Reviewerly/exHarmony huggingface dataset:
The VC Dimension of Metric Balls under Fréchet and Hausdorff Distances

Abstract The Vapnik–Chervonenkis dimension provides a notion of complexity for systems of sets. If the VC dimension is small, then knowing this can drastically simplify fundamental computational tasks such as classification, range counting, and density estimation through the use of sampling bounds. We analyze set systems where the ground set X is a set of polygonal curves in $$\mathbb {R}^d$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow> <mml:mi>R</mml:mi> </mml:mrow> <mml:mi>d</mml:mi> </mml:msup> </mml:math> and the sets $$\mathcal {R}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>R</mml:mi> </mml:math> are metric balls defined by curve similarity metrics, such as the Fréchet distance and the Hausdorff distance, as well as their discrete counterparts. We derive upper and lower bounds on the VC dimension that imply useful sampling bounds in the setting that the number of curves is large, but the complexity of the individual curves is small. Our upper and lower bounds are either near-quadratic or near-linear in the complexity of the curves that define the ranges and they are logarithmic in the complexity of the curves that define the ground set.
Extraction of Temporal Events’ Frequency from Online News Channels

Retrieving important information from vast unstructured data is a great topic of interest. Such a large amount of web data can also be easily accessed; however, finding specific and structured information from unstructured text is itself a challenging task. Information extraction is a framework used to retrieve structured insights from text. Among extraction paradigms, event extraction is a method that provides answers to some questions (who did what, where, and when). Such answers can be used for the decision-making process. The underlying research focuses on extracting the frequency and place of happenings while incorporating the news headlines data generated by the online news channels. More precisely, events regarding crime and natural disasters such as earthquakes and their temporal frequency from online news headlines are extracted. For this purpose, data is collected through the scraping of two renowned news websites. The proposed event extraction approach used natural language processing libraries such as the NLTK toolkit, including Part of Speech Tagging (POS), Chunking, and Named Entity Recognition (NER), to effectively detect news and events from extracted headlines of news websites. Results present the frequency and place of crime and natural disaster events headlines in visual form. This research's second objective is to check the similarity of headlines of different news channels posted daily. We calculated the cosine similarity of headlines from six months of a dataset. An average of 0.510878 similarities is found, which is 51%.
The state of the art in swept-wavelength laser Fizeau interferometry

A powerful technique for interference fringe analysis uses tunable light sources. Originally developed to solve the problem of phase shifting in large aperture systems, the technique has evolved to the simultaneous measurement of multiple surfaces and optical thickness of optical assemblies and components. Here we review the principles and current state of the art for swept-wavelength interferometry for optical testing, including recent advances in digital holographic refocusing and environmental robustness using model-based data analysis. Applications for swept-wavelength interferometry span the full optical metrology space; we provide examples of the measurement of glass substrates for rigid data storage drives to planar waveguides for augmented and mixed reality.
Feature Selection and Feature Extraction: Highlights

In recent years, big data deluges have resulted in exciting data science opportunities. In particular, there is always a desire to extract the most from different data sources. To address it, a promising and recurring task is to perform feature selection and feature extraction. Specifically, the objective is to obtain the non-redundant and informative set of input features (also known as attributes or predictor variables) for downstream data science tasks. In this study, we highlight the existing approaches in both feature selection and feature extraction. In particular, benchmark comparisons are conducted for independent evaluations.
Adventures of a Light Blockchain Protocol in a Forest of Transactions: A Subset of a Story

The Ethereum blockchain is one of the most popular permissionless blockchains. A consequence of its popularity has been the growth of processing and data storage requirements for any node participating in the Ethereum blockchain network. For constrained devices such requirements are often infeasible to meet. To cater for such nodes, a so-called light protocol has been proposed for Ethereum where the responsibility of maintaining a correct state representation is delegated to light protocol servers. Previous research has identified dependence on external state management as a potential security vulnerability that exclusively impacts light nodes. Although a simple mitigation strategy is available, it comes at the expense of increased latency. In this work, we propose a new Ethereum node type, which we call a subset node, as an extension of the light protocol. Our proposal allows subset nodes to gain a lower latency than a pure light node with comparable or even higher security assurances by tracking and evaluating only a subset of all of the transactions issued on the blockchain. We provide a formal proof on the correctness of the blockchain state used by the subset node under the proposed model. To evaluate the practical feasibility of the subset node model, we analyze one year of historical transaction data from Ethereum, and demonstrate that a subset node tracking the state of a single account can achieve a significant reduction in storage and computational requirements when compared to a full node.
Predicting Actions in Videos and Action-Based Segmentation Using Deep Learning

