Reviewerly/exHarmony huggingface dataset:
Area-Coverage Planning for Spray-based Surface Disinfection with a Mobile Manipulator

The use of robots has significantly increased to fight highly contagious diseases like SARS-COV-2, Ebola, MERS, and others. One of the important applications of robots to fight such infectious diseases is disinfection. Manual disinfection can be a time-consuming, risky, labor-intensive, and mundane, and humans may fail to disinfect critical areas due to the resulting fatigue. Autonomous or semi-autonomous mobile manipulators mounted with a spray nozzle at the end-effector can be very effective in spraying disinfectant liquid for deep disinfection of objects and surfaces. In this paper, we present an area-coverage planning algorithm to compute a path that the nozzle follows to disinfect surfaces represented by their point clouds. We project the point cloud on a plane and produce a polygon on which we generate multiple spray paths using our branch and bound-based tree search area-coverage algorithm such that the spray paths cover the entire area of the polygon. An appropriate spray path is chosen using a robot capability map-based selection criterion. We generate mobile manipulator trajectories using successive refinement-based parametric optimization so that the paths for the nozzle are followed accurately. Thereafter, we need to make sure that the joint velocities of the mobile manipulator are regulated appropriately such that each point on the surface receives enough disinfectant spray. To this end, we compute the time intervals between the robot path waypoints such that enough disinfectant liquid is sprayed on all points of the point cloud that results in thorough disinfection of the surface, and the particular robot path is executed in the minimum possible time. We have implemented the area-coverage planning and mobile manipulator motion planning on five test scenarios in simulation using our ADAMMS-SD (Agile Dexterous Autonomous Mobile Manipulation System for Surface Disinfection) robot. We benchmark our spray path generation algorithm with three competing methods by showing that the generated paths are significantly more efficient in terms of area coverage and reducing disinfectant wastage. We also show the time interval computation between successive waypoints results in thorough disinfection of surfaces.
Sub-minute probabilistic solar forecasting for real-time stochastic simulations

Simulation needs to reflect reality, otherwise, it yields misleading results that are potentially harmful to the operators and decision makers who rely on that simulation. Current stochastic simulation methods for solar energy systems in smart grids mostly consider scenarios generated from a single low-frequency time series, which is unable to reflect the high-frequency fluctuation and changing uncertainty in the actual solar power output. To that end, this paper introduces a state-of-the-art probabilistic solar forecasting method, that provides remedies to the drawbacks, and thus benefits real-time stochastic simulations at large. Lasso-penalized quantile regression is paired with an analog-based preselection algorithm, and is used to forecast the irradiance over a 1 km by 1 km area, in sub-minute timescales. To capture the changing weather condition, the forecasting method uses online training, and updates its parameters and forecasts every few seconds. Despite the heavy computation required, the forecasting method is fast; it takes less than 1 s to complete a forecasting cycle. Through five benchmarking methods, ranging from the naïve climatology and persistence to the state-of-the-art analog ensemble (AnEn), the proposed method is shown to be able to attain an exceptionally high forecast skill in terms of pinball loss (up to a skill score of 55%, with AnEn being the reference model), which is unparalleled by all previous works that used the same dataset. To promote the future uptake of the method, the R code and the final forecast datasets are released on Github.
CeyMo: See More on Roads - A Novel Benchmark Dataset for Road Marking Detection

In this paper, we introduce a novel road marking bench-mark dataset for road marking detection, addressing the limitations in the existing publicly available datasets such as lack of challenging scenarios, prominence given to lane markings, unavailability of an evaluation script, lack of an-notation formats and lower resolutions. Our dataset consists of 2887 total images with 4706 road marking instances belonging to 11 classes. The images have a high resolution of 1920 × 1080 and capture a wide range of traffic, lighting and weather conditions. We provide road marking an-notations in polygons, bounding boxes and pixel-level segmentation masks to facilitate a diverse range of road marking detection algorithms. The evaluation metrics and the evaluation script we provide, will further promote direct comparison of novel approaches for road marking detection with existing methods. Furthermore, we evaluate the effectiveness of using both instance segmentation and object detection based approaches for the road marking detection task. Speed and accuracy scores for two instance segmentation models and two object detector models are provided as a performance baseline for our benchmark dataset. The dataset and the evaluation script is publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Non-interactive verifiable privacy-preserving federated learning

