Reviewerly/exHarmony huggingface dataset:
Linear and Range Counting under Metric-based Local Differential Privacy

Local differential privacy (LDP) enables private data sharing and analytics without the need for a trusted data collector. Error-optimal primitives (for, e.g., estimating means and item frequencies) under LDP have been well studied. For analytical tasks such as range queries, however, the best known error bound is dependent on the domain size of private data, which is potentially prohibitive. This deficiency is inherent as LDP protects the same level of indistinguishability between any pair of private data values for each data downer.In this paper, we utilize an extension of -LDP called MetricLDP or E-LDP, where a metric E defines heterogeneous privacy guarantees for different pairs of private data values and thus provides a more flexible knob than does to relax LDP and tune utility-privacy trade-offs. We show that, under such privacy relaxations, for analytical workloads such as linear counting, multi-dimensional range counting queries, and quantile queries, we can achieve significant gains in utility. In particular, for range queries under E-LDP where the metric E is the L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> distance function scaled by , we design mechanisms with errors independent on the domain sizes; instead, their errors depend on the metric E, which specifies in what granularity the private data is protected. We believe that the primitives we design for ELDP will be useful in developing mechanisms for other analytical tasks, and encourage the adoption of LDP in practice.
Anomaly Scoring for Prediction-Based Anomaly Detection in Time Series

Prediction-based anomaly detection methods for time series have been studied for decades and demonstrated to be useful in many applications. However, many predictors cannot accurately predict values around abrupt changes in time series, which may result in false detections or missed detections. In this paper, the problem is addressed using an anomaly scoring method for prediction-based anomaly detection. A Long Short-Term Memory (LSTM) network is used for prediction, and a dynamic thresholding method is used for anomaly extraction from prediction error sequences. The pattern of falsely-detected anomalies, or false positive sequences (FPS), in training data is learned by a clustering algorithm. A score is assigned to each detected anomaly in test data according to its distance to the nearest FPS pattern learned from training data. The effectiveness of this method is demonstrated by testing it on a variety of public time series datasets.
RainbowPIV with improved depth resolution—design and comparative study with TomoPIV

Abstract RainbowPIV is a recent imaging technology, proposed for time-resolved 3D-3C fluid velocity measurement using a single RGB camera. It dramatically simplifies hardware setup and calibration procedures as compared to alternative 3D-3C measurement approaches. RainbowPIV combines optical design and tailored reconstruction algorithms, and earlier preliminary studies have demonstrated its ability to extract physically constrained fluid vector fields. This article addresses the issue of limited axial resolution, the major drawback of the original RainbowPIV system. We validate the new system with a direct, quantitative comparison with four-camera Tomo-PIV on experimental data. The reconstructed flow vectors of the two approaches exhibit a high degree of consistency, with the RainbowPIV results explicitly guaranteeing physical properties, such as divergence free velocity fields for incompressible fluid flows.
DC-SMIL

Among skin cancers, melanoma is the most aggressive and most lethal form. Despite these terrible premises, an excision treatment carried out thanks to an early diagnosis is almost always decisive, guaranteeing the patient's survival. The early detection of melanoma is hampered by the extreme similarity of melanoma with other skin lesions such as dysplastic nevi. The current research is aimed at defining software solutions that support the computerized diagnosis of lesions for the detection of melanoma. To date, the proposals, both in terms of algorithms and frameworks, have focused on the dichotomous distinction of melanoma from benign lesions. However, the current debate on Dysplastic Nevi Syndrome (DNS), makes issues relating to the nature of the lesions, central to subjects who present a large number of moles throughout the body. In fact, individuals with DNS have a greater chance of being attacked by melanoma. The classification task relating to the distinction of dysplastic nevi from common ones is totally unexplored. In this document, we consider the difficult task of applying multiple-instance learning (MIL) approaches to discriminate melanoma from dysplastic nevi and outline an even more complex challenge related to the classification of dysplastic nevi from common ones. In particular, we introduce the application of a MIL approach that uses spherical separation surfaces. Since the results seem promising, we conclude that a MIL technique could be the basis of more sophisticated tools useful for detecting skin lesions.
Thirty years of credal networks: Specification, algorithms and complexity

Credal networks generalize Bayesian networks to allow for imprecision in probability values. This paper reviews the main results on credal networks under strong independence, as there has been significant progress in the literature during the last decade or so. We focus on computational aspects, summarizing the main algorithms and complexity results for inference and decision making. We address the question “What is really known about strong extensions of credal networks?” by looking at theoretical results and by presenting a short summary of real applications.
Online Anomaly Detection With Bandwidth Optimized Hierarchical Kernel Density Estimators