In this paper, we propose a technique to recognize multiple actions in a video using deep learning.The proposed approach is concerned with interpreting the overall context of a video and transforming it into one or more appropriate actions.In order to cope with multiple actions in a video, our proposed technique first determines the individual segments/shots in a video using intersections of color histograms.The segmented parts are then fed to the action recognition system comprising a combination of a Convolution Neural Network (CNN) and a Long-Short-Term Memory (LSTM) network trained on our action vocabulary.The segments are then labeled according to their predicted actions and a compact set of distinct actions is produced.Using the corpus generated by the shot detection phase, which includes the location of keyframes in shots, and start/end timestamps of a shot, we can also perform video segmentation based on an action query.Hence, the proposed technique can be used for a number of tasks such as content censoring, on-demand scene retrieval, video summarizing, and query-based scene/video retrieval, to name a few.The proposed technique also stands apart from the existing approaches which either do not take into account the motion information for action prediction or do not perform action-based video segmentation.The experimental results presented in this paper show that the proposed technique not only finds the complete set of actions present in the video but can also find all the relevant parts in a video based on an action query.
Analysis of Online Toxicity Detection Using Machine Learning Approaches

Social media and the Internet have become an integral part of how people spread and consume information. Over a period of time, social media evolved dramatically, and almost half of the population is using social media to express their views and opinions. Online hate speech is one of the drawbacks of social media nowadays, which needs to be controlled. In this paper, we will understand how hate speech originated and what are the consequences of it and trends of machine learning algorithms to solve an online hate speech problem. This study contributes by providing a systematic approach to help researchers to identify a new research direction and elucidating the shortcomings of the studies and model, as well as providing future directions to advance the field.
Private Proximity Retrieval Codes

A <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">private proximity retrieval</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PPR</i> ) scheme is a protocol which allows a user to retrieve the identities of all records in a database that are within some distance <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$r$ </tex-math></inline-formula> from the user’s record <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$x$ </tex-math></inline-formula> . The user’s <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">privacy</i> at each server is given by the fraction of the record <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$x$ </tex-math></inline-formula> that is kept private. In this paper, this research is initiated and protocols that offer trade-offs between privacy, computational complexity, and storage are studied. In particular, we assume that each server stores a copy of the database and study the required minimum number of servers by our protocol which provides a given privacy level. Each server receives a query in the protocol and the set of queries forms a code. The main focus in the paper is dedicated to studying the family of codes generated by the set of queries. These codes will be shown to satisfy a specific covering property and will be called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">private proximity retrieval intersection covering codes</i> . In particular, since the query every server receives is a codeword, the goal is to minimize the number of codewords in such a code which is the minimum number of servers required by the protocol. These codes are closely related to a family of codes known as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">covering designs</i> . We introduce several lower bounds on the sizes of such codes as well as several constructions. This work focuses on the case when the records are binary vectors together with the Hamming distance. Other metrics such as the Johnson metric are also investigated.
Space-efficient Huffman codes revisited

A canonical Huffman code is an optimal prefix-free compression code whose codewords enumerated in the lexicographical order form a list of binary words in non-decreasing lengths. Gagie et al. (2015) gave a representation of this coding capable of encoding and decoding a symbol in constant worst-case time. It uses σlg⁡ℓmax+o(σ)+O(ℓmax2) bits of space, where σ and ℓmax are the alphabet size and maximum codeword length, respectively. We refine their representation to reduce the space complexity to σlg⁡ℓmax(1+o(1)) bits while preserving the constant encode and decode times. Our algorithmic idea can be applied to any canonical code.
Towards Identifying, Understanding and Controlling Cumulative Revelations in Social Media

