Reviewerly/exHarmony huggingface dataset:
Introduction to the Special Section on Graph Technologies for User Modeling and Recommendation, Part 2

introduction Share on Introduction to the Special Section on Graph Technologies for User Modeling and Recommendation, Part 2 Authors: Xiangnan He University of Science and Technology of China, Hefei, China University of Science and Technology of China, Hefei, ChinaSearch about this author , Zhaochun Ren Shandong University, Qingdao, China Shandong University, Qingdao, ChinaSearch about this author , Emine Yilmaz Department of Computer Science, University College London, London, United Kingdom Department of Computer Science, University College London, London, United KingdomSearch about this author , Marc Najork Google Research, Mountain View, CA, United States Google Research, Mountain View, CA, United StatesSearch about this author , Tat-Seng Chua National University of Singapore, Singapore, Republic of Singapore National University of Singapore, Singapore, Republic of SingaporeSearch about this author Authors Info & Claims ACM Transactions on Information SystemsVolume 40Issue 3July 2022 Article No.: 42pp 1–5https://doi.org/10.1145/3490180Published:14 December 2021Publication History 0citation385DownloadsMetricsTotal Citations0Total Downloads385Last 12 Months385Last 6 weeks27 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Overlapping Cell Nuclei Segmentation in Digital Histology Images using Intensity-based Contours

Automated nuclei segmentation techniques in histopathological image analysis continue to improve. The machine learning model requires the annotation of large data sets which is a time-consuming, expensive, and laborious process. This segmentation is also limited in detecting touching or overlapping nuclei and considers any overlapping nuclei as a single nucleus. This is due to low contrast images, occultation, and diversity of cell nuclei. This work proposes an automated overlapping nuclei segmentation model with a U-net and an intensity-based contour technique in order to address these issues. In a previous study, a U-net segmentation model was trained with synthetic data, which was generated using a GAN model, where a small number of histopathology data was used to generate the synthetic data. This reduced the data limitation and need for nuclei annotation in the deep learning model. Initially in this study, the overlapping nuclei regions were not considered for segmentation by the network. Hence, an intensity-based contour line is proposed to separate overlapping nuclei regions. The distance transformation is utilized to define the center of each nucleus. The identification of local minima followed by intensity-based gradient weights is applied to obtain the final segmentation line of overlapping nuclei. The boundary of the overlapping nuclei is refined, and noise is removed in order to clearly describe each nuclei region. The proposed method results in 91.6% accuracy in separating the overlapping nuclei compared to other existing methods.
Optical Flow with Learning Feature for Deformable Medical Image Registration

Deformable medical image registration plays a vital role in medical image applications, such as placing different temporal images at the same time point or different modality images into the same coordinate system. Various strategies have been developed to satisfy the increasing needs of deformable medical image registration. One popular registration method is estimating the displacement field by computing the optical flow between two images. The motion field (flow field) is computed based on either gray-value or handcrafted descriptors such as the scale-invariant feature transform (SIFT). These methods assume that illumination is constant between images. However, medical images may not always satisfy this assumption. In this study, we propose a metric learning-based motion estimation method called Siamese Flow for deformable medical image registration. We train metric learners using a Siamese network, which produces an image patch descriptor that guarantees a smaller feature distance in two similar anatomical structures and a larger feature distance in two dissimilar anatomical structures. In the proposed registration framework, the flow field is computed based on such features and is close to the real deformation field due to the excellent feature representation ability of the Siamese network. Experimental results demonstrate that the proposed method outperforms the Demons, SIFT Flow, Elastix, and VoxelMorph networks regarding registration accuracy and robustness, particularly with large deformations.
A Lightweight Deep Gesture Recognition Model on Embedded Computing Platform

With the development of big data and highperformance hardware devices, the huge structure of neural network can be fitted efficiently and strongly, therefore, it is widely used as a common algorithm in the field of gesture recognition. However, in the application of low-cost embedded devices, the deployment of model is still relatively difficult. In this paper, in order to mitigate the mismatch between the powerful computing power required by the model and the embedded hardware computing resources, we use the channel pruning method to compact the model structure and reduce the parameters of model on the basis of 3D-SqueezeNet and 3D-MobileNetV2. Without losing accuracy, we reduce the parameters by 75%. We deploy the pruned gesture recognition model on NVIDIA® Jetson Nano™. In our experimental analysis, we achieve to recognize hand gestures containing three gesture-phonemes with an accuracy of 96% (in 100 classes). The speed of model inferring maintains at up to 33fps. Our code and pretrained models are publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Framelet transform and fuzzy clustering-based intelligent technique for speckle noise removal in ultrasound images

