Reviewerly/exHarmony huggingface dataset:
Online Easy Example Mining for Weakly-Supervised Gland Segmentation from Histology Images

Developing an AI-assisted gland segmentation method from histology images is critical for automatic cancer diagnosis and prognosis; however, the high cost of pixel-level annotations hinders its applications to broader diseases. Existing weakly-supervised semantic segmentation methods in computer vision achieve degenerative results for gland segmentation, since the characteristics and problems of glandular datasets are different from general object datasets. We observe that, unlike natural images, the key problem with histology images is the confusion of classes owning to morphological homogeneity and low color contrast among different tissues. To this end, we propose a novel method Online Easy Example Mining (OEEM) that encourages the network to focus on credible supervision signals rather than noisy signals, therefore mitigating the influence of inevitable false predictions in pseudo-masks. According to the characteristics of glandular datasets, we design a strong framework for gland segmentation. Our results exceed many fully-supervised methods and weakly-supervised methods for gland segmentation over 4.6% and 6.04% at mIoU, respectively. Code is available at https://github.com/xmed-lab/OEEM .
A hybrid method based on estimation of distribution algorithms to train convolutional neural networks for text categorization

Convolutional Neural Networks for text categorization allows the extraction of features from the text represented through word embedding. The high dimensionality of the texts themselves implies a larger number of network parameters and a more complex optimization surface. Artificial neural network training is an NP-Hard optimization problem, which has been addressed by methods based on partial derivatives of the objective function and presents several theoretical and practical limitations, such as the probability of convergence to local minimums. In this work, we propose a hybrid method based on the Estimation of Distribution Algorithms for training a Convolutional Neural Network. For this, we train together gradient-based methods with the Estimation of Multivariate Normal Algorithm and Univariate Marginal Distribution Algorithm by dividing the training process into two stages. The different variants obtained with the proposed method are compared with gradient-based methods on public benchmark datasets and statistical differences are analyzed by nonparametric tests. The proposed method increases the accuracy of the convolutional network applied to the text categorization task and overcome in about 0.22%–24% the state-of-the-art algorithms.
Determining reliable wide-strain-range equivalent stress–strain curves using 3D digital image correlation

We propose a method for obtaining reliable wide strain range equivalent stress–strain curves from tensile testing by applying 3D digital image correlation. Herein, both the front and side surfaces of the specimen were identified by the 45° tilting of the specimens. The deformation of the specimen was the most severe in the neck section, and the equivalent strain representing the entire specimen could be extracted from the neck regions on the side and front surfaces. In addition, the equivalent stress was obtained by calculating the reduction in the varying cross-sectional area from the strain information of the two surfaces. The resulting wide strain range equivalent stress–strain curves can describe nonlinear hardening behaviors up to higher strains than the conventional gauge length method. The proposed method can provide wide strain range equivalent stress–strain curves only through experiments without requiring separate finite element method simulations or correction factors.
Who’s Controlling My Device? Multi-User Multi-Device-Aware Access Control System for Shared Smart Home Environment

Multiple users have access to multiple devices in a smart home system – typically through a dedicated app installed on a mobile device. Traditional access control mechanisms consider one unique, trusted user that controls access to the devices. However, multi-user multi-device smart home settings pose fundamentally different challenges to traditional single-user systems. For instance, in a multi-user environment, users have conflicting, complex, and dynamically-changing demands on multiple devices that cannot be handled by traditional access control techniques. Moreover, smart devices from different platforms/vendors can share the same home environment, making existing access control obsolete for smart home systems. To address these challenges, in this paper, we introduce Kratos+ , a novel multi-user and multi-device-aware access control mechanism that allows smart home users to flexibly specify their access control demands. Kratos+ has four main components: user interaction module, backend server, policy manager, and policy execution module. Users can easily specify their desired access control settings using the interaction module that are translated into access control policies in the back-end server. The policy manager analyzes these policies, initiates automated negotiation between users to resolve conflicting demands, and generates final policies to enforce in smart home systems. We implemented Kratos+ as a platform-independent solution and evaluated its performance on real smart home deployments featuring multi-user scenarios with a rich set of configurations (337 different policies including 231 demand conflicts and 69 restriction policies). These configurations also included five different threats associated with access control mechanisms. Our extensive evaluations show that Kratos+ is very effective in resolving conflicting access control demands with minimal overhead. We also performed an extensive user study with 72 smart home users to better understand the user’s needs before designing the system and a usability study to evaluate the efficacy of Kratos+ in a real-life smart home environment.
Level of conceptual interoperability model for blockchain based systems