Federated Learning (FL) has received widespread attention for its ability to conduct collaborative learning without collecting raw data. Recently, it has became a reality that more accurate model training is achieved through the large-scale deployment of FL on resource-constrained device, where the communication is expensive and clients dropping out is common, such as mobile phone or IoT devices etc. However, shared local gradients make the privacy of local data in FL vulnerable, and the client is easily deceived by the server for the returned forged results. To solve these problems, the existing schemes either only consider the privacy protection requirements under the communication-limited but not involving verifiability, or consider the privacy-protection and verification separately, which incurs expensive computation and communication costs. It is a challenge to design a lightweight verifiable privacy preserving gradient aggregation scheme for large-scale resource-constrained clients under the communication-limited condition. In this paper, we proposed a non-interactive verifiable privacy-preserving FL based on dual-servers (NIVP-DS) architecture, which improves the efficiency and security of the system and is robust to clients dropping out, based on the constraints that the communication overhead between client and server not more than 2× that of plaintext computation. Based on NIVP-DS, an efficient privacy gradient aggregation scheme is presented by exploiting random matrix coding and secure 2-party computation. The scheme only costs O(M) fully linear operation in the client side under the communication constraints. In order to realize the verifiability, a cross-verification method is introduce, which is based on credible matrix exchange to extend the privacy aggregation scheme to a verifiable scheme. The method only costs little additional overhead, meanwhile, guarantees that one dishonest server cannot forge the aggregate results to deceive the honest client, even if it colludes with multiple clients. The effectiveness of NIVP-DS in practice is corroborated by experiments. The results show that the performance of both secure aggregation and verification are efficiency, and the additional overhead of verification is minimal.
Exploring more diverse network architectures for single image super-resolution

We propose a plug-and-play neural architecture search (NAS) method to explore diverse architectures for single image super-resolution (SISR). Unlike current NAS-based methods with the single path setting and pipeline setting, our proposed method achieves the trade-off between diverse network architectures and search cost. Our proposed method formulates the task in a differentiable manner, which inherits the architecture parameter optimization method from Discrete Stochastic Neural Architecture Search (DSNAS). Besides the straightforward searching of operations, we also search each node in a cell for the activation function, from-node, and skip-connection node, which diverse the searched architecture topologies. The individually searching of skip-connection node avoids skip-connection excessive phenomenon. Moreover, to alleviate the influence of inconsistent architecture between training and testing periods, we introduce random variables into the architecture parameter as regularization. Benchmark experiments show our state-of-the-art performance under specific parameters and FLOPs constraints. Compared with other NAS-based SISR methods, our proposed methods achieve better performance with less searching time and resources. The superior results further demonstrate the effectiveness of our proposed NAS methods.
$$\mathsf {Rabbit}$$: Efficient Comparison for Secure Multi-Party Computation

Secure comparison has been a fundamental challenge in privacy-preserving computation, since its inception as Yao’s millionaires’ problem (FOCS 1982). In this work, we present a novel construction for general n-party private comparison, secure against an active adversary, in the dishonest majority setting. For the case of comparisons over fields, our protocol is more efficient than the best prior work ( $$\mathsf {edaBits} $$ : Crypto 2020), with $$\sim 1.5\times $$ better throughput in most adversarial settings, over $$2.3\times $$ better throughput in particular in the passive, honest majority setting, and lower communication. Our comparisons crucially eliminate the need for bounded inputs as well as the need for statistical security that prior works require. An important consequence of removing this “slack” (a gap between the bit-length of the input and the MPC representation) is that multi-party computation (MPC) protocols can be run in a field of smaller size, reducing the overhead incurred by privacy-preserving computations. We achieve this novel construction using the commutative nature of addition over rings and fields. This makes the protocol both simple to implement and highly efficient and we provide an implementation in MP-SPDZ (CCS 2020).
Interpretable Diagnosis for Whole-Slide Melanoma Histology Images Using Convolutional Neural Network

At present, deep learning-based medical image diagnosis had achieved high performance in several diseases. However, the black-box nature of the convolutional neural network (CNN) limits their role in diagnosis. In this study, a novel interpretable diagnosis pipeline using the CNN model was proposed. Furthermore, a sizeable melanoma database that contains 841 digital whole-slide images (WSIs) was built to train and evaluate the model. The model achieved strong melanoma classification ability (0.962 areas under the receiver operating characteristic, 0.887 sensitivity, and 0.925 specificity). Moreover, the proposed model outperformed the existing schemes in terms of accuracy that is 20 pathologists (0.933 vs 0.732 accuracy). Finally, the gradient-weighted class activation mapping (Grad-CAM) method was used to show the inner logic of the proposed model and its feasibility to improve diagnosis process in healthcare. The mechanism of feature heat maps which is visualized through a saliency mapping has demonstrated that features learned or extracted by the proposed model are compatible with the accepted pathological features. Conclusively, the proposed model provides a rapid and accurate diagnosis by locating the distinctive features of melanoma to build doctors' trust in the CNNs' diagnosis results.
Can You Trust Your Autonomous Car? Interpretable and Verifiably Safe Reinforcement Learning