Purpose Speckle noise removal in ultrasound images is one of the important tasks in biomedical-imaging applications. Many filtering -based despeckling methods are discussed in many existing works. Two-dimensional (2-D) transforms are also used enormously for the reduction of speckle noise in ultrasound medical images. In recent years, many soft computing-based intelligent techniques have been applied to noise removal and segmentation techniques. However, there is a requirement to improve the accuracy of despeckling using hybrid approaches. Design/methodology/approach The work focuses on double-bank anatomy with framelet transform combined with Gaussian filter (GF) and also consists of a fuzzy kind of clustering approach for despeckling ultrasound medical images. The presented transform efficiently rejects the speckle noise based on the gray scale relative thresholding where the directional filter group (DFB) preserves the edge information. Findings The proposed approach is evaluated by different performance indicators such as the mean square error (MSE), peak signal to noise ratio (PSNR) speckle suppression index (SSI), mean structural similarity and the edge preservation index (EPI) accordingly. It is found that the proposed methodology is superior in terms of all the above performance indicators. Originality/value Fuzzy kind clustering methods have been proved to be better than the conventional threshold methods for noise dismissal. The algorithm gives a reconcilable development as compared to other modern speckle reduction procedures, as it preserves the geometric features even after the noise dismissal.
Learning Performance Facilitation in a Sensor-Based Intelligent Classroom

The paper presents an analysis of the recently published scientific papers about the application of wireless sensor networks and wearable sensors for the organization of intelligent educational environments, which is a predisposing feature for the learning performance improvement of the students. A conceptual model of a sensor-based intelligent classroom is proposed that consists of a sensor set for monitoring some physiological parameters of students and for controlling the classroom conditions. The sensor changes are used as indicators for proposing learning objects with a suitable level of complexity. A prototype wireless sensor network based on LoRa, which relies on a custom private data communication protocol, is designed, implemented and tested. We also describe briefly the practical design and implementation of two types of nodes (communication and sensor nodes) used within the network for sensor data gathering, filtering and transmission.
Generalized amplitude-phase demodulation for isotropic image decomposition and analysis

It is well known that the amplitude-phase demodulation based on Hilbert transform (HT) plays a fundamental role in signal processing. Also, the Bedrosian principle (BP), the key to HT, determines the exponential form of the complex signals out of real ones. In this paper, the novel BP on 2D HT was proposed in details. At first, a few 2D BPs would be revisited with some new interpretation for 2D time-frequency analysis. Then the new generalized BP (GBP) with new spirit of Riesz transform (RT) were derived with novel features. On one hand, various BPs would be employed jointly for image pattern analysis via our particular design. On the other hand, various π/2 phase shifting operations for the multiplicative signal would be systematically performed in 2D images through BPs so that we could obtain more rational image features. Furthermore, the amplitude-phase demodulation method based on GBP for the isotropic image decomposition would be presented as well. The theoretical analysis and comparison experiments on composite and natural images have been presented to demonstrate the efficiency of our contributions in time-frequency analysis and pattern recognition along with feature extraction.
Fuzzy Sets in the Problems of Assessing the Quality of Mobile Applications

The article discusses the issues of building a base of fuzzy inference rules for solving problems of assessing the quality of mobile applications. The solution to this problem is based on the use of the main provisions of the PNST277-2018 standard &#x201C;Comparative tests of mobile applications for smartphones&#x201D;. It is shown that the hierarchical organization of software quality models is a prerequisite for creating a base of rules for fuzzy inference. A general algebraic algorithm for constructing the structure of a mobile application quality model is presented. An example of describing fuzzy rules for the implementation of the problem of assessing the characteristics of the maintainability of a mobile application is given. It is proposed to use the Fussy Logic Toolbox MatLab package as a tool for creating a fuzzy expert system to support the process of comparative research of the quality of mobile applications.
Existence and consequences of dormant features: Evidence from L2 acquisition of Chinese object ellipsis

