Reviewerly/exHarmony huggingface dataset:
Weakly-Supervised Domain Adaptation of Deep Regression Trackers via Reinforced Knowledge Distillation

Deep regression trackers are among the fastest tracking algorithms available, and therefore suitable for real-time robotic applications. However, their accuracy is inadequate in many domains due to distribution shift and overfitting. In this letter we overcome such limitations by presenting the first methodology for domain adaption of such a class of trackers. To reduce the labeling effort we propose a weakly-supervised adaptation strategy, in which reinforcement learning is used to express weak supervision as a scalar application-dependent and temporally-delayed feedback. At the same time, knowledge distillation is employed to guarantee learning stability and to compress and transfer knowledge from more powerful but slower trackers. Extensive experiments on five different robotic vision domains demonstrate the relevance of our methodology. Real-time speed is achieved on embedded devices and on machines without GPUs, while accuracy reaches significant results.
Toward Facilitating Team Formation and Communication Through Avatar Based Interaction in Desktop-Based Immersive Virtual Environments

Millions of students worldwide have adopted online learning due to the isolation restrictions imposed by the Covid-19 pandemic. In this context, video conferencing platforms have garnered immense popularity as tools for teaching. However, these tools have several limitations compared to real-world encounters, especially in activities involving collaboration and teamwork. A growing number of researchers and educators have turned to avatar-based communication platforms, such as Mozilla Hubs, as alternatives that can complement video conferencing in social and teaching activities. Several previous research efforts have focused on developing tools that implement avatar-based communication systems or have explored creating activities in these 3D virtual spaces, such as poster sessions in scientific conferences or the classroom environment. In this work, we describe our semester-long efforts to develop Mozilla Hubs rooms toward promoting interaction and communication to help students self-form teams in the context of an introductory virtual reality course at the University of Florida. We describe hands-on activities to prepare students to use Mozilla Hubs effectively, including teaching them skills to customize and create avatars. We describe the implementation of three virtual rooms developed based on researchers' observations and students' survey responses. By observing students' behavior and communication patterns in those rooms, we propose a set of guidelines for building virtual rooms that can promote communication, interaction, and teamwork. We discuss the rooms' design, students' attendance, and avatar choices. Our findings suggest that highly detailed, small, closed spaces are preferred over large, open spaces with few details when promoting interaction and collaboration among students.
ParaSCI: A Large Scientific Paraphrase Dataset for Longer Paraphrase Generation

We propose ParaSCI, the first large-scale paraphrase dataset in the scientific field, including 33,981 paraphrase pairs from ACL (ParaSCI-ACL) and 316,063 pairs from arXiv (ParaSCI-arXiv).Digging into characteristics and common patterns of scientific papers, we construct this dataset though intra-paper and inter-paper methods, such as collecting citations to the same paper or aggregating definitions by scientific terms.To take advantage of sentences paraphrased partially, we put up PDBERT as a general paraphrase discovering method.The major advantages of paraphrases in ParaSCI lie in the prominent length and textual diversity, which is complementary to existing paraphrase datasets.ParaSCI obtains satisfactory results on human evaluation and downstream tasks, especially long paraphrase generation.
Utilizing asynchronous email interviews for health research: overview of benefits and drawbacks

Abstract Objective Through collating observations from various studies and complementing these findings with one author’s study, a detailed overview of the benefits and drawbacks of asynchronous email interviewing is provided. Through this overview, it is evident there is great potential for asynchronous email interviews in the broad field of health, particularly for studies drawing on expertise from participants in academia or professional settings, those across varied geographical settings (i.e. potential for global public health research), and/or in circumstances when face-to-face interactions are not possible (e.g. COVID-19). Results Benefits of asynchronous email interviewing and additional considerations for researchers are discussed around: (i) access transcending geographic location and during restricted face-to-face communications; (ii) feasibility and cost; (iii) sampling and inclusion of diverse participants; (iv) facilitating snowball sampling and increased transparency; (v) data collection with working professionals; (vi) anonymity; (vii) verification of participants; (viii) data quality and enhanced data accuracy; and (ix) overcoming language barriers. Similarly, potential drawbacks of asynchronous email interviews are also discussed with suggested remedies, which centre around: (i) time; (ii) participant verification and confidentiality; (iii) technology and sampling concerns; (iv) data quality and availability; and (v) need for enhanced clarity and precision.
HULK: An Energy Efficiency Benchmark Platform for Responsible Natural Language Processing

