Reviewerly/exHarmony huggingface dataset:
Statistical Dependence Analyses of Operational Flight Data Used for Landing Reconstruction Enhancement

The RTS smoother is widely used for state estimation and it is utilized here to increase the data quality with respect to physical coherence and to increase resolution. The purpose of this paper is to enhance the performance of the RTS smoother to reconstruct an aircraft landing using on board recorded data only. Thereby, errors and uncertainties of operational flight data (e.g. altitude, attitude, position, speed) recorded during flights of civil aircraft are minimized. These data can be used for subsequent analyses in terms of flight safety or efficiency, which is commonly referred to as Flight Data Monitoring (FDM). Statistical assumptions of the smoother theory are not always verified during application but (consciously or not) assumed to be fulfilled. These assumptions can hardly be verified prior to the smoother application, however, they can be verified using the results of an initial smoother iteration and modifications of specific smoother characteristics can be suggested. This project specifically verifies assumptions on the measurement noise characteristics. Variance and covariance of the measurement noise can be checked after the initial smoother application. It is discovered that these characteristics change over time and should be accounted for with a time varying covariance matrix. This sequence of matrices is estimated by kernel smoothing and replaces an initially assumed fixed and diagonal covariance matrix used for the first smoother run. The results of this second smoother iteration are mostly improved compared to the initial iteration, i.e. the errors are significantly reduced. Subsequently, the remaining dependence structures of the residuals of the second smoother iteration can be captured by copula models. Their interpretation is useful for a revision of the physical model utilized by the RTS smoother.
SoteriaFL: A Unified Framework for Private Federated Learning with Communication Compression

To enable large-scale machine learning in bandwidth-hungry environments such as wireless networks, significant progress has been made recently in designing communication-efficient federated learning algorithms with the aid of communication compression. On the other end, privacy-preserving, especially at the client level, is another important desideratum that has not been addressed simultaneously in the presence of advanced communication compression techniques yet. In this paper, we propose a unified framework that enhances the communication efficiency of private federated learning with communication compression. Exploiting both general compression operators and local differential privacy, we first examine a simple algorithm that applies compression directly to differentially-private stochastic gradient descent, and identify its limitations. We then propose a unified framework SoteriaFL for private federated learning, which accommodates a general family of local gradient estimators including popular stochastic variance-reduced gradient methods and the state-of-the-art shifted compression scheme. We provide a comprehensive characterization of its performance trade-offs in terms of privacy, utility, and communication complexity, where SoteraFL is shown to achieve better communication complexity without sacrificing privacy nor utility than other private federated learning algorithms without communication compression.
Document Navigability: A Need for Print-Impaired

Printed documents continue to be a challenge for blind, low-vision, and other print-disabled (BLV) individuals. In this paper, we focus on the specific problem of (in-)accessibility of internal references to citations, footnotes, figures, tables and equations. While sighted users can flip to the referenced content and flip back in seconds, linear audio narration that BLV individuals rely on makes following these references extremely hard. We propose a vision based technique to locate the referenced content and extract metadata needed to (in subsequent work) inline a content summary into the audio narration. We apply our technique to citations in scientific documents and find it works well both on born-digital as well as scanned documents.
ck-means, a novel unsupervised learning method that combines fuzzy and crispy clustering methods to extract intersecting data

Clustering data is a popular feature in the field of unsupervised machine learning. Most algorithms aim to find the best method to extract consistent clusters of data, but very few of them intend to cluster data that share the same intersections between two features or more. This paper proposes a method to do so. The main idea of this novel method is to generate fuzzy clusters of data using a Fuzzy C-Means (FCM) algorithm. The second part involves applying a filter that selects a range of minimum and maximum membership values, emphasizing the border data. A {\mu} parameter defines the amplitude of this range. It finally applies a k-means algorithm using the membership values generated by the FCM. Naturally, the data having similar membership values will regroup in a new crispy cluster. The algorithm is also able to find the optimal number of clusters for the FCM and the k-means algorithm, according to the consistency of the clusters given by the Silhouette Index (SI). The result is a list of data and clusters that regroup data sharing the same intersection, intersecting two features or more. ck-means allows extracting the very similar data that does not naturally fall in the same cluster but at the intersection of two clusters or more. The algorithm also always finds itself the optimal number of clusters.
Predicative Possession in Learning English: Syntactic Study

