Reviewerly/exHarmony huggingface dataset:
Affine-Invariant Ensemble Transform Methods for Logistic Regression

Abstract We investigate the application of ensemble transform approaches to Bayesian inference of logistic regression problems. Our approach relies on appropriate extensions of the popular ensemble Kalman filter and the feedback particle filter to the cross entropy loss function and is based on a well-established homotopy approach to Bayesian inference. The arising finite particle evolution equations as well as their mean-field limits are affine-invariant. Furthermore, the proposed methods can be implemented in a gradient-free manner in case of nonlinear logistic regression and the data can be randomly subsampled similar to mini-batching of stochastic gradient descent. We also propose a closely related SDE-based sampling method which again is affine-invariant and can easily be made gradient-free. Numerical examples demonstrate the appropriateness of the proposed methodologies.
Interpretable Control by Reinforcement Learning

In this paper, three recently introduced reinforcement learning (RL) methods are used to generate human-interpretable policies for the cart-pole balancing benchmark. The novel RL methods learn human-interpretable policies in the form of compact fuzzy controllers and simple algebraic equations. The representations as well as the achieved control performances are compared with two classical controller design methods and three non-interpretable RL methods. All eight methods utilize the same previously generated data batch and produce their controller offline - without interaction with the real benchmark dynamics. The experiments show that the novel RL methods are able to automatically generate well-performing policies which are at the same time human-interpretable. Furthermore, one of the methods is applied to automatically learn an equation-based policy for a hardware cart-pole demonstrator by using only human-player-generated batch data. The solution generated in the first attempt already represents a successful balancing policy, which demonstrates the methods applicability to real-world problems.
“Contro L’Odio”: A Platform for Detecting, Monitoring and Visualizing Hate Speech against Immigrants in Italian Social Media

The paper describes the Web platform built within the project "Contro l'Odio", for monitoring and contrasting discrimination and hate speech against immigrants in Italy.It applies a combination of computational linguistics techniques for hate speech detection and data visualization tools on data drawn from Twitter.It allows users to access a huge amount of information through interactive maps, also tuning their view, e.g.visualizing the most viral tweets and interactively reducing the inherent complexity of data.Educational courses for high school students have been developed which are centered on the platform and focused on the deconstruction of negative stereotypes against immigrants, Rom and religious minorities, and on the creation of positive narratives.The data collected and analyzed by the platform are also currently used for benchmarking activities within an evaluation campaign, and for paving the way to new projects against hate.
Classification of American Sign Language by Applying a Transfer Learned Deep Convolutional Neural Network

Having the community with more than 500,000 deaf and mute English speaking people, sign alphabet detection has become a domain of interest among the researchers for the last decade. Previously, a lot of signs of progress have been made in accurate recognition of the American Sign Language. Both convolutional neural networks and traditional machine learning classifiers have been applied formerly for the recognition process. In this study, we've considered an American Sign Language dataset having 36 classes of English characters and digits. The latest research on this real-world dataset achieved an accuracy of 90%. However, in our research, we introduced modified inceptionV3 architecture for the detection of American Sign characters and obtained overall correctness of 98.81% which outperformed all the previous studies by a notable margin.
Learning from Graph Propagation via Ordinal Distillation for One-Shot Automated Essay Scoring

One-shot automated essay scoring (AES) aims to assign scores to a set of essays written specific to a certain prompt, with only one manually scored essay per distinct score. Compared to the previous-studied prompt-specific AES which usually requires a large number of manually scored essays for model training (e.g., about 600 manually scored essays out of totally 1000 essays), one-shot AES can greatly reduce the workload of manual scoring. In this paper, we propose a Transductive Graph-based Ordinal Distillation (TGOD) framework to tackle the task of one-shot AES. Specifically, we design a transductive graph-based model as a teacher model to generate pseudo labels of unlabeled essays based on the one-shot labeled essays. Then, we distill the knowledge in the teacher model into a neural student model by learning from the high confidence pseudo labels. Different from the general knowledge distillation, we propose an ordinal-aware unimodal distillation which makes a unimodal distribution constraint on the output of student model, to tolerate the minor errors existed in pseudo labels. Experimental results on the public dataset ASAP show that TGOD can improve the performance of existing neural AES models under the one-shot AES setting and achieve an acceptable average QWK of 0.69.
Automatic Video Summarization from Cricket Videos Using Deep Learning

