Reviewerly/exHarmony huggingface dataset:
Advanced energy consumption system for smart farm based on reactive energy utilization technologies

Over the decades, both agriculture and energy grids have encountered significant challenges, such as the lack of power supply to agricultural farms and the difficulties of renewable energy use in the electricity sector. To overcome and address these issues, this paper presents an idea of merging smart farm and renewable energy, using surplus renewable energy for the agriculture sector using the smart farm for renewable energy supplies. First, demonstrate the framework to connect a smart farm energy system and analyze its feasibility and advantages. In this paper, Reactive Energy Utilization Technology (REUT) has been used as an efficient energy consumption system for the smart farm. It is used to predict the optimum wind power scenario in the smart farm by monitoring continuous control variables, with the lowest voltage reached in each scenario, and minimized actual total power losses. A combined system consisting of smart farming production, as well as renewable energy consumption, has been evaluated in experimental results. Experimental results and discussion shows that the proposed method has better renewable energy consumption, accuracy(96.7%), and efficiency(95.6%) for smart farming.
Deep Learning Implementation of Facemask and Physical Distancing Detection with Alarm Systems

COVID-19 or Severe Acute Respiratory Syndrome Corona virus-2 is an extremely transmissible virus that is discharged through breathing droplets released from an infected individual who is talking, sneezing, or coughing. Close interaction with a person infected or through touching a contaminated surface and object can spread the virus rapidly. As of now, there is no vaccine to combat the COVID-19, and the best way to protect the person from a virus is to avoid being exposed to it. Wearing a facemask that covers the nose and mouth in a public setting and repeatedly cleansing of hands or the use of at least 70% alcohol-based disinfectants is a practice to avoid virus exposure. Deep Learning technology has demonstrated its achievement in recognition and classification by processing images. The research study uses deep learning techniques that identify if the person is wearing a facemask or not and check if the persons in the area observe physical distancing. The collected image data contains 20,000 images, uniformly crop images in 224x224 pixels, and attained an accuracy rate of 97% during the training of the model. The developed system is implemented using Python and OpenCV through TensorFlow that recognizes persons wearing a facemask or not wearing and measures the physical distance between each person. It signals an alarm and captures facial images upon detecting persons not wearing a mask and does not observe physical distancing. This study is beneficial in combating the spread of the virus and avoiding contact with the virus.
Outcomes of Contact Tracing in San Francisco, California—Test and Trace During Shelter-in-Place

This case series evaluates case investigation and contact tracing outcomes in San Francisco, California, during shelter-in-place restrictions during the COVID-19 pandemic.
Fine-Tuning BERT for Multi-Label Sentiment Analysis in Unbalanced Code-Switching Text

Previous research on sentiment analysis mainly focuses on binary or ternary sentiment analysis in monolingual texts. However, in today's social media such as micro-blogs, emotions are often expressed in bilingual or multilingual text called code-switching text, and people's emotions are complex, including happiness, sadness, angry, afraid, surprise, etc. Different emotions may exist together, and the proportion of each emotion in the code-switching text is often unbalanced. Inspired by the recently proposed BERT model, we investigate how to fine-tune BERT for multi-label sentiment analysis in code-switching text in this paper. Our investigation includes the selection of pre-trained models and the fine-tuning methods of BERT on this task. To deal with the problem of the unbalanced distribution of emotions, a method based on data augmentation, undersampling and ensemble learning is proposed to get balanced samples and train different multi-label BERT classifiers. Our model combines the prediction of each classifier to get the final outputs. The experiment on the dataset of NLPCC 2018 shared task 1 shows the effectiveness of our model for the unbalanced code-switching text. The F1-Score of our model is higher than many previous models.
The Application of Machine Learning in Self-Adaptive Systems: A Systematic Literature Review

Context: Self-adaptive systems have been studied in software engineering over the past few decades attempting to address challenges within the field. There is a continuous significant need to fully understand the behavior and characteristics of the systems that operate in dynamic environments. By learning the behavior pattern of the environment, we can avoid unnecessary adaptations imbalance efforts for adaptation. As such, there exist research in the area of machine learning aimed at understanding dynamic environments regarding self-adaptive systems. Objective: This study aims to help software practitioners to address adaptation concerns by performing a systematic literature review that provides a comprehensive overview of using machine learning (ML) in self-adaptive systems. We summarize state-of-the-art Of the ML approaches used to handle self-adaptation to help software engineers in the proper selection of ML techniques based on the adaptation concern. Method: This review examines research published between 2001 and 2019 on ML implementation in self-adaptive systems, focusing on the adaptation aspects and purposes. The review was conducted by analyzing major scientific databases that resulted in 78 primary studies from 315 papers from an automatic search. Result: Finally, this study recommends three future research directions to enhance the application of machine learning in self-adaptive systems.
CPGAN: Content-Parsing Generative Adversarial Networks for Text-to-Image Synthesis