The current study investigates second language acquisition of Chinese object ellipsis to probe the development of features transferred from learners’ native language without robust confirming or disconfirming evidence in the second language (L2) input. It is argued that Chinese allows object ellipsis licensed by a verb with a [V Case ] feature but not by a verb with a [V non-Case ] feature. In contrast, Korean allows object ellipsis to be licensed by both types of verbs, whilst English prohibits both. An acceptability judgement task was conducted among first language (L1) English and L1 Korean L2 Chinese learners from elementary to advanced levels, with the results showing that the [V non-Case ] feature was assembled in the Chinese grammars of English and Korean elementary L2 learners; however, it gradually lost its vigour and licensing power for object ellipsis in intermediate L2 grammars and was successfully removed from licensing object ellipsis in advanced L2 grammars. These findings support predictions by Yuan regarding a feature’s dormant status and modify Yuan’s predictions regarding a dormant feature’s consequences.
Towards a Comprehensive Cohort Visualization of Patients with Inflammatory Bowel Disease

This paper reports on a joint project with medical experts on inflammatory bowel disease (IBD). Patients suffering from IBD, e.g. Crohn's disease or ulcerative colitis, do not have a reduced life expectancy and disease progressions easily span several decades. We designed a visualization to highlight information that is vital for comparing patients and progressions, especially with respect to the treatments administered over the years. Medical experts can interactively determine the amount of information displayed and can synchronize the progressions to the beginning of certain treatments and medications. While the visualization was designed in close collaboration with IBD experts, we additionally evaluated our approach with 35 participants to ensure good usability and accessibility. The paper also highlights the future work on similarity definition and additional visual features in this on-going project.
Hacking the Medium: Shaping the creative constraints of network architectures in multiplicitous media artworks

In this article we discuss our practice-based research into effective architectures and creative workflows for creatively coding massive multidevice light and sound installation artworks. We discuss the challenges of working with networked multidevice systems and illustrate these challenges with examples of the type of content that one may wish to display on these systems. We then consider how the structuring of a creative framework can strongly influence how an artist approaches the creation of such work, eases the process of creative search and discovery and reduces the time cost and risk of solving technical problems of architecture design. We take a design perspective on how to make effective creativity support tools and also consider a holistic perspective on creative practice that attempts to satisfy creative ideals grounded in the reality of practice.
A Hybrid Framework to Improve Data Security in Cloud Computing

Cloud computing is defined as the management and provision of resources, software, applications, and information as services over the internet. Numerous organizations are adopting cloud services because of the economy and technology changes. Over the past year, cloud computing has made life easier for other people or organizations by allowing people to have access to information from anywhere in the world through the internet. However, cloud computing introduces a series of security concerns, which data security is a major concern. Data security is basically linked with any security in the cloud. If the network or infrastructure security is compromised, the possibility there is a possibility of data being affected. Data security has three important principles that are Confidentiality, Integrity, and Availability (CIA) that needs to always be maintained. Understanding the security concerns in cloud computing is an important requirement for those who are planning to move to the cloud solution. Thus, this paper aims to investigate and develop a hybrid data security framework to mitigate data security challenges in the cloud, focusing on legal frameworks such as POPIA and GDPR. A survey was conducted to investigate which techniques are more effective in protecting data and the evaluation ensured that CIA triads are covered. The limitation on the existing techniques is that they address only one principle or two, but not all three at the same time. The best technique among the others will be used integrated when designing the proposed framework in order to address CIA. Having more than one security technique to address data security will improve the security in the cloud and will also bring more business to cloud service providers when customers see the security in data enhanced.
Blockchain Address Transparency with DNS

Blockchain systems have gained popularity in the last few years. The services they provide go from monetary transactions to digital forms of identification and many others. However, blockchain addresses are long numbers that are not adequate for human use, as they are hard to remember and manage. This work considers the use of the Domain Name System (DNS) as a means for users of any blockchain to use domain names to identify blockchain addresses. Similarly to the way DNS stores mappings of IP addresses to domain names, we offer a solution that allows it to also keep mappings of blockchain addresses to domain names. Our solution, Domain Name System for Blockchain-type Addresses (DNSBA), allows any blockchain address to be easily substituted by an associated domain name.
CNN Accelerator with Non-Blocking Network Design

In this paper, we designed a new hardware architecture that uses non-blocking network for accelerating the convolutional neural network (CNN). Unlike many other CNN accelerator which only capable of supporting a specific type of network model, by making use of the rearrangeability of non-blocking network, we can provide high flexibility and high parallelism. We successfully implemented our CNN accelerator on Xilinx Virtex UltraScale+ FPGA VCU128 Evaluation Kit and evaluated it by running CNN model, LeNet-5.
A platform for developing and fine tuning adaptive 3D navigation techniques for the immersive web

