Reviewerly/exHarmony huggingface dataset:
Enhanced Image-Based Endoscopic Pathological Site Classification Using an Ensemble of Deep Learning Models

In vivo diseases such as colorectal cancer and gastric cancer are increasingly occurring in humans. These are two of the most common types of cancer that cause death worldwide. Therefore, the early detection and treatment of these types of cancer are crucial for saving lives. With the advances in technology and image processing techniques, computer-aided diagnosis (CAD) systems have been developed and applied in several medical systems to assist doctors in diagnosing diseases using imaging technology. In this study, we propose a CAD method to preclassify the in vivo endoscopic images into negative (images without evidence of a disease) and positive (images that possibly include pathological sites such as a polyp or suspected regions including complex vascular information) cases. The goal of our study is to assist doctors to focus on the positive frames of endoscopic sequence rather than the negative frames. Consequently, we can help in enhancing the performance and mitigating the efforts of doctors in the diagnosis procedure. Although previous studies were conducted to solve this problem, they were mostly based on a single classification model, thus limiting the classification performance. Thus, we propose the use of multiple classification models based on ensemble learning techniques to enhance the performance of pathological site classification. Through experiments with an open database, we confirmed that the ensemble of multiple deep learning-based models with different network architectures is more efficient for enhancing the performance of pathological site classification using a CAD system as compared to the state-of-the-art methods.
Multi-Occupancy Fall Detection Using Non-Invasive Thermal Vision Sensor

Falling is a common issue within the aging population. The immediate detection of a fall is key to guarantee early and immediate attention to avoid other potential immobility risks and reduction in recovery time. Video-based approaches for monitoring fall detection, although being highly accurate, are largely perceived as being intrusive if deployed within living environments. As an alternative, thermal vision-based methods can be deployed to offer a more acceptable level of privacy. To date, thermal vision-based fall detection methods have largely focused on single-occupancy scenarios, which are not fully representative of real living environments with multi-occupancy. This work proposes a non-invasive thermal vision-based approach of multi-occupancy fall detection (MoT-LoGNN) which discriminates between a fall or no-fall. The approach consists of four major components: i) a multi-occupancy decomposer, ii) a sensitivity-based sample selector, iii) the T-LoGNN for single-occupancy fall detection, and iv) a fine-tuning mechanism. The T-LoGNN consists of a robust neural network minimizing a Localized Generalization Error (L-GEM) and thermal image features extracted by a Convolutional Neural Network (CNN). Comparing to other methods, the MoT-LoGNN achieved the highest average accuracy of 98.39% within the context of a multi-occupancy fall detection experiment.
Decoding Surface Touch Typing from Hand-Tracking

We propose a novel text decoding method that enables touch typing on an uninstrumented flat surface. Rather than relying on physical keyboards or capacitive touch, our method takes as input hand motion of the typist, obtained through hand-tracking, and decodes this motion directly into text. We use a temporal convolutional network to represent a motion model that maps the hand motion, represented as a sequence of hand pose features, into text characters. To enable touch typing without the haptic feedback of a physical keyboard, we had to address more erratic typing motion due to drift of the fingers. Thus, we incorporate a language model as a text prior and use beam search to efficiently combine our motion and language models to decode text from erratic or ambiguous hand motion. We collected a dataset of 20 touch typists and evaluated our model on several baselines, including contact-based text decoding and typing on a physical keyboard. Our proposed method is able to leverage continuous hand pose information to decode text more accurately than contact-based methods and an offline study shows parity (73 WPM, 2.38% UER) with typing on a physical keyboard. Our results show that hand-tracking has the potential to enable rapid text entry in mobile environments.
Sociocultural Dimensions of Tracking Health and Taking Care

The field of personal health informatics has received increasing attention within the CSCW and HCI communities as health tracking becomes more affordable, accessible, and pervasive. Chronic disease management, in particular, presents tremendous potential for intervention given patients' ability to now actively participate in their care through tracking. The focus on 'personal' in health informatics, however, obfuscates the role of other cultural and ecological factors that might shape health tracking behaviors, and important information from alternative sources could be ignored by virtue of being subjective, complex, or simply hard to collect. To dig deeper into these negative spaces that may go untracked, uncover potential sources of important health information, and more completely understand current tracking practices, we embarked on an interview study with patients with cardiac diseases in Bangalore, India. In this paper, we present these patients' current health management approaches that are culturally situated, identifying both motivations and barriers to tracking, their attitudes towards online information, as well as cultural and ecological influences on their perceptions of cardiac care. We then discuss the interplay between our findings and current notions of, and approaches towards, patient empowerment and datafication of health.
Genetic relationship between hydrocarbon system evolution and Carlin-type gold mineralization: Insights from ReOs pyrobitumen and pyrite geochronology in the Nanpanjiang Basin, South China

