Reviewerly/exHarmony huggingface dataset:
Continuous Adaptation for Interactive Object Segmentation by Learning from Corrections

In interactive object segmentation a user collaborates with a computer vision model to segment an object. Recent works employ convolutional neural networks for this task: Given an image and a set of corrections made by the user as input, they output a segmentation mask. These approaches achieve strong performance by training on large datasets but they keep the model parameters unchanged at test time. Instead, we recognize that user corrections can serve as sparse training examples and we propose a method that capitalizes on that idea to update the model parameters on-the-fly to the data at hand. Our approach enables the adaptation to a particular object and its background, to distributions shifts in a test set, to specific object classes, and even to large domain changes, where the imaging modality changes between training and testing. We perform extensive experiments on 8 diverse datasets and show: Compared to a model with frozen parameters, our method reduces the required corrections (i) by 9%–30% when distribution shifts are small between training and testing; (ii) by 12%–44% when specializing to a specific class; (iii) and by 60% and 77% when we completely change domain between training and testing.
T2Pair: Secure and Usable Pairing for Heterogeneous IoT Devices

Secure pairing is key to trustworthy deployment and application of Internet of Things (IoT) devices. However, IoT devices lack conventional user interfaces, such as keyboards and displays, which makes many traditional pairing approaches inapplicable. Proximity-based pairing approaches are very usable, but can be exploited by co-located malicious devices. Approaches based on a user's physical operations on IoT devices are more secure, but typically require inertial sensors, while many devices do not satisfy this requirement. A secure and usable pairing approach that can be applied to heterogeneous IoT devices still does not exist. We develop a technique, Universal Operation Sensing, which allows an IoT device to sense the user's physical operations on it without requiring inertial sensors. With this technique, a user holding a smartphone or wearing a wristband can finish pairing in seconds through some very simple operations, e.g., pressing a button or twisting a knob. Moreover, we reveal an inaccuracy issue in original fuzzy commitment and propose faithful fuzzy commitment to resolve it. We design a pairing protocol using faithful fuzzy commitment, and build a prototype system named Touch-to-Pair (T2Pair, for short). The comprehensive evaluation shows that it is secure and usable.
The Virtual Reality Questionnaire Toolkit

In this work, we present the VRQuestionnaireToolkit, which enables the research community to easily collect subjective measures within virtual reality (VR). We contribute a highly customizable and reusable open-source toolkit which can be integrated in existing VR projects rapidly. The toolkit comes with a pre-installed set of standard questionnaires such as NASA TLX, SSQ and SUS Presence questionnaire. Our system aims to lower the entry barrier to use questionnaires in VR and to significantly reduce development time and cost needed to run pre-, in between- and post-study questionnaires.
PoEWAL: A lightweight consensus mechanism for blockchain in IoT

Blockchain in IoT applications obviates the need for dependence on a single trusted authority, enhancing the potential for scalability and reliability. Existing consensus methods used in blockchain require high energy consumption, massive computational power with trusted authorities, or proof for mining a block. Resource constrained IoT devices need a lightweight and low latency consensus mechanism. Trust-free probabilistic consensus methods allow every node to participate in the consensus, enhancing the robustness and reliability of transactions as compared to absolute consensus methods. We propose a lightweight probabilistic proof of elapsed work and luck (PoEWAL) consensus method for non-cooperative blockchain in IoT environments. PoEWAL consumes less energy, requires less computational power and has low latency. We analyzed suitability of PoEWAL for resource constrained devices, using Contiki Cooja simulator. Experimental results show that energy consumption is low at various difficulty levels, demonstrating the feasibility of PoEWAL. Furthermore, PoEWAL is compared with such existing probabilistic consensus methods as proof of work, proof of stake, proof of activity, algorand, and proof of authority, in terms of energy, consensus time, and network latency, to confirm that PoEWAL is a suitable consensus method for blockchain in IoT.
Studying Software Developer Expertise and Contributions in Stack Overflow and GitHub

Knowledge and experience are touted as both the necessary and sufficient conditions to make a person an expert. This paper attempts to investigate this issue in the context of software development by studying software developer's expertise based on their activity and experience on GitHub and Stack Overflow platforms. We study how developers themselves define the notion of an "expert", as well as why or why not developers contribute to online collaborative platforms. We conducted an exploratory survey with 73 software developers and applied a mixed methods approach to analyze the survey results. The results provided deeper insights into how an expert in the field could be defined. Further, the study provides a better understanding of the underlying factors that drive developers to contribute to GitHub and Stack Overflow, and the challenges they face when participating on either platform.The quantitative analysis showed that JavaScript remains a popular language, while knowledge and experience are the key factors driving expertise. On the other hand, qualitative analysis showed that soft skills such as effective and clear communication, analytical thinking are key factors defining an expert. We found that both knowledge and experience are only necessary but not sufficient conditions for a developer to become an expert, and an expert would necessarily have to possess adequate soft skills. Lastly, an expert's contribution to GitHub seems to be driven by personal factors, while contribution to Stack Overflow is motivated more by professional drivers (i.e., skills and expertise). Moreover, developers seem to prefer contributing to GitHub as they face greater challenges while contributing to Stack Overflow.
Software reuse analytics using integrated random forest and gradient boosting machine learning algorithm

