Reviewerly/exHarmony huggingface dataset:
Learning with User-Level Privacy

We propose and analyze algorithms to solve a range of learning tasks under user-level differential privacy constraints. Rather than guaranteeing only the privacy of individual samples, user-level DP protects a user's entire contribution ($m \ge 1$ samples), providing more stringent but more realistic protection against information leaks. We show that for high-dimensional mean estimation, empirical risk minimization with smooth losses, stochastic convex optimization, and learning hypothesis class with finite metric entropy, the privacy cost decreases as $O(1/\sqrt{m})$ as users provide more samples. In contrast, when increasing the number of users $n$, the privacy cost decreases at a faster $O(1/n)$ rate. We complement these results with lower bounds showing the worst-case optimality of our algorithm for mean estimation and stochastic convex optimization. Our algorithms rely on novel techniques for private mean estimation in arbitrary dimension with error scaling as the concentration radius $\tau$ of the distribution rather than the entire range. Under uniform convergence, we derive an algorithm that privately answers a sequence of $K$ adaptively chosen queries with privacy cost proportional to $\tau$, and apply it to solve the learning tasks we consider.
A Distributed Security SDN Cluster Architecture for Smart Grid Based on Blockchain Technology

This paper proposes a smart grid distributed security architecture based on blockchain technology and SDN cluster structure, referred to as ClusterBlock model, which combines the advantages of two emerging technologies, blockchain and SDN. The blockchain technology allows for distributed peer-to-peer networks, where the network can ensure the trusted interaction of untrusted nodes in the network. At the same time, this article adopts the design of an SDN controller distributed cluster to avoid single point of failure and balance the load between equipment and the controller. A cluster head was selected in each SDN cluster, and it was used as a blockchain node to construct an SDN cluster head blockchain. By combining blockchain technology, the security and privacy of the SDN communication network can be enhanced. At the same time, this paper designs a distributed control strategy and network attack detection algorithm based on blockchain consensus and introduces the Jaccard similarity coefficient to detect the network attacks. Finally, this paper evaluates the ClusterBlock model and the existing model based on the OpenFlow protocol through simulation experiments and compares the security performance. The evaluation results show that the ClusterBlock model has more stable bandwidth and stronger security performance in the face of DDoS attacks of the same scale.
An Empirical Study of the Effectiveness of an Ensemble of Stand-alone Sentiment Detection Tools for Software Engineering Datasets

Sentiment analysis in software engineering (SE) has shown promise to analyze and support diverse development activities. Recently, several tools are proposed to detect sentiments in software artifacts. While the tools improve accuracy over off-the-shelf tools, recent research shows that their performance could still be unsatisfactory. A more accurate sentiment detector for SE can help reduce noise in analysis of software scenarios where sentiment analysis is required. Recently, combinations, i.e., hybrids of stand-alone classifiers are found to offer better performance than the stand-alone classifiers for fault detection. However, we are aware of no such approach for sentiment detection for software artifacts. We report the results of an empirical study that we conducted to determine the feasibility of developing an ensemble engine by combining the polarity labels of stand-alone SE-specific sentiment detectors. Our study has two phases. In the first phase, we pick five SE-specific sentiment detection tools from two recently published papers by Lin et al. [ 29 , 30 ], who first reported negative results with stand alone sentiment detectors and then proposed an improved SE-specific sentiment detector, POME [ 29 ]. We report the study results on 17,581 units (sentences/documents) coming from six currently available sentiment benchmarks for software engineering. We find that the existing tools can be complementary to each other in 85-95% of the cases, i.e., one is wrong but another is right. However, a majority voting-based ensemble of those tools fails to improve the accuracy of sentiment detection. We develop Sentisead, a supervised tool by combining the polarity labels and bag of words as features. Sentisead improves the performance (F1-score) of the individual tools by 4% (over Senti4SD [ 5 ]) – 100% (over POME [ 29 ]). The initial development of Sentisead occurred before we observed the use of deep learning models for SE-specific sentiment detection. In particular, recent papers show the superiority of advanced language-based pre-trained transformer models (PTM) over rule-based and shallow learning models. Consequently, in a second phase, we compare and improve Sentisead infrastructure using the PTMs. We find that a Sentisead infrastructure with RoBERTa as the ensemble of the five stand-alone rule-based and shallow learning SE-specific tools from Lin et al. [ 29 , 30 ] offers the best F1-score of 0.805 across the six datasets, while a stand-alone RoBERTa shows an F1-score of 0.801.
Machine learning for inversing FeO and TiO2 content on the Moon: Method and comparison

