Reviewerly/exHarmony huggingface dataset:
Accelerating Forward and Backward Private Searchable Encryption Using Trusted Execution

Searchable encryption (SE) is one of the key enablers for building encrypted databases. It allows a cloud server to search over encrypted data without decryption. Dynamic SE additionally includes data addition and deletion operations to enrich the functions of encrypted databases. Recent attacks exploiting the leakage in dynamic operations drive the rapid development of SE schemes revealing less information while performing updates; they are also known as forward and backward private SE. Newly added data is no longer linkable to queries issued before, and deleted data is no longer searchable in queries issued later. However, those advanced SE schemes reduce the efficiency of SE, especially in the communication cost between the client and server. In this paper, we resort to the hardware-assisted solution, aka Intel SGX, to ease the above bottleneck. Our key idea is to leverage SGX to take over most tasks of the client, i.e., tracking keyword states along with data addition and caching deleted data. However, handling large datasets is non-trivial due to the I/O and memory constraints of SGX. We further develop batch data processing and state compression techniques to reduce the communication overhead between the SGX and untrusted server and minimise the memory footprint within the enclave. We conduct a comprehensive set of evaluations on both synthetic and real-world datasets, which confirm that our designs outperform the prior art.
A convenient 3D reconstruction model based on parallel-axis structured light system

Conventional high-accuracy three-dimensional (3D) reconstruction methods usually involve complicated and time-consuming system calibration. This paper presents a novel and simple 3D reconstruction model based on a parallel-axis structured light system. Compared to the traditional structured light system, the proposed system contains a perspective projection unit and a telecentric imaging unit. Instead of obtaining the complicated structured light system parameters, the proposed model only requires the retrieval of a product matrix which could be easily calibrated by using an ordinary white plane. For an arbitrary point A on the object surface, it is mathematically proved that the product matrix of its phase map and height distribution is proportional to the distance between A and projector optical center in XY plane, and independent of the depth in Z direction. Experiments were carried out to measure a step-height object and a cylindrical surface, and the results verify that the proposed method can achieve high-accuracy 3D shape measurement.
Root identification in minirhizotron imagery with multiple instance learning

In this paper, multiple instance learning (MIL) algorithms to automatically perform root detection and segmentation in minirhizotron imagery using only image-level labels are proposed. Root and soil characteristics vary from location to location, thus, supervised machine learning approaches that are trained with local data provide the best ability to identify and segment roots in minirhizotron imagery. However, labeling roots for training data (or otherwise) is an extremely tedious and time-consuming task. This paper aims to address this problem by labeling data at the image level (rather than the individual root or root pixel level) and train algorithms to perform individual root pixel level segmentation using MIL strategies. Three MIL methods (multiple instance adaptive cosine coherence estimator, multiple instance support vector machine, multiple instance learning with randomized trees) were applied to root detection and compared to non-MIL approches. The results show that MIL methods improve root segmentation in challenging minirhizotron imagery and reduce the labeling burden. In our results, multiple instance support vector machine outperformed other methods. The multiple instance adaptive cosine coherence estimator algorithm was a close second with an added advantage that it learned an interpretable root signature which identified the traits used to distinguish roots from soil and did not require parameter selection.
Count- and Similarity-Aware R-CNN for Pedestrian Detection

Recent pedestrian detection methods generally rely on additional supervision, such as visible bounding-box annotations, to handle heavy occlusions. We propose an approach that leverages pedestrian count and proposal similarity information within a two-stage pedestrian detection framework. Both pedestrian count and proposal similarity are derived from standard full-body annotations commonly used to train pedestrian detectors. We introduce a count-weighted detection loss function that assigns higher weights to the detection errors occurring at highly overlapping pedestrians. The proposed loss function is utilized at both stages of the two-stage detector. We further introduce a count-and-similarity branch within the two-stage detection framework, which predicts pedestrian count as well as proposal similarity. Lastly, we introduce a count and similarity-aware NMS strategy to identify distinct proposals. Our approach requires neither part information nor visible bounding-box annotations. Experiments are performed on the CityPersons and CrowdHuman datasets. Our method sets a new state-of-the-art on both datasets. Further, it achieves an absolute gain of 2.4% over the current state-of-the-art, in terms of log-average miss rate, on the heavily occluded (HO) set of CityPersons test set. Finally, we demonstrate the applicability of our approach for the problem of human instance segmentation. Code and models are available at: https://github.com/Leotju/CaSe .
Preserving Semantic Neighborhoods for Robust Cross-Modal Retrieval