Computation-intensive pretrained models have been taking the lead of many natural language processing benchmarks such as GLUE (Wang et al., 2018).However, energy efficiency in the process of model training and inference becomes a critical bottleneck.We introduce HULK, a multi-task energy efficiency benchmarking platform for responsible natural language processing.With HULK, we compare pretrained models' energy efficiency from the perspectives of time and cost.Baseline benchmarking results are provided for further analysis.The fine-tuning efficiency of different pretrained models can differ significantly among different tasks, and fewer parameter number does not necessarily imply better efficiency.We analyzed such a phenomenon and demonstrated the method for comparing the multi-task efficiency of pretrained models.Our platform is available at https:// hulkbenchmark.github.io/.
A CNN-Based High-Accuracy Registration for Remote Sensing Images

In this paper, a convolutional neural network-based registration framework is proposed for remote sensing to improve the registration accuracy between two remote-sensed images acquired from different times and viewpoints. The proposed framework consists of four stages. In the first stage, key-points are extracted from two input images—a reference and a sensed image. Then, a patch is constructed at each key-point. The second stage consists of three processes for patch matching—candidate patch pair list generation, one-to-one matched label selection, and geometric distortion compensation. One-to-one matched patch pairs between two images are found, and the exact matching is found by compensating for geometric distortions in the matched patch pairs. A global geometric affine parameter set is computed using the random sample consensus algorithm (RANSAC) algorithm in the third stage. Finally, a registered image is generated after warping the input sensed image using the affine parameter set. The proposed high-accuracy registration framework is evaluated using the KOMPSAT-3 dataset by comparing the conventional frameworks based on machine learning and deep-learning-based frameworks. The proposed framework obtains the least root mean square error value of 34.922 based on all control points and achieves a 68.4% increase in the matching accuracy compared with the conventional registration framework.
PDANet: Pyramid density-aware attention based network for accurate crowd counting

Crowd counting, i.e., estimating the number of people in crowded areas, has attracted much interest in the research community. Although many attempts have been reported, crowd counting remains an open real-world problem due to the vast density variations and severe occlusion within the interested crowd area. In this paper, we propose a novel Pyramid Density-Aware Attention based network, abbreviated as PDANet, which leverages the attention, pyramid scale feature, and two branch decoder modules for density-aware crowd counting. The PDANet utilizes these modules to extract features of different scales while focusing on the relevant information and suppressing the misleading information. We also address the variation of crowdedness levels among different images with a Density-Aware Decoder (DAD) modules. For this purpose, a classifier is constructed to evaluate the density level of the input features and then passes them to the corresponding high and low density DAD modules. Finally, we generate an overall density map by considering the summation of low and high crowdedness density maps. Meanwhile, we employ different losses aiming to achieve a precise density map for the input scene. Extensive evaluations conducted on the challenging benchmark datasets well demonstrate the superior performance of the proposed PDANet in terms of the accuracy of counting and generated density maps over the well-known state-of-the-art approaches.
Automatic Modulation Classification of Real Signals in AWGN Channel Based on Sixth-Order Cumulants

Automatic modulation classification (AMC)represents an important integral part of modern communication systems.While novel AMC algorithms based on complex neural network structures showed significant performance improvements, in practical applications low algorithm complexity of AMC algorithms based on higherorder cumulants still makes them very attractive.AMC algorithm based on sixth-order cumulants showed very good performance in this context, especially when it comes to distinguishing Binary Phase Shift Keying (BPSK) signals from complex constellations.Still, no further analysis of expected performance with other real constellations was presented for this algorithm so far.In this paper, the performance was explored in a wider context of real signals classification, by observing various Pulse Amplitude Modulation (PAM) constellations, whose statistical features are presented for the first time.Their classification performance was tested via Monte Carlo simulations, and explained through the presence of bias under conditions of strong additive white Gaussian noise channel, reported in this paper for real signals for the first time.One new approach in AMC is proposed, which ensures improvement in the classification of real signal constellations.Achieved improvement is confirmed in many Monte Carlo experiments, where the proposed new AMC scheme is tested versus the most popular standard higher-order cumulantsbased algorithms.
The impact of hyper-parameter tuning for landscape-aware performance regression and algorithm selection

Automated algorithm selection and configuration methods that build on exploratory landscape analysis (ELA) are becoming very popular in Evolutionary Computation. However, despite a significantly growing number of applications, the underlying machine learning models are often chosen in an ad-hoc manner. We show in this work that three classical regression methods are able to achieve meaningful results for ELA-based algorithm selection. For those three models -- random forests, decision trees, and bagging decision trees -- the quality of the regression models is highly impacted by the chosen hyper-parameters. This has significant effects also on the quality of the algorithm selectors that are built on top of these regressions. By comparing a total number of 30 different models, each coupled with 2 complementary regression strategies, we derive guidelines for the tuning of the regression models and provide general recommendations for a more systematic use of classical machine learning models in landscape-aware algorithm selection. We point out that a choice of the machine learning model merits to be carefully undertaken and further investigated.
Advanced fuzzy cognitive maps: state-space and rule-based methodology for coronary artery disease detection

