Reviewerly/exHarmony huggingface dataset:
SU-Net and DU-Net Fusion for Tumour Segmentation in Histopathology Images

In this work, a fusion framework[1] is proposed for automatic cancer detection and segmentation in whole-slide histopathology images [2], [3]. The framework includes two parts of fusion, multi-scale fusion, and sub-datasets fusion. For a particular type of cancer, histopathological images often demonstrate large morphological variances, the performance of an individual trained network is usually limited. We develop a fusion model that integrates two types of U-net structures: Shallow U-net (SU-net) and Deep U-net (DU-net), trained with a variety of multiple re-scaled images and different subsets of images, and finally ensemble a unified output. Smoothing and noise elimination are conducted using convolutional Conditional Random Fields (CRFs)[4]. The proposed model is validated on Automatic Cancer Detection and Classification in Whole-slide Lung Histopathology (ACDC@LungHP) challenge in ISBI2019 and Digestive-System Pathological Detection and Segmentation Challenge 2019(DigestPath 2019) in MICCAI2019. Our method achieves a dice coefficient of 0.7968 in ACDC@LungHP and 0.773 in DigestPath 2019, and the result of ACDC@LungHP challenge is ranked in third place on the board.
Double Attention Convolutional Neural Network for Driver Action Recognition

Driver action recognition is an important technology of Advanced Driver Assistant System (ADAS). Recently, distracted driving recognition with deep learning, especially Convolutional Neural Network (CNN) is very popular, however, the feature extracting in traditional CNN is very sensitive to the true circumstance of driving. It will take a lot of time to train the CNN model with huge amounts of data to gain better robustness. To solve this problem, we introduce the attention mechanism of human visual perception system, which can identify outstanding image regions and magnify their effects while suppressing irrelevant and potentially confusing information of other regions. In this paper, we propose a novel Double Attention CNN (DACNN) to pay attention to salient regions to recognize distracted driving, which combines the local features with the global features to build an attention model, and we use two attention sub-modules to focus on the head, arms and hands of driver, respectively. Our experiment result shows that the recognition accuracy and robustness of our proposed method are improved comparing with CNN without attention sub-module and with one attention sub-module.
Mobile Installation of Water Treatment in the Aftermath of Emergency Situations

A new technological scheme of water treatment is proposed, combining reagent methods with hydrodynamic activation of coagulation, flocculation and sorption processes, with baromembrane methods of purification. A method of organizing the process of filtering water after treatment with reagents using a tangential filter made using self-propagating high-temperature synthesis technology is proposed. Jet agitators – ejectors were developed and investigated, the efficiency of using ejectors as devices for dosing and mixing of reagents with treated water was shown. An experimental model of the installation was created and it was successfully tested on model as well as real polluted waters. Purified water obtained on an experimental sample of the installation fully complies with the hygienic requirements and standards established for drinking water.
Bardzell's "Feminist HCI" Legacy: Analyzing Citational Patterns

In this late-breaking work, we describe the legacy of feminist theory within HCI literature, focusing on Shaowen Bardzell's seminal publication "Feminist HCI: Taking Stock and Outlining an Agenda for Design," which was one of the first to propose adoption of feminist theories into HCI research and practice. We conducted a citation analysis of 70 published texts that cited this paper, using the Harwood functions to identify how feminist theory concepts have been cited in HCI and whether the implementation of proposed frameworks has taken place. This paper was mostly given 'credit,' and most frequently 'signposted' to keep readers on track of the topical issues in HCI, with little evidence of explicit use or extension of proposed frameworks. These results demonstrate a largely one-dimensional impact, characterized by a lack of deep engagement in feminist theories. We identify opportunities to expand feminist approach to further improve research and practice in HCI.
Neural Network Insights of Blockchain Technology in Manufacturing Improvement

In recent years, blockchain technology has garnered phenomenal interest across various industries due to its disruptive potential on business improvement. The capability of blockchain technology in enhancing efficient data management and providing traceability offers significant value to manufacturing. Given the increasing traction of blockchain application, the confluence of blockchain technology with other emergent technologies is poised to transform the manufacturing industry. This study addresses the paucity of scholarly research by deciphering how blockchain technology can contribute to manufacturing improvement. Based on neural network analysis, this study maps the future pathways in blockchain technology research in manufacturing improvement by revealing the emerging themes, offer recommendations, and raise the call for more studies.
TxChain: Efficient Cryptocurrency Light Clients via Contingent Transaction Aggregation