The spatial association of hydrocarbons with metalliferous ore deposits is found worldwide and is particularly common to Carlin-type gold systems. Both liquid oil and pyrobitumen are found in Carlin-type gold deposits of North Nevada, USA and the Nanpanjiang Basin, South China. However, the temporal and genetic association of hydrocarbons and gold mineralization are still debated. To this end, using rhenium‑osmium (ReOs) geochronology of pyrobitumen and gold-bearing pyrite from the Laizishan and Banqi reservoirs and the Yata Carlin-type gold deposit in the Nanpanjiang Basin, we consider hydrocarbons played a critical role in the mineralization process. A ReOs age of 228 ± 16 Ma obtained for highly mature pyrobitumen suggests that liquid oil cracking occurred during the Late Triassic in the Laizishan and Banqi reservoirs. This age is in agreement with the modelled thermal history of the Nanpanjiang Basin. Additionally, a broadly identical gold-bearing pyrite ReOs age of 218 ± 25 Ma from Yata Carlin-type gold deposit which is in agreement with ages reported for other Carlin-type gold deposits in the Nanpanjiang Basin (e.g., in-situ SIMS UPb rutile = 213.6 ± 5.4 Ma, ReOs arsenopyrite = 204 ± 19 Ma - 235 ± 33 Ma and RbSr illite = 212.8 ± 4.6 Ma) suggests the auriferous Carlin-type systems of the Nanpanjiang Basin also formed during the Late Triassic. Integrating our ReOs data, with recent liquid hydrocarbon experimental data and fluid inclusion data from both reservoirs and gold deposits within the Nanpanjiang Basin, a methane (CH4) dominated thermochemical sulfate reduction (TSR) process, which introduced hydrogen sulfide (H2S) into basinal fluid and ultimately led to the deposition of gold-bearing pyrite by sulfidation, is considered to be the genetic link between of pyrobitumen and gold-bearing pyrite mineralization of the Carlin-type systems of the Nanpanjiang Basin.
On the Memory Underutilization: Exploring Disaggregated Memory on HPC Systems

Large-scale high-performance computing (HPC) systems consist of massive compute and memory resources tightly coupled in nodes. We perform a large-scale study of memory utilization on four production HPC clusters. Our results show that more than 90% of jobs utilize less than 15% of the node memory capacity, and for 90% of the time, memory utilization is less than 35%. Recently, disaggregated architecture is gaining traction because it can selectively scale up a resource and improve resource utilization. Based on these observations, we explore using disaggregated memory to support memory-intensive applications, while most jobs remain intact on HPC systems with reduced node memory. We designed and developed a user-space remote-memory paging library to enable applications exploring disaggregated memory on existing HPC clusters. We quantified the impact of access patterns and network connectivity in benchmarks. Our case studies of graph-processing and Monte-Carlo applications evaluated the impact of application characteristics and local memory capacity and highlighted the potential of throughput scaling on disaggregated memory.
Personalized Re-ranking with Item Relationships for E-commerce

Re-ranking is a critical task for large-scale commercial recommender systems. Given the initial ranked lists, top candidates are re-ranked to improve the accuracy of the ranking results. However, existing re-ranking strategies are sub-optimal due to (i) most prior works do not consider explicit item relationships, like being substitutable or complementary, which may mutually influence the user satisfaction on other items in the lists, and (ii) they usually apply an identical re-ranking strategy for all users, with personalized user preferences and intents ignored. To resolve the problem, we construct a heterogeneous graph to fuse the initial scoring information and item relationships information. We develop a graph neural network based framework, IRGPR, to explicitly model transitive item relationships by recursively aggregating relational information from multi-hop neighborhoods. We also incorporate a novel intent embedding network to embed personalized user intents into the propagation. We conduct extensive experiments on real-world datasets, demonstrating the effectiveness of IRGPR in re-ranking. Further analysis reveals that modeling the item relationships and personalized intents are particularly useful for improving the performance of re-ranking.
Covid-19 pandemic and the prospects of education in South Africa

The Covid-19 pandemic has caused havoc in the world, radically changing our lives and raising new and old questions, both existential and educational. This pandemic has revealed the underbelly of South African society in general and its education system more specifically-it has laid bare the gross inequalities that are the legacies of apartheid and the consequences of neoliberal capitalism. Drawing on ideas articulated in the four introductory chapters of the International Handbook of Curriculum Research, edited by William Pinar in 2014, this article discusses Covid-19 and the prospects of education in South Africa. The article shows how understanding the wisdom of indigenous traditions along with the moral dimensions of education, race, and the new technologies of surveillance, neoliberalism, and education can provide a nuanced awareness of the nature of the Covid-19 pandemic. It then explores the implications of such insights for the field of curriculum studies and, where relevant, for the school curriculum. It concludes by showing how these broad themes intersect and gel around the notion of Ubuntu-currere.
Hybrid Implementation of Twofish, AES, ElGamal and RSA Cryptosystems