The abundance of multimodal data (e.g. social media posts) has inspired interest in cross-modal retrieval methods. Popular approaches rely on a variety of metric learning losses, which prescribe what the proximity of image and text should be, in the learned space. However, most prior methods have focused on the case where image and text convey redundant information; in contrast, real-world image-text pairs convey complementary information with little overlap. Further, images in news articles and media portray topics in a visually diverse fashion; thus, we need to take special care to ensure a meaningful image representation. We propose novel within-modality losses which encourage semantic coherency in both the text and image subspaces, which does not necessarily align with visual coherency. Our method ensures that not only are paired images and texts close, but the expected image-image and text-text relationships are also observed. Our approach improves the results of cross-modal retrieval on four datasets compared to five baselines.
Rethinking Architecture Selection in Differentiable NAS

Differentiable Neural Architecture Search is one of the most popular Neural Architecture Search (NAS) methods for its search efficiency and simplicity, accomplished by jointly optimizing the model weight and architecture parameters in a weight-sharing supernet via gradient-based algorithms. At the end of the search phrase, the operations with the largest architecture parameters will be selected to form the final architecture, with the implicit assumption that the values of architecture parameters reflect the operation strength. While much has been discussed about the supernet's optimization, the architecture selection process has received little attention. We provide empirical and theoretical analysis to show that the magnitude of architecture parameters does not necessarily indicate how much the operation contributes to the supernet's performance. We propose an alternative perturbation-based architecture selection that directly measures each operation's influence on the supernet. We re-evaluate several differentiable NAS methods with the proposed architecture selection and find that it is able to extract significantly improved architectures from the underlying supernets consistently. Furthermore, we find that several failure modes of Darts can be greatly alleviated with the proposed selection method, indicating that much of the poor generalization observed in Darts can be attributed to the failure of magnitude-based architecture selection rather than entirely the optimization of its supernet. Our code will be made publicly available shortly.
Fuzzy-based Sentiment Analysis System for Analyzing Student Feedback and Satisfaction

The feedback collection and analysis has remained an important subject matter for long. The traditional techniques for student feedback analysis are based on questionnaire-based data collection and analysis. However, the ... | Find, read and cite all the research you need on Tech Science Press
Dark Patterns at Scale: Findings from a Crawl of 11K Shopping Websites

Dark patterns are user interface design choices that benefit an online service by coercing, steering, or deceiving users into making unintended and potentially harmful decisions. We present automated techniques that enable experts to identify dark patterns on a large set of websites. Using these techniques, we study shopping websites, which often use dark patterns to influence users into making more purchases or disclosing more information than they would otherwise. Analyzing ~53K product pages from ~11K shopping websites, we discover 1,818 dark pattern instances, together representing 15 types and 7 broader categories. We examine these dark patterns for deceptive practices, and find 183 websites that engage in such practices. We also uncover 22 third-party entities that offer dark patterns as a turnkey solution. Finally, we develop a taxonomy of dark pattern characteristics that describes the underlying influence of the dark patterns and their potential harm on user decision-making. Based on our findings, we make recommendations for stakeholders including researchers and regulators to study, mitigate, and minimize the use of these patterns.
Polish university libraries social networking services during the COVID-19 pandemic spring term lockdown

During the spring 2020 COVID-19 lockdown, University libraries made available digital content of varying types. This article assesses the scope and breadth of content published by 18 Polish university libraries, how libraries compared in their approach to using social media, and the level of engagement and collaboration with patrons during a time when the physical library was closed. Data collection consisted of gathering social networking site information as used by Polish university libraries with the Fanpage Karma tool. The Fanpage Karma tool allowed the researcher to analyse and compare the fan pages of individual university library social networking sites. The tool simplifies the process to calculate key variables such as the number of user reactions to the library content; the number of posts, comments, likes, and dislikes; the average daily number of posts made; the most popular text, images, videos, links; and the kind of hashtags used by librarians. Findings indicate Polish university libraries most frequently used Facebook during the lockdown followed by Instagram and Twitter. To a much smaller extent, libraries also used YouTube. Not all Polish university libraries made equal use of social media during the lockdown despite the availability of content and ease of use.
First-Order Newton-Type Estimator for Distributed Estimation and Inference

This paper studies distributed estimation and inference for a general statistical problem with a convex loss that could be non-differentiable. For the purpose of efficient computation, we restrict ourselves to stochastic first-order optimization, which enjoys low per-iteration complexity. To motivate the proposed method, we first investigate the theoretical properties of a straightforward Divide-and-Conquer Stochastic Gradient Descent (DC-SGD) approach. Our theory shows that there is a restriction on the number of machines and this restriction becomes more stringent when the dimension $p$ is large. To overcome this limitation, this paper proposes a new multi-round distributed estimation procedure that approximates the Newton step only using stochastic subgradient. The key component in our method is the proposal of a computationally efficient estimator of $\Sigma^{-1} w$, where $\Sigma$ is the population Hessian matrix and $w$ is any given vector. Instead of estimating $\Sigma$ (or $\Sigma^{-1}$) that usually requires the second-order differentiability of the loss, the proposed First-Order Newton-type Estimator (FONE) directly estimates the vector of interest $\Sigma^{-1} w$ as a whole and is applicable to non-differentiable losses. Our estimator also facilitates the inference for the empirical risk minimizer. It turns out that the key term in the limiting covariance has the form of $\Sigma^{-1} w$, which can be estimated by FONE.
Efficient anonymous authentication scheme for automatic dependent surveillance‐broadcast system with batch verification

