Reviewerly/exHarmony huggingface dataset:
Data Sampling Methods for Analyzing Publishers Conduct from Highly Imbalanced Dataset in Web Advertising

Due to the involvement of a huge amount of expenditure, the Pay-per-Click digital advertising model is affected by click fraud. The rare presence of illegitimate publishers than the legitimates makes the class distribution skewed which in turn biases the learning model towards the majority class. Moreover, the dynamic disguise activities of publishers make the task harder to investigate the actual status labels of the publishers, resulting in less accurate predictions. However, features reported in the literature detect click fraud based on analyzing the publishers’ conduct but are unable to cope with the changing behavior of publishers. Therefore, we proposed a framework addressing two challenging issues: a) eight new conditional features are proposed to better capture the dynamics of click fraud behavior by merging two or more attributes over publishers on finer-grained time intervals. b) Examined the impact of data sampling methods on learners’ performance for the effective identification of publisher’s activity. The performance of the classification algorithms is assessed using average precision, recall, and f-measure and validated using 10-fold cross-validation. Experimental results illustrate the potential of the proposed features over a balanced dataset in improving the average precision of the learners in discriminating the fraudsters among genuine publishers.
Trimming outliers using trees

Prediction of building energy consumption using machine learning models has been a focal point of research for decades. However, some causes of forecast errors, particularly data quality, have not been adequately addressed, which may affect the accuracy of forecasting models and subsequent energy management. To solve the issue of data quality, a classifier that can automatically detect time series anomalies is the goal that researchers have been pursuing. Large-scale Energy Anomaly Detection (LEAD), a community competition hosted on the Kaggle platform, was created for this purpose as well as to provide a foundation for benchmarking solutions. In this competition, 200 energy time series worldwide with labeled anomalies were provided to train a classification model to predict anomalies of another 206 unseen time series. The proposed winning solution is a tree-based supervised learning anomaly classifier with ROC-AUC score as high as 0.9866 on private leaderboard. This article describes and analyzes in depth a variety of commonly employed techniques for improving the classification model. Among these strategies, feature engineering requires the most effort and dominates all other techniques; value-changing features that can represent the level of time-series variation have a particularly positive impact. Besides, the classification accuracy of solutions in the competition can serve as a benchmark for future research on supervised learning of energy anomaly detection.
Deep learning-based computed tomographic image super-resolution via wavelet embedding

Effort to realize high-resolution medical images have been made steadily. In particular, super resolution technology based on deep learning is making excellent achievement in computer vision recently. In this study, we developed a model that can dramatically increase the spatial resolution of medical images using deep learning technology, and we try to demonstrate the superiority of proposed model by analyzing it quantitatively. We simulated the computed tomography images with various detector pixel size and tried to restore the low-resolution image to high resolution image. We set the pixel size to 0.5, 0.8 and 1 mm2 for low resolution image and the high-resolution image, which were used for ground truth, was simulated with 0.25 mm2 pixel size. The deep learning model that we used was a fully convolution neural network based on residual structure. The result image demonstrated that proposed super resolution convolution neural network improve image resolution significantly. We also confirmed that PSNR and MTF was improved up to 38% and 65% respectively. The quality of the prediction image is not significantly different depending on the quality of the input image. In addition, the proposed technique not only increases image resolution but also has some effect on noise reduction. In conclusion, we developed deep learning architectures for improving image resolution of computed tomography images. We quantitatively confirmed that the proposed technique effectively improves image resolution without distorting the anatomical structures.
FIRE: knowledge-enhanced recommendation with feature interaction and intent-aware attention networks

To solve the information overload issue and enhance the user experience of various web applications, recommender systems aim to better model user interests and preferences. Knowledge Graphs (KGs), consisting of real-world objective facts and fruitful entities, play a vital role in recommender systems. Recently, a technological trend has been to develop end-to-end Graph Neural Networks (GNNs)-based knowledge-aware recommendation (a.k.a., Knowledge Graph Recommendation, KGR) models. Unfortunately, current GNNs-based KGR approaches focus on how to capture high-order feature information on KGs while neglecting the following two crucial limitations: 1) The explicitly high-order feature interaction and fusion mechanism and 2) The valid user intent modelling mechanism. As such, these issues lead to insufficient user/item representation learning capability and unsatisfactory KGR performance. In this work, we present a novel Knowledge-enhanced Re commendation with F eature I nteraction and Intent-aware Attention Networks (FIRE) to address the latent intent modelling and high-order feature interaction deficiencies ignored by existing KGR methods. Based on the prototype user/item representation learning leveraging the GNNs-based approach, our model offers the following major improvements: One is the innovative use of Convolutional Neural Networks (CNNs) that perform vertical convolutional (a.k.a., bit-level convolutional) and horizontal convolutional (a.k.a., vector-level convolutional) processes to model multi-granular high-order feature interactions to enhance item-side representation learning. Another is to model users' latent intent factors by utilizing a two-level attention mechanism (i.e., node- and intent-level attention mechanism) to enhance user-side representation learning. Extensive experiments on three KGs domain public datasets demonstrate that our method outperforms the existing state-of-the-art baseline. Last but not least, numerous ablation- and model studies demystify the working mechanism and elucidate the plausibility of the proposed model.
Software Implementation and Benchmarking of TinyJAMBU on Programmable Logic Controllers

