Reviewerly/exHarmony huggingface dataset:
Hybrid Methodology: Combining Ethnography, Cognitive Science, and Machine Learning to Inform the Development of Context‐Aware Personal Computing and Assistive Technology

The not‐too‐distant future may bring more ubiquitous personal computing technologies seamlessly integrated into people's lives, with the potential to augment reality and support human cognition. For such technology to be truly assistive to people, it must be context‐aware. Human experience of context is complex, and so the early development of this technology benefits from a collaborative and interdisciplinary approach to research—what the authors call “hybrid methodology”—that combines (and challenges) the frameworks, approaches, and methods of machine learning, cognitive science, and anthropology. Hybrid methodology suggests new value ethnography can offer, but also new ways ethnographers should adapt their methodologies, deliverables, and ways of collaborating for impact in this space. This paper outlines a few of the data collection and analysis approaches emerging from hybrid methodology, and learnings about impact and team collaboration, that could be useful for applied ethnographers working on interdisciplinary projects and/or involved in the development of ubiquitous assistive technologies.
Application of Convolutional Neural Networks for Fall Detection Using Multiple Cameras

Currently one of the most important research issue for artificial intelligence and computer vision tasks is the recognition of human falls. Due to the current exponential increase in the use of cameras is it common to use vision-based approach for fall detection and classification systems. On another hand deep learning algorithms have transformed the way that we see vision-based problems. The Convolutional Neural Network (CNN) as deep learning technique offers more reliable and robust solutions on detection and classification problems. Focusing only on a vision-based approach, for this work we used images from a new public multimodal data set for fall detection (UP-Fall Detection dataset) published by our research team. In this chapter we present fall detection system using a 2D CNN analyzing multiple camera information. This method analyzes images in fixed time window frames extracting features using an optical flow method that obtains information of relative motion between two consecutive images. For experimental results, we tested this approach in UP-Fall Detection dataset. Results showed that our proposed multi-vision-based approach detects human falls achieving 95.64% in accuracy with a simple CNN network architecture compared with other state-of-the-art methods.
A Design and Development Research Approach to Redesigning an Online Module for Education 4.0

This paper elucidates the development of Malaysian Studies 3 (MS3) module for Education 4.0, using design and development research (DDR) approach. This MS3 module is for learning at scale (L@S) using Taylor's Integrated Moodle eLearning System (TIMeS). MS3 is one of the university core module (UCM) in Taylor's University (TU), Malaysia. Transforming the MS3 module to be taught a virtual learning environment (VLE) is the solution proposed to mitigate the lack of space on campus for a massive number of students per course. The learning design in this VLE must also fulfill the requirements of the Education 4.0 era, which caters to today's learners in the digital age. In applying an agile model Successive Approximation Model 2 (SAM2) as the project's research design framework, the process of development will go through three iterations of refinements. Future research shall further analyze and reflect upon the elements of learning design during the process of development to conceptualize a learning design strategy framework (LDSF) to ease the process of course transformation to a VLE. Further, a framework can ensure course designed is aligned to the new Taylor's Curriculum Framework (TCF) and meets the quality for L@S in TIMeS.
Sub-Word Based Mongolian Offline Handwriting Recognition

Mongolian is an agglutinative language, which re-sults in a large number of words derived from the same stems connecting different suffixes. This morphological richness leads to high out-of-vocabulary (OOV) rates and causes problems of data sparsity. In this paper, our proposed recognition system is composed of three parts: handwritten image preprocessing, mapping of images to grapheme sequences, and sub-word-based language model (LM) decoding. We present a sub-word-based n-gram LM to solve the high OOV rate problem. According to the characteristics of Mongolian, we modified the traditional token passing algorithm to improve decoding speed and to easy to combine with any n-gram LM. We evaluated the performance of sub-words at different levels on the open Mongolian offline handwriting dataset (MHW). The bi-syllable 2-gram LM showed the best performance, with 18.32% and 23.22% word-error rates (WERs) on two test sets. Our various experiments show that, this method can predict in vocabulary words with a higher accuracy rate and also predict OOV words with a certain accuracy rate.
A Calculus for Language Transformations

In this paper we propose a calculus for expressing algorithms for programming languages transformations. We present the type system and operational semantics of the calculus, and we prove that it is type sound. We have implemented our calculus, and we demonstrate its applicability with common examples in programming languages. As our calculus manipulates inference systems, our work can, in principle, be applied to logical systems.
Consistency of Proof-of-Stake Blockchains with Concurrent Honest Slot Leaders