Video Summarization is a technique that reduces the viewing duration of a video by extracting important parts from a large video. As most of the cricket matches are long-lasting, the audience preferably needs to have the summarized format of the large content. In this paper, we intend to speed up large-scale video processing by providing a concise synopsis that still conveys the story of a cricket match. For addressing this challenge, we develop a Deep Cricket Summarization Network (DCSN) that automatically extracts key-shots from an input video. Due to the limitations of the few available datasets in this domain, we introduce a new dataset named CricSum. The quality of the generated summary highly relies on users' subjective understandings. So we demonstrate the efficiency of the proposed summarization system using the Mean Opinion Score (MOS) technique. The automatically generated summarized videos collectively obtained MOS value 4 out of 5.
HealthyBlockchain for Global Patients

An emerging healthcare delivery model is enabling a new era of clinical care based on well-informed decision-making processes. Current healthcare information systems (HISs) fall short of adopting this model due to a conflict between information security needed to implement the new model and those already enforced locally to support traditional care models. Meanwhile, in recent times, the healthcare sector has shown a substantial interest in the potential of using blockchain technology for providing quality care to patients. No blockchain solution proposed so far has fully addressed emerging cross-organization information-sharing needs in healthcare. In this paper, we aim to study the use of blockchain in equipping struggling HISs to cope with the demands of the new healthcare delivery model, by proposing HealthyBlockchain as a granular patient-centered ledger that digitally tracks a patient’s medical transactions all along the treatment pathway to support the care teams. The patient-centered ledger is a neutral tamper-proof trail time-stamp block sequence that governs distributed patient information across the decentralized discrete HISs. HealthyBlockchain connects patients, clinicians, and healthcare providers to facilitate a transparent, trustworthy, and secure supporting platform.
Identification of Straggler Node in Distributed Environment Using Soft Computing Algorithms

Node straggling is most impactful thing to improve the Quality of Service (QoS) in distributed as well as cloud environment. On the other hand, the reason for which node discharges are caused is complicated. The precedents work mainly focus on detecting Stragglers, optimizing the level of programming and analysis of the root cause. Without order to help users automate their services, these approaches cannot provide valuable information. We in proposed new approach, a general method that incorporates framework and characteristics of the system for the analysis of root cause for detection of stragglers in the big data system, which basically reduce the process execution time and generate job failure issues during execution. We proposed an framework for straggler node detection from distributed environment using machine learning algorithm based on large scale job performed log data. After execution of machine learning algorithm system will predict straggler nodes list and dynamically eliminate such nodes from execution list. Q-Learning based reinforcement learning algorithm initially proposed for execution and validate the system result in multi node environments.
Automatic Intent-Slot Induction for Dialogue Systems

Automatically and accurately identifying user intents and filling the associated slots from their spoken language are critical to the success of dialogue systems. Traditional methods require manually defining the DOMAIN-INTENT-SLOT schema and asking many domain experts to annotate the corresponding utterances, upon which neural models are trained. This procedure brings the challenges of information sharing hindering, out-of-schema, or data sparsity in open domain dialogue systems. To tackle these challenges, we explore a new task of automatic intent-slot induction and propose a novel domain-independent tool. That is, we design a coarse-to-fine three-step procedure including Role-labeling, Concept-mining, And Pattern-mining (RCAP): (1) role-labeling: extracting key phrases from users’ utterances and classifying them into a quadruple of coarsely-defined intent-roles via sequence labeling; (2) concept-mining: clustering the extracted intent-role mentions and naming them into abstract fine-grained concepts; (3) pattern-mining: applying the Apriori algorithm to mine intent-role patterns and automatically inferring the intent-slot using these coarse-grained intent-role labels and fine-grained concepts. Empirical evaluations on both real-world in-domain and out-of-domain datasets show that: (1) our RCAP can generate satisfactory SLU schema and outperforms the state-of-the-art supervised learning method; (2) our RCAP can be directly applied to out-of-domain datasets and gain at least 76% improvement of F1-score on intent detection and 41% improvement of F1-score on slot filling; (3) our RCAP exhibits its power in generic intent-slot extractions with less manual effort, which opens pathways for schema induction on new domains and unseen intent-slot discovery for generalizable dialogue systems.
OrgBox: Supporting Cognitive and Metacognitive Activities During Exploratory Search