Typical methods for text-to-image synthesis seek to design effective generative architecture to model the text-to-image mapping directly. It is fairly arduous due to the cross-modality translation. In this paper we circumvent this problem by focusing on parsing the content of both the input text and the synthesized image thoroughly to model the text-to-image consistency in the semantic level. Particularly, we design a memory structure to parse the textual content by exploring semantic correspondence between each word in the vocabulary to its various visual contexts across relevant images during text encoding. Meanwhile, the synthesized image is parsed to learn its semantics in an object-aware manner. Moreover, we customize a conditional discriminator to model the fine-grained correlations between words and image sub-regions to push for the text-image semantic alignment. Extensive experiments on COCO dataset manifest that our model advances the state-of-the-art performance significantly (from 35.69 to 52.73 in Inception Score).
Robust circular marker localization under non-uniform illuminations based on homomorphic filtering

Circular markers are widely used in image-based measurement fields, e.g. vision system calibration, displacement measurement, and vibration monitoring. Generally, the marker positioning error inevitably leads to inaccurate results. As one of the key factors, the non-uniform illumination seriously affects the localization precision of circular markers. In order to reduce the influences of uneven lighting, a homomorphic filtering-gray weighted centroid method (HF-GWCM) is presented in this work. Firstly, the basic principles of both HF and its combination with GWCM are introduced. Subsequently, some simulation examples are conducted to analyze the localization accuracy of concentric circle markers under non-uniform illumination situations, which comprise the influences of HF parameters and different localization methods. Finally, a single camera imaging system with multiple light sources is employed to perform some tests for the effectiveness validation of HF-GWCM. Both positioning accuracy and stability of HF-GWCM are analyzed under different uneven illumination situations, including lighting movement, multiple light sources, and shaded region. Results demonstrate that HF-GWCM owns the ability to resist the interference of non-uniform illuminations, and it is also an effective way to improve the localization accuracy of circular markers.
Exploring how deprecated Python library APIs are (not) handled

In this paper, we present the first exploratory study of deprecated Python library APIs to understand the status quo of API deprecation in the realm of Python libraries. Specifically, we aim to comprehend how deprecated library APIs are declared and documented in practice by their maintainers, and how library users react to them. By thoroughly looking into six reputed Python libraries and 1,200 GitHub projects, we experimentally observe that API deprecation is poorly handled by library contributors, which subsequently introduce difficulties for Python developers to resolve the usage of deprecated library APIs. This empirical evidence suggests that our community should take immediate actions to appropriately handle the deprecation of Python library APIs.
Regularizing extreme learning machine by dual locally linear embedding manifold learning for training multi-label neural network classifiers

Multi-label learning has been received much attention due to its applicability in machine learning problems. In current years, quite few approaches based on either extreme learning machine (ELM) or radial basis function (RBF) neural network have been proposed with the aim of increasing the efficiency of the multi-label classification. Most existing multi-label learning algorithms focus on information about the feature space. In this paper, our major intention is to regularize the objective function of multi-label learning methods via Locally Linear Embedding (LLE). To achieve this goal, two neural network architectures namely Multi-Label RBF (ML-RBF) and Multi-Label Multi Layer ELM (ML-ELM) are utilized. Then, a regularized multi-label learning method via feature manifold learning (RMLFM) and a regularized multi-label learning method via dual-manifold learning (RMLDM) are established for training two network structures. RMLDM simultaneously exploits the geometry structure of both feature and data space. Furthermore, eight different configurations of applying training algorithms (i.e., RMLFM and RMLDM) to model architectures (i.e., ML-RBF and ML-ELM) are considered for conducting comparisons. The validity and effectiveness of these eight classifiers are indicated by a number of experimental studies on several multi-label datasets. Furthermore, the experiments indicate that the efficiency of the classification can be improved considerably against some cutting-the-edge multi-label techniques for the neural classifiers in which the dual-manifold learning is used as the training method.
Deep Learning for Heterogeneous Human Activity Recognition in Complex IoT Applications