The discussion on predicative possession is actually a fairly broad discussion. The aspect studied in this paper is looking at the construction of ownership verbs in the English possession predicate. Viewed from the point of view of the study this paper is grouped into language studies in terms of syntax. The method used is descriptive qualitative method. The theory in this paper refers to the theory of Quirk (1973), Halliday (1985), Cook (1989), and Jackendoff (1977). The data used in this paper are taken from The British National Corpus (http://www.notcorp.ox.ac.uk/). The data collected contains sentences with the predicate of ownership in English with the verb “have”, “own”, “possess”, and “belong to”. From the results of research on the predicative possession in English, it can be concluded as follows. Lingual elements that can fill the possession predicate are a) The "have" possession predicate, which consists of several patterns as follows: active pattern (S+V+O), (S+V+O+A), (S+V+O+OC), and (S+V+O+OC+A); b) The "own" possession predicate consists on the following patterns: active sentence patterns (S+V+O), (S+V+O+A), (S+V+O+OC), and (S+V+O+OC+A) and passive sentence patterns with patterns (S+V+O), (S+V+O+A), (S+V+O+OC) and (S+V+O+OC+A) c) The "possess" possession predicate consists on several patterns as follows: active pattern (S+V+O), (S+V+O+A), (S+V+O+OC) dan (S+ V+O+OC+A); d). The "belong to" possession predicate consists of the following patterns: Active sentences (S+V+O), (S+V+O+A), (S+V+O+OC), and (S+V+O+OC +A).
Primal Estimated Subgradient Solver for SVM for Imbalanced Classification

We aim to demonstrate in experiments that our cost sensitive PEGASOS SVM achieves good performance on imbalanced data sets with a Majority to Minority Ratio ranging from 8.6:1 to 130:1 and to ascertain whether the including intercept (bias), regularization and parameters affects performance on our selection of datasets. Although many resort to SMOTE methods, we aim for a less computationally intensive method. We evaluate the performance by examining the learning curves. These curves diagnose whether we overfit or underfit or whether the random sample of data chosen during the process was not random enough or diverse enough in dependent variable class for the algorithm to generalized to unseen examples. We will also see the background of the hyperparameters versus the test and train error in validation curves. We benchmark our PEGASOS Cost-Sensitive SVM's results of Ding's LINEAR SVM DECIDL method. He obtained an ROC-AUC of .5 in one dataset. Our work will extend the work of Ding by incorporating kernels into SVM. We will use Python rather than MATLAB as python has dictionaries for storing mixed data types during multi-parameter cross-validation.
Existence and Minimax Theorems for Adversarial Surrogate Risks in Binary Classification

Adversarial training is one of the most popular methods for training methods robust to adversarial attacks, however, it is not well-understood from a theoretical perspective. We prove and existence, regularity, and minimax theorems for adversarial surrogate risks. Our results explain some empirical observations on adversarial robustness from prior work and suggest new directions in algorithm development. Furthermore, our results extend previously known existence and minimax theorems for the adversarial classification risk to surrogate risks.
Converting 2D magnetic resource imagining brain tumors to 3D structure using depth map machine learning techniques

&lt;span&gt;The use &lt;span&gt;of medical imaging technology aids clinicians in recognizing and assessing patient problems, as well as improving treatment procedures. However, while conducting complex procedures such as the excision of brain tumors, the knowledge and biological research gathered from 2D images are insufficient. Converting 2D images to 3D images may assist doctors in determining the size, shape, and sharp area of tumor cells in the brain. The feasibility of translating 2D medical image data to a 3D model is described in this work. A suggested framework for predicting the size, shape, and location of a brain tumor using a minimized genetic machine learning method, and then converting the tumor information into 3D images using a depth map estimation approach after detecting the tumor information. When the tumor is located, the left and right view data are combined to form a 3D magnetic resonance imaging reconstruction. We used mixed reality methods to minimize file size while preserving the greatest quality of the model during a brain surgical operation.&lt;/span&gt;&lt;/span&gt;
On Waring numbers of henselian rings

Let $n>1$ be a positive integer. Let $R$ be a henselian local ring with residue field $k$ of $n$th level $s_n(k)$. We give some upper and lower bounds for the $n$th Waring number $w_n(R)$ in terms of $w_n(k)$ and $s_n(k)$. In large number of cases we are able to compute $w_n(R)$. Similar results for the $n$th Waring number of the total ring of fractions of $R$ are obtained. We then provide applications. In particular we compute $w_n(\mathbb{Z}_p)$ and $w_n(\mathbb{Q}_p)$ for $n\in\{3,4,5\}$ and any prime $p$.
Semantics based English-Arabic machine translation evaluation

