Reviewerly/exHarmony huggingface dataset:
Finance 4.0—A Socio-Ecological Finance System

Abstract This contribution develops the framework of a novel, socio-ecological finance system that enables the incentivization of environmentally friendly behavior, socially responsible production, resource recycling, sharing and more. We call this system Finance 4.0—where Finance 1.0 refers to a physical coin-based system, Finance 2.0 to a fiat currency system, Finance 3.0 to blockchain finance, while Finance 4.0 stands for a multi-dimensional, real-time feedback system that combines blockchain technology with the Internet of Things. Instead of “Finance 4.0,” we will also often use the abbreviation “FIN4.” In comparison with citizen scores such as the Chinese social credit score, the FIN4 approach is different in a variety of aspects: FIN4 is not aimed at punishment and control, but rather at helping to encourage, empower and coordinate sustainable and other favorable action. It is being built for local, temporary measurements and feedback, not for global surveillance and control. It seeks to protect the privacy of people rather than to keep information about everyone and every action forever. It is not focused on the individual in the sense of profiling and targeting, but on favorable interactions and our social and environmental goals. It offers a multi-dimensional choice of options instead of trying to determine every individual’s action through behavioral manipulation and control. It has a participatory opt-in nature, where people can co-create the incentive system in such a way that they can reach their goals more successfully. With multi-dimensional incentivization, it becomes possible to advance several goals simultaneously; thereby, many more people can benefit from the interactions they engage in, as multi-dimensional value exchange increases the solution space enormously.
Boosting High-Level Vision with Joint Compression Artifacts Reduction and Super-Resolution

Due to the limits of bandwidth and storage space, digital images are usually down-scaled and compressed when transmitted over networks, resulting in loss of details and jarring artifacts that can lower the performance of high-level visual tasks. In this paper, we aim to generate an artifact-free high-resolution image from a low-resolution one compressed with an arbitrary quality factor by exploring joint compression artifacts reduction (CAR) and super-resolution (SR) tasks. First, we propose a context-aware joint CAR and SR neural network (CAJNN) that integrates both local and non-local features to solve CAR and SR in one-stage. Finally, a deep reconstruction network is adopted to predict high quality and high-resolution images. Evaluation on CAR and SR benchmark datasets shows that our CAJNN model outperforms previous methods and also takes 26.2% shorter runtime. Based on this model, we explore addressing two critical challenges in high-level computer vision: optical character recognition of low-resolution texts, and extremely tiny face detection. We demonstrate that CAJNN can serve as an effective image preprocessing method and improve the accuracy for real-scene text recognition (from 85.30% to 85.75%) and the average precision for tiny face detection (from 0.317 to 0.611).
An Efficient Algorithm for Complete Linkage Clustering with a Merging Threshold

In recent years, one of the serious challenges envisaged by experts in the field of data science is dealing with the gigantic volume of data, piling up at a high speed. Apart from collecting this avalanche of data, another major problem is extracting useful information from it. Clustering is a highly powerful data mining tool capable of finding hidden information from a totally unlabelled dataset. Complete Linkage Clustering is a distance-based Hierarchical clustering algorithm, well-known for providing highly compact clusters. The algorithm because of its high convergence time is unsuitable for large datasets, and hence our paper proposes a preclustering method that not only reduces the convergence time of the algorithm but also makes it suitable for partial clustering of streaming data. The proposed preclustering algorithm uses triangle inequality to take a clustering decision without comparing a pattern with all the members of a cluster, unlike the classical Complete Linkage algorithm. The preclusters are then subjected to an efficient Complete Linkage algorithm for getting the final set of compact clusters in a relatively shorter time in comparison to all those existing variants where the pairwise distance between all the patterns are required for the Clustering process.
Performing with Me: Enhancing Audience-Performer Interaction in An Immersive Virtual Play

Immersive theater in virtual reality (VR) has been increasingly popular in recently years. However, it is difficult for the audience to immerse in the play while interacting with the performer who usually presented as virtual avatars. In this paper, we present a novel approach for enhancing audience-performer interaction based on the theory of immersive theater. We integrated three types of interaction into a virtual play: individual-based interaction, scenario-based interaction, and narrative-based interaction. We reported the findings of a pilot qualitative study and identified three design recommendations: (1) letting the audience decide how the scenario goes, (2) making clear the identity of the audience, and (3) increasing the audience's freedom to explore in VR space. These recommendations could be adapted for future VR theater experience.
EMICS’21: Eye Movements as an Interface to Cognitive State