Abstract Every day, people post information about themselves and others on online social networks, making such information accessible within their social circles but also, potentially, way beyond. While the information posted may seem benign or innocuous, small pieces of information, when tied together, can potentially reveal much more about the person than intended. Such cumulative revelations could expose them to risks such as identity theft, fraud or loss of employment. This paper describes findings from interviews about people's online interactions, focusing on their requirements and desires for improved ways to identify, understand and control cumulative revelations arising from their social media profiles that could put them at risk of grave consequences. These findings motivate our future work on how to better raise awareness among social media users of the risks and consequences to which combinations of posts may lead.
An Efficient Image-to-Image Translation HourGlass-based Architecture for Object Pushing Policy Learning

Humans effortlessly solve pushing tasks in everyday life but unlocking these capabilities remains a challenge in robotics because physics models of these tasks are often inaccurate or unattainable. State-of-the-art data-driven approaches learn to compensate for these inaccuracies or replace the approximated physics models altogether. Nevertheless, approaches like Deep Q-Networks (DQNs) suffer from local optima in large state-action spaces. Furthermore, they rely on well-chosen deep learning architectures and learning paradigms. In this paper, we propose to frame the learning of pushing policies (where to push and how) by DQNs as an image-to-image translation problem and exploit an Hourglass-based architecture. We present an architecture combining a predictor of which pushes lead to changes in the environment with a state-action value predictor dedicated to the pushing task. Moreover, we investigate positional information encoding to learn position-dependent policy behaviors. We demonstrate in simulation experiments with a UR5 robot arm that our overall architecture helps the DQN learn faster and achieve higher performance in a pushing task involving objects with unknown dynamics.
Investigating Knowledge-Based Exploration-Exploitation Balance in a Minimalist Swarm Optimiser

One of the key challenges in evolutionary, swarm and population-based optimisers is the balance between exploration and exploitation. The reliance on both guided and stochastic search in these algorithms allows researchers to take different approaches to the topic. This work uses a minimalist, vector-stripped swarm optimiser to present a theoretical analysis on the behaviour of the particles. Being a population-based continuous optimiser, dispersive flies optimisation or DEO, bears several similarities with the well-known particle swarm optimisers, differential evolution algorithms and their bare-bones variants. The distinctive feature of this algorithm is its sheer reliance on particles positions to update the population. The minimalist nature of the algorithm reduces the challenges of understanding particles oscillation around constantly changing centres, particles' influence on one another, and their trajectories. This work presents a unified exploration-exploitation probability study which is derived from six scenarios in order to examine the population's dimensional behaviour in each iteration. This paves the way to propose and investigate adaptable, diversity promoting mechanisms. The proposed methods, which may be extendable to other optimisers, are then examined on a comprehensive set of benchmarks, and finally applied to high-dimensional tomographic reconstruction which is an important inverse problem in medical and industrial imaging.
Privacy preserving neural networks for electronic health records de-identification

Over the last decade, significant improvements and efforts in digitizing healthcare provided us with a sizeable collection of electronic medical records. These Electronic Health Records (EHRs), especially the clinical narratives, brimming with hidden knowledge to be discovered, contain sensitive information about the patient. For this reason, medical institutions are legally prohibited from publishing these data in the raw format to the public. Hence, the recent surge towards finding a solution for de-identification detects and removes sensitive information from clinical narratives. Since 2016, we have seen several deep learning-based approaches for de-identification, which achieved over 98% accuracy. However, these models are trained with sensitive information and can unwittingly memorize some of its training data, and a careful analysis of these models can reveal patients' data. In this work, we propose a differentially private ensemble framework for de-identification, allowing medical researchers to collaborate through publicly publishing the de-identification models. To the best of our knowledge, this is the first privacy-preserving machine learning approach for the de-identification of EHRs. Experiments in three different datasets showed competitive results compared to the state-of-the-art methods with guaranteed differential privacy.
How Much Are “Many People” on Facebook? Interpretations of Vague Quantifiers in Online and Offline Contexts

In an increasingly interconnected world, many people handle large parts of their communication online, often via social networking sites (SNS). In contrast to face-to-face communication, messages on SNS are accessible by potentially unknown and large audiences. However, it is an open question what users actually perceive as a large audience, or else as many people in SNS contexts. Exploring this question from a psycholinguistic perspective, we investigated the meaning of vague quantifiers such as “few” or “many” with regard to audiences in different contexts in two experiments. In Experiment 1, participants assigned numbers to quantifiers describing audiences in online versus offline and private versus public contexts. In Experiment 2, including the same items as Experiment 1, participants rated the appropriateness of specific numbers of people that were described by a quantifier. Our results show, for example, that people assigned larger numbers to quantifiers for online than for offline contexts. This was also true when access to the information was supposed to be restricted which implies a (scalar) change of privacy expectations.
Question-Answering System with Linguistic Summarization

