Reviewerly/exHarmony huggingface dataset:
Smart Watering System Security Technologies using Blockchain

Water quality monitoring and its use in smart agriculture and other crucial domains is an important step towards a system based on risk analysis on how data is transmitted over the network. In this article, the emphasis is on the security of the transmitted data and is based on the developed project SWAM (Smart WAter Management system for better environmental sustainability). The security services used in the current paper are based on an architecture that provides a set of security guarantees capable of mitigating certain threats relevant to the water quality monitoring system used in agriculture. These services are beneficial for irrigation system operators because the devices are authenticated, then the data is sent from the station and reaches the platform and is not modified in the sense of simulating erroneous measurements. The general purpose of the article is to describe the security aspect, elements and factors of the SWAM architecture. Security is required at all IoT levels, for example, at the device level, including sensors for water quality monitoring and communication level (generally through the Gateway).
Measuring Haptic Experience: Elaborating the HX model with scale development

Designers increasingly employ haptic feedback with the aim to improve user experience (UX). Designers and researchers currently use qualitative methods or demos for feedback, but neither approach scales to large studies or remote work. We build upon the recent Haptic Experience (HX) model and report on progress towards measuring the five constructs for designing haptic experiences: Harmony, Expressivity, Autotelics, Immersion, and Realism. We describe initial findings from scale development, specifically, from item generation (N=23) and exploratory factor analysis (N=261). Our results provide initial evidence that vibrotactile experiences are effectively modeled by five factors, enriched description of each factor, and guidelines for quantitatively measuring HX.
Real-time semantic segmentation with weighted factorized-depthwise convolution

Semantic segmentation has achieved great success with the popularity of convolutional neural networks (CNNs). However, the huge computational burden restricts the application of most existing networks on edge devices with strict inference time constraints. To solve this problem, a weighted factorized-depthwise convolution network (WFDCNet) is presented in this paper, which contains full- dimensional continuous separation convolution (FCS) modules and a lateral asymmetric pyramid fusion (LAPF) module, aiming to obtain high accuracy without damaging inference speed. Specifically, the FCS module enables the calculation of each dimension to be completed independently in a continuous separation process and uses simplified SE (SSE) attention layer to adjust the channels, achieving the extensive extraction of feature information. The LAPF module is able to eliminate semantic divergence and fuse feature maps of three different scales to realize the combination of multiple information from the front-end and the back-end network. WFDCNet shows superior performance on Cityscapes, Camvid, Mapillary Vistas and COCO-Stuff datasets. Especially, the experimental results demonstrate that our network achieves 73.7% mIoU on Cityscapes dataset, with the inference speed of 102.6FPS on a single RTX 2080 Ti GPU, and 17.2FPS on Jetson TX2.
CODEs: Chamfer Out-of-Distribution Examples against Overconfidence Issue

Overconfident predictions on out-of-distribution (OOD) samples is a thorny issue for deep neural networks. The key to resolve the OOD overconfidence issue inherently is to build a subset of OOD samples and then suppress predictions on them. This paper proposes the Chamfer OOD examples (CODEs), whose distribution is close to that of in-distribution samples, and thus could be utilized to alleviate the OOD overconfidence issue effectively by suppressing predictions on them. To obtain CODEs, we first generate seed OOD examples via slicing&splicing operations on in-distribution samples from different categories, and then feed them to the Chamfer generative adversarial network for distribution transformation, without accessing to any extra data. Training with suppressing predictions on CODEs is validated to alleviate the OOD overconfidence issue largely without hurting classification accuracy, and outperform the state-of-the-art methods. Besides, we demonstrate CODEs are useful for improving OOD detection and classification.
Item-side ranking regularized distillation for recommender system

Recent recommender system (RS) have adopted large and sophisticated model architecture to better understand the complex user-item relationships, and accordingly, the size of the recommender is continuously increasing. To reduce the high inference costs of the large recommender, knowledge distillation (KD), which is a model compression technique from a large pre-trained model (teacher) to a small model (student), has been actively studied for RS. The state-of-the-art method is based on the ranking distillation approach, which makes the student preserve the ranking orders among items predicted by the teacher. In this work, we propose a new regularization method designed to maximize the effect of the ranking distillation in RS. We first point out an important limitation and a room for improvement of the state-of-the-art ranking distillation method based on our in-depth analysis.Then, we introduce the item-side ranking regularization, which can effectively prevent the student with limited capacity from being overfitted and enables the student to more accurately learn the teacher’s prediction results. We validate the superiority of the proposed method by extensive experiments on real-world datasets.
Optical Entanglement of Distinguishable Quantum Emitters