We improve the fundamental security threshold of Proof-of-Stake (PoS) blockchain protocols, re ecting for the rst time the positive e ect of rounds with multiple honest leaders.Current analyses of the longest-chain rule in PoS blockchain protocols reduce consistency to the dynamics of an abstract, round-based block creation process that is determined by three probabilities: •, the probability that a round has at least one adversarial leader;• , the probability that a round has a single honest leader; and• , the probability that a round has multiple, but honest, leaders.We present a consistency analysis that achieves the optimal threshold + > .This is a rst in the literature and can be applied to both the simple synchronous setting and the setting with bounded delays.Moreover, we achieve the optimal consistency error -Θ( ) where is the con rmation time.The consistency analyses in Ouroboros Praos (Eurocrypt 2018) and Genesis (CCS 2018) assume that the probability of a uniquely honest round exceeds that of the other two events combined (i.e., -> ); the analyses in Sleepy Consensus (Asiacrypt 2017) and Snow White (Fin.Crypto 2019) assume that a uniquely honest round is more likely than an adversarial round (i.e., > ).Thus existing analyses either incur a penalty for multiply honest rounds, or treat them neutrally.In addition, previous analyses completely break down when uniquely honest rounds become less frequent, i.e., < .Our new results can be directly applied to improve consistency of these existing protocols.We emphasize that these thresholds determine the critical tradeo between honest majority, network delays, and consistency error.We complement our results with a consistency analysis in the setting where uniquely honest slots are rare, even letting = 0, under the added assumption that honest players adopt a consistent chain selection rule.Our analysis provides a direct connection between the Ouroboros analysis by Blum et al. (SODA 2020) focusing on "relative margin" and the Sleepy consensus analysis focusing on "strong pivots."
DMFP: A Dynamic Multi-faceted Fine-Grained Preference Model for Recommendation

The time signals behind a user's historical behaviors are important for better inferring what she prefers to interact with at the next time. For the attention-based recommendation methods, relative position encoding and time intervals division are two common ways to model the time signal behind each behavior. They either only consider the relative position of each behavior in the behavior sequence, or process the continuous temporal features into discrete category features for subsequent tasks, which can hardly capture the dynamic preferences of a user. In addition, although the existing recommendation methods have considered both long-term preference and short-term preference, they ignore the fact that the long-term preference of a user may be multi-faceted, and it is difficult to learn a user's fine-grained short-term preference. In this paper, we propose a Dynamic Multi-faceted Fine-grained Preference model (DMFP), where the multi-hops attention mechanism and the feature-level attention mechanism together with a vertical convolution operation are adopted to capture users' multi-faceted long-term preference and fine-grained short-term preference, respectively. Therefore, DMFP can better support the next item recommendation. Extensive experiments on three real-world datasets illustrate that our model can improve the effectiveness of the recommendation compared with the state-of-the-art methods.
Exploring users’ attitudes towards prosthesis aesthetics in the UK and Greece

Previous studies have highlighted the importance of prostheses on users' wellbeing; however, the effects of the prosthetic appearance on users' lives have not been thoroughly explored. The aim of this study was to explore how the aesthetics of prosthetic limbs affected users in two countries with different cultures: the UK and Greece. Interpretative phenomenological analysis was used based on semi-structured interviews, alongside probes. Seven participants (nUK = 4; nGR = 3) were recruited, based on purposive methods. All the participants were adults, who had limb-loss due to amputation. The results regarding the role of prosthetic limbs, with respect to their appearance, revealed one theme related to users' personal life, and two opposing themes regarding users' social lives. Prosthetic limbs with unattractive appearance negatively affected participants' wellbeing, whilst expressive prostheses, an alternative prosthetic type that is individually designed to highlight the user's identity, could increase their self-confidence. Regardless of the extent to which participants were conscious about the aesthetics of their prostheses, they indicated that expressive prostheses were more attractive. Therefore, they could educate society and help users be more easily accepted. However, caution needs to be paid in the case of collectivistic societies, as expressive prostheses could increase users' stigmatization.
Accelerating Deep Learning Systems via Critical Set Identification and Model Compression

