Reviewerly/exHarmony huggingface dataset:
Intelligent Optimization of the Financial Sharing Path Based on Accounting Big Data

In order to solve the problems of inconsistent accounting and untimely accounting information, this paper proposes a research method of intelligent optimization of financial sharing path based on big data of accounting. This paper analyzes the current working situation of the financial sharing service center of large- and medium-sized enterprises and expounds the reasons for the blockchain. The author embeds the blockchain technology in the financial sharing service center, with a view to optimizing the design of the current FSSC’s functions in terms of the scope of daily business work, the effectiveness of accounting information processing, and the utilization of financial analysis decisions, and finally analyzes the corporate effect of applying the financial sharing architecture based on blockchain technology. The results show that compared with the financial sharing score under the traditional mode, the financial sharing score under the blockchain technology is higher, and the former is 69.675 points, and the latter is 80.6340 points. From this, we can conclude that financial sharing under blockchain technology has significant advantages. This framework realizes the effective allocation of enterprise information resources through finance to achieve real industry finance integration.
A new approach for social group detection based on spatio-temporal interpersonal distance measurement

Visual-based social group detection aims to cluster pedestrians in crowd scenes according to social interactions and spatio-temporal position relations by using surveillance video data. It is a basic technique for crowd behaviour analysis and group-based activity understanding. According to the theory of proxemics study, the interpersonal relationship between individuals determines the scope of their self-space, while the spatial distance can reflect the closeness degree of their interpersonal relationship. In this paper, we proposed a new unsupervised approach to address the issues of interaction recognition and social group detection in public spaces, which remits the need to intensely label time-consuming training data. First, based on pedestrians' spatio-temporal trajectories, the interpersonal distances among individuals were measured from static and dynamic perspectives. Combined with proxemics' theory, a social interaction recognition scheme was designed to judge whether there is a social interaction between pedestrians. On this basis, the pedestrians are clustered to identify if they form a social group. Extensive experiments on our pedestrian dataset “SCU-VSD-Social” annotated with multi-group labels demonstrated that the proposed method has outstanding performance in both accuracy and complexity.
Generic go to go: dictionary-passing, monomorphisation, and hybrid

Go is a popular statically-typed industrial programming language. To aid the type safe reuse of code, the recent Go release (Go 1.18) published early 2022 includes bounded parametric polymorphism via generic types. Go 1.18 implements generic types using a combination of monomorphisation and call-graph based dictionary-passing called hybrid. This hybrid approach can be viewed as an optimised form of monomorphisation that statically generates specialised methods and types based on possible instantiations. A monolithic dictionary supplements information lost during monomorphisation, and is structured according to the program’s call graph. Unfortunately, the hybrid approach still suffers from code bloat, poor compilation speed, and limited code coverage. In this paper we propose and formalise a new non-specialising call-site based dictionary-passing translation. Our call-site based translation creates individual dictionaries for each type parameter, with dictionary construction occurring in place of instantiation, overcoming the limitations of hybrid. We prove it correct using a novel and general bisimulation up to technique. To better understand how different generics translation approaches work in practice, we benchmark five translators, Go 1.18, two existing monomorphisation translators, our dictionary-passing translator, and an erasure translator. Our findings reveal several suggestions for improvements for Go 1.18— specifically how to overcome the expressiveness limitations of generic Go and improve compile time and compiled code size performance of Go 1.18.
Deep Neural Networks for Fine-Grained Surveillance of Overdose Mortality

Abstract Surveillance of drug overdose deaths relies on death certificates for identification of the substances that caused death. Drugs and drug classes can be identified through the International Classification of Diseases, Tenth Revision (ICD-10), codes present on death certificates. However, ICD-10 codes do not always provide high levels of specificity in drug identification. To achieve more fine-grained identification of substances on death certificate, the free-text cause-of-death section, completed by the medical certifier, must be analyzed. Current methods for analyzing free-text death certificates rely solely on lookup tables for identifying specific substances, which must be frequently updated and maintained. To improve identification of drugs on death certificates, a deep-learning named-entity recognition model was developed, utilizing data from the Kentucky Drug Overdose Fatality Surveillance System (2014–2019), which achieved an F1-score of 99.13%. This model can identify new drug misspellings and novel substances that are not present on current surveillance lookup tables, enhancing the surveillance of drug overdose deaths.
Smart Certificate using Blockchain

