Reviewerly/exHarmony huggingface dataset:
Analysis and prediction of railway accident risks using machine learning

The harmful consequences of rail accidents, which sometimes lead to loss of life and the destruction of the system and its environment, are at the basis of the implementation of a &quot;experience feedback&quot; (REX) system considered as the essential means to promote the improvement of safety. REX seeks to identify adverse events with a view to highlighting all the causes that contributed to the occurrence of a particular accident and therefore to avoid at least the reproduction of new accidents and similar incidents. Accident and incident investigation reports provide a wealth of informative information for accident prevention. It would be appropriate to exploit these reports in order to extract the relevant information and suggest ways to avoid the reproduction of adverse events. In this context, knowledge of the causes of accidents results mainly from the contribution of lessons learned and experiences gained, whether positive or negative. However, the exploitation of information and the search for lessons from past events is a crucial step in the REX process. This process of analyzing and using data from experience can be facilitated if there are methods and tools available to technical investigators. It seems advisable to consider the use of artificial intelligence (AI) techniques and in particular automatic learning methods in order to understand the origins and circumstances of accidents and therefore propose solutions to avoid the reproduction of similar insecurity events. The fact that the lessons one can learn from a REX depends on the experiences of the situations experienced in the past, constitutes in itself a key argument in favor of machine learning. Thus, the identification of knowledge about rail accidents and incidents and share them among REX actors constitute a process of learning sequences of undesirable events. The approach proposed in this manuscript for the prevention of railway accidents is a hybrid method built around several algorithms and uses several methods of automatic learning: Learning by classification of concepts, Rule-based machine learning (RBML) and Case-based reasoning (CBR).
Attention Based Shared Representation for Multi-task Stance Detection and Sentiment Analysis

Stance detection and sentiment analysis are two important problems that have gained significant attention in recent time. While stance detection corresponds to detecting the attitude/position (i.e., favor, against, and none) of a person towards any specific event or topic, sentiment analysis deals with determining the opinion expressed by a person for a topic, event, product or a service (i.e., positive, negative, and neutral). We envisage these two problems to have a good correlation. For e.g., information about favor stance can help in the prediction of positive sentiment or negative sentiment can help in predicting the against stance and so on. Motivated by this, in our current work, we propose a multi-task deep neural framework to investigate whether sentiment helps in stance detection and the vice-versa. Our proposed method makes use of an attention-based shared representation for multi-task stance detection and sentiment analysis. We also deploy an attention mechanism to learn the joint-association between the words present in a tweet and a topic. We evaluate our proposed approach on the benchmark dataset of SemEval-2016 Task 6. The proposed multi-task approach yields higher performance compared to the state-of-the-art systems for both stance detection and sentiment analysis.
Collision Avoidance for Indoor Service Robots Through Multimodal Deep Reinforcement Learning

In this paper, we propose an end-to-end approach to endow indoor service robots with the ability to avoid collisions using Deep Reinforcement Learning (DRL). The proposed method allows a controller to derive continuous velocity commands for an omnidirectional mobile robot using depth images, laser measurements, and odometry based speed estimations. The controller is parameterized by a deep neural network, and trained using DDPG. To improve the limited perceptual range of most indoor robots, a method to exploit range measurements through sensor integration and feature extraction is developed. Additionally, to alleviate the reality gap problem due to training in simulations, a simple processing pipeline for depth images is proposed. As a case study we consider indoor collision avoidance using the Pepper robot. Through simulated testing we show that our approach is able to learn a proficient collision avoidance policy from scratch. Furthermore, we show empirically the generalization capabilities of the trained policy by testing it in challenging real-world environments. Videos showing the behavior of agents trained using the proposed method can be found at https://youtu.be/ypC39m4BlSk .
Dynamics of analog logic-gate networks for machine learning

We describe the continuous-time dynamics of networks implemented on Field Programable Gate Arrays (FPGAs). The networks can perform Boolean operations when the FPGA is in the clocked (digital) mode; however, we run the programed FPGA in the unclocked (analog) mode. Our motivation is to use these FPGA networks as ultrafast machine-learning processors, using the technique of reservoir computing. We study both the undriven dynamics and the input response of these networks as we vary network design parameters, and we relate the dynamics to accuracy on two machine-learning tasks.
A Methodological Assistant for Use Case Diagrams

