Reviewerly/exHarmony huggingface dataset:
Effective truth discovery under local differential privacy by leveraging noise-aware probabilistic estimation and fusion

Truth discovery is an effective way to eliminate data inconsistency by integrating different worker-provided values. Although directly conducting non-private truth discovery approaches based on uploaded noisy values after adding Laplace noise for continuous inputs guarantees rigorous local differential privacy (LDP), it may result in poor performance due to the lot of contained noise. First, the injected noise for privacy protection randomly sampled from Laplace distribution may be excessive even with a large privacy budget, as the above distribution is unbounded and drops sharply with respect to the x-axis. Built-in Gaussian noise also usually exists within these uploaded noisy values, which may also have a negative effect on the aggregated truths under LDP and makes the problem investigated in this paper far more challenging. In this paper, we focus on obtaining accurate truths in the above cases under rigorous LDP for continuous inputs, and present a novel solution TESLA. The key idea of this solution is that we let injected noise for privacy protection and inherent Gaussian noise only weakly negatively affect the weight estimation and true aggregation. In particular, we design a runtime filtering mechanism (RFM) to obtain the supremum and infimum for the values after adding Laplace noise by considering these two types of noise together. Moreover, we develop a probabilistic fusion mechanism (PFM) to get the fused values by adaptively using the obtained supremum and infimum. Furthermore, we devise a probabilistic weight mechanism (PWM) to obtain a more accurate weight for each worker. Therefore, truth discovery can be conducted based on the new weight of each worker and the filtered values. We provide theoretical analyses of TESLA’s utility, privacy and complexity. Experimental results demonstrate the effectiveness and efficiency of TESLA. We also extend and verify TESLA over typical mean estimation as well as standard deviation calculation, and various machine learning tasks (e.g., logistic regression, support vector machine (SVM) and neural network). Experimental results also demonstrate its superiority.
A federated collaborative recommendation model for privacy-preserving distributed recommender applications based on microservice framework

The wide adoption of IoT technologies has accelerated the accumulation of big data. Recommender systems (RS) is one of the most effective methods to extract user interested items from the huge volume of big data. However, implementing a recommender system over the distributed error-prone IoT devices faces two challenges. On the one hand, the distributed IoT devices may randomly fail to deliver its local data due to hardware malfunction, which may cause unavailability of the recommender system. Moreover, collecting the raw data from the distributed IoT devices may cause data privacy leakage issue, since the privacy data of user-item interaction records may be abused by vicious parties. In view of these challenges, we propose a federated collaborative recommendation model based on microservice framework in this paper to implement privacy preserving distributed recommendation applications. Firstly, we utilize the federated learning framework to train the collaborative recommendation model, where the raw data on each distributed device is kept locally and only item related model parameters are exposed to train the federated recommendation model. Moreover, we adopt the microservice framework to encapsulate different functions of the federated recommender model. Each distributed device can participate in the federated training process via service registration and service discovery function of the microservice framework. Furthermore, we enhance the typical Neural Collaborative Filtering model with the proposed FedNeuMF model by fusing auxiliary user profiles and item attributes to improve the recommendation accuracy. Finally, we conduct a set of experiments on three real-world datasets to check the performance of our proposal.
Urns &amp; Tubes

Urn models play an important role to express various basic ideas in probability theory. Here we extend this urn model with tubes. An urn contains coloured balls, which can be drawn with probabilities proportional to the numbers of balls of each colour. For each colour a tube is assumed. These tubes have different sizes (lengths). The idea is that after drawing a ball from the urn it is dropped in the urn of the corresponding colour. We consider two associated probability distributions. The first-full distribution on colours gives for each colour the probability that the corresponding tube is full first, before any of the other tubes. The negative distribution on natural numbers captures for a number k the probability that all tubes are full for the first time after k draws. This paper uses multisets to systematically describe these first-full and negative distributions in the urns & tubes setting, in fully multivariate form, for all three standard drawing modes (multinomial, hypergeometric, and Polya).
Harvesting publication data to the institutional repository from Scopus, Web of Science, Dimensions and Unpaywall using a custom R Script