With continued improvements in wireless sensing technology, the notion of the Internet of Things (IoT) has been widely adopted and has become pervasive owing to its broad applications in scenarios such as ambient assisted living, smart healthcare, and smart homes. In that regard, human activity recognition (HAR) is a vital element of intelligent systems to undertake persistent surveillance of human behavior. Due to the omnipresent impact of smartphones in each person’s life, smartphone inertial sensors are used as a case study for this research. Most of the conventional approaches regard HAR as a time-series classification problem; yet, the accuracy of recognition degrades for heterogeneous sensors. In this article, we investigate encoding sensory heterogeneous HAR (HHAR) data into three-channel image representation (i.e., RGB), hence treat the HHAR task as an image classification problem. Since present convolutional network models are computationally heavy when deployed in the IoT environment, we propose a lightweight model image encoded HHAR, called multiscale image-encoded HHAR (MS-IE-HHAR). The model employs a hierarchical multiscale extraction (HME) module followed by an improved spatialwise and channelwise attention (ISCA) module to form the main architecture of the model. The HME module is formed by a group of residually connected shuffle group convolutions (SG-Conv) to extract and learn image representations from different receptive fields while reducing the number of network parameters. The ISCA module combines a lightweight spatialwise attention (SwA) block and an improved channelwise attention (CwA) module to enable the network to pay instructive attention to spatial correlations as well as channel interdependency information. Finally, two widely available HHAR public data sets (i.e., HHAR UCI, and MHEALTH) were used to evaluate the performance of the proposed models with accuracy over 98% and 99%, respectively, demonstrating the model superiority for modeling HAR from heterogeneous data sources.
MaD-DLS: Mean and Deviation of Deep and Local Similarity for Image Quality Assessment

When human visual system (HVS) looks at a scene, it extracts various features from the image about the scene to understand it. The extracted features are compared with the stored memory on the analogous scene to judge their similarity <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> . By analyzing to the similarity, HVS understands the scene presented on eyes. Based on the neurobiological basis, we propose a 2D full reference (FR) image quality assessment (IQA) method, named mean and deviation of deep and local similarity (MaD-DLS) that compares similarity between many original and distorted deep feature maps from convolutional neural networks (CNNs). MaD-DLS uses a deep learning algorithm, but since it uses the convolutional layers of a pre-trained model, it is free from training. For pooling of local quality scores within a deep similarity map, we employ two important descriptive statistics, (weighted) mean and standard deviation and name it mean and deviation (MaD) pooling. The two statistics each have the physical meaning: the weighted mean reflects effect of visual saliency on quality, whereas the standard deviation reflects effect of distortion distribution within the image on it. Experimental results show that MaD-DLS is superior or competitive to the existing methods and the MaD pooling is effective. The MATLAB source code of MaD-DLS will be available online soon.
Readiness of Quantum Optimization Machines for Industrial Applications

There have been multiple attempts to demonstrate that quantum annealing and, in particular, quantum annealing on quantum annealing machines, has the potential to outperform current classical optimization algorithms implemented on CMOS technologies. The benchmarking of these devices has been controversial. Initially, random spin-glass problems were used, however, these were quickly shown to be not well suited to detect any quantum speedup. Subsequently, benchmarking shifted to carefully crafted synthetic problems designed to highlight the quantum nature of the hardware while (often) ensuring that classical optimization techniques do not perform well on them. Even worse, to date a true sign of improved scaling with the number of problem variables remains elusive when compared to classical optimization techniques. Here, we analyze the readiness of quantum annealing machines for real-world application problems. These are typically not random and have an underlying structure that is hard to capture in synthetic benchmarks, thus posing unexpected challenges for optimization techniques, both classical and quantum alike. We present a comprehensive computational scaling analysis of fault diagnosis in digital circuits, considering architectures beyond D-wave quantum annealers. We find that the instances generated from real data in multiplier circuits are harder than other representative random spin-glass benchmarks with a comparable number of variables. Although our results show that transverse-field quantum annealing is outperformed by state-of-the-art classical optimization algorithms, these benchmark instances are hard and small in the size of the input, therefore representing the first industrial application ideally suited for testing near-term quantum annealers and other quantum algorithmic strategies for optimization problems.
Low Power Reconfigurability and Reduced Crosstalk in Integrated Photonic Circuits Fabricated by Femtosecond Laser Micromachining

Femtosecond laser writing is a powerful technique that allows rapid and cost-effective fabrication of photonic integrated circuits with unique three-dimensional geometries. In particular, the possibility to reconfigure such devices by thermo-optic phase shifters represents a paramount feature, exploited to produce adaptive and programmable circuits. However, the scalability is strongly limited by the flaws of current thermal phase shifters, which require hundreds of milliwatts to operate and exhibit large thermal crosstalk. In this work, thermally-insulating three-dimensional microstructures are exploited to decrease the power needed to induce a 2{\pi} phase shift down to 37 mW and to reduce the crosstalk to a few percent. Further improvement is demonstrated when operating in vacuum, with sub-milliwatt power dissipation and negligible crosstalk. These results pave the way towards the demonstration of complex programmable integrated photonic circuits fabricated by femtosecond laser writing, thus opening exciting perspectives in integrated quantum photonics.
Spatial-Temporal Graph Convolutional Networks for Sign Language Recognition

