Reviewerly/exHarmony huggingface dataset:
Pattern discovery and disentanglement on relational datasets

Abstract Machine Learning has made impressive advances in many applications akin to human cognition for discernment. However, success has been limited in the areas of relational datasets, particularly for data with low volume, imbalanced groups, and mislabeled cases, with outputs that typically lack transparency and interpretability. The difficulties arise from the subtle overlapping and entanglement of functional and statistical relations at the source level. Hence, we have developed Pattern Discovery and Disentanglement System (PDD), which is able to discover explicit patterns from the data with various sizes, imbalanced groups, and screen out anomalies. We present herein four case studies on biomedical datasets to substantiate the efficacy of PDD. It improves prediction accuracy and facilitates transparent interpretation of discovered knowledge in an explicit representation framework PDD Knowledge Base that links the sources, the patterns, and individual patients. Hence, PDD promises broad and ground-breaking applications in genomic and biomedical machine learning.
Value and activity-based orientations of university students: The choice between safety and innovativeness

Introduction. The study addresses the issues of value and activity-based orientations in order to understand and explore personal and professional development processes in in the current higher education context with the main focus on innovativeness. The study aims to reveal the specifics of the relationship between value and activity-based orientations of university students in terms of their choice between safety and innovativeness. Materials and Methods. The research methodology involves questionnaires grouped according to three research vectors: 1) university students’ cultural value orientations (R. Inglehart’s Survey) and individual value preferences (Schwartz Value Survey); 2) activity-based orientations (psychological characteristics of activity) were evaluated using the authors’ scale of personal readiness for activity based on E. Y. Mandrikova, D. A. Leontiev and E. N. Osin, E. Diener’s questionnaires; c) to evaluate university students’ innovative orientations, N. M. Lebedeva and A. N. Tatarko’s technique was applied. The study was conducted in Tomsk universities and involved 260 students. Statistical analysis included descriptive statistics, factor analysis, and regression analysis. Results. The factor and regression analyses revealed that choosing the ‘innovativeness vector’ by university students in the context of their personal and professional development is determined by personal qualities associated with innovativeness: creativity, risk for success and orientation to the future. Choosing the ‘safety vector’ is more inherent in modern university students and is determined by the degree of their adherence to traditional values and by their personal readiness for activity, the core of which is purposefulness and consistency in achieving the life goals. Conclusions. The results obtained indicate the necessity for developing appropriate programmes of psychological support for university students in their personal and professional development processes in order to involve them in research, innovative and entrepreneurial activities.
History-Augmented Collaborative Filtering for Financial Recommendations

In many businesses, and particularly in finance, the behavior of a client might drastically change over time. It is consequently crucial for recommender systems used in such environments to be able to adapt to these changes. In this study, we propose a novel collaborative filtering algorithm that captures the temporal context of a user-item interaction through the users' and items' recent interaction histories to provide dynamic recommendations. The algorithm, designed with issues specific to the financial world in mind, uses a custom neural network architecture that tackles the non-stationarity of users' and items' behaviors. The performance and properties of the algorithm are monitored in a series of experiments on a G10 bond request for quotation proprietary database from BNP Paribas Corporate and Institutional Banking.
Local Interactions That Contribute Minimal Frustration Determine Foldability

Earlier experiments suggest that the evolutionary information (conservation and coevolution) encoded in protein sequences is necessary and sufficient to specify the fold of a protein family. However, there is no computational work to quantify the effect of such evolutionary information on the folding process. Here we explore the role of early folding steps for sequences designed using coevolution and conservation through a combination of computational and experimental methods. We simulated a repertoire of native and designed WW domain sequences to analyze early local contact formation and found that the N-terminal β-hairpin turn would not form correctly due to strong non-native local contacts in unfoldable sequences. Through a maximum likelihood approach, we identified five local contacts that play a critical role in folding, suggesting that a small subset of amino acid pairs can be used to solve the "needle in the haystack" problem to design foldable sequences. Thus, using the contact probability of those five local contacts that form during the early stage of folding, we built a classification model that predicts the foldability of a WW sequence with 81% accuracy. This classification model was used to redesign WW domain sequences that could not fold due to frustration and make them foldable by introducing a few mutations that led to the stabilization of these critical local contacts. The experimental analysis shows that a redesigned sequence folds and binds to polyproline peptides with a similar affinity as those observed for native WW domains. Overall, our analysis shows that evolutionary-designed sequences should not only satisfy the folding stability but also ensure a minimally frustrated folding landscape.
Observational/hydrographic data of the South Atlantic Ocean published as LOD