Institutional repositories are established tools for archiving and increasing the visibility and availability of academic outputs. Although the potential benefits of institutional repositories are well researched and many funders and institutions already mandate open access publishing via gold or green open access routes, institutional repositories often struggle with lack of growth and sustained workflows for content recruitment. Institutions have come up with various (and often creative) workflows for populating their repositories, including institutional open access mandates, library-mediated self-archiving, fully or partially automated content harvesting and integrations between repositories and Current Research Information Systems (CRIS). Zayed University launched the ZU Scholars1 institutional repository in fall 2021. Since the beginning, a semi-automated workflow was introduced to populate the repository with publication data from Scopus, Web of Science, Dimensions and Unpaywall using a custom R script. Full text files are added automatically for all Creative Commons licensed articles. This article describes the data harvesting and conversion process, its current limitations and plans for future development. The article also reviews similar content harvesting projects in the context of institutional repositories.
A Developmental Approach for Training Deep Belief Networks

Abstract Deep belief networks (DBNs) are stochastic neural networks that can extract rich internal representations of the environment from the sensory data. DBNs had a catalytic effect in triggering the deep learning revolution, demonstrating for the very first time the feasibility of unsupervised learning in networks with many layers of hidden neurons. These hierarchical architectures incorporate plausible biological and cognitive properties, making them particularly appealing as computational models of human perception and cognition. However, learning in DBNs is usually carried out in a greedy, layer-wise fashion, which does not allow to simulate the holistic maturation of cortical circuits and prevents from modeling cognitive development. Here we present iDBN , an iterative learning algorithm for DBNs that allows to jointly update the connection weights across all layers of the model. We evaluate the proposed iterative algorithm on two different sets of visual stimuli, measuring the generative capabilities of the learned model and its potential to support supervised downstream tasks. We also track network development in terms of graph theoretical properties and investigate the potential extension of iDBN to continual learning scenarios. DBNs trained using our iterative approach achieve a final performance comparable to that of the greedy counterparts, at the same time allowing to accurately analyze the gradual development of internal representations in the deep network and the progressive improvement in task performance. Our work paves the way to the use of iDBN for modeling neurocognitive development.
Identity-Preserved Human Posture Detection in Infrared Thermal Images: A Benchmark

Human pose estimation has a variety of real-life applications, including human action recognition, AI-powered personal trainers, robotics, motion capture and augmented reality, gaming, and video surveillance. However, most current human pose estimation systems are based on RGB images, which do not seriously take into account personal privacy. Although identity-preserved algorithms are very desirable when human pose estimation is applied to scenarios where personal privacy does matter, developing human pose estimation algorithms based on identity-preserved modalities, such as thermal images concerned here, is very challenging due to the limited amount of training data currently available and the fact that infrared thermal images, unlike RGB images, lack rich texture cues which makes annotating training data itself impractical. In this paper, we formulate a new task with privacy protection that lies between human detection and human pose estimation by introducing a benchmark for IPHPDT (i.e., Identity-Preserved Human Posture Detection in Thermal images). This task has a threefold novel purpose: the first is to establish an identity-preserved task with thermal images; the second is to achieve more information other than the location of persons as provided by human detection for more advanced computer vision applications; the third is to avoid difficulties in collecting well-annotated data for human pose estimation in thermal images. The presented IPHPDT dataset contains four types of human postures, consisting of 75,000 images well-annotated with axis-aligned bounding boxes and postures of the persons. Based on this well-annotated IPHPDT dataset and three state-of-the-art algorithms, i.e., YOLOF (short for You Only Look One-level Feature), YOLOX (short for Exceeding YOLO Series in 2021) and TOOD (short for Task-aligned One-stage Object Detection), we establish three baseline detectors, called IPH-YOLOF, IPH-YOLOX, and IPH-TOOD. In the experiments, three baseline detectors are used to recognize four infrared human postures, and the mean average precision can reach 70.4%. The results show that the three baseline detectors can effectively perform accurate posture detection on the IPHPDT dataset. By releasing IPHPDT, we expect to encourage more future studies into human posture detection in infrared thermal images and draw more attention to this challenging task.
Ethereum-Based Information System for Digital Higher Education Registry and Verification of Student Achievement Documents

Blockchain is a new and modern technology that is gradually being used in various fields due to its ability to decentralize and organize secure and reliable data exchange and storage. One of the related research areas generating increasing interest is the field of education, with particular focus on the digitization and automation of educational management processes and the ability to store and verify digital documents about student progress. The main goal of this study is to develop a platform that creates a unified digital register of students’ educational achievements, which is one of the most pressing issues in the field of education, based on the Ethereum blockchain architecture. Blockchain is expensive; therefore, there is a need to consider performance criteria when evaluating any decision made about the technology, especially the most important aspects such as predicting traffic behavior, estimating transaction costs and providing the necessary indicators of system quality and functionality. However, most research ignores the evaluation of performance indicators, such as throughput, the speed of transactions and the amount of data stored in the Ethereum blockchain database, which are the main evaluation criteria. This paper aims to eliminate this gap by evaluating the performance of the developed platform and by discussing the obtained experimental results. Thus, the main results of this work are the design and deployment of a blockchain platform and the analysis of its transaction costs. We conclude that the proposed blockchain solution is applicable as a system for the accounting and verification of loans and student academic achievements.
Software variability in service robotics