Eye movement recording has been extensively used in HCI and offers the possibility to understand how information is perceived and processed by users. Hardware developments provide the ubiquitous accessibility of eye recording, allowing eye movements to enter common usage as a control modality. Recent A.I. developments provide powerful computational means to make predictions about the user. However, the connection between eye movements and cognitive state has been largely under-exploited in HCI. Despite the rich literature in psychology, a deeper understanding of its usability in practice is still required. This virtual EMICS workshop will provide an opportunity to discuss possible application scenarios and HCI interfaces to infer users’ mental state from eye movements. It will bring together researchers across disciplines with the goal of expanding shared knowledge, discussing innovative research directions and methods, fostering future collaborations around the use of eye movements as an interface to cognitive state.
Topic-Aware Dialogue Generation with Two-Hop Based Graph Attention

Generating on-topic responses and understanding the background information of context are both significant for dialogue generation. However, few works simultaneously concentrate on these two issues. For this purpose, we propose an open-domain topic-aware dialogue generation model via joint learning. We first design two-hop based static graph attention mechanism to enhance the semantic representations of context, and then two auxiliary sub-tasks are introduced. Topic Predictor module is designed to focus on the most pertinent topics and Language Modeling module further facilitates learning richer information from context. Experimental study has shown the proposed model’s promising potential. In particular, our model predicts the most topics that best match the query per response. Besides, further analysis proves that our model can generate more diversified and informative responses.
Evaluating Epidemiological Risk by Using Open Contact Tracing Data: Correlational Study

Background During the 2020s, there has been extensive debate about the possibility of using contact tracing (CT) to contain the SARS-CoV-2 pandemic, and concerns have been raised about data security and privacy. Little has been said about the effectiveness of CT. In this paper, we present a real data analysis of a CT experiment that was conducted in Italy for 8 months and involved more than 100,000 CT app users. Objective We aimed to discuss the technical and health aspects of using a centralized approach. We also aimed to show the correlation between the acquired contact data and the number of SARS-CoV-2–positive cases. Finally, we aimed to analyze CT data to define population behaviors and show the potential applications of real CT data. Methods We collected, analyzed, and evaluated CT data on the duration, persistence, and frequency of contacts over several months of observation. A statistical test was conducted to determine whether there was a correlation between indices of behavior that were calculated from the data and the number of new SARS-CoV-2 infections in the population (new SARS-CoV-2–positive cases). Results We found evidence of a correlation between a weighted measure of contacts and the number of new SARS-CoV-2–positive cases (Pearson coefficient=0.86), thereby paving the road to better and more accurate data analyses and spread predictions. Conclusions Our data have been used to determine the most relevant epidemiological parameters and can be used to develop an agent-based system for simulating the effects of restrictions and vaccinations. Further, we demonstrated our system's ability to identify the physical locations where the probability of infection is the highest. All the data we collected are available to the scientific community for further analysis.
Exploiting Acceleration of the Target for Visual Object Tracking

Discriminative Correlation Filters (DCF) based trackers have achieved remarkable performance in visual object tracking in recent years. The trackers represent the target with hand-craft features or deep features. To reduce the computational cost and irrelevant information, such trackers choose the region of interest (ROI) as a search window to search the target rather than search exhaustively in each frame. The size of search window, which is fixed in most trackers, can greatly affect the performance of tracker. In order to characterize the motion state of the target, this paper presents the velocity and acceleration in the field of visual object tracking. It is observed that the acceleration can change the search window dynamically and achieve better performance. Experiments on the popular datasets OTB50 and OTB100 demonstrate that the DCF based trackers including the state-of-the-art (SOTA) trackers improve their performance by exploiting acceleration.
PIF: Anomaly detection via preference embedding

We address the problem of detecting anomalies with respect to structured patterns. To this end, we conceive a novel anomaly detection method called PIF, that combines the advantages of adaptive isolation methods with the flexibility of preference embedding. Specifically, we propose to embed the data in a high dimensional space where an efficient tree-based method, PI-Forest, is employed to compute an anomaly score. Experiments on synthetic and real datasets demonstrate that PIF favorably compares with state-of-the-art anomaly detection techniques, and confirm that PI-Forest is better at measuring arbitrary distances and isolate points in the preference space.
Real-Time Person Removal from Video