We propose a novel unsupervised anomaly detection algorithm that can work for sequential data from any complex distribution in a truly online framework with mathematically proven strong performance guarantees. First, a partitioning tree is constructed to generate a doubly exponentially large hierarchical class of observation space partitions, and every partition region trains an online kernel density estimator (KDE) with its own unique dynamical bandwidth. At each time, the proposed algorithm optimally combines the class estimators to sequentially produce the final density estimation. We mathematically prove that the proposed algorithm learns the optimal partition with kernel bandwidths that are optimized in both region-specific and time-varying manner. The estimated density is then compared with a data-adaptive threshold to detect anomalies. Overall, the computational complexity is only linear in both the tree depth and data length. In our experiments, we observe significant improvements in anomaly detection accuracy compared with the state-of-the-art techniques.
Visibility-Based Persistent Monitoring of Piecewise Linear Features on a Terrain Using Multiple Aerial and Ground Robots

Persistent monitoring on terrains using mobile robotic sensors requires coordinated planning. Terrain features add visibility obstacles and limited fuel capacity of aerial robots leads to range restrictions that make the problem challenging. We address the visual-monitoring problem on piecewise linear features within a terrain using multiple mobile robots for persistent operations. The planner must account for visual coverage, refueling aerial robots during the mission, and placement of refueling depots while also utilizing the available sensor diversity to minimize overall costs for the monitoring mission. Building on previous works on visibility in specific classes of polygons and fuel-constrained routing, we develop a discrete representation of the problem that allows the design and application of discrete optimization techniques to find optimal solutions. We develop a mixed-integer linear programming (MILP) formulation and discuss a branch-and-cut implementation to compute exact solutions. We also develop a construction heuristic based on the idea of competitive construction of robot paths using a step-increment strategy. We report the results from computational simulations and illustrate proof of concept using experiments on real robots. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article is motivated by the need to perform persistent monitoring in applications, such as border patrol and perimeter surveillance. Unmanned aerial and ground robots can be used to perform these activities uninterruptedly. However, aerial robots have limited fuel capacity and need periodic refueling. Hence, the number of refueling depots and their placement within the environment also affects the monitoring task. Also, due to terrain variation, robots are subject to limited visibility. Therefore, we need to consider refueling constraints and terrain visibility aspects while planning optimal routes for the robots to perform visual monitoring. In this article, we present a general optimal routing formulation to compute exact solutions. We also present a fast heuristic for real-time applications that produce feasible solutions. The algorithms are validated in simulations. We also show a proof of concept using experiments in limited outdoor settings.
UGC-VIDEO: Perceptual Quality Assessment of User-Generated Videos

Recent years have witnessed an ever-expanding volume of user-generated content (UGC) videos available on the Internet. Nevertheless, progress on perceptual quality assessment of UGC videos still remains quite limited. A distinguished characteristic of UGC videos in the complete video production and delivery chain is that they often undergo multiple compression stages before ultimately viewed and there does not exist the pristine source after they are uploaded to the hosting platform. To facilitate the UGC video quality assessment (VQA), we create a UGC video perceptual quality assessment database. It contains 50 source videos collected from TikTok with diverse content, along with multiple transcoded versions generated by different coding standards and quantization levels. Subjective quality assessment has been conducted to evaluate the video quality. Furthermore, we benchmark the database using existing quality assessment algorithms, and potential room is observed to further improve the accuracy of UGC video quality measures.
An effective component-based age-invariant face recognition using Discriminant Correlation Analysis

Face is an important characteristic for biometric identification and verification systems, as it holds the needed information about individual’s identity. However, it is the characteristic most impacted by aging process, and facial aging is the main effect which decreases significantly the performance of face recognition algorithms. The main idea of this study relies on the fact that aging affects facial components such as mouth, eyes and nose differently. Therefore, we suggest to consider face as an independent component set, and each facial component (eyes, mouth, and nose) will be processed separately, and we propose an effective component-based method for age-invariant face recognition using a Discriminant Correlation Analysis(DCA) as a feature-level fusion algorithm to combine Deep-based features computed from separated facial components, and a Support Vector Machine (SVM) as a classifier. To evaluate the proposed approach, a comprehensive experimental study is performed on a widely used face-aging database, namely FG-NET, also the proposed method has been compared with a Particle Swarm Optimization (PSO)-based score-level fusion method. Experimental results show that the proposed component-based system worked effectively for face recognition across age progression, as it achieves significant high mean accuracy rates.
Smart and Secure Voting Machine using Biometrics