Automatic Dependent Surveillance-Broadcast (ADS-B) system provides significant improvements in air traffic control system such as optimal routing in non-radar environments with a new level of safety and efficiency. The ADS-B system is being installed in many aircrafts in recent year. The ADS-B equipped aircraft broadcasts the air traffic information to nearby aircraft and ground stations either once or twice per second. Because of the open channel communication atmosphere, the ADS-B system is affected by many security attacks. Many authentication and batch verification schemes are available to provide data integrity and source authentication for the ADS-B system. But they are suffering from computational overhead and communication overhead. Hence, an efficient anonymous authentication scheme is proposed for the ADS-B system with batch verification based on Message Recovery Signature (MRS). The security and performance analysis section ensure that the proposed scheme provides essential security features with less computational and communication overhead comparison with the other existing schemes.
Variational quantum eigensolver for approximate diagonalization of downfolded Hamiltonians using generalized unitary coupled cluster ansatz

In this paper, we discuss the utilization of variational quantum solver (VQE) and recently introduced generalized unitary coupled cluster (GUCC) formalism for the diagonalization of downfolded/effective Hamiltonians in active spaces. In addition to effective Hamiltonians defined by the downfolding of a subset of virtual orbitals we also consider their form defined by freezing core orbitals, which enables us to deal with larger systems. We also consider various solvers to identify solutions of the GUCC equations. We use N2, H2O, and C2H4, as benchmark systems to illustrate the performance of the combined framework.
DAGN: Discourse-Aware Graph Network for Logical Reasoning

Recent QA with logical reasoning questions requires passage-level relations among the sentences.However, current approaches still focus on sentence-level relations interacting among tokens.In this work, we explore aggregating passage-level clues for solving logical reasoning QA by using discourse-based information.We propose a discourse-aware graph network (DAGN) that reasons relying on the discourse structure of the texts.The model encodes discourse information as a graph with elementary discourse units (EDUs) and discourse relations, and learns the discourseaware features via a graph network for downstream QA tasks.Experiments are conducted on two logical reasoning QA datasets, Re-Clor and LogiQA, and our proposed DAGN achieves competitive results.
Pinball Loss Twin Support Vector Clustering

Twin Support Vector Clustering (TWSVC) is a clustering algorithm inspired by the principles of Twin Support Vector Machine (TWSVM). TWSVC has already outperformed other traditional plane based clustering algorithms. However, TWSVC uses hinge loss, which maximizes shortest distance between clusters and hence suffers from noise-sensitivity and low re-sampling stability. In this article, we propose Pinball loss Twin Support Vector Clustering (pinTSVC) as a clustering algorithm. The proposed pinTSVC model incorporates the pinball loss function in the plane clustering formulation. Pinball loss function introduces favorable properties such as noise-insensitivity and re-sampling stability. The time complexity of the proposed pinTSVC remains equivalent to that of TWSVC. Extensive numerical experiments on noise-corrupted benchmark UCI and artificial datasets have been provided. Results of the proposed pinTSVC model are compared with TWSVC, Twin Bounded Support Vector Clustering (TBSVC) and Fuzzy c-means clustering (FCM). Detailed and exhaustive comparisons demonstrate the better performance and generalization of the proposed pinTSVC for noise-corrupted datasets. Further experiments and analysis on the performance of the above-mentioned clustering algorithms on structural MRI (sMRI) images taken from the ADNI database, face clustering, and facial expression clustering have been done to demonstrate the effectiveness and feasibility of the proposed pinTSVC model.
Generalized jump functions

Jump functions are the most studied non-unimodal benchmark in the theory of evolutionary algorithms (EAs). They have significantly improved our understanding of how EAs escape from local optima. However, their particular structure - to leave the local optimum the EA can only jump directly to the global optimum - raises the question of how representative the recent findings are.
Big data, big challenges: risk management of financial market in the digital economy