Solid-state quantum emitters are promising candidates for the realization of quantum networks, owing to their long-lived spin memories, high-fidelity local operations, and optical connectivity for long-range entanglement. However, due to differences in local environment, solid-state emitters typically feature a range of distinct transition frequencies, which makes it challenging to create optically mediated entanglement between arbitrary emitter pairs. We propose and demonstrate an efficient method for entangling emitters with optical transitions separated by many linewidths. In our approach, electro-optic modulators enable a single photon to herald a parity measurement on a pair of spin qubits. We experimentally demonstrate the protocol using two silicon-vacancy centers in a diamond nanophotonic cavity, with optical transitions separated by 7.4 GHz. Working with distinguishable emitters allows for individual qubit addressing and readout, enabling parallel control and entanglement of both colocated and spatially separated emitters, a key step toward scaling up quantum information processing systems.
Ensemble learning-based approach for improving generalization capability of machine reading comprehension systems

Machine Reading Comprehension (MRC) is an active field in natural language processing with many successful developed models in recent years. Despite their high in-distribution accuracy, these models suffer from two issues: high training cost and low out-of-distribution accuracy. Even though some approaches have been presented to tackle the generalization problem, they have high, intolerable training costs. In this paper, we investigate the effect of ensemble learning as a light approach to improve out-of-distribution generalization of MRC systems by aggregating the outputs of some pre-trained base models without retraining a big model. After separately training the base models with different structures on different datasets, they are ensembled using weighting and stacking approaches in probabilistic and non-probabilistic settings. Three configurations are investigated including heterogeneous, homogeneous, and hybrid on eight datasets and six state-of-the-art models. We identify the important factors in the effectiveness of ensemble methods. Also, we compare the robustness of ensemble and fine-tuned models against data distribution shifts. The experimental results show the effectiveness and robustness of the ensemble approach in improving the out-of-distribution accuracy of MRC systems, especially when the base models are similar in accuracies.
Performance enhancement of dual axis solar tracker system for solar panels using proteus ISIS 7.6 software package

In this article, a standalone solar tracking system is intended and successfully synchronized using proteus 7.6 ISIS. Best at instantly aligning a solar panel with the position of the sun, resulting in a 40% increase in efficiency. A low-cost solar tracking detector is being used to rotate the solar panel paired to the motor at such a specific angle to rotate 180°s in 8 steps per day with better accuracy. A microcontroller (Atmega3285P) is used as a control unit, and ADC ports are used to interconnect sensor units. To interconnect a solar array, the motor driver ULN 293D is also used to rotate the solar panel at the highest solar power angle. The modeling is performed in two ways, using four LDRs and eight LDRs, and the results of both are measured. The simulation and analysis results show that the maximum current drawn by the solar tracking sensor is less than 0.5 mA. “Proteus 7.6 ISIS” and “Arduino UNO” and “Code Vision AVR” are used to write the program code and to burn over Atmega3285P for parameters estimation.
Self-Supervised 3D Human Pose Estimation with Multiple-View Geometry

We present a self-supervised learning algorithm for 3D human pose estimation of a single person based on a multiple-view camera system and 2D body pose estimates for each view. To train our model, represented by a deep neural network, we propose a four-loss function learning algorithm, which does not require any 2D or 3D body pose ground-truth. The proposed loss functions make use of the multiple-view geometry to reconstruct 3D body pose estimates and impose body pose constraints across the camera views. Our approach utilizes all available camera views during training, while the inference is single-view. In our evaluations, we show promising performance on Human3.6M and HumanEva benchmarks, while we also present a generalization study on MPI-INF-3DHP dataset, as well as several ablation results. Overall, we outperform all self-supervised learning methods and reach comparable results to supervised and weakly-supervised learning approaches. Our code and models are publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Source Code: https://gi-thub.com/vru2020jpose_3D/.
Design, Validity and Effect of an Intra-Curricular Program for Facilitating Self-Regulation of Learning Competences in University Students with the Support of the 4Planning App

Background: Smartphone applications have the potential to support university students for the benefit of learning in higher education. Objective: To design and evaluate the effect of an intra-curricular program using a mobile application on self-regulated learning strategies in university students. Method: The 4Planning mobile application was designed following a systematic literature review, expert judgement and application. The instrument to assess the effect of the intervention was the SRL Readiness Practices Scale, with a one-factor structure (CFI = 0.98; TLI = 0.97; RMSEA = 0.05) with reliability of α = 0.89. The design was quasi-experimental with pre- and post-test measures and experimental and control groups. The sample consisted of 473 first-year university students (37.02% male) from seven Chilean universities with a mean age of 19.35 (SD = 2.49). Statistical analysis was an ANOVA performed in R software version 4.0.3. Results: Statistically significant differences were identified in the levels of self-regulated learning between the experimental group compared to the control group. Conclusion: The development of the 4Planning app proved to be effective in promoting the development of self-regulated learning strategies in university students.
ELECTRA for Neural Coreference Resolution in Italian