India, considered being one of the largest democratic countries in the world. Besides this country have the largest number of population and a number of politicians in each party. The fate of the country and citizens is in the hand of the political representative of the country, who was chosen by the citizens of the country by voting. This gives an illustration of how much the fundamental right to vote is important. But according to research, in 2014, in common election, in India, only 66.4% of valid citizens cast their votes. This may represent that the voters do not value or care about their right to vote or maybe there is a problem with the voting system. The voting system has a few problems with it. One of the major problems in existing voting system, which is EVM (Electronic Voting Machine), in that the voter cannot verify whether his vote is registered or not and even if it is registered it is not possible to know if it is registered under the correct candidate the voter had cast his vote too. So, in this paper, we propose a system or method which will solve that problem of the current voting system available in India.
AE-LSTM Based Deep Learning Model for Degradation Rate Influenced Energy Estimation of a PV System

With the increase in penetration of photovoltaics (PV) into the power system, the correct prediction of return on investment requires accurate prediction of decrease in power output over time. Degradation rates and corresponding degraded energy estimation must be known in order to predict power delivery accurately. Solar radiation plays a key role in long-term solar energy predictions. A combination of auto-encoder and long short-term memory (AE-LSTM) based deep learning approach is adopted for long-term solar radiation forecasting. First, the auto-encoder (AE) is trained for the feature extraction, and then fine-tuning with long short-term memory (LSTM) is done to get the final prediction. The input data consist of clear sky global horizontal irradiance (GHI) and historical solar radiation. After forecasting the solar radiation for three years, the corresponding degradation rate (DR) influenced energy potentials of an a-Si PV system is estimated. The estimated energy is useful economically for planning and installation of energy systems like microgrids, etc. The method of solar radiation forecasting and DR influenced energy estimation is compared with the traditional methods to show the efficiency of the proposed method.
Avoiding blocking by scheduling transactions using quantum annealing

Quantum annealers are a special kind of quantum computers for solving optimization problems. In this paper, we investigate the benefits of quantum annealers in the field of transaction synchronization. In particular, we show how transactions using the 2-phase-locking protocol can be optimally distributed to any number of available machines to reduce transaction waiting times. Therefore an instance of the problem will be transformed into a formula that is accepted by quantum annealers. In an experimental evaluation, the runtime on a quantum annealer outperforms the runtime of traditional algorithms to solve combinatorial problems like simulated annealing already for small problem sizes.
Quantum Language Model With Entanglement Embedding for Question Answering

Quantum language models (QLMs) in which words are modeled as a quantum superposition of sememes have demonstrated a high level of model transparency and good post-hoc interpretability. Nevertheless, in the current literature, word sequences are basically modeled as a classical mixture of word states, which cannot fully exploit the potential of a quantum probabilistic description. A quantum-inspired neural network (NN) module is yet to be developed to explicitly capture the nonclassical correlations within the word sequences. We propose a NN model with a novel entanglement embedding (EE) module, whose function is to transform the word sequence into an entangled pure state representation. Strong quantum entanglement, which is the central concept of quantum information and an indication of parallelized correlations among the words, is observed within the word sequences. The proposed QLM with EE (QLM-EE) is proposed to implement on classical computing devices with a quantum-inspired NN structure, and numerical experiments show that QLM-EE achieves superior performance compared with the classical deep NN models and other QLMs on question answering (QA) datasets. In addition, the post-hoc interpretability of the model can be improved by quantifying the degree of entanglement among the word states.
Convolutional neural network applied for nanoparticle classification using coherent scatterometry data

The analysis of 2D scattering maps generated in scatterometry experiments for detection and classification of nanoparticles on surfaces is a cumbersome and slow process. Recently, deep learning techniques have been adopted to avoid manual feature extraction and classification in many research and application areas, including optics. In the present work, we collected experimental datasets of nanoparticles deposited on wafers for four different classes of polystyrene particles (with diameters of 40, 50, 60, and 80 nm) plus a background (no particles) class. We trained a convolutional neural network, including its architecture optimization, and achieved 95% accurate results. We compared the performance of this network to an existing method based on line-by-line search and thresholding, demonstrating up to a twofold enhanced performance in particle classification. The network is extended by a supervisor layer that can reject up to 80% of the fooling images at the cost of rejecting only 10% of original data. The developed Python and PyTorch codes, as well as dataset, are available online.
Improving Estimation Accuracy Prediction of Software Development Effort: A Proposed Ensemble Model