Safe and efficient behavior are the key guiding principles for autonomous vehicles. Manually designed rule-based systems need to act very conservatively to ensure a safe operation. This limits their applicability to real-world systems. On the other hand, more advanced behaviors, i.e., policies, learned through means of reinforcement learning (RL) suffer from non-interpretability as they are usually expressed by deep neural networks that are hard to explain. Even worse, there are no formal safety guarantees for their operation. In this paper we introduce a novel pipeline that builds on recent advances in imitation learning and that can generate safe and efficient behavior policies. We combine a reinforcement learning step that solves for safe behavior through the introduction of safety distances with a subsequent innovative safe extraction of decision tree policies. The resulting decision tree is not only easy to interpret, it is also safer than the neural network policy trained for safety. Additionally, we formally prove the safety of trained RL agents for linearized system dynamics, showing that the learned and extracted policy successfully avoids all catastrophic events.
EvoGAN: An evolutionary computation assisted GAN

The image synthesis technique is relatively well established which can generate facial images that are indistinguishable even by human beings. However, all of these approaches uses gradients to condition the output, resulting in the outputting the same image with the same input. Also, they can only generate images with basic expression or mimic an expression instead of generating compound expression. In real life, however, human expressions are of great diversity and complexity. In this paper, we propose an evolutionary algorithm (EA) assisted GAN, named EvoGAN, to generate various compound expressions with any accurate target compound expression. EvoGAN uses an EA to search target results in the data distribution learned by GAN. Specifically, we use the Facial Action Coding System (FACS) as the encoding of an EA and use a pre-trained GAN to generate human facial images, and then use a pre-trained classifier to recognize the expression composition of the synthesized images as the fitness function to guide the search of the EA. Combined random searching algorithm, various images with the target expression can be easily sythesized. Quantitative and Qualitative results are presented on several compound expressions, and the experimental results demonstrate the feasibility and the potential of EvoGAN. The source code is available at https://github.com/ECNU-Cross-Innovation-Lab/EvoGAN.
Multi-functional photonic processors using coherent network of micro-ring resonators

Silicon photonics have widespread applications in optical communications, photonic sensors, and quantum information processing systems. Different photonic integrated circuits often require similar basic functional elements such as tunable filters, optical switches, wavelength de-multiplexers, optical delay lines, and polarization crosstalk unscrambling. Other optical signal processing functional elements may be needed in specific applications, for example, the differentiation with respect to time of time-varying optical signals and the implementation of very high extinction interferometers in some integrated quantum photonic circuits. Just as reconfigurable electronic processors in microelectronics have advantages in terms of ready availability and low cost from large-volume generic manufacturing and are useful for configuration into different functionalities in the form of field-programmable gate arrays, here, we show how an integrated coherent network of micro-ring resonators can be used in reconfigurable photonic processors. We demonstrate the implementation of optical filters, optical delay lines, optical space switching fabric, high extinction ratio Mach–Zehnder interferometer, and photonic differentiation in a reconfigurable network where the control of the phase in the different arms of the coherent network can determine the implemented functionality.
PSSL

Personalized search plays a crucial role in improving user search experience owing to its ability to build user profiles based on historical behaviors. Previous studies have made great progress in extracting personal signals from the query log and learning user representations. However, neural personalized search is extremely dependent on sufficient data to train the user model. Data sparsity is an inevitable challenge for existing methods to learn high-quality user representations. Moreover, the overemphasis on final ranking quality leads to rough data representations and impairs the generalizability of the model. To tackle these issues, we propose a Personalized Search framework with Self-supervised Learning (PSSL) to enhance data representations. Specifically, we adopt a contrastive sampling method to extract paired self-supervised information from sequences of user behaviors in query logs. Four auxiliary tasks are designed to pre-train the sentence encoder and the sequence encoder used in the ranking model. They are optimized by contrastive loss which aims to close the distance between similar user sequences, queries, and documents. Experimental results on two datasets demonstrate that our proposed model PSSL achieves state-of-the-art performance compared with existing baselines.
Text-Based Emotion Recognition in English and Polish for Therapeutic Chatbot