Some classic machine translation (MT) Evaluation methods, such as the bilingual evaluation understudy score (BLEU), have notably underperformed in evaluating machine translations for morphologically rich languages like Arabic. However, the recent remarkable advancements in the domain of word vectors and sentence vectors have opened up new research avenues for low-resource languages. This paper proposes a novel linguistic-based evaluation method for English-translated sentences in Arabic. The proposed approach includes penalties based on length, positions, and context-based schemes such as part-of-speech tagging (POS) and multilingual sentenceBERT (SBERT) models for machine translation evaluation. The proposed technique is tested using pearson correlation as a performance evaluation parameter and compared with state-of-the-art techniques. The experimental results demonstrate that the proposed model evidently outperforms other MT evaluation methods such as BLEU.
Reduced Robust Random Cut Forest for Out-of-Distribution Detection in Machine Learning Models

Most machine learning based regressors extract in-formation from data collected via past observations to make predictions in the future.Consequently, when input to these trained models is data with significantly different statistical properties from data used for training, there is no guarantee of accurate prediction.Consequently, using these models on out of distribution input data may result in a completely different predicted outcome from the desired one, which is not only erroneous but can also be hazardous in some cases.Successful deployment of these machine learning models in any system requires a detection system, which should be able to distinguish between out-of-distribution and in-distribution data (i.e.similar to training data).In this paper, we introduce a novel approach for this detection process using Reduced Robust Random Cut Forest (RRRCF) data-structure, which can be used on both small and large datasets.Similarly, to the Robust Random Cut Forest (RRCF), RRRCF is a structured, but reduced representation of the training data sub-space in form of cut-trees.Empirical results of this method on both low and high dimensional data showed that inference about data being in/out of training distribution can be made efficiently and the model is easy to train with no difficult hyper-parameter tuning.The paper discusses two different use-cases for testing and validating results.
Towards Using Promises for Multi-Agent Cooperation in Goal Reasoning

Reasoning and planning for mobile robots is a challenging problem, as the world evolves over time and thus the robot's goals may change. One technique to tackle this problem is goal reasoning, where the agent not only reasons about its actions, but also about which goals to pursue. While goal reasoning for single agents has been researched extensively, distributed, multi-agent goal reasoning comes with additional challenges, especially in a distributed setting. In such a context, some form of coordination is necessary to allow for cooperative behavior. Previous goal reasoning approaches share the agent's world model with the other agents, which already enables basic cooperation. However, the agent's goals, and thus its intentions, are typically not shared. In this paper, we present a method to tackle this limitation. Extending an existing goal reasoning framework, we propose enabling cooperative behavior between multiple agents through promises, where an agent may promise that certain facts will be true at some point in the future. Sharing these promises allows other agents to not only consider the current state of the world, but also the intentions of other agents when deciding on which goal to pursue next. We describe how promises can be incorporated into the goal life cycle, a commonly used goal refinement mechanism. We then show how promises can be used when planning for a particular goal by connecting them to timed initial literals (TILs) from PDDL planning. Finally, we evaluate our prototypical implementation in a simplified logistics scenario.
Low-Precision Stochastic Gradient Langevin Dynamics

While low-precision optimization has been widely used to accelerate deep learning, low-precision sampling remains largely unexplored. As a consequence, sampling is simply infeasible in many large-scale scenarios, despite providing remarkable benefits to generalization and uncertainty estimation for neural networks. In this paper, we provide the first study of low-precision Stochastic Gradient Langevin Dynamics (SGLD), showing that its costs can be significantly reduced without sacrificing performance, due to its intrinsic ability to handle system noise. We prove that the convergence of low-precision SGLD with full-precision gradient accumulators is less affected by the quantization error than its SGD counterpart in the strongly convex setting. To further enable low-precision gradient accumulators, we develop a new quantization function for SGLD that preserves the variance in each update step. We demonstrate that low-precision SGLD achieves comparable performance to full-precision SGLD with only 8 bits on a variety of deep learning tasks.
Measuring Lower Bounds of Local Differential Privacy via Adversary Instantiations in Federated Learning