Abstract The term Cleaner Production (CP) for Production Companies is contemplated as influential to get sustainable production. CP mainly deals with three R's that is, reuse, reduce, and recycle. For software enterprise, the software reuse plays a pivotal role. Software reuse is a process of producing new products or software from the existing software by updating it. To extract useful information from the existing software data mining comes into light. The algorithms used for software reuse face issues related to maintenance cost, accuracy, and performance. Also, the currently used algorithm does not give accurate results on whether the component of software can be reused. Machine Learning gives the best results to predicate if the given software component is reusable or not. This paper introduces an integrated Random Forest and Gradient Boosting Machine Learning Algorithm (RFGBM) which test the reusability of the given software code considering the object‐oriented parameters such as cohesion, coupling, cyclomatic complexity, bugs, number of children, and depth inheritance tree. Further, the proposed algorithm is compared with J48, AdaBoostM1, LogitBoost, Part, One R, LMT, JRip, DecisionStump algorithms. Performance metrices like accuracy, error rate, Relative Absolute Error, and Mean Absolute Error are improved using RFGBM. This algorithm also utilizes data preprocessing with the help of an unsupervised filter to remove the missing value for efficiency improvement. Proposed algorithm outperforms existing in term of performance parameters.
Blockchain Technology Application for Value-Added Tax Systems

The utilization of new technology in the form of blockchain technology for a Value Added Tax (VAT) acceptance system is relatively new and has not been widely encountered thus far. This research analyzes how blockchain technology can be applied to a VAT system, particularly for electronic invoices (e-Invoice). A qualitative approach was used in this study to analyze blockchain technology models that could be applied in a VAT system. The results of this study indicate that due to its characteristics, blockchain technology can only be applied to taxpayer data that do not require privacy. Data that are considered safe if distributed to nodes in the blockchain technology network include the Tax Invoice Serial Number (TISN). A TISN system based on blockchain technology will produce a faster and more efficient system. Transactions on the TISN in Indonesia can also be monitored and tracked directly by the Directorate General of Taxation (DGT). Blockchain technology can be applied in the TISN system by using a permissioned private blockchain type.
Using mobile applications for self-directed learning of academic vocabulary among university students

The current study investigated the contribution of a mobile application for self-directed and autonomous learning of academic vocabulary among English as a Foreign language (EFL) university students. Furthermore, we examined the long-term impacts of mobile-assisted vocabulary learning, by using a pre-, post-, and delayed post-test design. The participants were 38 third- and fourth-year university students in experimental (N = 20) and control (N = 18) groups, selected based on convenience sampling procedures. During a semester, students in the experimental group used a mobile application (i.e. AWL builder), while the control group used traditional materials. The participants’ receptive vocabulary knowledge was tested three times. The findings revealed that both groups improved their vocabulary knowledge (Sig. < 0.000). Nonetheless, the participants in the experimental group attained better results in both post- and delayed post-test, and the observed mean differences were statistically significant (Sig. < 0.000, Partial Eta Squared = 0.356). Moreover, a significant effect for time was found for mobile-assisted vocabulary learning (Sig. < 0.000, Partial Eta Squared = 0.907). The results highlight the potential offered by mobile applications for self-directed learning of academic vocabulary and promise implications for EFL vocabulary learning.
Contrast to Divide: Self-Supervised Pre-Training for Learning with Noisy Labels

The success of learning with noisy labels (LNL) methods relies heavily on the success of a warm-up stage where standard supervised training is performed using the full (noisy) training set. In this paper, we identify a "warm-up obstacle": the inability of standard warm-up stages to train high quality feature extractors and avert memorization of noisy labels. We propose "Contrast to Divide" (C2D), a simple framework that solves this problem by pre-training the feature extractor in a self-supervised fashion. Using self-supervised pre-training boosts the performance of existing LNL approaches by drastically reducing the warm-up stage's susceptibility to noise level, shortening its duration, and improving extracted feature quality. C2D works out of the box with existing methods and demonstrates markedly improved performance, especially in the high noise regime, where we get a boost of more than 27% for CIFAR-100 with 90% noise over the previous state of the art. In real-life noise settings, C2D trained on mini-WebVision outperforms previous works both in WebVision and ImageNet validation sets by 3% top-1 accuracy. We perform an in-depth analysis of the framework, including investigating the performance of different pre-training approaches and estimating the effective upper bound of the LNL performance with semi-supervised learning. Code for reproducing our experiments is available at https://github.com/ContrastToDivide/C2D
Revisiting EmbodiedQA: A Simple Baseline and Beyond

