Reviewerly/exHarmony huggingface dataset:
Annotation of Text Corpora by Sentiment and Presence of Irony within a Project of Citizen Science

The paper is devoted to construction of a sentence corpus annotated by the general sentiment into 4 classes (positive, negative, neutral, and mixed), a corpus of phrasemes annotated by the sentiment into 3 classes (positive, negative, and neutral), and a corpus of sentences annotated by the presence or absence of irony. The annotation was done by volunteers within the project “Prepare texts for algorithms” on the portal “People of science”. The existing knowledge on the domain regarding each task was the basis to develop guidelines for annotators. A technique of statistical analysis of the annotation result based on the distributions and agreement measures of the annotations performed by various annotators was also developed. For the annotation of sentences by irony and phrasemes by the sentiment the agreement measures were rather high (the full agreement rate of 0.60--0.99), whereas for the annotation of sentences by the general sentiment the agreement was low (the full agreement rate of 0.40), presumably, due to the higher complexity of the task. It was also shown that the results of automatic algorithms of detecting the sentiment of sentences improved by 12–13 % when using a corpus for which all the annotators (from 3 till 5) had the agreement, in comparison with a corpus annotated by only one volunteer.
Justice and Truth: A Leibnizian Perspective on Modern Jurisprudence

The purpose of this essay is to outline the significance of Leibniz’s philosophy of law for the present. The essay traces the main features of Leibniz’s theory and points out what further developments of his approach are pending today. Finally, it shows how similar Leibniz’s basic convictions are to those of Ronald Dworkin.
A Small Gain Analysis of Single Timescale Actor Critic

.We consider a version of actor-critic which uses proportional step-sizes and only one critic update with a single sample from the stationary distribution per actor step. We provide an analysis of this method using the small gain theorem. Specifically, we prove that this method can be used to find a stationary point, and that the resulting sample complexity improves the state of the art for actor-critic methods to \(O (\mu^{-2} \epsilon^{-2} )\) to find an \(\epsilon\) -approximate stationary point where \(\mu\) is the condition number associated with the critic.Keywordsreinforcement learningactor criticnonconvex optimizationMSC codes93E3590-08
Retrievability in an integrated retrieval system: an extended study

Abstract Retrievability measures the influence a retrieval system has on the access to information in a given collection of items. This measure can help in making an evaluation of the search system based on which insights can be drawn. In this paper, we investigate the retrievability in an integrated search system consisting of items from various categories, particularly focussing on datasets, publications and variables in a real-life digital library. The traditional metrics, that is, the Lorenz curve and Gini coefficient, are employed to visualise the diversity in retrievability scores of the three retrievable document types (specifically datasets, publications, and variables). Our results show a significant popularity bias with certain items being retrieved more often than others. Particularly, it has been shown that certain datasets are more likely to be retrieved than other datasets in the same category. In contrast, the retrievability scores of items from the variable or publication category are more evenly distributed. We have observed that the distribution of document retrievability is more diverse for datasets as compared to publications and variables.
Effects of Spatial Characteristics on the Human–Robot Communication Using Deictic Gesture in Construction

Construction robots are expected to frequently communicate in situ improvisations with human workers to adapt and change their workflow and methods. One way to achieve this is through deictic gestures that are one of the most effective forms of human–robot interaction (HRI) in delivering spatial information. Nevertheless, the limited coverage of deictic gestures in large-scale environments poses some challenges for both humans and robots in leveraging such techniques for HRI in construction. To identify the feasibility of deictic gestures in the construction domain and find applicable solutions for improving performance, this study aims to extend current knowledge on the performance in communicating positional information using deictic gestures by investigating the effects of spatial characteristics on spatial referencing, focusing on the target configuration, target distance, and relative position of human and robot. We observed that the recognition and estimation of deictic gestures were affected by the target plane, target position, and the target layout and that the robot performance was significantly reduced as the distance between the human and robot increased. The findings of this study demonstrate the challenges in spatial referencing within a large-scale environment and highlight the need for bidirectional communication in HRI.
Sending-or-not-sending twin-field quantum key distribution with redundant space

We propose to adopt redundant space such as polarization mode in the sending-or-not-sending twin-field quantum key distribution (TF-QKD) in the Fock space. With the help of redundant space such as photon polarization, we can postselect events according to the outcome of the observation to the additional quantity. This compresses the bit-flip error rate in the postselected events of the SNS protocol. The calculation shows that the method using redundant space can greatly improve the performance in practical TF-QKD, especially when the total number of pulses is small.
Deterministic quantum computation with one-clean-qubit model as an open quantum system