In recent years, the impact of Neural Language Models has changed every field of Natural Language Processing. In this scenario, coreference resolution has been among the least considered task, especially in language other than English. This work proposes a coreference resolution system for Italian, based on a neural end-to-end architecture integrating ELECTRA language model and trained on OntoCorefIT, a novel Italian dataset built starting from OntoNotes. Even if some approaches for Italian have been proposed in the last decade, to the best of our knowledge, this is the first neural coreference resolver aimed specifically to Italian. The performance of the system is evaluated with respect to three different metrics and also assessed by replacing ELECTRA with the widely-used BERT language model, since its usage has proven to be effective in the coreference resolution task in English. A qualitative analysis has also been conducted, showing how different grammatical categories affect performance in an inflectional and morphological-rich language like Italian. The overall results have shown the effectiveness of the proposed solution, providing a baseline for future developments of this line of research in Italian.
Validation on machine reading comprehension software without annotated labels: a property-based method

Machine Reading Comprehension (MRC) in Natural Language Processing has seen great progress recently. But almost all the current MRC software is validated with a reference-based method, which requires well-annotated labels for test cases and tests the software by checking the consistency between the labels and the outputs. However, labeling test cases of MRC could be very costly due to their complexity, which makes reference-based validation hard to be extensible and sufficient. Furthermore, solely checking the consistency and measuring the overall score may not be sensible and flexible for assessing the language understanding capability. In this paper, we propose a property-based validation method for MRC software with Metamorphic Testing to supplement the reference-based validation. It does not refer to the labels and hence can make much data available for testing. Besides, it validates MRC software against various linguistic properties to give a specific and in-depth picture on linguistic capabilities of MRC software. Comprehensive experimental results show that our method can successfully reveal violations to the target linguistic properties without the labels. Moreover, it can reveal problems that have been concealed by the traditional validation. Comparison according to the properties provides deeper and more concrete ideas about different language understanding capabilities of the MRC software.
Optimized histogram computation model using cuckoo search for color image contrast distortion

This paper proposes a novel optimized histogram modification framework using a cuckoo search (CS) algorithm for brightness preserved contrast enhancement. The histogram modification has been modeled as an optimization problem and the CS algorithm is used to find an optimum solution. The proposed method's chief objective is to find a fair tradeoff between contrast improvement and mean-brightness preservation. A new target histogram has been generated using the CS algorithm using a target function composed of tuning parameters. The extent of the image contrast range can be adjusted by selecting appropriately designed tuning parameters. The proposed technique can control the enhancement with the values chosen to fix the enhancement level and retain the enhanced image features. Finally, the optimized histogram generated from the CS algorithm is employed in the histogram equalization (HE) process to generate the final enhanced image. Its performance is validated with qualitative and quantitative assessment against well-known state-of-the-art techniques.
Restoration and enhancement of breast ultrasound images using extended complex diffusion based unsharp masking

Ultrasound is a well-known imaging modality for the interpretation of breast cancer. It is playing very important role for breast cancer detection that are missed by mammograms. The image acquisition is usually affected by the presence of noise, artifacts, and distortion. To overcome such type of issues, there is a need of image restoration and enhancement to improve the quality of image. This paper proposes a single framework for denoising and enhancement of ultrasound images, where a smoothing filter is replaced with an extended complex diffusion-based filter in an unsharp masking technique. The performance evaluation of the proposed method is tested on real ultrasound breast cancer images database and synthetic ultrasound image. The performance evaluation comprises qualitative and quantitative evaluation along with comparative analysis of pre-existing and proposed method. The quantitative evaluation metrics are mean squared error, peak-signal-to-noise ratio, correlation parameter, normalized absolute error, universal quality index, similarity structure index, edge preservation index, a measure of enhancement, a measure of enhancement by entropy, and second derivative like measurement. The result specifies that the proposed method is better suited approach for the removal of speckle noise which follows Rayleigh distribution, restoration of information, enhancement of abnormalities, and proper edge preservation.
A Synthesis-Based Approach for Thermal-to-Visible Face Verification