The process of removing special areas in a video or repairing the damaged parts in a video segment is known as video painting. In this work, the proposed painting algorithm in real time can remove unwanted human objects from the video. On a canvas, the algorithm makes the video frame by frame and then performs segmentation to assign binary values to the frame’s segmented pixels. A value of 1 is given to the human objects, and 0 is given to all other objects. Now, we can distinguish the (1) values from the rest using a simple formula and the algorithm will fill those values with (0) pixels. Later, we will provide the conclusion and future scope of our work.
Real-Time Object Detection Using Yolo Algorithm for Blind People

Vision is one of the foremost necessary sense that human beings use to interact with the surrounding objects. There are more than 200 visually challenges people in this world and being visually challenged obstruct lots of daily activities. Hence it is very important for blind person to know what objects they are interacting with and understand their surroundings. In this project we have created a website, which help the blind people to identify different objects in the surrounding using YOLO V3 algorithm. This integrates different technologies to build a rich website which not only helps to recognize different object in the visually challenged persons surrounding in real time but also guides them through an audio output. YOLO (You Only Look at Once) algorithm is used for object detection and recognition. This algorithm gives very close accuracy for object detection in real time and studies have also proven the this algorithm is faster and better than other object detection algorithms.
Social Rehabilitation Practices of Inclusion In The Educational Process In Working With Children With Disabilities

Coming into being and development of inclusive education in contemporary Ukrainian society are analysed. The tasks of educational institutions in creating an inclusive space are defined, and the approaches to work with children with disabilities are identified. Works of foreign and domestic scholars and practitioners who studied the problems of inclusion of children with disabilities in the educational process and in the context of social rehabilitation activities are studied. Emphasis is placed on theoretical analysis of social rehabilitation practices of inclusion. A mechanism for implementing social rehabilitation practices of inclusion in the educational process of working with children with disabilities is suggested.
SIAMATSN: Real-Time Carotid Plaque Tracking and Segmentation of Ultrasonic Videos

In ultrasound video, the tracking and segmentation of carotid plaques are prerequisite for the analysis of plaque properties. The problem at hand is quite challenging as some issues such as low ultrasound image resolution, large variation between frames and low tracking efficiency need to addressed. Our method, Siamese automatic tracking and segmentation network (SiamATSN), is an end-to-end deep learning method. Multiple dual attention region proposal network (DARPN) blocks are developed to integrate multi-level features for better target detection. The DARPN block is composed of spatial-wise attention module, channel-wise attention module, and Region Proposal Network. Moreover, a fusion module is further introduced to capture long-range contextual clues. At last, concat module which combines low and high resolution features is embedded in the decoder path to attain a more precise segmentation. Extensive experiments are conducted and the experimental results showed that our approach outperforms the state-of-the-art deep learning based methods.
A Framework to Establish an Assistance System by Using Reality Technology in Maintenance

Maintenance is an increasingly complex and knowledge-intensive field. In order to address these challenges, assistance systems based on augmented, mixed, or virtual reality can be applied. Therefore, the objective of this paper is to present a framework that can be used to identify, select, and implement an assistance system based on reality technology in the maintenance environment. The development of the framework is based on a systematic literature review and subject matter expert interviews. The framework provides the best technological and economic solution in several steps. The validation of the framework is carried out through a case study.
Integrative Review on Vision-Based Dynamic Indian Sign Language Recognition Systems

Human-computer interaction is capable of solving the complex problems and challenges faced by human beings, among many of the complex jobs Sign Language Recognition is one of them. Therefore, automatically detecting the sign language is a broad area of research many works have been done in this area, and still, the work is going on. A variety of sign languages can be found throughout the globe sometimes the sign languages can be diversified by the country or region, the sign language(SL) which is available in India is known as the Indian Sign Language(ISL). Indian sign language requires the involvement of both hands, face, and upper body part movement which makes it difficult from the other single-handed sign languages. If we compare the Static gesture identification with the dynamic gesture identification, it's obvious that the former is easier. In real-life scenarios, a system should have the ability to identify the continuous and dynamic gestures, so that it can become an interface between the hearing impaired people and the normal people. Therefore, an Integrative review has been presented here which strongly summarizes the works on Indian Sign Language Recognition(ISLR) systems capable of identifying the dynamic and continuous Vision-based gestures without using any gloves or sensor-enabled wearables.
Cloud Computing Offered Capabilities: Threats to Software Vendors