The Level of Conceptual Interoperability Model (LCIM) is a widely used framework that represents inter-relationship among interoperability and composability of different information systems. Although this model has been successfully applied to various domains such as cybernetics and informatics, there are many challenges in directly adopting the model for blockchain-based systems. This paper identifies those challenges and proposes a new Level of Conceptual Interoperability Model for blockchain systems based on the original LCIM. We define five different levels of interoperability for blockchain-based systems and theoretically evaluate the level of interoperability (LOI) achieved by different blockchain networks. The evaluation outcomes show that there exists technical interoperability (Level 1) between Bitcoin and Ethereum networks, whereas Solana and Binance achieve pragmatic interoperability (Level 4) by conveying state changes with the Ethereum network and Polkadot achieve dynamic interoperability (level 5) by suitably conveying state changes within the ecosystem of its networks. We present case studies that demonstrate how the proposed LCIM for blockchain systems map various real-world applications to their respective levels.
Conditional Anonymous Remote Healthcare Data Sharing Over Blockchain

As an important carrier of healthcare data, Electronic Medical Records (EMRs) generated from various sensors, i.e., wearable, implantable, are extremely valuable research materials for artificial intelligence and machine learning. The efficient circulation of EMRs can improve remote medical services and promote the development of the related healthcare industry. However, in traditional centralized data sharing architectures, the balance between privacy and traceability still cannot be well handled. To address the issue that malicious users cannot be locked in the fully anonymous sharing schemes, we propose a trackable anonymous remote healthcare data storing and sharing scheme over decentralized consortium blockchain. Through an "on-chain & off-chain" model, it relieves the massive data storage pressure of medical blockchain. By introducing an improved proxy re-encryption mechanism, the proposed scheme realizes the fine-gained access control of the outsourced data, and can also prevent the collusion between semi-trusted cloud servers and data requestors who try to reveal EMRs without authorization. Compared with the existing schemes, our solution can provide a lower computational overhead in repeated EMRs sharing, resulting in a more efficient overall performance.
Enhancement of thin-section image using super-resolution method with application to the mineral segmentation and classification in tight sandstone reservoir

The accurate characterization of rock and fluid properties in porous media of oil reservoir using thin sections depends on reliable segmentation and classification of the involved phases. However, in tight sandstone reservoirs, rock image segmentation and classification become a challenging task due to the limitation of resolution on representing the pores, throats, and other minerals at the microscale size. The resolution of an image has thus become a critical factor. Therefore, this study aims to use the super-resolution technique, which can enhance the image resolution by deep learning methods to overcome the limitation of original image resolution, and significantly improve the traditional segmentation and classification results. A Generative Adversarial Network (GAN)-based Super-resolution(SR) model was used as a pre-processing step to enhance the resolution of a given image. Then the effectiveness of super-resolution technique in post-processing procedures like segmentation and classification is evaluated using a tight sandstone data set from Ordos basin. The performance of segmentation with super-resolution enhancement is compared among Level set, Simple Linear Iterative Clustering (SLIC), and Watershed. As numerical test results reflect, segmentation in super-resolution image can achieve the segmentation of tiny minerals, pores, throats, and other blurry edges which can't be correctly segmented in the original image. Furthermore, in classification, we use a Convolutional Neural Network (CNN) model and a logistic regression model to demonstrate the advantage of SR enhancement. After the super-resolution process, the classification accuracy of CNN and logistic models have both improved for over 10%. The comparisons and analyses are presented to show that super-resolution could significantly improve these post-processing procedures. In addition, we discuss the potentials and limitations of applying super-resolution in segmentation and classification.
Posture and sequence recognition for Bharatanatyam dance performances using machine learning approaches

Understanding the underlying semantics of performing arts like dance is a challenging task. Analysis of dance is useful to preserve cultural heritage, make video recommendation systems, and build tutoring systems. To create such a dance analysis application, three aspects of dance analysis must be addressed: (1) segment the dance video to find representative action elements, (2) recognize the detected action elements, and (3) recognize sequences formed by combining action elements according to specific rules. This paper attempts to address the three fundamental problems of dance analysis raised above, with a focus on Indian Classical Dance, em Bharatanatyam. Since dance is driven by music, we use both musical and motion information to extract action elements. The action elements are then recognized using machine learning and deep learning techniques. Finally, the Hidden Markov Model (HMM) and Long Short-Term Memory (LSTM) are used to recognize the dance sequence.
Self-supervised coarse-to-fine monocular depth estimation using a lightweight attention module