In this article, we present the results of our experiments on sentiment and emotion recognition for English and Polish texts, aiming to work in the context of a therapeutic chatbot. We created a dedicated dataset by adding samples of neutral texts to an existing English-language emotion-labeled corpus. Next, using neural machine translation, we developed a Polish version of the English database. A bilingual, parallel corpus created in this way, named CORTEX (CORpus of Translated Emotional teXts), labeled with three sentiment polarity classes and nine emotion classes, was used for experiments on classification. We employed various classifiers: Naïve Bayes, Support Vector Machines, fastText, and BERT. The results obtained were satisfactory: we achieved the best scores for the BERT-based models, which yielded accuracy of over 90% for sentiment (3-class) classification and almost 80% for emotion (9-class) classification. We compared the results for both languages and discussed the differences. Both the accuracy and the F1-scores for Polish turned out to be slightly inferior to those for English, with the highest difference visible for BERT.
Personal big data pricing method based on differential privacy

Personal big data can greatly promote social management, business applications, and personal services, and bring certain economic benefits to users. The difficulty with personal big data security and privacy protection lies in realizing the maximization of the value of personal big data and in striking a balance between data privacy protection and sharing on the premise of satisfying personal big data security and privacy protection. Thus, in this paper, we propose a personal big data pricing method based on differential privacy (PMDP). We design two different mechanisms of positive and reverse pricing to reasonbly price personal big data. We perform aggregate statistics on an open dataset and extensively evaluated its performance. The experimental results show that PMDP can provide reasonable pricing for personal big data and fair compensation to data owners, ensuring an arbitrage-free condition and finding a balance between privacy protection and data utility.
Student Engagement Dataset

A major challenge for online learning is the inability of systems to support student emotion and to maintain student engagement. In response to this challenge, computer vision has become an embedded feature in some instructional applications. In this paper, we propose a video dataset of college students solving math problems on the educational platform MathSpring.org with a front facing camera collecting visual feedback of student gestures. The video dataset is annotated to indicate whether students’ attention at specific frames is engaged or wandering. In addition, we train baselines for a computer vision module that determines the extent of student engagement during remote learning. Baselines include state-of-the-art deep learning image classifiers and traditional conditional and logistic regression for head pose estimation. We then incorporate a gaze baseline into the MathSpring learning platform, and we are evaluating its performance with the currently implemented approach.
King Abdulaziz University Breast Cancer Mammogram Dataset (KAU-BCMD)

The current era is characterized by the rapidly increasing use of computer-aided diagnosis (CAD) systems in the medical field. These systems need a variety of datasets to help develop, evaluate, and compare their performances fairly. Physicians indicated that breast anatomy, especially dense ones, and the probability of breast cancer and tumor development, vary highly depending on race. Researchers reported that breast cancer risk factors are related to culture and society. Thus, there is a massive need for a local dataset representing breast cancer in our region to help develop and evaluate automatic breast cancer CAD systems. This paper presents a public mammogram dataset called King Abdulaziz University Breast Cancer Mammogram Dataset (KAU-BCMD) version 1. To our knowledge, KAU-BCMD is the first dataset in Saudi Arabia that deals with a large number of mammogram scans. The dataset was collected from the Sheikh Mohammed Hussein Al-Amoudi Center of Excellence in Breast Cancer at King Abdulaziz University. It contains 1416 cases. Each case has two views for both the right and left breasts, resulting in 5662 images based on the breast imaging reporting and data system. It also contains 205 ultrasound cases corresponding to a part of the mammogram cases, with 405 images as a total. The dataset was annotated and reviewed by three different radiologists. Our dataset is a promising dataset that contains different imaging modalities for breast cancer with different cancer grades for Saudi women.
Fraud and Data Availability Proofs: Detecting Invalid Blocks in Light Clients

Light clients, also known as Simple Payment Verification (SPV) clients, are nodes which only download a small portion of the data in a blockchain, and use indirect means to verify that a given chain is valid. Instead of validating blocks, they assume that the chain favoured by the blockchain’s consensus algorithm only contains valid blocks, and that the majority of block producers are honest. By allowing such clients to receive fraud proofs generated by fully validating nodes that show that a block violates the protocol rules, and combining this with probabilistic sampling techniques to verify that all of the data in a block actually is available to be downloaded so that fraud can be detected, we can eliminate the honest-majority assumption for block validity, and instead make much weaker assumptions about a minimum number of honest nodes that rebroadcast data. Fraud and data availability proofs are key to enabling on-chain scaling of blockchains while maintaining a strong assurance that on-chain data is available and valid. We present, implement, and evaluate a fraud and data availability proof system.