Use case driven analysis is the corner stone of software and systems modeling in UML and SysML, respectively. Whether many books and tutorials have discussed the use of use case diagrams, students and industry practitioners regularly face methodological problems in writing good use cases. This paper defines a methodological assistant that helps designing use case diagrams relying on formalized rules and reuse of previous diagrams. The methodological assistant is implemented in Python. It is interfaced with the free SysML software TTool, and with Cameo Systems Modeler.
XML-Based Information Fusion Architecture based on Cloud Computing Ecosystem

Considering cloud computing from an organizational and end user computing point of view, it is a new paradigm for deploying, managing and offering services through a shared infrastructure. Current development of cloud computing applications, however, are the lack of a uniformly approach to cope with the heterogeneous information fusion. This leads cloud computing to inefficient development and a low potential reuse. This study addresses these issues to propose a novel Web 2.0 Mashups as a Service, called WMaaS, which is a fundamental cloud service model. The WMaaS is developed based on a XML-based Mashups Architecture (XMA) that is composed of Web 2.0 Mashups technologies, including Web Data, Web API, Web Interaction, and Web Presentation to associate with existing service models. To demonstrate the feasibility of this approach, this study implemented a Ubiquitous Location-based Service System (ULSS) that is a cloud computing application developed based on WMaaS to provide continuous and location-based schedule information for organization monitoring and end user needs.
Prediction, Consistency, Curvature: Representation Learning for Locally-Linear Control

Many real-world sequential decision-making problems can be formulated as optimal control with high-dimensional observations and unknown dynamics. A promising approach is to embed the high-dimensional observations into a lower-dimensional latent representation space, estimate the latent dynamics model, then utilize this model for control in the latent space. An important open question is how to learn a representation that is amenable to existing control algorithms? In this paper, we focus on learning representations for locally-linear control algorithms, such as iterative LQR (iLQR). By formulating and analyzing the representation learning problem from an optimal control perspective, we establish three underlying principles that the learned representation should comprise: 1) accurate prediction in the observation space, 2) consistency between latent and observation space dynamics, and 3) low curvature in the latent space transitions. These principles naturally correspond to a loss function that consists of three terms: prediction, consistency, and curvature (PCC). Crucially, to make PCC tractable, we derive an amortized variational bound for the PCC loss function. Extensive experiments on benchmark domains demonstrate that the new variational-PCC learning algorithm benefits from significantly more stable and reproducible training, and leads to superior control performance. Further ablation studies give support to the importance of all three PCC components for learning a good latent space for control.
Telepresence-place-based foreign language learning and its design principles

Real world places are important for foreign language (FL) learning since they provide rich and authentic language and cultural contexts for learners. However, numerous FL learners are unable to travel to target-language environments for language learning purposes. This article develops the concept of telepresence-place-based FL learning and examines its design principles through a mixed-method case study. Eleven adult English learners in China and four native-speaking English instructors participated in a series of learning activities at an arboretum on the campus of a university in the US. Based on the analysis of surveys, interviews, field notes, and learning activity videos, the findings show that using telepresence robots outside the classroom provides unique learning affordance for learners, especially when proper design principles are followed in designing the learning activities.
ICTD Research in Latin America: literature review, scholar feedback, and recommendations

Reviews of the ICTD literature have noted a scarcity of studies about Latin American countries. We investigate (1) what are the alternatives to ICTD and English-language ICTD publication venues researchers utilize to disseminate their work and why they may do so, and (2) what methodological, theoretical, and contextual characteristics these researchers bring to their publications. The study takes a two-pronged approach to answer these questions: a survey of researchers who have conducted ICTD research in Latin America and an analysis of their ICTD publications. We find that researchers use an array of specific alternative and additional terms to describe ICTD research, that methodological and theoretical characteristics of the literature resemble ICTD in general, and that contextual coverage of the region is lacking. Our results prompt a set of recommendations for better incorporating scholarship about Latin America in the ICTD field as well as improving global coverage of the ICTD community.
Completeness Theorems for First-Order Logic Analysed in Constructive Type Theory

We study various formulations of the completeness of first-order logic phrased in constructive type theory and mechanised in the Coq proof assistant. Specifically, we examine the completeness of variants of classical and intuitionistic natural deduction and sequent calculi with respect to model-theoretic, algebraic, and game semantics. As completeness with respect to standard model-theoretic semantics is not readily constructive, we analyse the assumptions necessary for particular syntax fragments and discuss non-standard semantics admitting assumption-free completeness. We contribute a reusable Coq library for first-order logic containing all results covered in this paper.
ALOHA: Artificial Learning of Human Attributes for Dialogue Agents