Abstract Self-supervised monocular depth estimation has been widely investigated and applied in previous works. However, existing methods suffer from texture-copy, depth drift, and incomplete structure. It is difficult for normal CNN networks to completely understand the relationship between the object and its surrounding environment. Moreover, it is hard to design the depth smoothness loss to balance depth smoothness and sharpness. To address these issues, we propose a coarse-to-fine method with a normalized convolutional block attention module (NCBAM). In the coarse estimation stage, we incorporate the NCBAM into depth and pose networks to overcome the texture-copy and depth drift problems. Then, we use a new network to refine the coarse depth guided by the color image and produce a structure-preserving depth result in the refinement stage. Our method can produce results competitive with state-of-the-art methods. Comprehensive experiments prove the effectiveness of our two-stage method using the NCBAM.
Human-centered AI

A new synthesis is emerging that integrates Artificial Intelligence (AI) technologies with Human-Computer Interaction to produce Human-Centered AI (HCAI). Advocates of this new synthesis seek to amplify, augment, and enhance human abilities, so as to empower people, build their self-efficacy, support creativity, recognize responsibility, and promote social connections.
Research on IoT Forensics System Based on Blockchain Technology

In recent years, mobile edge computing (MEC) has become a research hotspot in academia. The Internet of Things (IoT) is an excellent way to build the infrastructure required for a MEC environment. Its rich digital tracking repository can provide insights into people's daily activities at home and elsewhere. Meanwhile, due to the open connectivity of the Internet of things devices, they can easily become the target of network attacks and be used by criminals as criminal tools. As a result, civil and criminal cases have increased year by year. This article conducts in-depth research on IoT forensics. By comparing its difference with traditional digital forensics (DF), the definition of IoT forensics is given. We have systematically sorted out the research results since the concept of IoT forensics was proposed in 2013 and proposed a generalized IoT forensics model. By studying blockchain technology and introducing it into the IoT forensics framework, a blockchain-based IoT forensics architecture is further proposed. Further, an alliance chain IoT forensics system is proposed. From the perspective of the data provider and the data visitor, the process of evidence storage and forensics of the IoT system is discussed. Finally, taking Unmanned Aerial Vehicle (UAV) forensics as an example, we give an experiment of IoT forensics analysis.
Distributed three-level QR codes based on visual cryptography scheme

Owing to the large storage and fast machine recognition, QR codes have been widely utilized in many fields such as mobile payment, website navigation and user identity authentication. However, any QR code reader can access to the message contained in the QR code, the security becomes a major challenge to QR codes for privacy usage scenarios. Moreover, the management of QR codes for users are also inconvenient, since the human vision is hard to distinguish a QR code from the others. To solve the security and management problems, we propose the three-level QR codes for a group of participants. The first-level management information and the second-level public information are recognizable for the human vision and QR code reader device, respectively. The third-level privacy information is protected using visual cryptography scheme, and can be decoded using simple and non-cryptography computations. Furthermore, the shares can be stored or transferred in not only e-format but also print-format and photo-format, leading to the broad applicability. Experimental results and analysis demonstrate that the proposed scheme can encode three-level information into several distributed QR codes, and has more advantages compared with the previous schemes.
(Retracted) Hybrid whale optimization algorithm-Levy flight approach for multilevel thresholding image segmentation

Determining the optimal threshold for multilevel image segmentation is a stimulating task. Image segmentation has many applications, such as content-based image retrieval, medical imaging, object detection, object recognition, machine vision, etc. In multilevel thresholding, multiple thresholds are used to segment complex images, and the images are segmented into multiple levels to extract meaningful information for further image analysis. Where threshold number is small, p-tile, Otsu, moment preserving, and entropy thresholding methods attain good accuracy. These classical thresholding methods are time-consuming, computation expensive, and unable to produce good segmentation accuracy with increased threshold numbers. To overcome these problems, the classical thresholding methods are utilized as objective functions with nature-inspired metaheuristic algorithms such as whale optimization algorithm (WOA), simplified swarm optimization (SSO), sine cosine algorithm (SCA), BAT algorithm, WOA-thresholding, and black widow optimization (BWO) to determine optimal multiple thresholds. Nature-inspired metaheuristic algorithms are widely used to search for an optimal solution for global optimization problems. WOA is widely used to find the optimal solution in search space. WOA suffers from entrapment into local optima due to premature convergence behavior. We introduce hybridization of WOA with Levy flight trajectory and named as hybrid whale optimization algorithm-Levy flight (HWOAL), which is utilized to find optimal multiple thresholds for multilevel image segmentation. Levy flight trajectory is utilized to increase diversity in the swarm population. The efficacy of HWOAL is tested on 23 benchmark optimization functions (F1 to F23) and compared with WOA, SSO, SCA, and BAT algorithms. Experiment arms that HWOAL is efficient and makes WOA faster, enhances the ability of exploitation and exploration phase, and can avoid getting stuck into local optima. The segmentation performance of the proposed HWOAL method has been compared with other algorithms, such as WOA, SSO, SCA, BAT, and other two recent segmentation algorithms, such as WOA-TH and BWO on several benchmark images (BSD 300). The experimental result is carried in 30 trials and analyzed based on objective fitness value, optimal multilevel threshold values, segmentation quality measures, such as mean square error, peak signal-to-noise ratio, structural similarity index, average difference, and computation time to compute optimal multilevel threshold values. The experiment conducted by the authors shows that the proposed HWOAL method is efficient, produces better fitness value, and segmentation metrics for multilevel image segmentation than other algorithms.
A novel privacy protection scheme for location-based services using collaborative caching