This article describes the publication of occurrences of Southern Elephant Seals Mirounga leonina (Linnaeus, 1758) as Linked Open Data in two environments (marine and coastal). The data constitutes hydrographic measurements of instrumented animals and observation data collected during censuses between 1990 and 2017. The data scheme is based on the previously developed ontology BiGe-Onto and the new version of the Semantic Sensor Network ontology (SSN). We introduce the network of ontologies used to organize the data and the transformation process to publish the dataset. In the use case, we develop an application to access and analyze the dataset. The linked open dataset and the related visualization tool turned data into a resource that can be located by the international community and thus increase the commitment to its sustainability. The data, coming from Península Valdés (UNESCO World Heritage), is available for interdisciplinary studies of management and conservation of marine and coastal protected areas which demand reliable and updated data.
VinVL: Revisiting Visual Representations in Vision-Language Models

This paper presents a detailed study of improving visual representations for vision language (VL) tasks and develops an improved object detection model to provide object-centric representations of images. Compared to the most widely used \emph{bottom-up and top-down} model \cite{anderson2018bottom}, the new model is bigger, better-designed for VL tasks, and pre-trained on much larger training corpora that combine multiple public annotated object detection datasets. Therefore, it can generate representations of a richer collection of visual objects and concepts. While previous VL research focuses mainly on improving the vision-language fusion model and leaves the object detection model improvement untouched, we show that visual features matter significantly in VL models. In our experiments we feed the visual features generated by the new object detection model into a Transformer-based VL fusion model \oscar \cite{li2020oscar}, and utilize an improved approach \short\ to pre-train the VL model and fine-tune it on a wide range of downstream VL tasks. Our results show that the new visual features significantly improve the performance across all VL tasks, creating new state-of-the-art results on seven public benchmarks. We will release the new object detection model to public.
A Multi-objective Tracking Task Assignment Algorithm Based on Game Theory

Abstract Aiming at the relationship between competition and cooperation among sensors in multi-target tracking task allocation, this paper proposes a game theory-based method to allocate sensor resources for multi-target tracking tasks. The dissertation first establishes a target tracking model and designs a game theory payment function based on the model. Then a multi-sensor multi-target tracking task allocation game model is designed. Finally, a multi-target tracking task allocation algorithm based on game theory is given. Compared with other allocation algorithms, the average tracking error of the proposed allocation algorithm is reduced from 68.5630m to 19.7601m through experiments.
Teaching Formal Methods for Fun Using Maude

In this paper I try to identify some general criteria for teaching an undergraduate formal methods course in a “fun” way. Based on those criteria, I have developed an introductory formal methods course using rewriting logic and Maude. I explain why Maude is a suitable formal method for such a course, give an overview of the course and its textbook, and summarize student feedback to the course.
Validation of a Simulation-Based Pre-Assessment Process for Solar Photovoltaic Technology Implemented on Rooftops of South African Shopping Centres

The existing processes that determines the yield of a photovoltaic (PV) system before construction commences are fairly unstructured. Research that defines a structured process for simulation purposes is limited. This article builds on prior research where a proposed structured pre-assessment process, which may be applied before construction commences, was generated so that electricity yield from a PV system can be predicted with a higher degree of accuracy, and then subsequently optimized. By implementing the proposed pre-assessment process, calculating the future return on investment (ROI) by private investors is simplified, given that the existing process is restrictive. The research used the results from a South African case study over 24 months to ascertain the validity of the proposed pre-assessment process. The validation process includes analyzing the load demand of the shopping centre before and after the PV system was constructed, comparing the electricity yield from the PV system to the simulation results obtained in the preceding research, and amending the proposed pre-assessment process accordingly for improved electricity estimation. The case study shopping centre operates in Johannesburg, Gauteng, and consumes approximately 5000 kVA under maximum load.
Face Detection Algorithm Based on Deep Residual Network

Abstract With the rapid development of artificial intelligence, face detection technology is widely used in our daily lives, such as mobile payment, video conferencing and personal identification. However, face the scenario while the face been blocking or crowding, the face detection accuracy would be greatly reduced. Therefore, in this paper, a high-precision face detection algorithm based on deep residual network has been proposed to solve this issue. Firstly, adding neural framework branches based on the Resnet-50 to improve the detection accuracy. Then import the soft-NMS method to enhance the robustness of algorithm. Experimental test on the public data set FDDB, the results indicate that the accuracy of this algorithm can reach 94.2% with good robustness, both accuracy and speed are better than the previous algorithm.
Mammographic Image Classification Using Deep Neural Network for Computer-Aided Diagnosis