The student academic records maintained by the university system manually are vulnerable to easily being modified or tampered with.These academic records of students are proof of the student's performance for all semesters till the degree is completed.They are supposed to be present at the time of the interview or at the university if he wants to continue their studies further.The issue with the conventional approach is that it is simple for a malicious person to alter it so that it affects the student's grade in any way.This study aims to use blockchain to create safe Smart Certificates.It offers a viable alternative for issuing, confirming, and exchanging certificates without fear of their integrity being compromised.
Hard Negatives or False Negatives

Neural ranking models (NRMs) have become one of the most important techniques in information retrieval (IR). Due to the limitation of relevance labels, the training of NRMs heavily relies on negative sampling over unlabeled data. In general machine learning scenarios, it has shown that training with hard negatives (i.e., samples that are close to positives) could lead to better performance. Surprisingly, we find opposite results from our empirical studies in IR. When sampling top-ranked results (excluding the labeled positives) as negatives from a stronger retriever, the performance of the learned NRM becomes even worse. Based on our investigation, the superficial reason is that there are more false negatives (i.e., unlabeled positives) in the top-ranked results with a stronger retriever, which may hurt the training process; The root is the existence of pooling bias in the dataset constructing process, where annotators only judge and label very few samples selected by some basic retrievers. Therefore, in principle, we can formulate the false negative issue in training NRMs as learning from labeled datasets with pooling bias. To solve this problem, we propose a novel Coupled Estimation Technique (CET) that learns both a relevance model and a selection model simultaneously to correct the pooling bias for training NRMs. Empirical results on three retrieval benchmarks show that NRMs trained with our technique can achieve significant gains on ranking effectiveness against other baseline strategies.
Predicting Intraoperative Hypoxemia with Hybrid Inference Sequence Autoencoder Networks

We present an end-to-end model using streaming physiological time series to predict near-term risk for hypoxemia, a rare, but life-threatening condition known to cause serious patient harm during surgery. Inspired by the fact that a hypoxemia event is defined based on a future sequence of low SpO2 (i.e., blood oxygen saturation) instances, we propose the hybrid inference network (hiNet) that makes hybrid inference on both future low SpO2 instances and hypoxemia outcomes. hiNet integrates 1) a joint sequence autoencoder that simultaneously optimizes a discriminative decoder for label prediction, and 2) two auxiliary decoders trained for data reconstruction and forecast, which seamlessly learn contextual latent representations that capture the transition from present states to future states. All decoders share a memory-based encoder that helps capture the global dynamics of patient measurement. For a large surgical cohort of 72,081 surgeries at a major academic medical center, our model outperforms strong baselines including the model used by the state-of-the-art hypoxemia prediction system. With its capability to make real-time predictions of near-term hypoxemic at clinically acceptable alarm rates, hiNet shows promise in improving clinical decision making and easing burden of perioperative care.
X-Vision: Explainable Image Retrieval by Re-Ranking in Semantic Space

We present X-Vision, an explainable AI (XAI) driven image retrieval system based on a re-ranking approach to support non-expert users. We generate textual explanations such as, ''This image is similar to query image in color by Y%, shape by Z%'' along with visual explanations that compare image features. Besides the XAI goal of making AI systems transparent, we address the semantic gap between user's perception and model ranking, which arises in content based image retrieval (CBIR). We attempt to explain the notion of similarity in images in a query-by-example scenario, starting with relatively simple features such as color, texture, objects, background-foreground segments, moving to semantic representations learned from hidden layers of deep networks. The base retrieval model compares the query vector with other image feature vectors to create rankings. This result list is transferred to a semantic feature space that allows rule-based re-rankings. The core contribution of this work is a re-ranking algorithm for generating explanations. Our re-ranking improves retrieval performance (MAP) when compared with a base ranker, a random baseline, and recent CBIR baseline rankers on PASCAL VOC data. We evaluate XAI focused aspects of user trust in an eye-tracker based user study, we find that explanations supported users in the search process and understanding the notion of similarity.
Evaluating Interpolation and Extrapolation Performance of Neural Retrieval Models