The DQC1 complexity class, or power of one qubit model, is examined as an open quantum system. We study the dynamics of a register of qubits carrying out a DQC1 algorithm and show that, for any algorithm in the complexity class, the evolution of the logical qubit can be described as an open quantum system undergoing a dynamics which is unital. Unital quantum channels respect the Tasaki-Crooks fluctuation theorem and we demonstrate how this is captured by the thermodynamics of the logical qubit. As an application, we investigate the equilibrium and non-equilibrium thermodynamics of the DQC1 trace estimation algorithm. We show that different computational inputs, i.e. different traces being estimated, lead to different energetic exchanges across the register of qubits and that the temperature of the logical qubit impacts the magnitude of fluctuations experienced and quality of the algorithm.
Iterative heterogeneous graph learning for knowledge graph-based recommendation

Incorporating knowledge graphs into recommendation systems has attracted wide attention in various fields recently. A Knowledge graph contains abundant information with multi-type relations among multi-type nodes. The heterogeneous structure reveals not only the connectivity but also the complementarity between the nodes within a KG, which helps to capture the signal of potential interest of the user. However, existing research works have limited abilities in dealing with the heterogeneous nature of knowledge graphs, resulting in suboptimal recommendation results. In this paper, we propose a new recommendation method based on iterative heterogeneous graph learning on knowledge graphs (HGKR). By treating a knowledge graph as a heterogeneous graph, HGKR achieves more fine-grained modeling of knowledge graphs for recommendation. Specifically, we incorporate the graph neural networks into the message passing and aggregating of entities within a knowledge graph both at the graph and the semantic level. Furthermore, we designed a knowledge-perceiving item filter based on an attention mechanism to capture the user's potential interest in their historical preferences for the enhancement of recommendation. Extensive experiments conducted on two datasets in the context of two recommendations reveal the excellence of our proposed method, which outperforms other benchmark models.
Positioning Paradata: A Conceptual Frame for AI Processual Documentation in Archives and Recordkeeping Contexts

The emergence of sophisticated Artificial Intelligence (AI) and machine learning tools poses a challenge to archives and records professionals, who are accustomed to understanding and documenting the activities of human agents rather than the often-opaque processes of sophisticated AI functioning. Preliminary work has proposed the term paradata to describe the unique documentation needs that emerge for archivists using AI tools to process records in their collections. For the purposes of archivists working with AI, paradata is conceptualized here as information recorded and preserved about records’ processing with AI tools; it is a category of data that is defined both by its relationship with other datasets and by the documentary purpose it serves. This article surveys relevant literature across three contexts to scope the relevant scholarship that archivists may draw upon to develop appropriate AI documentation practices. From the statistical social sciences and the visual heritage fields, the article discusses existing definitions of paradata and its ambiguous, often contextually dependent relationship with existing metadata categories. Approaching the problem from a sociotechnical perspective, literature on Explainable Artificial Intelligence (XAI) insists pointedly that explainability be attuned to specific users’ stated needs—needs that archivists may better articulate using the framework of paradata. Most importantly, the article situates AI as a challenge to accountability, transparency, and impartiality in archives by introducing an unfamiliar non-human agency, one that pushes the limits of existing archival practice and demands the development of new concepts and vocabularies to shape future technological and methodological developments in archives.
Identity Feature Disentanglement for Visible-Infrared Person Re-Identification

Visible-infrared person re-identification (VI-ReID) task aims to retrieve persons from different spectrum cameras (i.e., visible and infrared images). The biggest challenge of VI-ReID is the huge cross-modal discrepancy caused by different imaging mechanisms. Many VI-ReID methods have been proposed by embedding different modal person images into a shared feature space to narrow the cross-modal discrepancy. However, these methods ignore the purification of identity features, which results in identity features containing different modal information and failing to align well. In this article, an identity feature disentanglement method is proposed to disentangle the identity features from identity-irrelevant information, such as pose and modality. Specifically, images of different modalities are first processed to extract shared features that reduce the cross-modal discrepancy preliminarily. Then the extracted feature of each image is disentangled into a latent identity variable and an identity-irrelevant variable. In order to enforce the latent identity variable to contain as much identity information as possible and as little identity-irrelevant information, an ID-discriminative loss and an ID-swapping reconstruction process are additionally designed. Extensive quantitative and qualitative experiments on two popular public VI-ReID datasets, RegDB and SYSU-MM01, demonstrate the efficacy and superiority of the proposed method.
Progressive scene text erasing with self-supervision