As of March 2021, TinyJAMBU is one of the 12 finalists in the National Institute of Standards and Technology (NIST) competition launched to standardize lightweight cryptographic algorithms, suitable for resource-constrain devices (e.g. RFID sensors, microcontrollers, embedded devices, industrial controllers). This paper reports on the software implementation details of TinyJAMBU on a Siemens S7–1200 Programmable Logic Controller. We evaluate the execution speed and memory requirements of each variant of the algorithm on this industrial controller.
Emotion Recognition from Microblog Managing Emoticon with Text and Classifying using 1D CNN

Microblog, an online-based broadcast medium, is a widely used forum for people to share their thoughts and opinions.Recently, Emotion Recognition (ER) from microblogs is an inspiring research topic in diverse areas.In the machine learning domain, automatic emotion recognition from microblogs is a challenging task, especially, for better outcomes considering diverse content.Emoticon becomes very common in the text of microblogs as it reinforces the meaning of content.This study proposes an emotion recognition scheme considering both the texts and emoticons from microblog data.Emoticons are considered unique expressions of the users' emotions and can be changed by the proper emotional words.The succession of emoticons appearing in the microblog data is preserved and a 1D Convolutional Neural Network (CNN) is employed for emotion classification.The experimental result shows that the proposed emotion recognition scheme outperforms the other existing methods while tested on Twitter data.
Speeding Up Optimization-based Motion Planning through Deep Learning

Planning collision-free motions for robots with many degrees of freedom is challenging in environments with complex obstacle geometries. Recent work introduced the idea of speeding up the planning by encoding prior experience of successful motion plans in a neural network. However, this "neural motion planning" did not scale to complex robots in unseen 3D environments as needed for real-world applications. Here, we introduce "basis point set", well-known in computer vision, to neural motion planning as a modern compact environment encoding enabling efficient supervised training networks that generalize well over diverse 3D worlds. Combined with a new elaborate training scheme, we reach a planning success rate of 100%. We use the network to predict an educated initial guess for an optimization-based planner (OMP), which quickly converges to a feasible solution, massively outperforming random multi-starts when tested on previously unseen environments. For the DLR humanoid Agile Justin with 19DoF and in challenging obstacle environments, optimal paths can be generated in 200ms using only a single CPU core. We also show a first successful real-world experiment based on a high-resolution world model from an integrated 3D sensor.
RAGO: Recurrent Graph Optimizer For Multiple Rotation Averaging

This paper proposes a deep recurrent Rotation Averaging Graph Optimizer (RAGO) for Multiple Rotation Averaging (MRA). Conventional optimization-based methods usually fail to produce accurate results due to corrupted and noisy relative measurements. Recent learning-based approaches regard MRA as a regression problem, while these methods are sensitive to initialization due to the gauge freedom problem. To handle these problems, we propose a learnable iterative graph optimizer minimizing a gauge-invariant cost function with an edge rectification strategy to mitigate the effect of inaccurate measurements. Our graph optimizer iteratively refines the global camera rotations by minimizing each node's single rotation objective function. Besides, our approach iteratively rectifies relative rotations to make them more consistent with the current camera orientations and observed relative rotations. Furthermore, we employ a gated recurrent unit to improve the result by tracing the temporal information of the cost graph. Our framework is a real-time learning-to-optimize rotation averaging graph optimizer with a tiny size deployed for real-world applications. RAGO outperforms previous traditional and deep methods on real-world and synthetic datasets. The code is available at https://github.com/sfu-gruvi-3dv/RAGO
Back to the Roots: Linking User Stories to Requirements Elicitation Conversations

