entities and relations, plays a vital role in a wide range of applications. It is crucial for knowledge graph embedding models to model and infer various relation patterns, such as symmetry/antisymmetry, inversion, and composition. However, most existing methods fail to model the non-commutative composition pattern, which is essential, especially for multi-hop reasoning. To address this issue, we propose a new model called Rotate3D, which maps entities to the three-dimensional space and defines relations as rotations from head entities to tail entities. By using the non-commutative composition property of rotations in the three-dimensional space, Rotate3D can naturally preserve the order of the composition of relations. Experiments show that Rotate3D outperforms existing state-of-the-art models for link prediction and path query answering. Further case studies demonstrate that Rotate3D can effectively capture various relation patterns with a marked improvement in modeling the composition pattern.
EyeBill-PH: A Machine Vision of Assistive Philippine Bill Recognition Device for Visually Impaired

The value of money can be quickly grasped by the fact that virtually all economic, social, and other tasks are carried out and done using money. With dramatic shifts in global growth and other total human needs, the value of money is rising day after day. Money is an important asset that will allow you to run your life. The exchange of goods for products is an older custom, and you cannot buy something you want without any money. It's straightforward to use money, you just have to glance at the currency instantly, pull the appropriate sum of cash out, and that's it. But that may become a challenging job for those who cannot see. Among the most significant issues facing visually impaired people is the identification of money, particularly for paper currency. In this study, it focuses on this problem and is based on a machine vision technique called object detection. The study used Raspberry Pi 4 as a microcontroller, Pi Camera as its capturing device, and a speaker for audio in announcing the detected bill. EyeBill-PH has been done with an overall testing accuracy of 86.3%.
Cryptanalysis and Improvement of a Pairing-Free Certificateless Aggregate Signature in Healthcare Wireless Medical Sensor Networks

The healthcare wireless medical sensor network is gradually changing the traditional mode of medical treatments with the rapid development of Internet of Things. Specifically, patients' healthcare data can be continuously collected by medical sensor nodes and transmitted to the medical specialists for disease monitoring, diagnosis and treatments. Recently, due to its advantages of low computational and communication overheads in a multiuser environment, the certificateless aggregate signature (CLAS) scheme has been adopted to prevent the sensitive healthcare data from being tampered and damaged, thereby ensuring the integrity and authenticity of data. In order to further improve the efficiency of CLAS schemes for the sensor nodes with limited resources, several CLAS schemes without bilinear pairing have been proposed. However, security issues prevent them from being fully applied in the practical scenarios. In this article, we analyze the security of a pairing-free CLAS scheme proposed by Liu et al. [IEEE Internet of Things Journal, vol. 7, no. 6, pp. 5256-5266, 2020] by pointing out that their scheme is insecure against adversaries. After that, we introduce an improved scheme to solve the security vulnerability. The security proofs show that our improved scheme is existentially unforgeable against chosen message attacks under the random oracle model. In addition, the length of the aggregate signature in our proposal does not increase with the growth of the number of users, which greatly reduces the communication cost. Finally, the efficiency of our scheme is illustrated through both performance analyses and comparisons of related work.
Adaptive robot navigation with collision avoidance subject to 2nd-order uncertain dynamics

This paper considers the problem of robot motion planning in a workspace with obstacles for systems with uncertain 2nd-order dynamics. In particular, we combine closed form potential-based feedback controllers with adaptive control techniques to guarantee the collision-free robot navigation to a predefined goal while compensating for the dynamic model uncertainties. We base our findings on sphere world-based configuration spaces, but extend our results to arbitrary star-shaped environments by using previous results on configuration space transformations. Moreover, we propose an algorithm for extending the control scheme to decentralized multi-robot systems. Finally, extensive simulation results verify the theoretical findings.
Neural Attentive Travel package Recommendation via exploiting long-term and short-term behaviors

Travel package recommendation is a critical task in the tourism e-commerce recommender systems. Recently, an increasing number of studies proposed various travel package recommendation algorithms to improve Online Travel Agencies (OTAs) service, such as collaborative filtering-based, matrix factorization-based and neural network-based methods. Despite their value, however, the main challenges that incorporating complex descriptive information of the travel packages and capturing complicated users’ long-term preferences for fine-grained travel package recommendation are still not fully resolved. In terms of these issues, this paper propose a novel model named Neural Attentive Travel package Recommendation (NATR) for tourism e-commerce by combining users’ long-term preferences with short-term preferences. Specifically, NATR mainly contains two core modules, namely, travel package encoder and user encoder. The travel package encoder module is developed to learn a unified travel package representation by an attentive multi-view learning approach including word-level and view-level attention mechanisms. The user encoder module is designed to study long-term and short-term preference of the user by Bidirectional Long Short-Term Memory (Bi-LSTM) neural networks with package-level attention mechanism. In addition, we further adopt a gated fusion approach to coalesce these two kinds of preferences for learning high-quality the user’s representation. Extensive experiments are conducted on a real-life tourism e-commerce dataset, the results demonstrate the proposed model yields significant performance advantages over several competitive methods. Further analyses from different attention weights provide insights of attentive multi-view learning and gated fusion network, respectively.
Extreme learning machine ensemble model for time series forecasting boosted by PSO: Application to an electric consumption problem

Ensemble Model is a tool that has attracted attention due to its capability to improve the outcome performance of emerging techniques to solve the modelling and classifying problem. However, the feasibility of applying intelligent algorithms to build the Ensemble Model presents a challenge of its own. In this work, an Extreme Learning Machine ensemble is applied to the Time Series modelling problem. We develop a thorough study of the models that will be candidates to compose the ensemble, obtaining statistical results of optimal topologies to solve each Time Series problem. The proposed method for the ensemble is the weighted averaging method, where the parameters (weights) are tuned with the Particle Swarm Optimization algorithm. Lastly, the ensemble is tested first in the well known Santa Fe Time Series Competition benchmark. Given the obtained satisfactory results, the ensemble is secondly tested in a real problem of Spain’s electric consumption forecasting. It is demonstrated that the PSO is a suitable algorithm to optimize Extreme Learning Machine ensemble and that the proposed strategy can obtain good results in both Time Series problems.
Scale-Space Autoencoders for Unsupervised Anomaly Segmentation in Brain MRI

Brain pathologies can vary greatly in size and shape, ranging from few pixels (i.e. MS lesions) to large, space-occupying tumors. Recently proposed Autoencoder-based methods for unsupervised anomaly segmentation in brain MRI have shown promising performance, but face difficulties in modeling distributions with high fidelity, which is crucial for accurate delineation of particularly small lesions. Here, similar to these previous works, we model the distribution of healthy brain MRI to localize pathologies from erroneous reconstructions. However, to achieve improved reconstruction fidelity at higher resolutions, we learn to compress and reconstruct different frequency bands of healthy brain MRI using the laplacian pyramid. In a range of experiments comparing our method to different State-of-the-Art approaches on three different brain MR datasets with MS lesions and tumors, we show improved anomaly segmentation performance and the general capability to obtain much more crisp reconstructions of input data at native resolution. The modeling of the laplacian pyramid further enables the delineation and aggregation of lesions at multiple scales, which allows to effectively cope with different pathologies and lesion sizes using a single model.
Adaptive Text Recognition Through Visual Matching

This work addresses the problems of generalization and flexibility for text recognition in documents. We introduce a new model that exploits the repetitive nature of characters in languages, and decouples the visual decoding and linguistic modelling stages through intermediate representations in the form of similarity maps. By doing this, we turn text recognition into a visual matching problem, thereby achieving generalization in appearance and flexibility in classes.