The FeO and TiO2 contents are critical for distinguishing petrological properties of the Moon and for studying the distribution of the lunar maria and its multi-period volcanic activity. Traditional methods used the ratio between spectral reflectances to estimate FeO and TiO2 contents, which are empirical models. The development of machine learning algorithms offered new ideas for solving inversion problems, and these algorithms can automatically mine the data for potential correlations wherever possible. In this work, by using the Kaguya Multiband Imager data, we construct an optimized spectral inversion model using the Convolutional Neural Network (CNN) algorithm to produce a map of the FeO and TiO2 content on the lunar surface. The CNN models were compared with the traditional linear model and the Random Forest (RF) model. The results were indicated that the CNN models had higher accuracy and the CNN model eliminated the shortcoming of the RF model that the inversion results were limited by the training data, and certainly optimizes the impact of data striping. The CNN models can better describe the nonlinear relationship between spectral reflectance and oxide content. This also provides the basis for the inversion of the other oxides (e.g., MgO, Al2O3, CaO and SiO2). These new maps from the CNN model provide reference information for further studies of the geological evolution of the Moon.
An empirical assessment of different kernel functions on the performance of support vector machines

Artificial intelligence (AI) and machine learning (ML) have influenced every part of our day-to-day activities in this era of technological advancement, making a living more comfortable on the earth. Among the several AI and ML algorithms, the support vector machine (SVM) has become one of the most generally used algorithms for data mining, prediction and other (AI and ML) activities in several domains. The SVM’s performance is significantly centred on the kernel function (KF); nonetheless, there is no universal accepted ground for selecting an optimal KF for a specific domain. In this paper, we investigate empirically different KFs on the SVM performance in various fields. We illustrated the performance of the SVM based on different KF through extensive experimental results. Our empirical results show that no single KF is always suitable for achieving high accuracy and generalisation in all domains. However, the gaussian radial basis function (RBF) kernel is often the default choice. Also, if the KF parameters of the RBF and exponential RBF are optimised, they outperform the linear and sigmoid KF based SVM method in terms of accuracy. Besides, the linear KF is more suitable for the linearly separable dataset.
Adaptive UAV Swarm Mission Planning by Temporal Difference Learning

The prevalence of Unmanned Aerial Vehicles (UAVs) in precision agriculture has been growing rapidly. This paper tackles the UAV global mission planning problem by incorporating a greater capacity for human-machine teaming in the architecture of a flexibly autonomous, near-fully-distributed Mission Management System for UAV swarms. Subsequently, the two problems of global mission planning are solved simultaneously using an integrated solution. This consists of a geometric clustering algorithm which prioritizes the minimization of overall mission time, and an off-policy, model-free Temporal Difference Learning global agent capable of learning about an initially unknown mission environment through simulations. The latter component makes the solution adaptive to missions with different requirements.
Rectifying the Data Bias in Knowledge Distillation

Knowledge distillation is a representative technique for model compression and acceleration, which is important for deploying neural networks on resource limited devices. The knowledge transferred from teacher to student is the mapping of teacher model, or represented by all the input-output pairs. However, in practice the student model only learns from data pairs of the dataset that may be biased, and we think this limits the performance of knowledge distillation. In this paper, we first quantitatively define the uniformity of the sampled data for training, providing a unified view for methods that learn from biased data. Then we evaluate the uniformity on real world dataset and show that existing methods actually improve the uniformity of data. We further introduce two uniformity-oriented methods for rectifying the bias of data for knowledge distillation. Extensive experiments conducted on Face Recognition and Person Re-identification have shown the effectiveness of our method. Moreover, we analyze the sampled data on Face Recognition and show that better balance is achieved between races and between easy and hard samples. And this effect can be also confirmed in training the student model from scratch, resulting in a comparable performance with standard knowledge distillation.
Patterns of Polysemy and Homonymy in Contextualised Language Models

One of the central aspects of contextualised language models is that they should be able to distinguish the meaning of lexically ambiguous words by their contexts.In this paper we investigate the extent to which the contextualised embeddings of word forms that display multiplicity of sense reflect traditional distinctions of polysemy and homonymy.To this end, we introduce an extended, human-annotated dataset of graded word sense similarity and co-predication acceptability, and evaluate how well the similarity of embeddings predicts similarity in meaning.Both types of human judgements indicate that the similarity of polysemic interpretations falls in a continuum between identity of meaning and homonymy.However, we also observe significant differences within the similarity ratings of polysemes, forming consistent patterns for different types of polysemic sense alternation.Our dataset thus appears to capture a substantial part of the complexity of lexical ambiguity, and can provide a realistic test bed for contextualised embeddings.Among the tested models, BERT Large shows the strongest correlation with the collected word sense similarity ratings, but struggles to consistently replicate the observed similarity patterns.When clustering ambiguous word forms based on their embeddings, the model displays high confidence in discerning homonyms and some types of polysemic alternations, but consistently fails for others.
Error Identification for Machine Translation with Metric Embedding and Attention