A retrieval model should not only interpolate the training data but also extrapolate well to the queries that are different from the training data. While neural retrieval models have demonstrated impressive performance on ad-hoc search benchmarks, we still know little about how they perform in terms of interpolation and extrapolation. In this paper, we demonstrate the importance of separately evaluating the two capabilities of neural retrieval models. Firstly, we examine existing ad-hoc search benchmarks from the two perspectives. We investigate the distribution of training and test data and find a considerable overlap in query entities, query intent, and relevance labels. This finding implies that the evaluation on these test sets is biased toward interpolation and cannot accurately reflect the extrapolation capacity. Secondly, we propose a novel evaluation protocol to separately evaluate the interpolation and extrapolation performance on existing benchmark datasets. It resamples the training and test data based on query similarity and utilizes the resampled dataset for training and evaluation. Finally, we leverage the proposed evaluation protocol to comprehensively revisit a number of widely-adopted neural retrieval models. Results show models perform differently when moving from interpolation to extrapolation. For example, representation-based retrieval models perform almost as well as interaction-based retrieval models in terms of interpolation but not extrapolation. Therefore, it is necessary to separately evaluate both interpolation and extrapolation performance and the proposed resampling method serves as a simple yet effective evaluation tool for future IR studies.
A Platform for Argumentative Zoning Annotation and Scientific Summarization

Argumentative Zoning (AZ) is a tool to obtain informative summaries of scientific articles. Using AZ assumes the definition of the main rhetorical structure in scientific articles, which are, then, used for the summary creation. The unavailability of large AZ annotated benchmark datasets is a bottleneck to training AZ-based summarization algorithms. In this work, we present an annotation platform for an AZ that defines four categories (zones), Claim, Method, Result and Conclusion, that are used to label sentences selected from scientific articles. The proposed tool can be used both for collecting benchmark datasets, and to help the researchers to create their own sub-corpora.
Social cohesion and intercultural studies in the educational community

The article shows the important role of social cohesion as a social phenomenon, its impact on the development of society and intercultural communication. The social role of trust, in particular, in the development of social cohesion, is also shown. Social cohesion acquires acute relevance and importance in the current time of social transformations, as cohesion allows communities overcome various fluctuations and changes. The social cohesion as complex social phenomenon has been analyzed in the focus of intercultural studies. The aim of the research was to investigate the important spheres of social cohesion related to intercultural communication in the educational community, in particular, in the university community of National Pedagogical Dragomanov University. The research conducted several times, including the crisis states of society – pan-demic and war conflict in Ukraine. This research continues social cohesion studies, which are conducted in the National Pedagogical Dragomanov University since 2016, and demonstrates one of the current stages. The methodological tool is an adaptation of the "Social Cohesion Radar" model at the university community level. The study is based on primary data from the author's questionnaire. The important role of social cohesion, trust and values in intercultural communication is demonstrated.
A Two-Stage Deep Generative Model for Masked Face Synthesis

Research on face recognition with masked faces has been increasingly important due to the prolonged COVID-19 pandemic. To make face recognition practical and robust, a large amount of face image data should be acquired for training purposes. However, it is difficult to obtain masked face images for each human subject. To cope with this difficulty, this paper proposes a simple yet practical method to synthesize a realistic masked face for an unseen face image. For this, a cascade of two convolutional auto-encoders (CAEs) has been designed. The former CAE generates a pose-alike face wearing a mask pattern, which is expected to fit the input face in terms of pose view. The output of the former CAE is readily fed into the secondary CAE for extracting a segmentation map that localizes the mask region on the face. Using the segmentation map, the mask pattern can be successfully fused with the input face by means of simple image processing techniques. The proposed method relies on face appearance reconstruction without any facial landmark detection or localization techniques. Extensive experiments with the GTAV Face database and Labeled Faces in the Wild (LFW) database show that the two complementary generators could rapidly and accurately produce synthetic faces even for challenging input faces (e.g., low-resolution face of 25 × 25 pixels with out-of-plane rotations).
”I Should Feel Like I’m In Control”: Understanding Expectations, Concerns, and Motivations for the Use of Autonomous Navigation on Wheelchairs

Autonomous navigation on wheelchairs promises to be a significant frontier in the evolution of the power wheelchair as an assistive enabling device, and is increasingly explored among researchers for its potential to unlock more accessible navigation for wheelchair users. While developments on path-planning methods for wheelchairs is ongoing, there is a relative paucity of research on autonomous wheelchair navigation experiences which accommodate potential users’ needs. In this work, we present preliminary design considerations for the user experience for autonomous wheelchair navigation derived from a semi-structured interview with ten (10) current wheelchair users about their willingness to use and applicability of an autonomous navigation function. From this, nine (9) expressed a willingness to use autonomous navigation in the near future in a range of contexts, while expressing attitudes like expectations, concerns, and motivations for intent to use. To better understand the impetus for such attitudes, we conducted thematic analysis to reveal three high-order factors and associated qualities which together serve as a framework to help understand participants’ intent. Finally, we highlight three critical areas of focus to highlight opportunities and challenges for developers of a user-centered autonomous navigation experience for wheelchairs.
Posthuman to Inhuman: mHealth Technologies and the Digital Health Assemblage