Nowadays, for achieving information security and to provide security against unauthorized access cryptography plays an important role. To ensure high-security level there are different types of cryptographic methods. This paper presents implementation and analysis of new hybrid cryptosystems. Main objectives of this paper are to emphasize on better performance, maximum speed of an algorithm, checking effectiveness and comparison with other algorithms. In the paper is proposed two new hybrid algorithms using combination of both symmetric and asymmetric cryptographic algorithm such as Twofish, AES, RSA and ElGamal. To analyze results was used JAVA program implementation. The results shows that the proposed hybrid algorithm AES+RSA is significantly secure. However, Twofish + RSA hybrid has other advantages like better computation time, the size of cipher text, and the memory consumption.
Testing the Generalizability of an Automated Method for Explaining Machine Learning Predictions on Asthma Patients’ Asthma Hospital Visits to an Academic Healthcare System

Asthma puts a tremendous overhead on healthcare. To enable effective preventive care to improve outcomes in managing asthma, we recently created two machine learning models, one using University of Washington Medicine data and the other using Intermountain Healthcare data, to predict asthma hospital visits in the next 12 months in asthma patients. As is common in machine learning, neither model supplies explanations for its predictions. To tackle this interpretability issue of black-box models, we developed an automated method to produce rule-style explanations for any machine learning model's predictions made on imbalanced tabular data and to recommend customized interventions without lowering the prediction accuracy. Our method exhibited good performance in explaining our Intermountain Healthcare model's predictions. Yet, it stays unknown how well our method generalizes to academic healthcare systems, whose patient composition differs from that of Intermountain Healthcare. This study evaluates our automated explaining method's generalizability to the academic healthcare system University of Washington Medicine on predicting asthma hospital visits. We did a secondary analysis on 82,888 University of Washington Medicine data instances of asthmatic adults between 2011 and 2018, using our method to explain our University of Washington Medicine model's predictions and to recommend customized interventions. Our results showed that for predicting asthma hospital visits, our automated explaining method had satisfactory generalizability to University of Washington Medicine. In particular, our method explained the predictions for 87.6% of the asthma patients whom our University of Washington Medicine model accurately predicted to experience asthma hospital visits in the next 12 months.
Learning Effective Representations for Person-Job Fit by Feature Fusion

Person-job fit is to match candidates and job posts on online recruitment platforms using machine learning algorithms. The effectiveness of matching algorithms heavily depends on the learned representations for the candidates and job posts. In this paper, we propose to learn comprehensive and effective representations of the candidates and job posts via feature fusion. First, in addition to applying deep learning models for processing the free text in resumes and job posts, which is adopted by existing methods, we extract semantic entities from the whole resume (and job post) and then learn features for them. By fusing the features from the free text and the entities, we get a comprehensive representation for the information explicitly stated in the resume and job post. Second, however, some information of a candidate or a job may not be explicitly captured in the resume or job post. Nonetheless, the historical applications including accepted and rejected cases can reveal some implicit intentions of the candidates or recruiters. Therefore, we propose to learn the representations of implicit intentions by processing the historical applications using LSTM. Last, by fusing the representations for the explicit and implicit intentions, we get a more comprehensive and effective representation for person-job fit. Experiments over 10 months real data show that our solution outperforms existing methods with a large margin. Ablation studies confirm the contribution of each component of the fused representation. The extracted semantic entities help interpret the matching results in the case study.
A conceptual digital business model for construction logistics consultants, featuring a sociomaterial blockchain solution for integrated economic, material and information flows

In this paper, a new digital business model for independent construction logistics consultants, which features the conceptualization of a sociomaterial blockchain solution for integrated information, material and economic flows, is proposed. Theoretically, we offer an understanding of the economic flow, stress the optimization of construction logistics through flow integration, analyse current approaches to understanding blockchain, adopt sociomateriality to envision a suitable blockchain solution, and consider the way blockchain can constitute part of the value proposition of a related digital business model. Methodologically, we systematically reviewed the literature on blockchain-related construction research, and conducted empirical studies on independent logistics consultants in the Swedish context for more than a year. On the one hand, the literature review reveals that core blockchain properties can generate value for construction logistics (e.g. shared ledger structure and reduction of accounting rework) – however, apart from visions and prototypes, there currently exist no use cases, and potential implementational constraints and security issues are limitedly considered. One the other hand, the empirical findings show that independent construction logistics consultants in the sociomaterial Swedish context are suitable candidates for the proposed digital business model. By combining the literature and empirical insights, a permissioned private proof-of-authority blockchain solution integrating the supply chain flows in a generic sociomaterial setting is conceptualized. This solution is then embedded in the value proposition of a digital business model for an independent construction logistics consultant. The proposition includes, among others, improved process management and increased productivity, while the consultants’ competitive advantage through innovation is facilitated. Other business model segments, like key resources, are also updated via the blockchain solution, while some, like channels, are not significantly affected. To not hinder the realization of this digital business model, issues like the lack of blockchain awareness, and the existing power balances within sociomaterial constellations, have to be addressed.
Secure and Transparent KYC for Banking System Using IPFS and Blockchain Technology