In Embodied Question Answering (EmbodiedQA), an agent interacts with an environment to gather necessary information for answering user questions. Existing works have laid a solid foundation towards solving this interesting problem. But the current performance, especially in navigation, suggests that EmbodiedQA might be too challenging for the contemporary approaches. In this paper, we empirically study this problem and introduce 1) a simple yet effective baseline that achieves promising performance; 2) an easier and practical setting for EmbodiedQA where an agent has a chance to adapt the trained model to a new environment before it actually answers users questions. In this new setting, we randomly place a few objects in new environments, and upgrade the agent policy by a distillation network to retain the generalization ability from the trained model. On the EmbodiedQA v1 benchmark, under the standard setting, our simple baseline achieves very competitive results to the-state-of-the-art; in the new setting, we found the introduced small change in settings yields a notable gain in navigation.
Fast High-Dimensional Bilateral and Nonlocal Means Filtering

Existing fast algorithms for bilateral and nonlocal means filtering mostly work with grayscale images. They cannot easily be extended to high-dimensional data such as color and hyperspectral images, patch-based data, and flow-fields. In this paper, we propose a fast algorithm for high-dimensional bilateral and nonlocal means filtering. Unlike existing approaches, where the focus is on approximating the data (using quantization) or the filter kernel (via analytic expansions), we locally approximate the kernel using weighted and shifted copies of a Gaussian, where the weights and shifts are inferred from the data. The algorithm emerging from the proposed approximation essentially involves clustering and fast convolutions, and is easy to implement. Moreover, a variant of our algorithm comes with a guarantee (bound) on the approximation error, which is not enjoyed by existing algorithms. We present some results for high-dimensional bilateral and nonlocal means filtering to demonstrate the speed and accuracy of our proposal. Moreover, we also show that our algorithm can outperform the state-of-the-art fast approximations in terms of accuracy and timing.
New binary marine predators optimization algorithms for 0–1 knapsack problems

Recently, a novel meta-heuristic algorithm known as the marine predators algorithm (MPA) has been proposed for solving continuous optimization problems. Despite the significant superiority of MPA in solving continuous problems, the algorithm is not applicable to binary problems. This work proposes a binary version of MPA for solving the 0–1 knapsack (KP01) problem. To develop the binary variant of MPA (BMPA), a wide range of V-Shaped and S-shaped transfer functions is investigated for mapping continuous values to binary. The performance of a binary algorithm is first shown to heavily rely on the selection of an appropriate transfer function on a specific dataset. The performance of the proposed BMPA algorithm is then tested on a set of KP01 problems and compared to a number of existing algorithms. The results demonstate the merits of the BMPAs proposed in this work.
Energy-time entanglement-based dispersive optics quantum key distribution over optical fibers of 20 km

An energy-time entanglement-based dispersive optics quantum key distribution (DO-QKD) is demonstrated experimentally over optical fibers of 20 km. In the experiment, the telecom band energy-time entangled photon pairs are generated through spontaneous four wave mixing in a silicon waveguide. The arrival time of photons are registered for key generating and security test. High dimensional encoding in the arrival time of photons is used to increase the information per coincidence of photon pairs. The bin sifting process is optimized by a three level structure, which significantly reduces the raw quantum bit error rate (QBER) due to timing jitters of detectors and electronics. A raw key generation rate of 151kbps with QBER of 4.95% is achieved, under a time-bin encoding format with 4 bits per coincidence. This experiment shows that entanglement-based DO-QKD can be implemented in an efficient and convenient way, which has great potential in quantum secure communication networks in the future.
A comparative study of estimation methods in quantum tomography

As quantum tomography is becoming a key component of the quantum engineering toolbox, there is a need for a deeper understanding of the multitude of estimation methods available. Here we investigate and compare several such methods: maximum likelihood, least squares, generalised least squares, positive least squares, thresholded least squares and projected least squares. The common thread of the analysis is that each estimator projects the measurement data onto a parameter space with respect to a specific metric, thus allowing us to study the relationships between different estimators.
iBrownout: An Integrated Approach for Managing Energy and Brownout in Container-Based Clouds