We describe a search-assistance tool called the OrgBox, created to support users' organizational, cognitive, and metacognitive activities while performing exploratory search tasks. The OrgBox tool allows users to create and label "boxes" to organize information found during a search. The OrgBox was integrated with a custom built search system that allowed users to save, organize, and synthesize information using drag-and-drop actions. The OrgBox tool also encourages users to engage in metacognitive activities during their search process (e.g., planning their next steps, monitoring their progress, evaluating the information found so far). In this paper, we describe the features and implementation of the OrgBox tool. We also summarize result of two user studies conducted using the OrgBox that show its cognitive and metacognitive benefits to users.
Keyword-aware Abstractive Summarization by Extracting Set-level Intermediate Summaries

Abstractive summarization is useful in providing a summary or a digest of news or other web texts and enhancing users reading experience, especially when they are reading on small displays such as mobile phones. However, existing encoder-decoder summarization models have difficulty learning the latent alignment between source documents and summaries because of their vast disparity in length. In this paper, we propose a extractor-abstractor framework in which the keyword-based extractor selects a few sets of salient sentences from the input document and then the abstractor paraphrases these sets of sentences in parallel, which are more aligned to the summary, to generate the final summary. The new extractor and abstractor are pretrained from a set of “pseudo summaries” extracted by specially designed heuristics, and then further trained together in a reinforcement learning framework. The results show that the proposed model generates high-quality summaries with faster training speed and less training memory footprint, and outperforms the state-of-the-art models on CNN/Daily Mail, Webis-TLDR-17, Webis-Snippet-20, WikiHow and DUC-2002 datasets.
A survey on Artificial Intelligence based techniques to convert User Interface design mock-ups to code

In the software engineering cycle for User Interface (UI) designing, there is heavy back-and-forth of prototypes between the UI concept creater, UI designer and UI programmer before finalizing a design. This process is very time-consuming and requires extensive human effort. With the recent developments in Artificial Intelligence (AI) and its capability to automate repetitive tasks which require little to no creativity, the UI design cycle is an ideal candidate for its application. Many algorithms are being developed to shorten this cycle, allowing rapid prototyping, and ultimately reducing cost. Various algorithms are developed to work for different levels of fidelity in wireframes. In this paper we discuss the need and compare such intelligent techniques that have been developed fast-track this designing process.
Language in Digital Motion: From ABCs to Intermediality and Why This Matters for Language Learning

Abstract Linguistic communication has moved beyond simple alphabetic encoding to multimedia design, challenging the fit of structural theories of language to digital communication. This transition is barely evident in mobile language learning contexts where top-selling apps present language as a linguistic structure to be drilled, ironically bypassing the complex communicative potential of smart devices. This chapter overviews changing language norms from language as structure to language within multimodality and comparatively discusses multimodality from a social semiotics paradigm nested in linguistic theory and from Elleström’s intermediality paradigm. To illustrate how one could conceptualize multimodality from a perspective decentred from linguistics and leveraged to explain language use in multimedia contexts, the author examines two novel features of digital communication: emoji and conversational digital agents .
Extending Ordinary-Label Learning Losses to Complementary-Label Learning

Deep Neural Networks have provided excellent performance in a variety of applications. However, their superior performance comes with a huge expense of collecting a correctly-annotated large-scale training set, and a little impracticality of preparing such a training set in many applications. In this work, we study another natural type of weak supervision, complementary-label learning, to address this problem. Complementary-label learning refers to train the Deep Neural Networks by the usage of only complementary labels, and a complementary label indicates one of the classes that the sample does not belong to. This paper first presents a general risk formulation for complementary label learning through an adoption of arbitrary losses designed for ordinary-label learning. We then theoretically analyze that our method is applicable for any loss functions to learn deep neural networks with complementary labels in the framework of risk minimization. Experimental results on different benchmark datasets demonstrate that our approach outperforms current state-of-the-art methods.
RETA: A Schema-Aware, End-to-End Solution for Instance Completion in Knowledge Graphs