Protecting privacy in Location-based services (LBSs) has been one of the major concerns over the last few years. Users’ queries can reveal sensitive information to LBSs, thus giving an opportunity for the adversaries to track the querying user. Existing location privacy protection mechanisms either use a trusted third party (TTP) or a location obfuscation-based scheme. TTP-based schemes require users to report their location-related information to the TTP before sending a query to the location-based service provider (LSP). Once the TTP is compromised, users’ privacy can be breached. On the other hand, location obfuscation schemes achieve privacy at the cost of accuracy. We address these problems by proposing a collaborative caching-based privacy-preserving scheme for LBSs. The scheme prevents the users’ location information leakage from untrusted communication channels as well as LSP while maintaining the quality of service. The scheme uses a novel dummy location generation technique and a virtual identity mechanism to improve users’ location and identity privacy. Furthermore, one-time spatial groups are created with the nearby neighbors to access the localized services, thus providing less interaction with the untrusted LSP. The proposed work has the potential to preserve users’ location privacy and reduce the computation and communication costs of the system. The aforementioned claims are verified by the experimental results.
On the role of metaheuristic optimization in bioinformatics

Abstract Metaheuristic algorithms are employed to solve complex and large‐scale optimization problems in many different fields, from transportation and smart cities to finance. This paper discusses how metaheuristic algorithms are being applied to solve different optimization problems in the area of bioinformatics. While the text provides references to many optimization problems in the area, it focuses on those that have attracted more interest from the optimization community. Among the problems analyzed, the paper discusses in more detail the molecular docking problem, the protein structure prediction, phylogenetic inference, and different string problems. In addition, references to other relevant optimization problems are also given, including those related to medical imaging or gene selection for classification. From the previous analysis, the paper generates insights on research opportunities for the Operations Research and Computer Science communities in the field of bioinformatics.
Building effective recommender systems for tourists

Abstract Recommender systems (RSs) are personalized information search and discovery applications helping users to identify and choose useful items and information. In this paper, we focus on the tourism application scenario and its specific requirements. We discuss a novel RS approach that copes with the specific application constraints of the domain and produces recommendations that better match the true needs of tourists. We illustrate the proposed next POI recommendation approach in a case study and we compare it with a state‐of‐the‐art nearest neighbor‐based next item RS. With the analysis of this case study, we aim at illustrating the specific features of the compared approaches also with the goal to raise the discussion on RSs validation methods, with a particular attention to tourism applications. We finally discuss some significant limitations of current evaluation approaches that must be addressed in future studies.
Workout connections: Investigating social interactions in online group exercise classes

Exercising in a group context effectively motivates physical activity participation. However, gyms and health clubs closed in-person services due to the COVID-19 global pandemic. Online group exercise classes became an emerging service that attracted many participants. Exercisers are predicted to continue participation even after the restriction is lifted. However, it is unclear how such transition influenced exercisers' and fitness instructors' experiences. In this study, we conducted interviews with 13 exercisers and 11 fitness instructors who participated in synchronous online group exercise classes. We identified the opportunities and challenges that the participants faced in different types of social interactions, as well as how technology limited or facilitated participants' experiences. We reported an increase in responsibilities that the instructors need to take on. The online context also expanded the stakeholders in the social interactions by including other household members. We contributed to the HCI and CSCW community by presenting how social interactions occur in the online group exercise context, reporting the challenges and opportunities in transitioning social interactions from in-person to the online context, as well as proposing design implications to alleviate these challenges and realize the opportunities of the online context.