Modern distributed engines are increasingly deployed to accelerate large-scaled deep learning (DL) training jobs. While the parallelism of distributed workers/nodes promises the scalability, the computation and communication overheads of the underlying iterative solving algorithms, e.g., stochastic gradient decent, unfortunately become the bottleneck for distributed DL training jobs. Existing approaches address such limitations by designing more efficient synchronization algorithms and model compressing techniques, but do not adequately address issues relating to processing massive datasets. In this article, we propose ClipDL, which accelerates the deep learning systems by simultaneously decreasing the number of model parameters as well as reducing the computations on critical data only. The core component of ClipDL is the estimation of critical set based on the observation that large proportions of input data have little influence on model parameter updating in many prevalent DL algorithms. We implemented ClipDL on Spark (a popular distributed engine for big data) and BigDL (based on de-factor distributed DL training architecture, parameter server), and integrated it with representative model compression techniques. The exhaustive experiments on real DL applications and datasets show ClipDL accelerates model training process by an average of 2.32 times while only incurring accuracy losses of 1.86 percent.
Sanskrit-Gujarati Constituency Mapper for Machine Translation System

Looking at vastness, depth and precise nature of Sanskrit grammar and geographically wide proliferation of Gujarati language and its native speaker, it becomes necessary to spotlight on constituency characteristics and features of Sanskrit and Gujarati. Both the languages fall under Indo-Iranian language sub-tree, but there are grammatical divergences which are discussed here so as to reflect in implementation of Machine Translation System (MTS). The content revolves around divergence pattern for a rule base MT system, due to scarce or unavailability of parallel aligned corpora to incorporate statistical or Example based methodology. The Sanskrit grammatical constituents like indeclinables, pronouns, verbs and nouns are analyzed. The Sanskrit inflectional affixes are mapped to its Gujarati inflectional affixes for each equivalent grammar constituent.
Piecewise Rigid Scene Flow with Implicit Motion Segmentation

In this paper, we introduce a novel variational approach to estimate the scene flow from RGB-D images. We regularize the ill-conditioned problem of scene flow estimation in a unified framework by enforcing piecewise rigid motion through decomposition into rotational and translational motion parts. Our model crucially regularizes these components by an L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> “norm”, thereby facilitating implicit motion segmentation in a joint energy minimization problem. Yet, we also show that this energy can be efficiently minimized by a proximal primal-dual algorithm. By implementing this approximate L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> rigid motion regularization, our scene flow estimation approach implicitly segments the observed scene of into regions of nearly constant rigid motion. We evaluate our joint scene flow and segmentation estimation approach on a variety of test scenarios, with and without ground truth data, and demonstrate that we outperform current scene flow techniques.
Transepistemic abduction: reasoning across epistemic domains

Abstract Frequently people draw on different domains of knowledge to reach a conclusion that seems reasonable despite being difficult to justify from the perspective of a single domain. For example, there appears to be no reason for ethics to involve mathematics, nor is there a mechanism in mathematics to embrace moral questions; however, both ethics and mathematics are likely to be involved in resolving questions about how an autonomous vehicle should make decisions in a social context. In this paper, we present a specialized mode of reasoning, transepistemic abduction (TeA), which establishes how two agents, in order to satisfactorily explain a phenomenon, reason across two epistemic domains despite each agent being ignorant of the other’s domain knowledge. We formalize TeA with epistemic logic and provide a naturalized example that brings together a psychosocial agent and a computational agent for the analysis of subjective text. We find that interaction between agents is critical to the process of TeA and that there are limits to the formalization while remaining true to naturalized reasoning. We conclude with some important implications of these findings for future work in this area.
Evaluation of Bias in Sensitive Personal Information Used to Train Financial Models

Bias in data can have unintended consequences which propagate to the design, development, and deployment of machine learning models. In the financial services sector, this can result in discrimination from certain financial instruments and services. At the same time, data privacy is of paramount importance, and recent data breaches have seen reputational damage for large institutions. Presented in this paper is a trusted model-lifecycle management platform that attempts to ensure consumer data protection, anonymization, and fairness. Specifically, we examine how datasets can be reproduced using deep learning techniques to effectively retain important statistical features in datasets whilst simultaneously protecting data privacy and enabling safe and secure sharing of sensitive personal information beyond the current state-of-practice.
Shot Classification and Replay Detection in Broadcast Soccer Video

In this work, we have classified the frames of a broadcast soccer video into four classes, namely long shot, medium shot, close shot and logo frame. A two-stream deep neural network (DNN) model is proposed for the shot classification. Along with static image features, player attributes like count of the players in a frame, area, width and height of the players are used as features for the classification. The heterogeneous features are fed into the DNN model through a late fusion strategy. In addition to shot classification, we propose a model to detect replay within a soccer video. The logo frames are used to decide the temporal boundary of a replay segment. A majority class assignment strategy is employed to improve the accuracy of replay detection. The experimental results show that our method is at least 12% better than that of similar approaches.
A method for the outlier flights detection of the final approach based on FOQA data