Quality Estimation (QE) for Machine Translation has been shown to reach relatively high accuracy in predicting sentence-level scores, relying on pretrained contextual embeddings and human-produced quality scores.However, the lack of explanations along with decisions made by end-to-end neural models makes the results difficult to interpret.Furthermore, word-level annotated datasets are rare due to the prohibitive effort required to perform this task, while they could provide interpretable signals in addition to sentence-level QE outputs.In this paper, we propose a novel QE architecture which tackles both the wordlevel data scarcity and the interpretability limitations of recent approaches.Sentence-level and word-level components are jointly pretrained through an attention mechanism based on synthetic data and a set of MT metrics embedded in a common space.Our approach is evaluated on the Eval4NLP 2021 shared task and our submissions reach the first position in all language pairs.The extraction of metricto-input attention weights show that different metrics focus on different parts of the source and target text, providing strong rationales in the decision-making process of the QE model.
Compression of Plenoptic Point Cloud Attributes Using 6-D Point Clouds and 6-D Transforms

In this paper, we introduce a novel 6-D representation of plenoptic point clouds, enabling joint, non-separable transform coding of plenoptic signals defined along both spatial and angular (viewpoint) dimensions. This 6-D representation, which is built in a global coordinate system, can be used in both multi-camera studio capture and video fly-by capture scenarios, with various viewpoint (camera) arrangements and densities. We show that both the Region-Adaptive Hierarchical Transform (RAHT) and the Graph Fourier Transform (GFT) can be extended to the proposed 6-D representation to enable the non-separable transform coding. Our method is applicable to plenoptic data with either dense or sparse sets of viewpoints, and to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">complete</i> or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">incomplete</i> plenoptic data, while the state-of-the-art RAHT-KLT method, which is separable in spatial and angular dimensions, is applicable only to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">complete</i> plenoptic data. The “complete” plenoptic data refers to data that has, for each spatial point, one colour for every viewpoint (ignoring any occlusions), while “incomplete” data has colours only for the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">visible</i> surface points at each viewpoint. We demonstrate that the proposed 6-D RAHT and 6-D GFT compression methods are able to outperform the state-of-the-art RAHT-KLT method on 3-D objects with various levels of surface specularity, and captured with different camera arrangements and different degrees of viewpoint sparsity.
Severity level classification and detection of breast cancer using computer-aided mammography techniques

Breast cancer is the main cause of increase in the cancer death rate among women globally. Early diagnosis of the breast tumors at premature stages prevents the increase in the mortality rate all around the world. Mammography is currently the most efficient and effective way of diagnosing breast cancers to prevent the patients from unwanted therapies and biopsies. The main objective of the proposed work is to help the radiologist to identify the severity level by appropriate grading of the breast cancers using computer-aided mammography (CAM) techniques. The accurate detection of the breast tumors in the proposed work is done using a modified region growing (MRG) followed by a semantic segmentation algorithm. The proposed system uses two-stage classification systems in which the first stage uses an optimized genetic fuzzy classifier (OGFC) to classify the mammograms as normal and abnormal taking statistical features and the wavelet features as input. Further, the abnormal images are classified to identify the stages of the breast cancer as stage I, stage II, stage III, and stage IV using the second stage classification system which consists of a hybrid neural network optimized using genetic algorithms and trained using the grading dataset taking the shape and size features of the malignant tissue as input. The performance of the proposed system will be analyzed using the true-positive (TP), true-negative (TN), false-positive (FP), and false-negative (FN) values, and accuracy. The proposed system is evaluated using various digital mammograms.
Coping with Digital Extortion: An Experimental Study of Benefit Appeals and Normative Appeals

Digital extortion has emerged as a significant threat to organizations that rely on information technologies for their operations. Using human subject experimentation, we study the effectiveness of message appeals in encouraging defenders to adopt two mitigation strategies, investment in security and refusal to pay ransoms, to digital extortion threats. We explore two types of appeals, benefit and normative, for this purpose. We find that the decisions of the defenders (representing any organization that can be a potential victim) deviate from the predictions of game theory. However, given the strategic interactions between the defenders and the attacker as well as noisy decision-making behaviors, it is challenging to untangle the influence of the appeals on the defenders. We develop a structural model based on the quantal response equilibrium framework to measure how message appeals change the defenders’ utilities of investment and payment refusal. Although the interventions may be successful in increasing the utilities of investment and/or payment refusal, their impacts on investment rate and payment rate are mitigated by the attacker reducing ransoms. Thus, it is challenging for an intervention to significantly boost a community’s investment rate or to suppress the ransom payment rate. We characterize how security outcomes of a community (including expected ransom, attack rate, investment rate, and payment rate) vary with the defenders’ utilities of investment and pay refusal. This paper was accepted by Chris Forman, information systems.
An Advanced Deep Learning Approach for Multi-Object Counting in Urban Vehicular Environments