For conversational AI and virtual assistants to communicate with humans in a realistic way, they must exhibit human characteristics such as expression of emotion and personality. Current attempts toward constructing human-like dialogue agents have presented significant difficulties. We propose Human Level Attributes (HLAs) based on tropes as the basis of a method for learning dialogue agents that can imitate the personalities of fictional characters. Tropes are characteristics of fictional personalities that are observed recurrently and determined by viewers' impressions. By combining detailed HLA data with dialogue data for specific characters, we present a dataset, HLA-Chat, that models character profiles and gives dialogue agents the ability to learn characters' language styles through their HLAs. We then introduce a three-component system, ALOHA (which stands for Artificial Learning of Human Attributes), that combines character space mapping, character community detection, and language style retrieval to build a character (or personality) specific language model. Our preliminary experiments demonstrate that two variations of ALOHA, combined with our proposed dataset, can outperform baseline models at identifying the correct dialogue responses of chosen target characters, and are stable regardless of the character's identity, the genre of the show, and the context of the dialogue.
Differentiable reservoir computing

Much effort has been devoted in the last two decades to characterize the situations in which a reservoir computing system exhibits the so-called echo state (ESP) and fading memory (FMP) properties. These important features amount, in mathematical terms, to the existence and continuity of global reservoir system solutions. That research is complemented in this paper with the characterization of the differentiability of reservoir filters for very general classes of discrete-time deterministic inputs. This constitutes a novel strong contribution to the long line of research on the ESP and the FMP and, in particular, links to existing research on the input-dependence of the ESP. Differentiability has been shown in the literature to be a key feature in the learning of attractors of chaotic dynamical systems. A Volterra-type series representation for reservoir filters with semi-infinite discrete-time inputs is constructed in the analytic case using Taylor's theorem and corresponding approximation bounds are provided. Finally, it is shown as a corollary of these results that any fading memory filter can be uniformly approximated by a finite Volterra series with finite memory.
HealthChain: A Secure Scalable Health Care Data Management System Using Blockchain

Electronic Health Records (EHRs) are designed to manage the complexities of multi-institutional and lifetime medical records. As patients move among providers, their data become scattered across different organizations, losing easy access to past records. The records should be kept in digitized format with required protection from unauthorized access. There are many schemes to solve these issues, which includes central server based systems, cloud based systems and blockchain based systems. The trending solutions are cloud based and blockchain based solutions. Even though cloud based system provides much scalability through various mechanisms, it is trusted as a secure system in which data may be corrupted with or without the knowledge of cloud provider. The blockchain technology overcomes the barrier of trusting an agent. But the blockchain based system lacks scalability and requires huge storage space. The proposed system, HealthChain, provides a secure health care record management system with scalability and low storage space. HealthChain divides the whole system into regions and uses two kinds of blockchains, Private Blockchain: for intra-regional communication and, Consortium Blockchain: for inter-regional communication. The proposed system utilizes a consensus mechanism for mining.
Control-theoretic Approach to Malleability Cancellation by Attacked Signal Normalization

In this study, we propose a novel attack for encrypted control systems along with a cancellation method for the attacks. The attacks assign the poles of closed-loop systems by utilizing the malleability of homomorphic encryption schemes used in encrypted control systems. Additionally, a condition of destabilization by the attacks is provided under the assumption that adversaries are aware of the homomorphic encryption scheme that is being used. The proposed method is applicable for encrypted control systems with partially or fully homomorphic encryption schemes, and completely removes influence of certain type of attacks with obfuscated controller gain, which is obtained by QR decomposition. The validity of the proposed method is examined through a numerical simulation. The results confirm that the proposed method is effective in canceling the malleability of encrypted control systems.
Product Quantized Collaborative Filtering

Because of strict response-time constraints, efficiency of top-k recommendation is crucial for real-world recommender systems. Locality sensitive hashing and index-based methods usually store both index data and item feature vectors in main memory, so they handle a limited number of items. Hashing-based recommendation methods enjoy low memory cost and fast retrieval of items, but suffer from large accuracy degradation. In this paper, we propose product Quantized Collaborative Filtering (pQCF) for better trade-off between efficiency and accuracy. pQCF decomposes a joint latent space of users and items into a Cartesian product of low-dimensional subspaces, and learns clustered representation within each subspace. A latent factor is then represented by a short code, which is composed of subspace cluster indexes. A user's preference for an item can be efficiently calculated via table lookup. We then develop block coordinate descent for efficient optimization and reveal the learning of latent factors is seamlessly integrated with quantization. We further investigate an asymmetric pQCF, dubbed as QCF, where user latent factors are not quantized and shared across different subspaces. The extensive experiments with 6 real-world datasets show that pQCF significantly outperforms the state-of-the-art hashing-based CF and QCF increases recommendation accuracy compared to pQCF.
Adaptive GloVe and FastText Model for Hindi Word Embeddings