In recent years, visible-spectrum face verification systems have been shown to match the performance of experienced forensic examiners. However, such systems are ineffective in low-light and nighttime conditions. Thermal face imagery, which captures body heat emissions, effectively augments the visible spectrum, capturing discriminative facial features in scenes with limited illumination. Due to the increased cost and difficulty of obtaining diverse, paired thermal and visible spectrum datasets, not many algorithms and large-scale benchmarks for low-light recognition are available. This paper presents an algorithm that achieves state-of-the-art performance on both the ARL-VTF and TUFTS multi-spectral face datasets. Importantly, we study the impact of face alignment, pixel-level correspondence, and identity classification with label smoothing for multi-spectral face synthesis and verification. We show that our proposed method is widely applicable, robust, and highly effective. In addition, we show that the proposed method significantly outperforms face frontalization methods on profile-to-frontal verification. Finally, we present MILAB-VTF(B), a challenging multi-spectral face dataset that is composed of paired thermal and visible videos. To the best of our knowledge, with face data from 400 subjects, this dataset represents the most extensive collection of publicly available indoor and long-range outdoor thermal-visible face imagery. Lastly, we show that our end-to-end thermal-to-visible face verification system provides strong performance on the MILAB-VTF(B) dataset.
Image-Based Visual Servoing of Rotorcrafts to Planar Visual Targets of Arbitrary Orientation

This letter for the first time extends the virtual camera image-based visual servoing (IBVS) scheme to enable an underactuated rotorcraft UAV to regulate its translational motion and heading relative to a planar visual target of arbitrary orientation. The conversion from real camera images to virtual camera images of visual targets are proposed based on a set of rotation matrices. Hence, image moment features can be reused due to the simplicity and decoupled structure of the interaction matrix, and satisfactory 3D Cartesian trajectory of UAVs. In the design of the IBVS control law, the external disturbance and model uncertainties are estimated by an integral-based filter. In addition, to enable tracking of a moving visual target, a velocity estimator is developed. The global asymptotic stability of the error dynamics is proven. Both of the simulation and experimental results of tracking of a tilted moving planar target are provided to show the efficacy of the proposed IBVS scheme.
Developing clinical prediction models when adhering to minimum sample size recommendations: The importance of quantifying bootstrap variability in tuning parameters and predictive performance

Recent minimum sample size formula (Riley et al.) for developing clinical prediction models help ensure that development datasets are of sufficient size to minimise overfitting. While these criteria are known to avoid excessive overfitting on average, the extent of variability in overfitting at recommended sample sizes is unknown. We investigated this through a simulation study and empirical example to develop logistic regression clinical prediction models using unpenalised maximum likelihood estimation, and various post-estimation shrinkage or penalisation methods. While the mean calibration slope was close to the ideal value of one for all methods, penalisation further reduced the level of overfitting, on average, compared to unpenalised methods. This came at the cost of higher variability in predictive performance for penalisation methods in external data. We recommend that penalisation methods are used in data that meet, or surpass, minimum sample size requirements to further mitigate overfitting, and that the variability in predictive performance and any tuning parameters should always be examined as part of the model development process, since this provides additional information over average (optimism-adjusted) performance alone. Lower variability would give reassurance that the developed clinical prediction model will perform well in new individuals from the same population as was used for model development.
Unsupervised Person Re-Identification Via Nearest Neighbor Collaborative Training Strategy

Because of the lack of human-labeled data, the challenge of unsupervised person re-identification (re-ID) is to learn to generate correct pseudo labels for training. Unlike the human-labeled annotation, the generated pseudo labels contain the noise labels that harm the model’s performance. In this paper, we propose the Nearest Neighbors Collaborative Training (NNCT) strategy to mitigate the effects of noisy labels by utilizing information of the nearest neighbor of an image. The proposed NNCT trains the image and its nearest neighbor collaboratively, thereby enhancing the generalization capability of the network and shortening the distance with neighbors. To make training using the up-to-date nearest neighbor possible, we introduce a Pseudo Label Memory Bank (PLMB) to store the up-to-date labels of all images. The experimental results confirm the superiority of the proposed method, which surpasses state-of-the-arts on two mainstream person re-ID datasets, Market-1501, and DukeMTMC-reID in both fully unsupervised learning manner and Unsupervised Domain Adaptation (UDA) manner.
Latent-Space Scalability for Multi-Task Collaborative Intelligence

We investigate latent-space scalability for multi-task collaborative intelligence, where one of the tasks is object detection and the other is input reconstruction. In our proposed approach, part of the latent space can be selectively decoded to support object detection while the remainder can be decoded when input reconstruction is needed. Such an approach allows reduced computational resources when only object detection is required, and this can be achieved without reconstructing input pixels. By varying the scaling factors of various terms in the training loss function, the system can be trained to achieve various trade-offs between object detection accuracy and input reconstruction quality. Experiments are conducted to demonstrate the adjustable system performance on the two tasks compared to the relevant benchmarks.