Robots artificially replicate human capabilities thanks to their software, the main embodiment of intelligence. However, engineering robotics software has become increasingly challenging. Developers need expertise from different disciplines as well as they are faced with heterogeneous hardware and uncertain operating environments. To this end, the software needs to be variable-to customize robots for different customers, hardware, and operating environments. However, variability adds substantial complexity and needs to be managed-yet, ad hoc practices prevail in the robotics domain, challenging effective software reuse, maintenance, and evolution. To improve the situation, we need to enhance our empirical understanding of variability in robotics. We present a multiple-case study on software variability in the vibrant and challenging domain of service robotics. We investigated drivers, practices, methods, and challenges of variability from industrial companies building service robots. We analyzed the state-of-the-practice and the state-of-the-art-the former via an experience report and eleven interviews with two service robotics companies; the latter via a systematic literature review. We triangulated from these sources, reporting observations with actionable recommendations for researchers, tool providers, and practitioners. We formulated hypotheses trying to explain our observations, and also compared the state-of-the-art from the literature with the-state-of-the-practice we observed in our cases. We learned that the level of abstraction in robotics software needs to be raised for simplifying variability management and software integration, while keeping a sufficient level of customization to boost efficiency and effectiveness in their robots' operation. Planning and realizing variability for specific requirements and implementing robust abstractions permit robotic applications to operate robustly in dynamic environments, which are often only partially known and controllable. With this aim, our companies use a number of mechanisms, some of them based on formalisms used to specify robotic behavior, such as finite-state machines and behavior trees. To foster software reuse, the service robotics domain will greatly benefit from having software components-completely decoupled from hardware-with harmonized and standardized interfaces, and organized in an ecosystem shared among various companies.
DEGAIN: Generative-Adversarial-Network-Based Missing Data Imputation

Insights and analysis are only as good as the available data. Data cleaning is one of the most important steps to create quality data decision making. Machine learning (ML) helps deal with data quickly, and to create error-free or limited-error datasets. One of the quality standards for cleaning the data includes handling the missing data, also known as data imputation. This research focuses on the use of machine learning methods to deal with missing data. In particular, we propose a generative adversarial network (GAN) based model called DEGAIN to estimate the missing values in the dataset. We evaluate the performance of the presented method and compare the results with some of the existing methods on publicly available Letter Recognition and SPAM datasets. The Letter dataset consists of 20,000 samples and 16 input features and the SPAM dataset consists of 4601 samples and 57 input features. The results show that the proposed DEGAIN outperforms the existing ones in terms of root mean square error and Frechet inception distance metrics.
Enhancing Explainable Machine Learning by Reconsidering Initially Unselected Items in Feature Selection for Classification

Feature selection is a common step in data preprocessing that precedes machine learning to reduce data space and the computational cost of processing or obtaining the data. Filtering out uninformative variables is also important for knowledge discovery. By reducing the data space to only those components that are informative to the class structure, feature selection can simplify models so that they can be more easily interpreted by researchers in the field, reminiscent of explainable artificial intelligence. Knowledge discovery in complex data thus benefits from feature selection that aims to understand feature sets in the thematic context from which the data set originates. However, a single variable selected from a very small number of variables that are technically sufficient for AI training may make little immediate thematic sense, whereas the additional consideration of a variable discarded during feature selection could make scientific discovery very explicit. In this report, we propose an approach to explainable feature selection (XFS) based on a systematic reconsideration of unselected features. The difference between the respective classifications when training the algorithms with the selected features or with the unselected features provides a valid estimate of whether the relevant features in a data set have been selected and uninformative or trivial information was filtered out. It is shown that revisiting originally unselected variables in multivariate data sets allows for the detection of pathologies and errors in the feature selection that occasionally resulted in the failure to identify the most appropriate variables.
A comparative online sales forecasting analysis: Data mining techniques