According to the World Health Organization, 50% of deaths in European Union are caused by Cardiovascular Diseases (CVD), while 80% of premature heart diseases and strokes can be prevented. In this study, a Computer-Aided Diagnostic model for a precise diagnosis of Coronary Artery Disease (CAD) is proposed. The methodology is based on State Space Advanced Fuzzy Cognitive Maps (AFCMs), an evolution of the traditional Fuzzy Cognitive Maps. Also, a rule-based mechanism is incorporated, to further increase the knowledge of the proposed system and the interpretability of the decision mechanism. The proposed method is evaluated utilizing a CAD dataset from the Department of Nuclear Medicine of the University Hospital of Patras, in Greece. Several experiments are conducted to define the optimal parameters of the proposed AFCM. Furthermore, the proposed AFCM is compared with the traditional FCM approach and the literature. The experiments highlight the effectiveness of the AFCM approach, obtaining 85.47% accuracy in CAD diagnosis, showing an improvement of +7% over the traditional approach. It is demonstrated that the AFCM approach in developing Fuzzy Cognitive Maps outperforms the conventional approach, while it constitutes a reliable method for the diagnosis of Coronary Artery Disease.
Human Action Recognition Using CNN-SVM Model

In this paper, a pre-trained CNN model VGG16 with the SVM classifier is presented for the HAR task. The deep features are learned via the VGG16 pre-trained CNN model. The VGG 16 network is previously used for the image classification task. We used VGG16 for the signal classification of human activity, which is recorded by the accelerometer sensor of the mobile phone. The UniMiB dataset contains the 11771 samples of the daily life activity of humans. A Smartphone records these samples through the accelerometer sensor. The features are learned via the fifth max-pooling layer of the VGG16 CNN model and feed to the SVM classifier. The SVM classifier replaced the fully connected layer of the VGG16 model. The proposed VGG16-SVM model achieves effective and efficient results. The proposed method of VGG16-SVM is compared with the previously used schemes. The classification accuracy and F-Score are the evaluation parameters, and the proposed method provided 79.55% accuracy and 71.63% F-Score.
Are geochemical regime shifts identifiable in river waters? Exploring the compositional dynamics of the Tiber River (Italy)

Rivers are dynamic and sensitive systems that change their chemical composition from source to mouth. This is due to the influence of a set of variables controlled by hydro-litho-eco-atmospheric processes and anthropic pressures which are, in turn, affected by catchment attributes. This work proposes a new way of thinking about river geochemistry focused on environmental interconnections rather than single chemical variables. Abrupt changes in the system state (composition) of a certain environmental media, driven by perturbations, may trigger Geochemical Regime Shifts (GRSs). This eventuality is explored in the Tiber River (central Italy) chemistry by Compositional Data Analysis, robust Principal Component Analysis and score-distance graphs. Data variability and the interlinks between response and forcing variables are investigated for different drained areas. A potential GRS is detected for major elements in the lower reaches resulting from a threshold-like state response caused by lithological forcing. On the contrary, trace elements respond gradually to environmental drivers, showing no abrupt changes. The findings outline mechanisms and factors influencing the river's self-restoring capability at a basin-wide scale, providing a better comprehension of the circumstances controlling the equilibrium dynamics of river water systems.
Robust implementation of generative modeling with parametrized quantum circuits

Although the performance of hybrid quantum-classical algorithms is highly dependent on the selection of the classical optimizer and the circuit ansätze (Benedetti et al, npj Quantum Inf 5:45, 2019; Hamilton et al, 2018; Zhu et al, 2018), a robust and thorough assessment on-hardware of such features has been missing to date. From the optimizer perspective, the primary challenge lies in the solver’s stochastic nature, and their significant variance over the random initialization. Therefore, a robust comparison requires one to perform several training curves for each solver before one can reach conclusions about their typical performance. Since each of the training curves requires the execution of thousands of quantum circuits in the quantum computer, such a robust study remained a steep challenge for most hybrid platforms available today. Here, we leverage on Rigetti’s Quantum Cloud Services (QCS™) to overcome this implementation barrier, and we study the on-hardware performance of the data-driven quantum circuit learning (DDQCL) for three different state-of-the-art classical solvers, and on two-different circuit ansätze associated to different entangling connectivity graphs for the same task. Additionally, we assess the gains in performance from varying circuit depths. To evaluate the typical performance associated with each of these settings in this benchmark study, we use at least five independent runs of DDQCL towards the generation of quantum generative models capable of capturing the patterns of the canonical Bars and Stripes dataset. In this experimental benchmarking, the gradient-free optimization algorithms show an outstanding performance compared to the gradient-based solver. In particular, one of them had better performance when handling the unavoidable noisy objective function to be minimized under experimental conditions.
Encoding Time-Series Ground Motions as Images for Convolutional Neural Networks-Based Seismic Damage Evaluation