The recognition of sign language is a challenging task with an important role in society to facilitate the communication of deaf persons. We propose a new approach of Spatial-Temporal Graph Convolutional Network to sign language recognition based on the human skeletal movements. The method uses graphs to capture the signs dynamics in two dimensions, spatial and temporal, considering the complex aspects of the language. Additionally, we present a new dataset of human skeletons for sign language based on ASLLVD to contribute to future related studies.
Machine Learning Reveals the Seismic Signature of Eruptive Behavior at Piton de la Fournaise Volcano

Volcanic tremor is key to our understanding of active magmatic systems but, due to its complexity, there is still a debate concerning its origins and how it can be used to characterize eruptive dynamics. In this study we leverage machine learning (ML) techniques using 6 years of continuous seismic data from the Piton de la Fournaise volcano (La R\'eunion island) to describe specific patterns of seismic signals recorded during eruptions. These results unveil what we interpret as signals associated with various eruptive dynamics of the volcano, including the effusion of a large volume of lava during the August-October 2015 eruption, as well as the closing of the eruptive vent during the September-November 2018 eruption. The ML workflow we describe can easily be applied to other active volcanoes, potentially leading to an enhanced understanding of the temporal and spatial evolution of volcanic eruptiions
(Re)Configuring Hybrid Meetings: Moving from User-Centered Design to Meeting-Centered Design

Despite sophisticated technologies for representational fidelity in hybrid meetings, in which co-located and remote participants collaborate via video or audio, meetings are still often disrupted by practical problems with trying to include remote participants. In this paper, we use micro-analysis of three disruptive moments in a hybrid meeting from a global software company to unpack blended technological and conversational practices of inclusion and exclusion. We argue that designing truly valuable experiences for hybrid meetings requires moving from the traditional, essentialist, and perception-obsessed user-centered design approach to a phenomenological approach to the needs of meetings themselves. We employ the metaphor of 'configuring the meeting' to propose that complex ecologies of people, technology, spatial, and institutional organization must be made relevant in the process of design.
Monocular Real-Time Volumetric Performance Capture

We present the first approach to volumetric performance capture and novel-view rendering at real-time speed from monocular video, eliminating the need for expensive multi-view systems or cumbersome pre-acquisition of a personalized template model. Our system reconstructs a fully textured 3D human from each frame by leveraging Pixel-Aligned Implicit Function (PIFu). While PIFu achieves high-resolution reconstruction in a memory-efficient manner, its computationally expensive inference prevents us from deploying such a system for real-time applications. To this end, we propose a novel hierarchical surface localization algorithm and a direct rendering method without explicitly extracting surface meshes. By culling unnecessary regions for evaluation in a coarse-to-fine manner, we successfully accelerate the reconstruction by two orders of magnitude from the baseline without compromising the quality. Furthermore, we introduce an Online Hard Example Mining (OHEM) technique that effectively suppresses failure modes due to the rare occurrence of challenging examples. We adaptively update the sampling probability of the training data based on the current reconstruction accuracy, which effectively alleviates reconstruction artifacts. Our experiments and evaluations demonstrate the robustness of our system to various challenging angles, illuminations, poses, and clothing styles. We also show that our approach compares favorably with the state-of-the-art monocular performance capture. Our proposed approach removes the need for multi-view studio settings and enables a consumer-accessible solution for volumetric capture.
Category Level Object Pose Estimation via Neural Analysis-by-Synthesis

Many object pose estimation algorithms rely on the analysis-by-synthesis framework which requires explicit representations of individual object instances. In this paper we combine a gradient-based fitting procedure with a parametric neural image synthesis module that is capable of implicitly representing the appearance, shape and pose of entire object categories, thus rendering the need for explicit CAD models per object instance unnecessary. The image synthesis network is designed to efficiently span the pose configuration space so that model capacity can be used to capture the shape and local appearance (i.e., texture) variations jointly. At inference time the synthesized images are compared to the target via an appearance based loss and the error signal is backpropagated through the network to the input parameters. Keeping the network parameters fixed, this allows for iterative optimization of the object pose, shape and appearance in a joint manner and we experimentally show that the method can recover orientation of objects with high accuracy from 2D images alone. When provided with depth measurements, to overcome scale ambiguities, the method can accurately recover the full 6DOF pose successfully.