Today, a lot of research is carried out on word embeddings in NLP domain. The algorithms like GloVe, FastText are used to develop word embeddings. However, not enough work is done on Indian languages due to lack of resource availability. The datasets required for testing word embeddings are not available for Indian languages. In this paper, two algorithms are proposed - Adaptive GloVe model (AGM) and Adaptive FastText model (AFM). Adapting to the co-occurrence matrix generation process of the original GloVe model, AGM, leverages part of speech tags, morphological knowledge of the language. Assigning higher co-occurrence weight to words with same root, AGM, significantly improved accuracy of resultant word embeddings on syntactic datasets. Whereas, AFM improves the vocabulary building process of the original FastText model. The work involves generation of word embeddings for low resource language like Hindi using AGM and AFM and creation of necessary test datasets for evaluating word embeddings. AGM word embeddings showed morphological awareness, achieving 9% increase in accuracy on syntactic word analogy task, compared to original GloVe model. AFM outperformed FastText by 1% accuracy in word analogy task and 2 Spearman rank on word similarity task, providing state-of-the-art performance.
The Sentiment Analysis of Fintech Users Using Support Vector Machine and Particle Swarm Optimization Method

This research was conducted to analyze the sentiment of Fintech users in Tegal City, especially the Ovo application since it currently has a very massive market and promotion. Users choosing the Fintech application generally consider convenience, security, transaction suitability, convenience, and cashback. The problem is that the user's trust in Fintech is still in doubt. Currently the application provides reviews to share their experiences. With the number of reviews displayed, it needs analysis that can classify the review into positive or negative classes. This research is using experimental method. Data is taken from Google Play in the OVO Application. The method used is the method of Support Vector Machine (SVM) and Particle Swarm Optimization (PSO). Data processing is using the Application RapidMiner Studio 7.6.003. The result is that the Ovo application gets the best value with an accuracy of 82.33%. So that it can be concluded that the results of the review might convince users of other positive customer experiences while negative experiences can be used as a contribution to the thinking of the next product development to be better so they can compete.
Blockchain Technology: What Is It Good for?

Business executives, government leaders, investors, and researchers frequently ask the following three questions: (1) What exactly is blockchain technology? (2) What capabilities does it provide? (3) What are good applications? Here we answer these questions thoroughly, provide a holistic overview of blockchain technology that separates hype from reality, and propose a useful lexicon for discussing the specifics of blockchain technology in the future.
Generating Realistic Binarization Data with Generative Adversarial Networks

One of the limitations for using Deep Learning models to solve binarization tasks is that there is a lack of large quantities of labeled data available to train such models. Efforts to create synthetic data for binarization mostly rely on heuristic image processing techniques and generally lack realism. In this work, we propose a method to produce realistic synthetic data using an adversarially trained image translation model. We extend the popular CycleGAN model to be conditioned on the ground truth binarization mask as it translates images from the domain of synthetic images to the domain of real images. For evaluation, we train deep networks on synthetic datasets produced in different ways and measure their performance on the DIBCO datasets. Compared to not pretraining, we reduce error by 13% on average, and compared to pretraining on unrealistic data, we reduce error by 6%. Visually, we show that DGT-CycleGAN model produces more realistic synthetic data than other models.
Supporting Real‐Time Contextual Inquiry through Sensor Data

A key challenge in carrying out product design research is obtaining rich contextual information about use in the wild. We present a method that algorithmically mediates between participants, researchers, and objects in order to enable real‐time collaborative sensemaking. It facilitates contextual inquiry, revealing behaviours and motivations that frame product use in the wild. In particular, we are interested in developing a practice of use driven design, where products become research tools that generate design insights grounded in user experiences. The value of this method was explored through the deployment of a collection of Bluetooth speakers that capture and stream live data to remote but co‐present researchers about their movement and operation. Researchers monitored a visualisation of the real‐time data to build up a picture of how the speakers were being used, responding to moments of activity within the data, initiating text conversations and prompting participants to capture photos and video. Based on the findings of this explorative study, we discuss the value of this method, how it compares to contemporary research practices, and the potential of machine learning to scale it up for use within industrial contexts. As greater agency is given to both objects and algorithms, we explore ways to empower ethnographers and participants to actively collaborate within remote real‐time research.