Pre-requirements specification (pre-RS) traceability focuses on tracing requirements back to their sources. In comparison with post-RS traceability, pre-RS traceability is an under-explored area of research. Likely reasons for the limited studies are the scarcity of pre-RS resources, e.g., recorded requirements elicitation conversations such as interviews or workshops, and the challenges of linking requirements to informal, unstructured text. Building on the increasing use of digital communication tools that allow the recording and transcription of conversations, we explore the opportunity of linking requirements to the transcript of a requirements elicitation conversation. We introduce TRACE2CoNV, a prototype tool that aims at tracing user story requirements back to the relevant speaker turns in a conversation. TRACE2CoNV makes use of NLP techniques to determine the relevant speaker turns. As an initial validation, we take automatically generated transcripts from real-world requirements conversations, and we assess the effectiveness of TRACE2CoNV in supporting the process of identifying additional context for the requirements. The validation serves as a formative evaluation that guides the evolution of TRACE2CoNV and as a inspiration for future research in the field of conversational RE.
Reshaping the Future of Recruitment through Talent Reputation and Verifiable Credentials using Blockchain Technology

The emerging Web3 economy and the Blockchain's massive adoption in 2021 shape the industry's future driven by distributed ledgers and smart contracts. Blockchain technology enables traceability, fraud prevention, and faster settlement of business processes, including talent recruitment. The current recruitment requires Applicant Tracking System (ATS), Artificial Intelligence (AI), and third-party HR recruitment agencies for sorting and verifying job application documents in every recruitment cycle, which are ineffective and inefficient. This paper proposes a model to discover and recruit reputable talent by leveraging blockchain technology, which enables trustless networks and allows entities to conduct transactions without mutual trust, called the recruitment system. This paper also seeks to enhance the relationship among recruitment entities by adopting a transparent and incorruptible decentralized autonomous organization (DAO) model powered by blockchain technology.
A Digital Twin Framework for Telesurgery in the Presence of Varying Network Quality of Service

Remote telesurgery can enable expert surgeons to operate on patients in distant or underserved locales. However, network instability and delays hamper long-distance communication. To address this, we explore how a "digital twin," a 3D simulator that actively mirrors a real environment, can be applied to telesurgery. We focus on the Fundamentals of Laparoscopic Surgery peg transfer surgical training task. We present a framework that enables a teleoperator to perform this task over unstable or low-bandwidth communication channels using a digital twin. The surgeon remotely teleoperates the robot in our simulator, which abstracts their motions into commands and transmits them to the real robot for semi-autonomous execution. The system executes the transfer and then sends the real state of the pegboard back to the simulator. We present experiments that demonstrate that the operation of each portion of the framework in isolation maintains a high task success rate, and that the success rate of the digital twin framework is robust to network transmission instability and delays.
Generating Natural Images with Direct Patch Distributions Matching

Many traditional computer vision algorithms generate realistic images by requiring that each patch in the generated image be similar to a patch in a training image and vice versa. Recently, this classical approach has been replaced by adversarial training with a patch discriminator. The adversarial approach avoids the computational burden of finding nearest neighbors of patches but often requires very long training times and may fail to match the distribution of patches. In this paper we leverage the Sliced Wasserstein Distance to develop an algorithm that explicitly and efficiently minimizes the distance between patch distributions in two images. Our method is conceptually simple, requires no training and can be implemented in a few lines of codes. On a number of image generation tasks we show that our results are often superior to single-image-GANs, and can generate high quality images in a few seconds. Our implementation is publicly available at https://github.com/ariel415el/GPDM .
Operational entanglement detection based on $$\Lambda $$-moments

We introduce $\Lambda$-moments with respect to any positive map $\Lambda$. We show that these $\Lambda$-moments can effectively characterize the entanglement of unknown quantum states without theirs prior reconstructions. Based on $\Lambda$-moments necessary and sufficient separability criteria, as well as necessary optimized criteria are presented, which include the ones in [\href{https://link.aps.org/doi/10.1103/PhysRevLett.127.060504}{Phys. Rev. Lett. \textbf{127}, 060504 (2021)}] as special cases. Detailed example is given to show that our criteria can detect bound entanglement that can not be identified by positive partial transpose criterion, with the explicit measurement operators to experimentally measure the corresponding $\Lambda$-moments.
Artificial Intelligence for Breast US

US is a widely available, commonly used, and indispensable imaging modality for breast evaluation. It is often the primary imaging modality for the detection and diagnosis of breast cancer in low-resource settings. In addition, it is frequently employed as a supplemental screening tool via either whole breast handheld US or automated breast US among women with dense breasts. In recent years, a variety of artificial intelligence systems have been developed to assist radiologists with the detection and diagnosis of breast lesions on US. This article reviews the background and evidence supporting the use of artificial intelligence tools for breast US, describes implementation strategies and impact on clinical workflow, and discusses potential emerging roles and future directions.
Multi-Objective Evolutionary Architecture Search for Parameterized Quantum Circuits