Breast cancer detection is a crucial topic in the healthcare sector.Breast cancer is a major reason for the increased mortality rate in recent years among women, specifically in developed and underdeveloped countries around the world.The incidence rate is less in India than in developed countries, but awareness must be increased.This paper focuses on an efficient deep learning-based diagnosis and classification technique to detect breast cancer from mammograms.The model includes preprocessing, segmentation, feature extraction, and classification.At the initial level, Laplacian filtering is applied to identify the portions of edges in mammogram images that are highly sensitive to noise.Subsequently, segmentation is done using modified adaptively regularized kernel-based fuzzy C means (ARKFCM).Feature extraction is accomplished using the morphological, texture, and moment invariants.The corresponding feature values are provided as inputs to a deep neural network (DNN) model that classifies the normal and abnormal portions in the mammogram images.The performance of the proposed model is validated with the Mammographic Image Analysis Society (MIAS) database.The efficiency of the proposed classifier is experimentally proved by comparing the various classifiers with respect to their statistical performances.On an applied database, the proposed model offered a maximum classification with the highest accuracy level of 99.13%.
Analysis of Cloud Computing Usage on Performance: The Case of Turkish SMEs

Small and mid-size enterprises (SME) are considered the backbone of developing economies in terms of reducing poverty and unemployment.Although most SMEs are still reluctant in new technology adoption, such as cloud computing, some benefited from the services provided by cloud technology.This study aims to assess the performance evaluation of small and mid-sized enterprises (SMEs) regarding the usage of cloud computing in their activities.The quantitative study was conducted on a sample of 112 respondents employed in Turkish SMEs.A research model is utilized based on performance specific scales.Results signified cloud technology's positive impact on business performance.
Emergent quantum state designs from individual many-body wavefunctions

Quantum chaos in many-body systems provides a bridge between statistical and quantum physics with strong predictive power. This framework is valuable for analyzing properties of complex quantum systems such as energy spectra and the dynamics of thermalization. While contemporary methods in quantum chaos often rely on random ensembles of quantum states and Hamiltonians, this is not reflective of most real-world systems. In this paper, we introduce a new perspective: across a wide range of examples, a single non-random quantum state is shown to encode universal and highly random quantum state ensembles. We characterize these ensembles using the notion of quantum state $k$-designs from quantum information theory and investigate their universality using a combination of analytic and numerical techniques. In particular, we establish that $k$-designs arise naturally from generic states as well as individual states associated with strongly interacting, time-independent Hamiltonian dynamics. Our results offer a new approach for studying quantum chaos and provide a practical method for sampling approximately uniformly random states; the latter has wide-ranging applications in quantum information science from tomography to benchmarking.
An Enhancing Differential Evolution Algorithm with a Rank-Up Selection: RUSDE

Recently, the differential evolution (DE) algorithm has been widely used to solve many practical problems. However, DE may suffer from stagnation problems in the iteration process. Thus, we propose an enhancing differential evolution with a rank-up selection, named RUSDE. First, the rank-up individuals in the current population are selected and stored into a new archive; second, a debating mutation strategy is adopted in terms of the updating status of the current population to decide the parent’s selection. Both of the two methods can improve the performance of DE. We conducted numerical experiments based on various functions from CEC 2014, where the results demonstrated excellent performance of this algorithm. Furthermore, this algorithm is applied to the real-world optimization problem of the four-bar linkages, where the results show that the performance of RUSDE is better than other algorithms.
Multiview Self-Supervised Segmentation for OARs Delineation in Radiotherapy

Radiotherapy has become a common treatment option for head and neck (H&amp;N) cancer, and organs at risk (OARs) need to be delineated to implement a high conformal dose distribution. Manual drawing of OARs is time consuming and inaccurate, so automatic drawing based on deep learning models has been proposed to accurately delineate the OARs. However, state-of-the-art performance usually requires a decent amount of delineation, but collecting pixel-level manual delineations is labor intensive and may not be necessary for representation learning. Encouraged by the recent progress in self-supervised learning, this study proposes and evaluates a novel multiview contrastive representation learning to boost the models from unlabelled data. The proposed learning architecture leverages three views of CTs (coronal, sagittal, and transverse plane) to collect positive and negative training samples. Specifically, a CT in 3D is first projected into three 2D views (coronal, sagittal, and transverse planes), then a convolutional neural network takes 3 views as inputs and outputs three individual representations in latent space, and finally, a contrastive loss is used to pull representation of different views of the same image closer (“positive pairs”) and push representations of views from different images (“negative pairs”) apart. To evaluate performance, we collected 220 CT images in H&amp;N cancer patients. The experiment demonstrates that our method significantly improves quantitative performance over the state-of-the-art (from 83% to 86% in absolute Dice scores). Thus, our method provides a powerful and principled means to deal with the label-scarce problem.
Convolutional Neural Network-Based Discriminator for Outlier Detection