Navigating through a virtual environment is one of the major user tasks in the 3D web. Although hundreds of interaction techniques have been proposed to navigate through 3D scenes in desktop, mobile and VR headset systems, 3D navigation still poses a high entry barrier for many potential users. In this paper we discuss the design and implementation of a test platform to facilitate the creation and fine-tuning of interaction techniques for 3D navigation. We support the most common navigation metaphors (walking, flying, teleportation). The key idea is to let developers specify, at runtime, the exact mapping between user actions and virtual camera changes, for any of the supported metaphors. We demonstrate through many examples how this method can be used to adapt the navigation techniques to various people including persons with no previous 3D navigation skills, elderly people, and people with disabilities.
Anomaly detection and missing data imputation in building energy data for automated data pre-processing

Abstract A new trend in building automation is the implementation of smart energy management systems to measure and control building systems without a need for decision-making by human operators. Artificial intelligence can optimize these systems by predicting future demand to make informed decisions about how to efficiently operate individual equipment. These machine learning algorithms use historical data to learn demand trends and require high quality datasets in order to make accurate predictions. But because of issues with data transmission or sensor errors, real world datasets often contain outliers or have data missing. In most research settings, these values can be simply omitted, but in practice, anomalies compromise the automation system’s prediction accuracy, rendering it unable to maximize energy savings. This study explores different machine learning algorithms for anomaly detection for automatically pre-processing incoming data using a case study on an actual electrical demand in a hospital building in Japan, namely cluster-based techniques such as k-means clustering and neural network-based approaches such as the autoencoder. Once anomalies were identified, the missing data was filled with prediction values from a deep neural network model. The newly composed data was then evaluated based on detection accuracy, prediction accuracy and training time. The proposed method of processing anomaly values allows the prediction model to process collected data without interruption, and shows similar predictive accuracy as manually processing the data. These predictions allow energy systems to optimize HVAC equipment control, increasing energy savings and reducing peak building loads.
Determination of the optimal distribution for loading cargo vehicles using the IBM Qiskit VQE algorithm

This paper presents a solution of the problem of determining the optimal configuration of maximizing the loading of cargo vehicles –subject to appropriate constraints – using quantum algorithms that was proposed at the Airbus Quantum Computing Challenge. Two constrains are considered: Maximum Volume available, and Maximum possible Weight. The solution uses the Variational Quantum Eigensolver and extends a previous proposal to solve the knapsack problem enlarging the Hamiltonian employed. The solution developed was implemented in IBM Qiskit environment.
List scheduling algorithm of improved priority with considering load balance

Aiming at such problems as the load imbalance and low efficiency of DAG task scheduling in the current cloud computing environment, a list scheduling algorithm of improved priority with considering load balance (LS-IPLB) was proposed. In the algorithm, the state parameter change of virtual machine in the cloud computing cluster was abstracted into the parameter vector variation in the space, and the real-time measurement method for the load balance of cloud computing cluster was given, which was taken as an important parameter to select the weight of virtual machine. At the same time, the task priority was calculated through taking the task execution cost, task output value and communication cost between the tasks as the parameters. In addition, the task duplication strategy was used in the task scheduling to further optimize the scheduling process. The results show that the LS-IPLB algorithm can effectively shorten the completion time of DAG task graph, and can achieve good load balance.
Forensic Phonetics

This chapter introduces the application of phonetics to various legal situations involving speech. In these situations, the identity of a speaker is in question or the linguistic content is disputed. Forensic phonetics focuses on the differences between members of a speech community resulting from physiological differences as well as choices under the speaker’s control (whether conscious or not). While some of this idiosyncrasy is found at the phonological level, the most useful speaker-discriminatory information is often encoded in the fine phonetic detail. It is where such patterns show a good degree of within-speaker consistency and between-speaker difference that the examination of a particular phonetic feature becomes useful in forensic analysis. This chapter considers the place of analysis by ear (by both expert and naïve listeners), the quantitative phonetic analysis of the acoustic signal, as well as automatic methods of analysis. It also considers how best to arrive at a scientifically defensible probabilistic conclusion, and how such conclusions may be communicated in a legal context. Best practice for the teaching and learning of forensic phonetics is also discussed.