Knowledge Graph (KG) completion has been widely studied to tackle the incompleteness issue (i.e., missing facts) in modern KGs. A fact in a KG is represented as a triplet (h, r, t) linking two entities h and t via a relation r. Existing work mostly consider link prediction to solve this problem, i.e., given two elements of a triplet predicting the missing one, such as (h, r, ?). This task has, however, a strong assumption on the two given elements in a triplet, which have to be correlated, resulting otherwise in meaningless predictions, such as (Marie Curie, headquarters location, ?). In addition, the KG completion problem has also been formulated as a relation prediction task, i.e., when predicting relations r for a given entity h. Without predicting t, this task is however a step away from the ultimate goal of KG completion. Against this background, this paper studies an instance completion task suggesting r-t pairs for a given h, i.e., (h, ?, ?). We propose an end-to-end solution called RETA (as it suggests the Relation and Tail for a given head entity) consisting of two components: a RETA-Filter and RETA-Grader. More precisely, our RETA-Filter first generates candidate r-t pairs for a given h by extracting and leveraging the schema of a KG; our RETA-Grader then evaluates and ranks the candidate r-t pairs considering the plausibility of both the candidate triplet and its corresponding schema using a newly-designed KG embedding model. We evaluate our methods against a sizable collection of state-of-the-art techniques on three real-world KG datasets. Results show that our RETA-Filter generates of high-quality candidate r-t pairs, outperforming the best baseline techniques while reducing by 10.61%-84.75% the candidate size under the same candidate quality guarantees. Moreover, our RETA-Grader also significantly outperforms state-of-the-art link prediction techniques on the instance completion task by 16.25%-65.92% across different datasets.
Privacy-Preserving Search for a Similar Genomic Makeup in the Cloud

Increasing affordability of genome sequencing and, as a consequence, widespread availability of genomic data opens up new opportunities for the field of medicine, as also evident from the emergence of popular cloud-based offerings in this area, such as Google Genomics [1]. To utilize this data more efficiently, it is crucial that different entities share their data with each other. However, such data sharing is risky mainly due to privacy concerns. In this article, we attempt to provide a privacy-preserving and efficient solution for the “similar patient search” problem among several parties (e.g., hospitals) by addressing the shortcomings of previous attempts. We consider a scenario in which each hospital has its own genomic dataset and the goal of a physician (or researcher) is to search for a patient similar to a given one (based on a genomic makeup) among all the hospitals in the system. To enable this search, we propose a hierarchical index structure to index each hospital’s dataset with low memory requirement. Furthermore, we develop a novel privacy-preserving index merging mechanism that generates a common search index from individual indices of each hospital to significantly improve the search efficiency. We also consider the storage of medical information associated with genomic data of a patient (e.g., diagnosis and treatment). We allow access to this information via a fine-grained access control policy that we develop through the combination of standard symmetric encryption and ciphertext policy attribute-based encryption. Using this mechanism, a physician can search for similar patients and obtain medical information about the matching records if the access policy holds. We conduct experiments on large-scale genomic data and show the high efficiency of the proposed scheme.
Partially observable environment estimation with uplift inference for reinforcement learning based recommendation

Reinforcement learning (RL) aims at searching the best policy model for decision making, and has been shown powerful for sequential recommendations. The training of the policy by RL, however, is placed in an environment. In many real-world applications, the policy training in the real environment can cause an unbearable cost due to the exploration. Environment estimation from the past data is thus an appealing way to release the power of RL in these applications. The estimation of the environment is, basically, to extract the causal effect model from the data. However, real-world applications are often too complex to offer fully observable environment information. Therefore, quite possibly there are unobserved variables lying behind the data, which can obstruct an effective estimation of the environment. In this paper, by treating the hidden variables as a hidden policy, we propose a partially-observed multi-agent environment estimation (POMEE) approach to learn the partially-observed environment. To make a better extraction of the causal relationship between actions and rewards, we design a deep uplift inference network (DUIN) model to learn the causal effects of different actions. By implementing the environment model in the DUIN structure, we propose a POMEE with uplift inference (POMEE-UI) approach to generate a partially-observed environment with a causal reward mechanism. We analyze the effect of our method in both artificial and real-world environments. We first use an artificial recommender environment, abstracted from a real-world application, to verify the effectiveness of POMEE-UI. We then test POMEE-UI in the real application of Didi Chuxing. Experiment results show that POMEE-UI can effectively estimate the hidden variables, leading to a more reliable virtual environment. The online A/B testing results show that POMEE can derive a well-performing recommender policy in the real-world application.
Clustering activity at Mt Etna based on volcanic tremor: A case study

Abstract This paper deals with the classification of volcanic activity into three classes, referred to as Quite, Strombolian and Paroxysm. The main purpose is to give a measure of the reliability with which such a classification, typically carried out by experts, can be performed by Machine Learning algorithms, by using the volcanic tremor as a feature. Both supervised and unsupervised methods are considered. It is experimentally shown that at least the Paroxysm activity can be reliably classified. Performances are rigorously assessed, in comparison with the classification made by expert volcanologists, in terms of popular indices such as the f 1 -score and the Area under the ROC curve ( AuC ). The work is basically a case study carried out on a dataset recorded in the area of the Mt Etna volcano. However, as volcanic tremor is a geophysical signal widely available, considered methods and strategies can be easily applied to similar volcanic areas.