Noise in training data increases the tendency of many machine learning methods to overfit the training data, which undermines the performance. Outliers occur in big data as a result of various factors, including human errors. In this work, we present a novel discriminator model for the identification of outliers in the training data. We propose a systematic approach for creating training datasets to train the discriminator based on a small number of genuine instances (trusted data). The noise discriminator is a convolutional neural network (CNN). We evaluate the discriminator’s performance using several benchmark datasets and with different noise ratios. We inserted random noise in each dataset and trained discriminators to clean them. Different discriminators were trained using different numbers of genuine instances with and without data augmentation. We compare the performance of the proposed noise-discriminator method with seven other methods proposed in the literature using several benchmark datasets. Our empirical results indicate that the proposed method is very competitive to the other methods. It actually outperforms them for pair noise.
The Effect of Thickness-Based Dynamic Matching Mechanism on a Hyperledger Fabric-Based TimeBank System

In a community with an aging population, helping each other is a must society function. Lacking mutual trust makes the need for a fair and transparent service exchange platform on top of the public service administration’s list. We present an efficient blockchain-based TimeBank realization with a newly proposed dynamic service matching algorithm (DSMA) in this work. The Hyperledger Fabric (or Fabric in short), one of the well-known Consortium Blockchains, is chosen as our system realization platform. It provides the identity certification mechanism and has an extendable network structure. The performance of a DSMA is measured by the waiting time for a service to get a match, called the service-matching waiting time (SMWT). In our DSMA, the decision as to whether a service is to get a match or wait for a later chance depends dynamically on the total number of contemporarily available services (i.e., the thickness of the service market). To better the proposed TimeBank system’s service quality, a Dynamic Tuning Strategy (DTS) is designed to thicken the market size. Experimental results show that a thicker market makes on-chain nodes have more links, and in turn, they find a match easier (i.e., consume a shorter SMWT).
Addressing Action Oscillations through Learning Policy Inertia

Deep reinforcement learning (DRL) algorithms have been demonstrated to be effective on a wide range of challenging decision making and control tasks. However, these methods typically suffer from severe action oscillations in particular in discrete action setting, which means that agents select different actions within consecutive steps even though states only slightly differ. This issue is often neglected since we usually evaluate the quality of a policy using cumulative rewards only. Action oscillation strongly affects the user experience and even causes serious potential security menace especially in real-world domains with the main concern of safety, such as autonomous driving. In this paper, we introduce Policy Inertia Controller (PIC) which serves as a generic plug-in framework to off-the-shelf DRL algorithms, to enable adaptive balance between the optimality and smoothness in a formal way. We propose Nested Policy Iteration as a general training algorithm for PIC-augmented policy which ensures monotonically non-decreasing updates.Further, we derive a practical DRL algorithm, namely Nested Soft Actor-Critic. Experiments on a collection of autonomous driving tasks and several Atari games suggest that our approach demonstrates substantial oscillation reduction than a range of commonly adopted baselines with almost no performance degradation.
COVID-19 Contact Tracing as an Enduringly Important Public Health Tool

Michael A. Johansson, PhD; Talia M. Quandelacy, PhD, MPH; Sarah Kada, PhD; Pragati Venkata Prasad, MPH; Molly Steele, PhD, MPH; John T. Brooks, MD; Rachel B. Slayton, PhD, MPH; Matthew Biggerstaff, ScD, MPH; Jay C. Butler, MD
Active Feedback Learning with Rich Feedback

In this paper, we proposed rich feedback which contains multiple types of feedback to allow human teachers to provide a variety of useful information to the learning agent and modified Policy Shaping to accumulate the effects of rich feedback. Then we designed the ALPHA framework to actively request rich feedback and further developed it to use Deep Learning. The experimental results showed ALPHA with rich feedback can greatly improve learning and quickly lead the learning agent to the optimal solution.