The increased popularity of Linked Open Data (LOD) and advances in Natural Language Processing techniques have led to the development of Question Answering Systems (QASs) that utilize Knowledge Graphs as data sources. QASs perform well on simple questions providing precise and concise answers. Yet, most of them cannot process answers that contain a large volume of numerical values and are not able to provide users with answers in a human-friendly format. In this paper, we propose a user-defined method for constructing linguistic summarization of multi-feature data. It selects suitable summarizers and quantifiers and works with linguistic constraints imposed on the data. The method relies on definitions of linguistic terms constructed by users using an easy and simple graphical interface. Additionally, we introduce a Context-based User-defined Weighted Averaging (CUWA) operator. It allows determining an average value of data that satisfies multiple constraints that are account for the context defined by the user. We include several illustrative examples.
HDR video coding for aerial videos with VVC and AV1

Today, a huge variety of video codecs are available for compressing video, for a wide range of applications. Besides the traditional MPEG-2 and H.264 codecs, we now have H.265, VP9, AV1, H.266, EVC, and LC-EVC. H.266 succeeds H.265; EVC is essentially a light version of H.266/VVC; AV1 is a more advanced version of VP9; and LC-EVC is not a codec at all (nor related to EVC), but a codec enhancer. So of the remaining contenders for the next-generation video codec, we focus on perhaps the two most relevant: VVC and AV1. We take a practical approach, and focus on a specific application: high-quality, 4K aerial HDR video. Consumers love 4K UHD HDR. That moniker sells TVs. And among the most common way consumers can get a hold of capturing such content is with a high-end consumer drone. Modern drones can already capture 4K (and even 8K) in HDR, using the HLG transfer function, in 10-bit H.265, at 100 Mbs, creating clean content. We have collected some representative aerial HDR video clips to test with. In this context, we consider three issues: (a) convenience of conversion; (b) coding efficiency; and (c) playback support. We mention IP licensing issues in passing but focus on the technical merits as well as business prospects. In short, we find that both formats are usable, and moderately convenient to convert to. VVC has a slight edge in terms of coding efficiency (~15%). However, only one of these has even a possibility of wide playback support at this time: AV1. But that will change in time (~3-5 years). AV1 dates to March, 2018, VVC only to July, 2020.
Action Prediction During Human-Object Interaction Based on DTW and Early Fusion of Human and Object Representations

Action prediction is defined as the inference of an action label while the action is still ongoing. Such a capability is extremely useful for early response and further action planning. In this paper, we consider the problem of action prediction in scenarios involving humans interacting with objects. We formulate an approach that builds time series representations of the performance of the humans and the objects. Such a representation of an ongoing action is then compared to prototype actions. This is achieved by a Dynamic Time Warping (DTW)-based time series alignment framework which identifies the best match between the ongoing action and the prototype ones. Our approach is evaluated quantitatively on three standard benchmark datasets. Our experimental results reveal the importance of the fusion of human- and object-centered action representations in the accuracy of action prediction. Moreover, we demonstrate that the proposed approach achieves significantly higher action prediction accuracy compared to competitive methods.
A Novel Software Fault Prediction Approach To Predict Error-type Proneness in the Java Programs Using Stream X-Machine and Machine Learning

Software fault prediction makes software quality assurance process more efficient and economic. Most of the works related to software fault prediction have mainly focused on classifying software modules as faulty or not, which does not produce sufficient information for developers and testers. In this paper, we explore a novel approach using a streamlined process linking Stream X-Machine and machine learning techniques to predict if software modules are prone to having a particular type of runtime error in Java programs. In particular, Stream X-Machine is used to model and generate test cases for different types of Java runtime errors, which will be employed to extract error-type data from the source codes. This data is subsequently added to the collected software metrics to form new training data sets. We then explore the capabilities of three machine learning techniques (Support Vector Machine, Decision Tree, and Multi-layer Perceptron) for error-type proneness prediction. The experimental results showed that the new data sets could significantly improve the performances of machine learning models in terms of predicting error-type proneness.