Scene text erasing seeks to erase text contents from scene images and current state-of-the-art text erasing models are trained on large-scale synthetic data. Although data synthetic engines can provide vast amounts of annotated training samples, there are differences between synthetic and real-world data. In this paper, we employ self-supervision for feature representation on unlabeled real-world scene text images. A novel pretext task is designed to keep consistent among text stroke masks of image variants. We design the Progressive Erasing Network in order to remove residual texts. The scene text is erased progressively by leveraging the intermediate generated results which provide the foundation for subsequent higher quality results. Experiments show that our method significantly improves the generalization of the text erasing task and achieves state-of-the-art performance on public benchmarks.
Living on the Edge. On Bare and Non-Bare NCIs across Italo-Romance

This article describes and discusses some properties of the distribution of Negative Concord Items (NCIs) in the Italo-Romance domain, taking into account both varieties of Italian and varieties of other Italo-Romance languages. More precisely, the authors examine non-negative contexts, which allow the presence of NCIs. Across all non-negative contexts, bare/pronominal NCIs are systematically allowed in more contexts than complex ones, modulo the behavior of the specific variety in relation to non-negative contexts. The phenomenon can be accounted for by assuming that the structure of complex and bare NCIs is different not only in terms of null versus lexically realized NPs. The authors argue that bare NCIs, and possibly other quantificational elements, are not paired with a null DP but with a reduced structure, i.e., a classifier-like element which contains no lexical N.
Machine Learning-Based Agoraphilic Navigation Algorithm for Use in Dynamic Environments with a Moving Goal

This paper presents a novel development of a new machine learning-based control system for the Agoraphilic (free-space attraction) concept of navigating robots in unknown dynamic environments with a moving goal. Furthermore, this paper presents a new methodology to generate training and testing datasets to develop a machine learning-based module to improve the performances of Agoraphilic algorithms. The new algorithm presented in this paper utilises the free-space attraction (Agoraphilic) concept to safely navigate a mobile robot in a dynamically cluttered environment with a moving goal. The algorithm uses tracking and prediction strategies to estimate the position and velocity vectors of detected moving obstacles and the goal. This predictive methodology enables the algorithm to identify and incorporate potential future growing free-space passages towards the moving goal. This is supported by the new machine learning-based controller designed specifically to efficiently account for the high uncertainties inherent in the robot’s operational environment with a moving goal at a reduced computational cost. This paper also includes comparative and experimental results to demonstrate the improvements of the algorithm after introducing the machine learning technique. The presented experiments demonstrated the success of the algorithm in navigating robots in dynamic environments with the challenge of a moving goal.
Towards realistic privacy-preserving deep learning over encrypted medical data

Cardiovascular disease supposes a substantial fraction of healthcare systems. The invisible nature of these pathologies demands solutions that enable remote monitoring and tracking. Deep Learning (DL) has arisen as a solution in many fields, and in healthcare, multiple successful applications exist for image enhancement and health outside hospitals. However, the computational requirements and the need for large-scale datasets limit DL. Thus, we often offload computation onto server infrastructure, and various Machine-Learning-as-a-Service (MLaaS) platforms emerged from this need. These enable the conduction of heavy computations in a cloud infrastructure, usually equipped with high-performance computing servers. Unfortunately, the technical barriers persist in healthcare ecosystems since sending sensitive data (e.g., medical records or personally identifiable information) to third-party servers involves privacy and security concerns with legal and ethical implications. In the scope of Deep Learning for Healthcare to improve cardiovascular health, Homomorphic Encryption (HE) is a promising tool to enable secure, private, and legal health outside hospitals. Homomorphic Encryption allows for privacy-preserving computations over encrypted data, thus preserving the privacy of the processed information. Efficient HE requires structural optimizations to perform the complex computation of the internal layers. One such optimization is Packed Homomorphic Encryption (PHE), which encodes multiple elements on a single ciphertext, allowing for efficient Single Instruction over Multiple Data (SIMD) operations. However, using PHE in DL circuits is not straightforward, and it demands new algorithms and data encoding, which existing literature has not adequately addressed. To fill this gap, in this work, we elaborate on novel algorithms to adapt the linear algebra operations of DL layers to PHE. Concretely, we focus on Convolutional Neural Networks. We provide detailed descriptions and insights into the different algorithms and efficient inter-layer data format conversion mechanisms. We formally analyze the complexity of the algorithms in terms of performance metrics and provide guidelines and recommendations for adapting architectures that deal with private data. Furthermore, we confirm the theoretical analysis with practical experimentation. Among other conclusions, we prove that our new algorithms speed up the processing of convolutional layers compared to the existing proposals.
An Efficient Approach to Manage Natural Noises in Recommender Systems