In exploring the intra-active, relational and material connections between humans and non-humans, proponents of posthumanism advocate a questioning of the "human" beyond its traditional anthropocentric conceptualization. By referring specifically to controversial developments in mHealth applications, this paper critically diverges from posthuman accounts of human/non-human assemblages. Indeed, we argue that rather than "dissolving" the human subject, the power of assemblages lies in their capacity to highlight the antagonisms and contradictions that inherently affirm the importance of the subject. In outlining this claim, we propose a turn from the posthuman to the inhuman as a way of understanding the contemporary landscape of (digital) health.
Plant Science Data Integration, from Building Community Standards to Defining a Consistent Data Lifecycle

Abstract FAIR (Findable, Accessible, Interoperable, Reusable) data principles for plant research build upon experience from other life science domains such as genomics. But plant specificities, e.g. plant-environment interactions or phenotypes, require tailored solutions. Major global players have joined forces to answer that challenge with the Minimal Information About a Plant Phenotyping Experiment (MIAPPE, www.miappe.org ) that handles general metadata organization and its companion web service API, the Breeding API ( www.brapi.org ). Both rely on two established data standards, the MultiCrop Passport Descriptors (MCPD) for identification of plant genetic resources and the Crop Ontology ( www.cropontology.org ) for trait documentation. Researcher communities’ coordination and collaborative approaches have enabled the success and adoption of MIAPPE and led to a general data lifecycle description by ELIXIR Plant Sciences Community to identify gaps and needed developments. A priority has been placed on addressing the “first mile” of data publishing, i.e. the gathering and documentation of data by the researcher, which enables relevant data findability and reusability. Here we describe the existing ecosystem of tools and standards for plant scientists as well as their history, including their convergence through the use of MIAPPE for describing genotyping datasets.
Conceptual Model of a Smart Integrated Educational Environment

At present, we are witnessing a trend of gradual transition from traditional technologies of mass education to in-depth, practice-oriented training of professionals. In this regard, educational models based on the application of smart technologies for engineering information-educational spaces, which provide for the individualization of learning trajectories, flexibly combining the requirements of the employer with the possibilities of competence-oriented educational programs, are becoming increasingly important. The goal of the current study is to develop innovative educational programs of higher education through the integration of the components of the educational process into one information-educational space with the use of smart technologies, as well as to develop information search and logical-semantic algorithms for generating a variety of educational and methodological information, helping to form the most relevant educational and organizational and managerial content and organize the learning process under the predetermined parameters of specialist training in a particular subject area. As a result of the conducted research, the authors present their attempt at systematizing the variety of components of an information-education environment and conduct their ontological engineering in order to identify and subsequently integrate all components in the form of a global learning ontology.
Detecting Changes in User Emotions During Bicycle Riding by Sampling Facial Images

In the context of mobility as a Service (MaaS), bicycles are an important mode of transport for the first and last mile between the home and other transport modalities. Also, due to covid-19 bicycle users such as food delivery drivers and commuters to work are increasing. To investigate driving experience of bicycle users in context and improve MaaS service quality, we propose and describe a method to automatically detect changes in user emotions during bicycle riding by sampling facial images using a smartphone. We describe the proposed method and how we plan to use it in the future.
RCSketch: Sketch, Build, and Control Your Dream Vehicles

We present RCSketch, a system that lets children sketch their dream vehicles in 3D, build moving structures of those vehicles, and control them from multiple viewpoints. As a proof of concept, we implemented our system and designed five vehicles that could perform a wide variety of realistic movements.
Balanced softmax cross-entropy for incremental learning with and without memory

When incrementally trained on new classes, deep neural networks are subject to catastrophic forgetting which leads to an extreme deterioration of their performance on the old classes while learning the new ones. Using a small memory containing few samples from past classes has shown to be an effective method to mitigate catastrophic forgetting. However, due to the limited size of the replay memory, there is a large imbalance between the number of samples for the new and the old classes in the training dataset resulting in bias in the final model. To address this issue, we propose to use the Balanced Softmax Cross-Entropy and show that it can be seamlessly combined with state-of-the-art approaches for class-incremental learning in order to improve their accuracy while also potentially decreasing the computational cost of the training procedure. We further extend this approach to the more demanding class-incremental learning without memory setting and achieve competitive results with memory-based approaches. Experiments on the challenging ImageNet, ImageNet-Subset, and CIFAR100 benchmarks with various settings demonstrate the benefits of our approach.