Energy consumption of Cloud data centers has been a major concern of many researchers, and one of the reasons for huge energy consumption of Clouds lies in the inefficient utilization of computing resources. Besides energy consumption, another challenge of data centers is the unexpected loads, which leads to the overloads and performance degradation. Compared with VM consolidation and Dynamic Voltage Frequency Scaling that cannot function well when the whole data center is overloaded, brownout has shown to be a promising technique to handle both overloads and energy consumption through dynamically deactivating application optional components, which are also identified as containers/microservices. In this work, we propose an integrated approach to manage energy consumption and brownout in container-based cloud data centers. \color{black} We also evaluate our proposed scheduling policies with real traces in a prototype system. The results show that our approach reduces about 40%, 20% and 10% energy than the approach without power-saving techniques, brownout-overbooking approach and auto-scaling approach respectively while ensuring Quality of Service.
More Classifiers, Less Forgetting: A Generic Multi-classifier Paradigm for Incremental Learning

Overcoming catastrophic forgetting in neural networks is a long-standing and core research objective for incremental learning. Notable studies have shown regularization strategies enable the network to remember previously acquired knowledge devoid of heavy forgetting. Since those regularization strategies are mostly associated with classifier outputs, we propose a MUlti-Classifier (MUC) incremental learning paradigm that integrates an ensemble of auxiliary classifiers to estimate more effective regularization constraints. Additionally, we extend two common methods, focusing on parameter and activation regularization, from the conventional single-classifier paradigm to MUC. Our classifier ensemble promotes regularizing network parameters or activations when moving to learn the next task. Under the setting of task-agnostic evaluation, our experimental results on CIFAR-100 and Tiny ImageNet incremental benchmarks show that our method outperforms other baselines. Specifically, MUC obtains 3%–5% accuracy boost and 4%–5% decline of forgetting ratio, compared with MAS and LwF. Our code is available at https://github.com/Liuy8/MUC .
A high dynamic range optical detector for measuring single photons and bright light

Detecting light is fundamental to all optical experiments and applications. At the single photon level, the quantised nature of light requires specialised detectors, which typically saturate for more than one photon, rendering the measurement of bright light impossible. Saturation can be partially overcome by multiplexing single-photon-sensitive detectors, enabling measurement up to tens of photons. However, current approaches are still far from bridging the gap to bright light levels. Here, we report on a massively-multiplexed single-photon detector, which exhibits a dynamic range of 123 dB, from optical energies as low as $\mathbf{10^{-7}}$ photons per pulse to $\mathbf{\sim2.5\times10^{5}}$ photons per pulse. This allows us to calibrate a single photon detector directly to a power meter. The use of a single-photon sensitive detector further allows us to characterise the nonclassical features of a variety of quantum states. This device will find application where high dynamic range and single-photon sensitivity are required.
Comparison of a Deep Learning-Based Pose Estimation System to Marker-Based and Kinect Systems in Exergaming for Balance Training

Using standard digital cameras in combination with deep learning (DL) for pose estimation is promising for the in-home and independent use of exercise games (exergames). We need to investigate to what extent such DL-based systems can provide satisfying accuracy on exergame relevant measures. Our study assesses temporal variation (i.e., variability) in body segment lengths, while using a Deep Learning image processing tool (DeepLabCut, DLC) on two-dimensional (2D) video. This variability is then compared with a gold-standard, marker-based three-dimensional Motion Capturing system (3DMoCap, Qualisys AB), and a 3D RGB-depth camera system (Kinect V2, Microsoft Inc). Simultaneous data were collected from all three systems, while participants (N = 12) played a custom balance training exergame. The pose estimation DLC-model is pre-trained on a large-scale dataset (ImageNet) and optimized with context-specific pose annotated images. Wilcoxon's signed-rank test was performed in order to assess the statistical significance of the differences in variability between systems. The results showed that the DLC method performs comparably to the Kinect and, in some segments, even to the 3DMoCap gold standard system with regard to variability. These results are promising for making exergames more accessible and easier to use, thereby increasing their availability for in-home exercise.
Exploring Public Response to COVID-19 on Weibo with LDA Topic Modeling and Sentiment Analysis

It is necessary and important to understand public responses to crises, including disease outbreaks. Traditionally, surveys have played an essential role in collecting public opinion, while nowadays, with the increasing popularity of social media, mining social media data serves as another popular tool in opinion mining research. To understand the public response to COVID-19 on Weibo, this research collects 719,570 Weibo posts through a web crawler and analyzes the data with text mining techniques, including Latent Dirichlet Allocation (LDA) topic modeling and sentiment analysis. It is found that, in response to the COVID-19 outbreak, people learn about COVID-19, show their support for frontline warriors, encourage each other spiritually, and, in terms of taking preventive measures, express concerns about economic and life restoration, and so on. Analysis of sentiments and semantic networks further reveals that country media, as well as influential individuals and "self-media," together contribute to the information spread of positive sentiment.