Recommender systems search the underlying preferences of users according to their historical ratings and recommend a list of items that may be of interest to them. Rating information plays an important role in revealing the true tastes of users. However, previous research indicates that natural noises may exist in the historical ratings and mislead the recommendation results. To deal with natural noises, different methods have been proposed, such as directly removing noises, correcting noise by re-predicting, or using additional information. However, these methods introduce some new problems, such as data sparsity and introducing new sources of noise. To address the problems, we present a new approach to managing natural noises in recommendation systems. Firstly, we provide the detection criteria for natural noises based on the classifications of users and items. After the noises are detected, we correct them with threshold values weighted by probabilities. Experimental results show that the proposed method can effectively correct natural noise and greatly improve the quality of recommendations.
EasySpider: A No-Code Visual System for Crawling the Web

The web is a treasure trove for data that is increasingly used by computer scientists for building large machine learning models as well as non-computer scientists for social studies or marketing analyses. As such, web-crawling is an essential tool for both computational and non-computational scientists to conduct research. However, most of the existing web crawler frameworks and software products either require professional coding skills without an easy-to-use graphic user interface or are expensive and limited in features. They are thus not friendly to newbies and inconvenient for complicated web-crawling tasks.
Visual Item Selection With Voice Assistants

Interacting with voice assistants, such as Amazon Alexa to aid in day-to-day tasks has become a ubiquitous phenomenon in modern-day households. These voice assistants often have screens to provide visual content (e.g., images, videos) to their users. There is an increasing trend of users shopping or searching for products using these devices, yet, these voice assistants do not support commands or queries that contain visual references to the content shown on screen (e.g., “blue one”, “red dress”). We introduce a novel multi-modal visual shopping experience where the voice assistant is aware of the visual content shown on the screen and assists the user in item selection using natural language multi-modal interactions. We detail a practical, lightweight end-to-end system architecture spanning from model fine-tuning, deployment, to skill invocation on an Amazon Echo family device with a screen. We also define a niche “Visual Item Selection” task and evaluate whether we can effectively leverage publicly available multi-modal models, and embeddings produced from these models for the task. We show that open source contrastive embeddings like CLIP [30] and ALBEF [24] have zero-shot accuracy above for the “Visual Item Selection” task on an internally collected visual shopping dataset. By further fine-tuning the embeddings, we obtain further gains of 8.6% to 24.0% in relative accuracy improvement over a baseline. The technology that enables our visual shopping assistant is available as an Alexa Skill in the Alexa Skills store.
dexter: An R Package to Manage and Analyze Test Data

In this study, we present a package for R that is intended as a professional tool for the management and analysis of data from educational tests and useful both in high-stakes assessment programs and survey research. Focused on psychometric models based on the sum score as the scoring rule and having sufficient statistics for their parameters, dexter fully exploits the many theoretical and practical advantages of this choice: lack of unnecessary assumptions, stable and fast estimation, and powerful and sensible diagnostic techniques. It includes an easy to use data management system tailored to the structure of test data and compatible with the current paradigm of tidy data. Companion packages currently include a graphical user interface and support for multi-stage testing.
A New Annotation Method and Dataset for Layout Analysis of Long Documents

Parsing long documents, such as books, theses, and dissertations, is an important component of information extraction from scholarly documents. Layout analysis methods based on object detection have been developed in recent years to help with PDF document parsing. However, several challenges hinder the adoption of such methods for scholarly documents such as theses and dissertations. These include (a) the manual effort and resources required to annotate training datasets, (b) the scanned nature of many documents and the inherent noise present resulting from the capture process, and (c) the imbalanced distribution of various types of elements in the documents. In this paper, we address some of the challenges related to object detection based layout analysis for scholarly long documents. First, we propose an AI-aided annotation method to help develop training datasets for object detection based layout analysis. This leverages the knowledge of existing trained models to help human annotators, thus reducing the time required for annotation. It also addresses the class imbalance problem, guiding annotators to focus on labeling instances of rare classes. We also introduce ETD-ODv2, a novel dataset for object detection on electronic theses and dissertations (ETDs). In addition to the page images included in ETD-OD [1], our dataset consists of more than 16K manually annotated page images originating from 100 scanned ETDs, along with annotations for 20K page images primarily consisting of rare classes that were labeled using the proposed framework. The new dataset thus covers a diversity of document types, viz., scanned and born-digital, and is better balanced in terms of training samples from different object categories.