Cloud computing has permeated and penetrated every aspect of our lives both personal and professional. This technology has gained a phenomenal acceptance as it provides opportunities for organizations, by offering large collections of easily accessible data. This has transformed the way businesses are done. The ever-changing demands of users, fierce competitive activities, and rapidly evolving technology pose a great challenge to software vendors to be innovative and constantly seek to be on top of their game. Cloud computing allows business to scale efficiently as they constantly adjust their operations to the new realities that help them to optimize cost, quality, and time. The main objective of this project is to ascertain if cloud computing offered capability is a threat to software vendors, and the extent of the threat, thus proposing solutions. This study used multi-stage research technique and the research instrument being questionnaire. The completed questionnaire forms were collated, coded, and analyzed using both descriptive and parametric statistics. Quantitative data was coded and entered into statistical packages for social scientists and analyzed using descriptive statistics for expressing the demographic characteristics of the respondents. Qualitative data was analyzed based on the content of the responses. Responses with common themes or patterns were grouped together into coherent categories. Chi-square analysis and logistic analysis were used to show dependencies and relationships between the variables and also to determine the probability that software vendors are aware of cloud computing threats and the severity of those threats. Quantitative data was rendered in tables and explanation was presented in logical prose.
An Analysis of Customer Sentiments Towards Education Technology App: A Text Mining Approach

Education technologies (EdTech) are one of the fastest growing fields of contemporary teaching and main source of innovation that can enable revolutionary change in educational practice. Online customer reviews play a vital role in choosing among the various online platforms. Parents and teachers make use of these reviews to match with their preference. Similarly, the EdTech companies uses this information to improve their customer service. Importing the customer reviews from online websites and sentiment analysis on the reviews by customers as well as companies could be used for decision making to improve the service delivered by EdTech companies. Sentiment analysis is a supervised learning to identify positive, negative and neutral opinions from text which could be used to understand the overall sentiment of customers towards education technologies.
GeoBD2: Geospatial Big Data Deduplication Scheme in Fog Assisted Cloud Computing Environment

With the speedy expansion of Internet of Spatial Things, the enormous volume of geospatial big data is produced by the IoT devices. It gives rise to the new challenges for real time geospatial data processing and storing of reliable data in cloud system. The traditional geospatial cloud computing system is not efficient enough to process large volumetric of concurrent geospatial data. Consequently, fog assisted cloud computing environment has come into picture for achieving secure geospatial big data deduplication scheme. In this paper, we introduce a novel scheme GeoBD2 which defines the geo-deduplication structure to build an efficient geospatial bigdata deduplication scheme on fog assisted cloud computing framework. It also regulates which fog node needs to be traversed to investigate duplicate geospatial data rather than to traverse all the fog nodes. This can substantially enhance the efficiency of geospatial big data deduplication in fog assisted cloud environment. It also executes the performance analysis of the proposed scheme. By the experimental results, it is found that the proposed scheme has minimum overhead cost than the existing big data deduplication scheme.
When and Why a Model Fails? A Human-in-the-loop Error Detection Framework for Sentiment Analysis

Although deep neural networks have been widely employed and proven effective in sentiment analysis tasks, it remains challenging for model developers to assess their models for erroneous predictions that might exist prior to deployment.Once deployed, emergent errors can be hard to identify in prediction runtime and impossible to trace back to their sources.To address such gaps, in this paper we propose an error detection framework for sentiment analysis based on explainable features.We perform global-level feature validation with human-in-the-loop assessment, followed by an integration of global and locallevel feature contribution analysis.Experimental results show that, given limited human-inthe-loop intervention, our method is able to identify erroneous model predictions on unseen data with high precision.
Logics for reasoning about degrees of confirmation

Abstract In this paper, we present a first-order and a propositional logic for reasoning about degrees of confirmation. We define the appropriate formal languages and describe the corresponding classes of models. We provide infinitary axiomatizations for both logics and we prove that the axiomatizations are sound and strongly complete. We also show that our propositional logic is decidable. For some restrictions of the logics, we provide finitary axiomatic systems.
Data Augmentation for Meta-Learning

Conventional image classifiers are trained by randomly sampling mini-batches of images. To achieve state-of-the-art performance, practitioners use sophisticated data augmentation schemes to expand the amount of training data available for sampling. In contrast, meta-learning algorithms sample support data, query data, and tasks on each training step. In this complex sampling scenario, data augmentation can be used not only to expand the number of images available per class, but also to generate entirely new classes/tasks. We systematically dissect the meta-learning pipeline and investigate the distinct ways in which data augmentation can be integrated at both the image and class levels. Our proposed meta-specific data augmentation significantly improves the performance of meta-learners on few-shot classification benchmarks.