This study aims to improve the management efficiency of e-commerce platform and assists merchants on the e-commerce platforms in formulating a suitable sales plan urgently. Online sales forecasting analysis needs to be studied and shows that the management efficiency and operating income on an e-commerce platform is improved through accurate commodity sales forecasting. A novel online clothing sales forecasting model is proposed based on data mining technique. This study contributes to presenting the model references for e-commerce platform to make decisions on future sales and directions. (1) The gray correlation model was employed to mine the correlation degree between each feature and the clothing sales to select the features that have a great impact on clothing sales. (2) A sailfish optimization algorithm (SFO) algorithm with random disturbance strategy (SFOR) was proposed based on the SFO to improve the prediction effect of clothing sales. The benchmark function test results showed that the SFOR algorithm effectively avoided local extreme points. (3) The SFOR algorithm was used to solve the extreme learning machine (ELM) random parameter problem, and the SFOR-ELM-based online sales prediction model of clothing products suitable for multiple scenarios was constructed. In addition, three cases are applied to verify the SFOR-ELM-based online clothing sales forecast model. The verification results proved that SFOR-ELM achieved satisfactory prediction results, with its mean absolute percentage error values controlled below 5.1% and root mean square error values controlled below 16.2%.
Sensitivity of string compressors and repetitiveness measures

The sensitivity of a string compression algorithm $C$ asks how much the output size $C(T)$ for an input string $T$ can increase when a single character edit operation is performed on $T$. This notion enables one to measure the robustness of compression algorithms in terms of errors and/or dynamic changes occurring in the input string. In this paper, we analyze the worst-case multiplicative sensitivity of string compression algorithms, which is defined by $\max_{T \in \Sigma^n}\{C(T')/C(T) : ed(T, T') = 1\}$, where $ed(T, T')$ denotes the edit distance between $T$ and $T'$. For the most common versions of the Lempel-Ziv 77 compressors, we prove that the worst-case multiplicative sensitivity is upper bounded by a small constant, and give matching lower bounds. We generalize these results to the smallest bidirectional scheme $b$. In addition, we show that the sensitivity of a grammar-based compressor called GCIS is also a small constant. Further, we extend the notion of the worst-case sensitivity to string repetitiveness measures such as the smallest string attractor size $\gamma$ and the substring complexity $\delta$, and show that the worst-case sensitivity of $\delta$ is also a small constant. These results contrast with the previously known related results such that the size $z_{\rm 78}$ of the Lempel-Ziv 78 factorization can increase by a factor of $\Omega(n^{1/4})$ [Lagarde and Perifel, 2018], and the number $r$ of runs in the Burrows-Wheeler transform can increase by a factor of $\Omega(\log n)$ [Giuliani et al., 2021] when a character is prepended to an input string of length $n$. By applying our sensitivity bounds of $\delta$ or the smallest grammar to known results (c.f. [Navarro, 2021]), some non-trivial upper bounds for the sensitivities of important string compressors and repetitiveness measures including $\gamma$, $r$, LZ-End, RePair, LongestMatch, and AVL-grammar are derived.
HCDC: A novel hierarchical clustering algorithm based on density-distance cores for data sets with varying density

Cluster analysis is a crucial data mining technology widely used in image segmentation, language processing, and pattern recognition. Most existing clustering algorithms cannot identify complex shapes in manifold data sets and data sets with varying-density distribution, especially when clusters with significant differences in density are close to each other. Hierarchical clustering algorithms can identify data sets of arbitrary shapes. However, hierarchical clustering algorithms not only cannot cluster datasets with significant density variations but also have a high time cost. So in this paper, we propose a novel hierarchical clustering algorithm based on density-distance cores, called HCDC. It first selects the density-distance representative points for each point from the set of candidate representative points. Then it selects density-distance cores from all density-distance representatives. And it replaces the whole data set with density-distance cores and uses a new distance between them to apply hierarchical clustering. To avoid the influence of noise points in the dataset when finding density-distance cores, we also propose the noise point detection method and verify the feasibility of this method. In this paper, we compare our proposed algorithm with existing classical and novel algorithms on synthetic and real datasets. Experiments show that our algorithm clusters better than existing algorithms on complex-shaped datasets and datasets with different densities. On datasets with sparse and dense clusters close to each other, the ARI score of HCDC is more than 0.1 higher than that of LDP-MST. In particular, on the grid dataset, HCDC’s ARI score is 0.997 higher than LDP-MST. On DS3 and DS8, HCDC’s ARI score is more than 0.14 higher than the second-best algorithm, RNN-DBSCAN. Moreover, on the zoo dataset, HCDC’s ARI score is 0.15 and 0.6 higher than RNN-DBSCAN and LDP-MST, respectively. On the olivetti face dataset, HCDC is the only algorithm with an NMI score above 0.9 on photo1 and photo2 datasets.