Recent work on hybrid quantum-classical machine learning systems has demonstrated success in utilizing parameterized quantum circuits (PQCs) to solve the challenging reinforcement learning (RL) tasks, with provable learning advantages over classical systems, e.g., deep neural networks. While existing work demonstrates and exploits the strength of PQC-based models, the design choices of PQC architectures and the interactions between different quantum circuits on learning tasks are generally underexplored. In this work, we introduce a Multi-objective Evolutionary Architecture Search framework for parameterized quantum circuits (MEAS-PQC), which uses a multi-objective genetic algorithm with quantum-specific configurations to perform efficient searching of optimal PQC architectures. Experimental results show that our method can find architectures that have superior learning performance on three benchmark RL tasks, and are also optimized for additional objectives including reductions in quantum noise and model size. Further analysis of patterns and probability distributions of quantum operations helps identify performance-critical design choices of hybrid quantum-classical learning systems.
Variational Approach for Joint Kidney Segmentation and Registration from DCE-MRI Using Fuzzy Clustering with Shape Priors

The dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) technique has great potential in the diagnosis, therapy, and follow-up of patients with chronic kidney disease (CKD). Towards that end, precise kidney segmentation from DCE-MRI data becomes a prerequisite processing step. Exploiting the useful information about the kidney’s shape in this step mandates a registration operation beforehand to relate the shape model coordinates to those of the image to be segmented. Imprecise alignment of the shape model induces errors in the segmentation results. In this paper, we propose a new variational formulation to jointly segment and register DCE-MRI kidney images based on fuzzy c-means clustering embedded within a level-set (LSet) method. The image pixels’ fuzzy memberships and the spatial registration parameters are simultaneously updated in each evolution step to direct the LSet contour toward the target kidney. Results on real medical datasets of 45 subjects demonstrate the superior performance of the proposed approach, reporting a Dice similarity coefficient of 0.94 ± 0.03, Intersection-over-Union of 0.89 ± 0.05, and 2.2 ± 2.3 in 95-percentile of Hausdorff distance. Extensive experiments show that our approach outperforms several state-of-the-art LSet-based methods as well as two UNet-based deep neural models trained for the same task in terms of accuracy and consistency.
Rapid and robust endoscopic content area estimation: a lean GPU-based pipeline and curated benchmark dataset

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Distributed Kalman filtering over sensor networks with fading measurements and random link failures

This paper investigates the distributed state estimation problem for a linear time-invariant system characterized by fading measurements and random link failures. We assume that the fading effect of the measurements occurs slowly. Additionally, communication failures between sensors can affect the state estimation performance. To this end, we propose a Kalman filtering algorithm composed of a structural data fusion stage and a signal date fusion stage. The number of communications can be decreased by executing signal data fusion when a global estimate is required. Then, we investigate the stability conditions for the proposed distributed approach. Furthermore, we analyze the mismatch between the estimation generated by the proposed distributed algorithm and that obtained by the centralized Kalman filter. Lastly, numerical results verify the feasibility of the proposed distributed method.
Captured by Digitization

While digitization claims to provide efficiency, accessibility, expansion, speediness, and profit accumulation, it is actually colonizing every human activity. It has even become a purpose in itself. In this essay we focus on the digitization of legal practices and contents. We describe what digitization encompasses, how digitalization processes work, and to what extent they are able to replace juristic processes and produce legal outcomes. We are inspired by Walter Benjamin's essay on the influence of mechanical reproduction of the works of Art. Parallel to Benjamin's work on Art, we will analyze Law and the consequences of innovations such as mechanical mass (re)production and computerized digitization.
Three-wave mixing traveling-wave parametric amplifier with periodic variation of the circuit parameters

We report on the implementation of a near-quantum-limited, traveling-wave parametric amplifier that uses three-wave mixing (3WM). To favor amplification by 3WM, we use superconducting nonlinear asymmetric inductive element (SNAIL) loops, biased with a dc magnetic flux. In addition, we equip the device with dispersion engineering features to create a stopband at the second harmonic of the pump and suppress the propagation of the higher harmonics that otherwise degrade the amplification. With a chain of 440 SNAILs, the amplifier provides up to 20 dB gain and a 3-dB bandwidth of 1 GHz. The added noise by the amplifier is found to be less than one photon.