The know your customer or know your client (KYC) is a guideline for the banking system to validate a customer using identity, appropriateness, risk assessment in establishing a banking relationship. With the growing concern of security, the KYC process is complex and involves a high cost for completing for a single customer. In this work, we propose an economical, swift, secure, and transparent platform for KYC document verification for the Banking system through InterPlanetary File System (IPFS) and blockchain technology. The proposed system allows a customer to open an account at one Bank, complete the KYC process there, and generate a hash value using the IPFS network and share it using the blockchain technique. Upon receiving the private key, any Bank/financial organization can retrieve, store customer data (i.e., KYC) securely using IPFS network if the customer wishes to open another account in that Bank/financial organization. The proposed system can save time, money, and repetitive work during the KYC process when someone tries to open an account at multiple Banks.
Contact tracing

As pandemic wide spread results in locking down vital facilities, digital contact tracing is deemed as a key for re-opening. However, current efforts in digital contact tracing, running as mobile apps on users' smartphones, fall short in being effective and present two major weaknesses related to accessibility and apparent privacy concern augmentation. Indeed, accessibility is affected by several factors such as smartphone penetration, age, or socio-economic conditions. The privacy concern on the other hand comes from the fear of having a piece of technology that is monitoring us all the time, everywhere, even when contact tracing is irrelevant. This paper lays out the vision and guidelines for the next era of digital contact tracing, where the contact tracing functionality is moved from being personal responsibility to be the responsibility of facilities that users visit daily. Our proposal tackles the two aforementioned shortcomings by disengaging users from using their own smartphones and requiring facilities to provide the technological devices needed for contact tracing. By doing so, we reassure users that their contacts are only considered in places where manual contact tracing is not effective, and cease being recorded as soon as they leave the facilities they visit. A privacy-preserving architecture is proposed, which can be mandated as a prerequisite for any facility to re-open during or after the pandemic. We finally outline research opportunities and challenges revolving around contact tracing system design and data management.
Can You Read Me Now? Content Aware Rectification Using Angle Supervision

The ubiquity of smartphone cameras has led to more and more documents being captured by cameras rather than scanned. Unlike flatbed scanners, photographed documents are often folded and crumpled, resulting in large local variance in text structure. The problem of document rectification is fundamental to the Optical Character Recognition (OCR) process on documents, and its ability to overcome geometric distortions significantly affects recognition accuracy. Despite the great progress in recent OCR systems, most still rely on a pre-process that ensures the text lines are straight and axis aligned. Recent works have tackled the problem of rectifying document images taken in-the-wild using various supervision signals and alignment means. However, they focused on global features that can be extracted from the document’s boundaries, ignoring various signals that could be obtained from the document’s content.We present CREASE: Content Aware Rectification using Angle Supervision, the first learned method for document rectification that relies on the document’s content, the location of the words and specifically their orientation, as hints to assist in the rectification process. We utilize a novel pixel-wise angle regression approach and a curvature estimation side-task for optimizing our rectification model. Our method surpasses previous approaches in terms of OCR accuracy, geometric error and visual similarity.
Representation Learning on Visual-Symbolic Graphs for Video Understanding

Events in natural videos typically arise from spatio-temporal interactions between actors and objects and involve multiple co-occurring activities and object classes. To capture this rich visual and semantic context, we propose using two graphs: (1) an attributed spatio-temporal visual graph whose nodes correspond to actors and objects and whose edges encode different types of interactions, and (2) a symbolic graph that models semantic relationships. We further propose a graph neural network for refining the representations of actors, objects and their interactions on the resulting hybrid graph. Our model goes beyond current approaches that assume nodes and edges are of the same type, operate on graphs with fixed edge weights and do not use a symbolic graph. In particular, our framework: a) has specialized attention-based message functions for different node and edge types; b) uses visual edge features; c) integrates visual evidence with label relationships; and d) performs global reasoning in the semantic space. Experiments on challenging video understanding tasks, such as temporal action localization on the Charades dataset, show that the proposed method leads to state-of-the-art performance.