Object counting is an active research area that gained more attention in the past few years. In smart cities, vehicle counting plays a crucial role in urban planning and management of the Intelligent Transportation Systems (ITS). Several approaches have been proposed in the literature to address this problem. However, the resulting detection accuracy is still not adequate. This paper proposes an efficient approach that uses deep learning concepts and correlation filters for multi-object counting and tracking. The performance of the proposed system is evaluated using a dataset consisting of 16 videos with different features to examine the impact of object density, image quality, angle of view, and speed of motion towards system accuracy. Performance evaluation exhibits promising results in normal traffic scenarios and adverse weather conditions. Moreover, the proposed approach outperforms the performance of two recent approaches from the literature.
PanopTOP: a framework for generating viewpoint-invariant human pose estimation datasets

Human pose estimation (HPE) from RGB and depth images has recently experienced a push for viewpoint-invariant and scale-invariant pose retrieval methods. Current methods fail to generalize to unconventional viewpoints due to the lack of viewpoint-invariant data at training time. Existing datasets do not provide multiple-viewpoint observations and mostly focus on frontal views. In this work, we introduce PanopTOP, a fully automatic framework for the generation of semi-synthetic RGB and depth samples with 2D and 3D ground truth of pedestrian poses from multiple arbitrary viewpoints. Starting from the Panoptic Dataset [15], we use the PanopTOP framework to generate the PanopTOP31K dataset, consisting of 31K images from 23 different subjects recorded from diverse and challenging viewpoints, also including the top-view. Finally, we provide baseline results and cross-validation tests for our dataset, demonstrating how it is possible to generalize from the semi-synthetic to the real-world domain. The dataset and the code will be made publicly available upon acceptance.
Reflecting on Best Practices for Online Learning in a Post-COVID-19 World

When the novel coronavirus 2019 caused many schools to immediately go online in March 2020, many instructors had significant training and experience teaching residentially but little to no experience teaching online courses. All classes were immediately converted to online, and some schools are still uncertain as to when they will return to full traditional classroom settings. Regardless of online experience, all instructors were needed to learn to adapt to online teaching immediately. This change created a need for all faculty members to receive the training and support necessary to make the online process as smooth and effective as possible. In this Best Practices perspective, we identified useful and successful practices to help students learn in the online courses. With the knowledge of data driven support and awareness of effective online teaching strategies, instructors can make the most of online teaching sessions.
An MRI Study on Effects of Math Education on Brain Development Using Multi-Instance Contrastive Learning

This paper explores whether mathematical education has effects on brain development from the perspective of brain MRIs. While biochemical changes in the left middle front gyrus region of the brain have been investigated, we proposed to classify students by using MRIs from the intraparietal sulcus (IPS) region that was left untouched in the previous study. On the cropped IPS regions, the proposed model developed popular contrastive learning (CL) to solve the problem of multi-instance representation learning. The resulted data representations were then fed into a linear neural network to identify whether students were in the math group or the non-math group. Experiments were conducted on 123 adolescent students, including 72 math students and 51 non-math students. The proposed model achieved an accuracy of 90.24 % for student classification, gaining more than 5% improvements compared to the classical CL frame. Our study provides not only a multi-instance extension to CL and but also an MRI insight into the impact of mathematical studying on brain development.
Using Federated Learning to Achieve Proactive Context-Aware IoT Environments

The Internet of Things (IoT) is more present in our daily lives than ever before, turning everyday physical objects into smart devices. However, these devices often need excessive human interaction before reaching their best performance, making them time-consuming and reducing their usability. Nowadays, Artificial Intelligence (AI) techniques are being used to process data and to find ways to automate different behaviours. However, achieving learning models capable of handling any situation is a challenging task, worsened by time training restrictions. This paper proposes a Federated Learning solution to manage different IoT environments and provide accurate predictions, based on the user’s preferences. To improve the coexistence between devices and users, this approach makes use of other users’ previous behaviours in similar environments, and proposes predictions for newcomers to the federation. Also, for existing participants, it provides a closer personalization, immediate availability and prevents most manual interactions. The approach has been tested with synthetic and real data and identifies the actions to be performed with 94% accuracy on regular users.