Cryptocurrency light- or simplified payment verification (SPV) clients allow nodes with limited resources to efficiently verify execution of payments. Instead of downloading the entire blockchain, only block headers and selected transactions are stored. Still, the storage and bandwidth cost, linear in blockchain size, remain non-negligible, especially for smart contracts and mobile devices: as of April 2020, these amount to 50 MB in Bitcoin and 5 GB in Ethereum. Recently, two improved sublinear light clients were proposed: to validate the blockchain, NIPoPoWs and FlyClient only download a polylogarithmic number of block headers, sampled at random. The actual verification of payments, however, remains costly: for each verified transaction, the corresponding block must too be downloaded. This yields NIPoPoWs and FlyClient only effective under low transaction volumes. We present TxChain, a novel mechanism to maintain efficiency of light clients even under high transaction volumes. Specifically, we introduce the concept of contingent transaction aggregation, where proving inclusion of a single contingent transaction implicitly proves that n other transactions exist in the blockchain. To verify n payments, TxChain requires a only single transaction in the best ( $$n \le c$$ ), and transactions in the worst case ( $$n > c$$ ), where c is a blockchain constant. We deploy TxChain on Bitcoin without consensus changes and implement a hard fork for Ethereum. To demonstrate effectiveness in the cross-chain setting, we implement TxChain as a smart contract on Ethereum to efficiently verify Bitcoin payments.
Think before you speak: An investigation of eye activity patterns during conversations using eyewear

With the emergence of low-cost wearable hardware for eye activity analysis comes the opportunity to use pupil and blink behavior during conversations to improve human computer interaction. Conversations in general can be decomposed into four segments, i.e. listening, speaking, thinking (transition from listening to speaking) and waiting (transition from speaking to listening). However, pupil and blink behavior during these segments of conversations is not yet fully understood. In this paper, pupillary response and blink in conversations are analyzed at the conversation-level and segment-level. 24 participants undertook two natural conversational tasks with different levels of communication load, while their eye activity was continuously recorded by eyewear. More intense conversation was found to impose a significant increase in pupil diameter, that is, higher communication load produces larger increases in pupil size, like other mental tasks. During conversations, pupil diameter increased more during speaking than during listening among conversations, while blink showed no difference between listening and speaking. Comparing two the transition segments, pupil size was found to be larger after speaking than before speaking, while more blinks occurred before speaking than after speaking. These findings on eye activity in conversations could be used to infer a user's communication load or predict their conversational state, which can be applied in turn-taking prediction, interruption management systems or computer-mediated conversational systems.
IslamicHCI: Designing with and within Muslim Populations

In recent years there has been a growing body of work from the CHI communities that looks at designing for inclusivity and for the unique and specific constraints of diverse populations. This has included but is not limited to, work on designing within patriarchal contexts, designing around issues of gender and sexual orientation and designing around literacy. In tandem, local HCI initiatives such as ArabHCI [3] have emerged to address the misrepresentation of these populations in HCI research, highlighting the fact that Western originated design methods would require delicate adaptations to suit non-Western cultural contexts. With the same approach towards inclusivity and co-existence the aim of this workshop is to bring together HCI researchers and practitioners who engage in studies and interventions within Muslim majority communities around the world. The goal is to understand the Muslim identity and perceptions around it, the unique constraints and limitations within Muslim communities and to identify core issues and concerns within these populations. We will explore the following themes: refugees and islamophobia; Muslim feminism and Digital financial services.
Coherence and normalisation-by-evaluation for bicategorical cartesian closed structure

We present two proofs of coherence for cartesian closed bicategories. Precisely, we show that in the free cartesian closed bicategory on a set of objects there is at most one structural 2-cell between any parallel pair of 1-cells. We thereby reduce the difficulty of constructing structure in arbitrary cartesian closed bicategories to the level of 1-dimensional category theory. Our first proof follows a traditional approach using the Yoneda lemma. For the second proof, we adapt Fiore's categorical analysis of normalisation-by-evaluation for the simply-typed lambda calculus. Modulo the construction of suitable bicategorical structures, the argument is not significantly more complex than its 1-categorical counterpart. It also opens the way for further proofs of coherence using (adaptations of) tools from categorical semantics.
A Cost-sensitive Active Learning for Imbalance Data with Uncertainty and Diversity Combination

The class distributions of real-world classification datasets are usually imbalanced because many applications, such as network intrusion detection, tumor classification, financial risk identification, etc., exhibit imbalance natures that positive examples are rare. When labeling such data to create training sets for supervised learning, too many examples belonging to the majority class will be labeled, which dramatically increase the labeling cost and usually is unnecessary, because balanced datasets are more suitable for inducing good learners. To deal with this problem, this paper proposes a novel cost-sensitive active learning algorithm that combines the uncertainty and diversity measures to select training examples for an unlabeled sample pool. We use the proportions of the majority and the minority against the whole examples in the training dataset as the weights of the majority class and the minority class, respectively. With the class weights, the minor examples can obtain more emphasis when building learning models. Experimental results show that our proposed method can significantly reduce the label cost while improving the performance of learning models.
Revocable Attribute-Based Encryption Scheme with Arithmetic Span Program for Cloud-Assisted IoT