Aiming at the final approach of the flight which has high risk of fatal accidents, a data-driven method for outlier flights detection is proposed; firstly, three checking points and the performance parameters of final approach are chosen and extracted from the QAR data in FOQA station; secondly, for the outlier detection model training, DBSCAN is proposed when discrete parameters are included in the training datasets, and one-class SVM algorithm is proposed when discrete parameters are not included in the training datasets; finally, the outlier flights deviating from the group characteristics are detected with the trained models at checking point 1000 feet, 500 feet and 300 feet respectively, and the flights present outlier all through the checking points are filtered, which provides important decision support information for the final approach safety management.
Application of virtual reality simulation in firefighter training for the development of decision-making competences

This paper focuses on the training of firefighters, using simulated scenarios in virtual reality, to develop decision-making competences in the management of relief operations. Through the action research method, a training program was designed and improved to develop the competences considered critical in the field of decision making in the context of fire brigade interventions, using simulation exercises with scenarios in virtual reality. During two action research cycles, a pilot training course was conducted for a group of eight internal and external trainers from the Portuguese National Fire Service School, and a second course for eight fire officers from different Firefighters' corps. The data collected and the comparative analysis on the performance of the two groups of trainees, as well as the trainers' and trainees' perception in terms of evaluation of the training course, allowed to identify corrections and introduce changes, in the logic of continuous improvement of the training product. The results achieved in the research work demonstrated the adequacy of the training program and the use of virtual reality simulation, distinguishing a set of orientations that can be considered in similar formative contexts.
Table Rows Segmentation

We consider the Document Understanding problem of segmenting tables in rows. We propose a method that first enumerates virtual row separator candidates and then select the correct ones thanks to a classification task, solved using supervised structured machine learning. Interestingly, the task is the joint-classification of virtual separators and real text lines. We describe and tested several alternative candidate generation methods and report the results of our experiment for each, on two different types of registry books from the 19th century.
RemBit: a blockchain based solution for remittances to Ethiopia

Since its introduction in the Satoshi Nakamoto's Bitcoin white paper published in 2008, blockchain technology has been seen as a great opportunity to enable new business scenarios both for developed and developing countries. Considering that digital alternative payment methods represent a key factor to the smart cities development, in this study we propose an innovative payment solution aimed at improving the remittance flows by exploiting the potential of blockchain technology. Traditional banking remittance systems are characterized by high transaction fees and long transfer times, therefore they encourage the adoption of informal channels for money transfer with consequent losses in the economic system. By focusing our interest in a specific country of Sub-Saharan Africa, Ethiopia, we launch an innovative blockchain based system able to make the remittance process safer, faster and to allow secure and reliable transactions between the counterparts in data sharing. We describe its architecture, which enables a secure management of big data and the different stages of solution design. Furthermore, the system guarantees the compliance with the Ethiopian government's recommendations on the matter, in particular it allows to encourage formal data collection on remittances and to support the introduction of new technology in the payments system. In addition, its use by individuals could improve awareness and financial education in Ethiopia and it can help prevent money laundering and financing of terrorism.
Design of A Blockchain-based Employee Attendance System

Blockchain system is distributed and decentralized that make it can be applied to develop a tamper proof-employee attendance system. There are many attendance systems have been developed but most of it still use conventional systems and databases that have not been distributed yet. A conventional databases do not have special features in checking whether a piece of information has experienced unauthorized changes. On a blockchain based system, no administrator permission is allowed to editing or deleting data. Someone who inserts an information record on the blockchain will not be able to deny that he is doing the activity. Each party on the blockchain has access to the whole database and history. The blockchain-based employee attendance system is required to provide a database that keeps its reliability and integrity and tamper proof.
A New Wearable System for Head Gesture Recognition Designed to Control an Intelligent Wheelchair

This paper proposes and evaluates a new simple method of recognizing head movements in order to empower a tetraplegic to easily control his wheelchair. The input for the recognition system is one based on the capacitive signals acquired from a wearable system consisting of four capacitive sensors embedded in a necktie. The proposed non-contact wearable system is a very simple and cheap one, being accessible, and, above all, it is very easy to use. All processing and analyses were done offline and the obtained competitive results make our approach a very promising and feasible solution from both, economic and constructive point of view. A key advantage of the system resides in its high simplicity which considerably reduces the computational burden, making it suitable for real-time processing. Also, due to the capacitive sensors' characteristics and their arrangement, the head gesture recognition process becomes a straight one, leading to very high recognition accuracy.