Software effort estimation is an essential feature of software engineering for effective planning, controlling and delivering successful software projects. The overestimation and underestimation both are the key challenges for future software development. The failure to acknowledge the effort estimation accuracy may lead to customer disappointment, inaccurate estimation and hence, contribute to either poor software development process or project failure. The main aim of this research is to optimize the estimation accuracy prediction of software development effort to support software development firms and practitioners. In this paper, we propose an ensemble software effort estimation model based on Use Case Points (UCP), expert judgment and Case-Based Reasoning (CBR) techniques. This research is conducted through primary (a multi-case involving software companies) study to make an ensemble model. The estimation accuracy prediction of the proposed model will be evaluated by selecting projects from primary studies as case selections in applying a quantitative approach through industrial experts, archival data about estimates and evaluation metrics. The proposed model produced at the end of this research will be used by software development firms and practitioners as an instrument to estimate the effort required to develop new software projects at an earlier stage.
GMFAD: Towards Generalized Visual Recognition via Multilayer Feature Alignment and Disentanglement

The deep learning based approaches which have been repeatedly proven to bring benefits to visual recognition tasks usually make a strong assumption that the training and test data are drawn from similar feature spaces and distributions. However, such an assumption may not always hold in various practical application scenarios on visual recognition tasks. Inspired by the hierarchical organization of deep feature representation that progressively leads to more abstract features at higher layers of representations, we propose to tackle this problem with a novel feature learning framework, which is called GMFAD, with better generalization capability in a multilayer perceptron manner. We first learn feature representations at the shallow layer where shareable underlying factors among domains (e.g., a subset of which could be relevant for each particular domain) can be explored. In particular, we propose to align the domain divergence between domain pair(s) by considering both inter-dimension and inter-sample correlations, which have been largely ignored by many cross-domain visual recognition methods. Subsequently, to learn more abstract information which could further benefit transferability, we propose to conduct feature disentanglement at the deep feature layer. Extensive experiments based on different visual recognition tasks demonstrate that our proposed framework can learn better transferable feature representation compared with state-of-the-art baselines.
A hybrid acceleration strategy for nonparallel support vector machine

Nonparallel support vector machine is a binary classifier which has better sparsity and classification performance than other support vector machine models. However, it works slowly. Although sample selection is an effective acceleration approach, it will disturb the accuracy. Fortunately, another recently emerging screening method could speedup the solving process and keeps the accuracy unchanged. Motivated by the above research, a novel two-stage hybrid screening rule based on variational inequality and duality gap is proposed in this paper. It can reduce the scale of dual problem by deleting more redundant samples. Then, a better acceleration effect will be obtained. Meanwhile, it is still a safe method which means that the optimal solution remains unchanged. Moreover, Shrinking technique is embedded into the fast iterative algorithm DCDM to get further speedup. Thus the hybrid method can reduce the size of the problem, and accelerate the solving process greatly. Numerical experiments on ten benchmark data sets and a real Chinese red wine data set are conducted to verify the effectiveness of our hybrid acceleration strategy.
Multi-copy dynamic cloud data auditing model based on IMB tree

In order to improve the efficiency and security of multi-copy dynamic cloud data audit, a multi-copy dynamic cloud data auditing model (MCAM) based on IMB tree is proposed. The IMB tree makes the audit process fast and efficient while maintaining security. Multi-copy compression transmission with masking rate is introduced to better reduce migration time. The theoretical analysis shows that MCAM can resist forgery attacks in the audit process and avoid information leakage. In addition, compared to the Merkle tree auditing solution, MCAM has improved data migration efficiency by about 17% and saved 24% of time consumption.
Mid-Air Gesture Control of Multiple Home Devices in Spatial Augmented Reality Prototype

Touchless, mid-air gesture-based interactions with remote devices have been investigated as alternative or complementary to interactions based on remote controls and smartphones. Related studies focus on user elicitation of a gesture vocabulary for one or a few home devices and explore recommendations of respective gesture vocabularies without validating them by empirical testing with interactive prototypes. We have developed an interactive prototype based on spatial Augmented Reality (AR) of seven home devices. Each device responds to touchless gestures (identified from a previous elicitation study) via the MS Kinect sensor. Nineteen users participated in a two-phase test (with and without help provided by a virtual assistant) according to a scenario that required from each user to apply 41 gestural commands (19 unique). We report on main usability indicators: task success, task time, errors (false negative/positives), memorability, perceived usability, and user experience. The main conclusion is that mid-air interaction with multiple home devices is feasible, fairly easy to learn and apply, and enjoyable. The contributions of this paper are (a) validation of a previously elicited gesture set; (b) development of a spatial AR prototype for testing of mid-air gestures, and (c) extensive assessment of gestures and evidence in favor of mid-air interaction in smart environments.