Efficient user revocation and description of the access policy are essential to enhance the practicality of attribute-based encryption (ABE) in real-life scenarios, such as cloud-assisted IoT. Nevertheless, existing ABE works fail to balance the two vital indicators. Motivated by this, in this paper, we present a revocable ciphertext-policy attribute-based encryption with arithmetic span programs (R-CPABE-ASP) for cloud-assisted IoT. For the first time, the presented R-CPABE-ASP achieves efficient user revocation and expressive description of access policy simultaneously. In R-CPABE-ASP, each attribute involved in access policy is merely used once to check whether a user owns access to shared data. Hence, the R-CPABE-ASP work enables efficient data encryption compared with existing revocable ABE works by reducing unnecessary cost for defining access policy. Meanwhile, as shown in the outsourced version of R-CPABE-ASP, the costly part for users to decrypt the data is outsourced to powerful cloud servers. Therefore, users in our R-CPABE-ASP can access their data in a more efficient way by merely one exponential operation. Finally, we carry out detailed theoretical analysis and experimental simulations to evaluate the performance of our work. The results fairly show that our proposed work is efficient and feasible in cloud-assisted IoT.
The Multi-phase ReLU Activation Function

Deep Neural Networks have become the tool of choice for Machine Learning practitioners today. They have been successfully applied for solving a large class of learning problems both in the industry and academia, with applications in fields such as Computer Vision, Natural Language Processing, Big data Analytics and Bioinformatics. One important aspect of designing a neural network is the choice of the activation function to be used at the neurons of the different layers. Activation functions are used for introducing non-linearity into the neural network model so that the network can progressively learn more effective feature representations over the different layers. Several different activation functions have been studied and used in the literature, however, Linear, Sigmoid, Tanh and ReLU are the most commonly used activation functions. They are often selected empirically during the network design phase, rather than through a proper data driven process. In this work we study the problem of generalizing the single output ReLU activation to a multi-output variant using the idea of multi-phase ReLU where the parameters involved in the activation are learned through a data driven paradigm. We report results of experiments with the MNIST dataset using a two layer network that clearly demonstrates the efficacy of the multi-phase ReLU over the standard single output ReLU.
Using Twitter to Predict Chart Position for Songs

With the advent of social media, concepts such as forecasting and now casting became part of the public debate. Past successes include predicting election results, stock prices and forecasting events or behaviors. This work aims at using Twitter data, related to songs and artists that appeared on the top 10 of the Billboard Hot 100 charts, performing sentiment analysis on the collected tweets, to predict the charts in the future. Our goal was to investigate the relation between the number of mentions of a song and its artist, as well as the semantic orientation of the relevant posts and its performance on the subsequent chart. The problem was approached via regression analysis, which estimated the difference between the actual and predicted positions and moderated results. We also focused on forecasting chart ranges, namely the top 5, 10 and 20. Given the accuracy and F-score achieved compared to previous research, our findings are deemed satisfactory, especially in predicting the top 20.
Internet memes as multilayered re-contextualization vehicles in lay-political online discourse

Abstract It is well established that the internet meme has come to represent a highly creative discursive device used to “facilitate the […] communication of one’s own political beliefs, attitudes and orientations” ( Ross and Rivers 2017 : 1). Although internet memes and political internet memes in particular have been addressed to many communicative situations such as participatory culture (e.g., Jenkins 2006 ; Shifman 2014 ; Theocharis 2015 ), one aspect that has not been paid enough attention to concerns the forms in which users refer to individual political figures and events in political memes. This being said, the present paper focuses on referring strategies (see Kirner-Ludwig and Zimmermann 2015 ; Kirner-Ludwig 2020 ) as employed in political internet memes on Reddit, including direct and indirect quotes, citations and allusions. A specific focus is going to be on such political internet memes that employ pop cultural and telecinematic reference points and recontextualize them from their original into new target contexts (see Bublitz 2015 ; Gruber 2019 ). As shall be shown, practices such as combining constructed speech elements into recontextualized elements in political internet memes create multiple intertextual references that may enhance visibility, saliency and, thus, the ‘lifetime’ of a political meme.
Indefinite determiners in informal Italian: A preliminary analysis