Local differential privacy (LDP) gives a strong privacy guarantee to be used in a distributed setting like federated learning (FL). LDP mechanisms in FL protect a client's gradient by randomizing it on the client; however, how can we interpret the privacy level given by the randomization? Moreover, what types of attacks can we mitigate in practice? To answer these questions, we introduce an empirical privacy test by measuring the lower bounds of LDP. The privacy test estimates how an adversary predicts if a reported randomized gradient was crafted from a raw gradient $g_1$ or $g_2$. We then instantiate six adversaries in FL under LDP to measure empirical LDP at various attack surfaces, including a worst-case attack that reaches the theoretical upper bound of LDP. The empirical privacy test with the adversary instantiations enables us to interpret LDP more intuitively and discuss relaxation of the privacy parameter until a particular instantiated attack surfaces. We also demonstrate numerical observations of the measured privacy in these adversarial settings, and the worst-case attack is not realistic in FL. In the end, we also discuss the possible relaxation of privacy levels in FL under LDP.
DPDR: A novel machine learning method for the Decision Process for Dimensionality Reduction

This paper discusses the critical decision process of extracting or selecting the features in a supervised learning context. It is often confusing to find a suitable method to reduce dimensionality. There are pros and cons to deciding between a feature selection and feature extraction according to the data's nature and the user's preferences. Indeed, the user may want to emphasize the results toward integrity or interpretability and a specific data resolution. This paper proposes a new method to choose the best dimensionality reduction method in a supervised learning context. It also helps to drop or reconstruct the features until a target resolution is reached. This target resolution can be user-defined, or it can be automatically defined by the method. The method applies a regression or a classification, evaluates the results, and gives a diagnosis about the best dimensionality reduction process in this specific supervised learning context. The main algorithms used are the Random Forest algorithms (RF), the Principal Component Analysis (PCA) algorithm, and the multilayer perceptron (MLP) neural network algorithm. Six use cases are presented, and every one is based on some well-known technique to generate synthetic data. This research discusses each choice that can be made in the process, aiming to clarify the issues about the entire decision process of selecting or extracting the features.
On the Real Time Object Detection and Tracking

Object detection and tracking is widely used for detecting motions of objects present in images and video.Since last so many decades, numerous real time object detection and tracking methods have been proposed byresearchers. The proposed methods for objects to be tracked till date require some preceding informationassociated with moving objects. In real time object detection and tracking approach segmentation is the initialtask followed by background modeling for the extraction of predefined information including shape of the objects,position in the starting frame, texture, geometry and so on for further processing of the cluster pixels and videosequence of these objects. The object detection and tracking can be applied in the fields like computerized videosurveillance, traffic monitoring, robotic vision, gesture identification, human-computer interaction, militarysurveillance system, vehicle navigation, medical imaging, biomedical image analysis and many more. In thispaper we focus detailed technical review of different methods proposed for detection and tracking of objects. Thecomparison of various techniques of detection and tracking is the purpose of this work.
Hate Speech and Binary Exclusions in Europe: A Digital and Communicative Approach

Hate speech targeting homosexuals, transgender people and other sexual orientations, as well as gender identities that deviate from the prevailing traditional binary system pervades social networks and digital communication channels. As a result, it is causing the exclusion of these groups, which often opt for invisibility in order to survive. Freedom of expression is an essential and preferential right in Western democratic systems. Based on this premise, this paper delves into the European legal and jurisprudential framework on hate speech –especially, acts of transphobia, homophobia and violence based on sexual orientation and gender identity– as a limit to freedom of expression, when other fundamental values, such as dignity, are at stake. Based on an analysis of the main normative instruments that have attempted to define the concept, as well as recent case law on hate speech, the aim of this article is to outline a consensus and to establish stable parameters to configure a legal response –valid in the European context– to cases of homophobic or transphobic speech.
Certifiably Robust Policy Learning against Adversarial Communication in Multi-agent Systems

Communication is important in many multi-agent reinforcement learning (MARL) problems for agents to share information and make good decisions. However, when deploying trained communicative agents in a real-world application where noise and potential attackers exist, the safety of communication-based policies becomes a severe issue that is underexplored. Specifically, if communication messages are manipulated by malicious attackers, agents relying on untrustworthy communication may take unsafe actions that lead to catastrophic consequences. Therefore, it is crucial to ensure that agents will not be misled by corrupted communication, while still benefiting from benign communication. In this work, we consider an environment with $N$ agents, where the attacker may arbitrarily change the communication from any $C<\frac{N-1}{2}$ agents to a victim agent. For this strong threat model, we propose a certifiable defense by constructing a message-ensemble policy that aggregates multiple randomly ablated message sets. Theoretical analysis shows that this message-ensemble policy can utilize benign communication while being certifiably robust to adversarial communication, regardless of the attacking algorithm. Experiments in multiple environments verify that our defense significantly improves the robustness of trained policies against various types of attacks.