Purpose The purpose of the research is to assess the risk of the financial market in the digital economy through the quantitative analysis model in the big data era. It is a big challenge for the government to carry out financial market risk management in the big data era. Design/methodology/approach In this study, a generalized autoregressive conditional heteroskedasticity-vector autoregression (GARCH-VaR) model is constructed to analyze the big data financial market in the digital economy. Additionally, the correlation test and stationarity test are carried out to construct the best fit model and get the corresponding VaR value. Findings Owing to the conditional heteroscedasticity, the index return series shows the leptokurtic and fat tail phenomenon. According to the AIC (Akaike information criterion), the fitting degree of the GARCH model is measured. The AIC value difference of the models under the three distributions is not obvious, and the differences between them can be ignored. Originality/value Using the GARCH-VaR model can better measure and predict the risk of the big data finance market and provide a reliable and quantitative basis for the current technology-driven regulation in the digital economy.
Regularizing Nighttime Weirdness: Efficient Self-supervised Monocular Depth Estimation in the Dark

Monocular depth estimation aims at predicting depth from a single image or video. Recently, self-supervised methods draw much attention since they are free of depth annotations and achieve impressive performance on several daytime benchmarks. However, they produce weird outputs in more challenging nighttime scenarios because of low visibility and varying illuminations, which bring weak textures and break brightness-consistency assumption, respectively. To address these problems, in this paper we propose a novel framework with several improvements: (1) we introduce Priors-Based Regularization to learn distribution knowledge from unpaired depth maps and prevent model from being incorrectly trained; (2) we leverage Mapping-Consistent Image Enhancement module to enhance image visibility and contrast while maintaining brightness consistency; and (3) we present Statistics-Based Mask strategy to tune the number of removed pixels within textureless regions, using dynamic statistics. Experimental results demonstrate the effectiveness of each component. Mean-while, our framework achieves remarkable improvements and state-of-the-art results on two nighttime datasets. Code is available at https://github.com/w2kun/RNW.
SLO Script: A Novel Language for Implementing Complex Cloud-Native Elasticity-Driven SLOs

Service Level Objectives (SLOs) allow defining expected performance of cloud services, such that cloud service providers know what they guarantee and service consumers know what to expect. Most approaches focus on low-level SLOs, closely related to resources, e.g., average CPU or memory usage, and are usually bound to specific elasticity controllers. We present SLO Script, a language and accompanying framework, motivated by real-world, industrial needs to allow service providers to define complex, high-level SLOs in an orchestrator-independent manner. The main features of SLO Script include: i) novel abstractions (StronglyTypedSLO) with type safety features, ensuring compatibility between SLOs and elasticity strategies, ii) abstractions that enable decoupling of SLOs from elasticity strategies, iii) a strongly typed metrics API, and iv) an orchestrator-independent object model that enables language extensibility. We present a case study about a real-world, cloud-native application and evaluate our language while implementing a realistic Cost Efficiency SLO.
An analysis to understand the role of machine learning, robotics and artificial intelligence in digital marketing

Focusing on the assumption that both physical and cognitive activities may be substituted by Artificial Intelligence, Machine Learning, and Robotics, this study explored the role of Artificial Intelligence (AI) in Digital Marketing utilizing a descriptive research technique. An examination with professionals from various sectors linked to marketing and AI reveals that AI has an influence on marketing operations and will have a greater impact in the future. By integrating new data-driven methods to digital marketing strategies, exponential technical advancement provides chances for strategic edge. By identifying lessons from vast volumes of produced data, Machine learning (ML) can forecast future events and aid decision-making. This feature has a significant influence on and simplifies businesses' strategic decision-making processes. As per the study needs assessment, little is known about marketers' attitudes towards, and understanding of, ML technologies, as well as their uptake and use to support operational and strategic administration. Intelligent robotics and technologies are ideal for internet marketing. Everyone agrees. They can determine what consumers want, customize offers, simplify content creation, gather vast quantities of data, and apply it to business choices.
A New Probabilistic Ensemble Method for an Enhanced Day-Ahead PV Power Forecast

The penetration of nonprogrammable renewable energy sources, namely wind and solar technology, has greatly increased in the last decades and nowadays the shift toward green energy sources represents a priority worldwide. The high variability of the primary source challenges the grid operators in ensuring the stability and reliability of the electric grid. Machine learning algorithms, and in particular artificial neural networks, are one of the most reliable methods for photovoltaic (PV) energy production forecast. This article proposes a new ensemble method based on the probabilistic distribution of the trials, the probabilistic ensemble method (PEM). The proposed method has been tested on a three-years real case study, where the available days have been clustered according to the solar irradiation forecast. The days where the worst performance, in terms of nRMSE, was recorded mostly belonged to the totally cloudy days class, that has been therefore selected for the analysis. The PEM has been compared with the ensemble based on the mean value, achieving an improvement in the nRMSE metric up to 4.79% in 2017 in the totally cloudy days class.