Abstract This paper presents the results of a pilot study on the distribution of indefinite determiners in contexts with narrow scope interpretation in current informal Italian. It individuates the available forms and presents their diatopic distribution. The research is based on data collected through an online questionnaire designed to detect optionality. The results show that in narrow scope indefinite contexts, i. e., negative statements, both the zero determiner and the definite article are widespread throughout the country. The partitive determiner is only found in episodic sentences and is limited to restricted geographic areas. In all contexts and areas, a large degree of optionality is found. In some context and area, however, it is possible to identify one form more prominent than the others. This can be related to the context, which may favour some specialized meaning of one specific form, e. g., saliency and small quantity, or to diatopic variation due to language contact with the dialect, as shown by comparing present-day informal Italian with the dialectal data reported in AIS and analysed in (Cardinaletti and Giusti. 2018. Indefinite determiners: Variation and optionality in Italo-Romance. In Diego Pescarini &amp; Roberta D’Alessandro (eds.), Advances in Italian dialectology: Sketches of Italo-Romance grammars , 135–161. Amsterdam: Brill).
Radio via mobile phones: the intersecting logics of media technologies in Ghana

This article argues that the material history of mobile phones as they took shape in Ghana reveals them to be essential parts of radio’s infrastructure; one that is social, informal, and transnational. Using the radio tuning feature on mobile phones as an emblematic device, this article unpacks the sociotechnical infrastructure underpinning radio’s continued dominance in Ghana, revealing the intersecting logics that help to sustain the media technology landscape in the country.
A Deep Learning Approach for Human Action Recognition Using Skeletal Information

In this paper we present an approach toward human action detection for activities of daily living (ADLs) that uses a convolutional neural network (CNN). The network is trained on discrete Fourier transform (DFT) images that result from raw sensor readings, i.e., each human action is ultimately described by an image. More specifically, we work using 3D skeletal positions of human joints, which originate from processing of raw RGB sequences enhanced by depth information. The motion of each joint may be described by a combination of three 1D signals, representing its coefficients into a 3D Euclidean space. All such signals from a set of human joints are concatenated to form an image, which is then transformed by DFT and is used for training and evaluation of a CNN. We evaluate our approach using a publicly available challenging dataset of human actions that may involve one or more body parts simultaneously and for two sets of actions which resemble common ADLs.
Understanding the timing of eruption end using a machine learning approach to classification of seismic time series

The timing and processes that govern the end of volcanic eruptions are not yet fully understood, and there currently exists no systematic definition for the end of a volcanic eruption. Currently, end of eruption is established either by generic criteria (typically 90 days after the end of visual signals of eruption) or criteria specific to a given volcano. We explore the application of supervised machine learning classification methods: Support Vector Machine, Logistic Regression, Random Forest and Gaussian Process Classifiers and define a decisiveness index D to evaluate the consistency of the classifications obtained by these models. We apply these methods to seismic time series from two volcanoes chosen because they display contrasting styles of eruption: Telica (Nicaragua) and Nevado del Ruiz (Colombia). We find that, for both volcanic systems, the end-date we obtain by classification of seismic data is 2–4 months later than end-dates defined by the last occurrence of visual eruption (such as ash emission). This finding is in agreement with previous, general definitions of eruption end and is consistent across models. Our classifications have a higher correspondence of eruptive activity with visual activity than with database records of eruption start and end. We analyze the relative importance of the different features of seismic activity used in our models (e.g. peak event amplitude, daily event counts) and find little consistency between the two volcanic systems in terms of the most important features which determine whether activity is eruptive or non-eruptive. These initial results look promising and our approach may offer a robust tool to help determine when an eruption has ended in the absence of visual confirmation.
Understanding Window Management Interactions in AR Headset + Smartphone Interface

We envision a combinative use of an AR headset and a smartphone in the future that can provide a more extensive display and precise touch input simultaneously. In this way, the input/output interface of these two devices can fuse to redefine how a user can manage application windows seamlessly on the two devices. In this work, we conducted a formative interview with ten people to provide an understanding of how users would prefer to manage multiple windows on the fused interface. Our interview highlighted that the desire to use a smartphone as a window management interface shaped users' interaction practices of window management operations. This paper reports how their desire to use a smartphone as a window manager is manifested.
Watch my Painting: The Back of the Hand as a Drawing Space for Smartwatches

Smartwatches can be used independently from smartphones, but input tasks like messaging are cumbersome due to the small display size. Parts of the display are hidden during interaction, which can lead to incorrect input. For simplicity, instead of general text input a small set of answer options are often provided, but these are limited and impersonal. In contrast, free-form drawings can answer messages in a very personal way, but are difficult to produce on small displays. To enable precise drawing input on smartwatches we present a magnetic stylus that is tracked on the back of the hand. In an evaluation of several algorithms we show that 3D position estimation with a 7.5x20mm magnet reaches a worst-case 6% relative position error on the back of the hand. Furthermore, the results of a user study are presented, which show that in the case of drawing applications the presented technique is faster and more precise than direct finger input.