Traditional methods for seismic damage evaluation require manual extractions of intensity measures (IMs) to properly represent the record-to-record variation of ground motions. Contemporary methods such as convolutional neural networks (CNNs) for time series classification and seismic damage evaluation face a challenge in training due to a huge task of ground-motion image encoding. Presently, no consensus has been reached on the understanding of the most suitable encoding technique and image size (width × height × channel) for CNN-based seismic damage evaluation. In this study, we propose and develop a new image encoding technique based on time-series segmentation (TS) to transform acceleration (A), velocity (V), and displacement (D) ground motion records into a three-channel AVD image of the ground motion event with a pre-defined size of width × height. The proposed TS technique is compared with two time-series image encoding techniques, namely recurrence plot (RP) and wavelet transform (WT). The CNN trained through the TS technique is also compared with the IM-based machine learning approach. The CNN-based feature extraction has comparable classification performance to the IM-based approach. WT 1,000 × 100 results in the highest 79.5% accuracy in classification while TS 100 × 100 with a classification accuracy of 76.8% is most computationally efficient. Both the WT 1,000 × 100 and TS 100 × 100 three-channel AVD image encoding methods are promising for future studies of CNN-based seismic damage evaluation.
A Deep Neural Network-Based Method for Prediction of Dementia Using Big Data

The rise in dementia among the aging Korean population will quickly create a financial burden on society, but timely recognition of early warning for dementia and proper responses to the occurrence of dementia can enhance medical treatment. Health behavior and medical service usage data are relatively more accessible than clinical data, and a prescreening tool with easily accessible data could be a good solution for dementia-related problems. In this paper, we apply a deep neural network (DNN) to prediction of dementia using health behavior and medical service usage data, using data from 7031 subjects aged over 65 collected from the Korea National Health and Nutrition Examination Survey (KNHANES) in 2001 and 2005. In the proposed model, principal component analysis (PCA) featuring and min/max scaling are used to preprocess and extract relevant background features. We compared our proposed methodology, a DNN/scaled PCA, with five well-known machine learning algorithms. The proposed methodology shows 85.5% of the area under the curve (AUC), a better result than that using other algorithms. The proposed early prescreening method for possible dementia can be used by both patients and doctors.
A Digital Currency Architecture for Privacy and Owner-Custodianship

In recent years, electronic retail payment mechanisms, especially e-commerce and card payments at the point of sale, have increasingly replaced cash in many developed countries. As a result, societies are losing a critical public retail payment option, and retail consumers are losing important rights associated with using cash. To address this concern, we propose an approach to digital currency that would allow people without banking relationships to transact electronically and privately, including both internet purchases and point-of-sale purchases that are required to be cashless. Our proposal introduces a government-backed, privately-operated digital currency infrastructure to ensure that every transaction is registered by a bank or money services business, and it relies upon non-custodial wallets backed by privacy-enhancing technology such as blind signatures or zero-knowledge proofs to ensure that transaction counterparties are not revealed. Our approach to digital currency can also facilitate more efficient and transparent clearing, settlement, and management of systemic risk. We argue that our system can restore and preserve the salient features of cash, including privacy, owner-custodianship, fungibility, and accessibility, while also preserving fractional reserve banking and the existing two-tiered banking system. We also show that it is possible to introduce regulation of digital currency transactions involving non-custodial wallets that unconditionally protect the privacy of end-users.
On the Optimal Tilt Angle and Orientation of an On-Site Solar Photovoltaic Energy Generation System for Sabah’s Rural Electrification

An ongoing project to implement a mini standalone solar photovoltaic (PV) generation system of 2.5 kWp capacity at the eco-tourism centre of Liogu Ku Silou-Silou (EPLISSI), Sabah, was initiated in 2019. Since the solar panel support (ground mounting) used in this project will be erected separately, the main goals of this study are to estimate the optimum tilt angle, βopt, and orientation for the solar PV modules. To achieve these goals, the Liu and Jordan isotropic diffuse radiation model was used (1960). Another three isotropic diffuse sky radiation models (Koronakis model, Badescu model, and Tian model) were applied to estimate the optimum tilt angle with the orientation kept facing due south. For verification purposes, the PV power output data obtained from an online PV simulator known as Global Solar Atlas or GSA 2.3 was used and compared to the results of the four isotropic models. The results suggest that the Tian model is more suitable for approximating insolation, as it was proven to have the lowest difference among all models and is in close agreement with the result of the optimum tilt angle provided by GSA 2.3. However, the outcomes demonstrated from the isotropic models propose an error up to 30% (in the range of 31% to 32%) as compared to GSA 2.3.