Reviewerly/exHarmony huggingface dataset:
Deep Reinforcement Learning for Syntactic Error Repair in Student Programs

Novice programmers often struggle with the formal syntax of programming languages. In the traditional classroom setting, they can make progress with the help of real time feedback from their instructors which is often impossible to get in the massive open online course (MOOC) setting. Syntactic error repair techniques have huge potential to assist them at scale. Towards this, we design a novel programming language correction framework amenable to reinforcement learning. The framework allows an agent to mimic human actions for text navigation and editing. We demonstrate that the agent can be trained through self-exploration directly from the raw input, that is, program text itself, without either supervision or any prior knowledge of the formal syntax of the programming language. We evaluate our technique on a publicly available dataset containing 6975 erroneous C programs with typographic errors, written by students during an introductory programming course. Our technique fixes 1699 (24.4%) programs completely and 1310 (18.8%) program partially, outperforming DeepFix, a state-of-the-art syntactic error repair technique, which uses a fully supervised neural machine translation approach.
Multitask Learning for Object Localization With Deep Reinforcement Learning

In object localization, methods based on a top-down search strategy that focus on learning a policy have been widely researched. The performance of these methods relies heavily on the policy in question. This paper proposes a deep Q-network (DQN) that employs a multitask learning method to localize class-specific objects. This DQN agent consists of two parts, an action executor part and a terminal part. The action executor determines the action that the agent should perform and the terminal decides whether the agent has detected the target object. By taking advantage of the capability of feature learning in a multitask method, our method combines these two parts by sharing hidden layers and trains the agent using multitask learning. A detection dataset from the PASCAL visual object classes challenge 2007 was used to evaluate the proposed method, and the results show that it can achieve higher average precision with fewer search steps than similar methods.
A propagation-DNN: Deep combination learning of multi-level features for MR prostate segmentation

Prostate segmentation on Magnetic Resonance (MR) imaging is problematic because disease changes the shape and boundaries of the gland and it can be difficult to separate the prostate from surrounding tissues. We propose an automated model that extracts and combines multi-level features in a deep neural network to segment prostate on MR images.Our proposed model, the Propagation Deep Neural Network (P-DNN), incorporates the optimal combination of multi-level feature extraction as a single model. High level features from the convolved data using DNN are extracted for prostate localization and shape recognition, while labeling propagation, by low level cues, is embedded into a deep layer to delineate the prostate boundary.A well-recognized benchmarking dataset (50 training data and 30 testing data from patients) was used to evaluate the P-DNN. When compared it to existing DNN methods, the P-DNN statistically outperformed the baseline DNN models with an average improvement in the DSC of 3.19%. When compared to the state-of-the-art non-DNN prostate segmentation methods, P-DNN was competitive by achieving 89.9 ± 2.8% DSC and 6.84 ± 2.5 mm HD on training sets and 84.13 ± 5.18% DSC and 9.74 ± 4.21 mm HD on testing sets.Our results show that P-DNN maximizes multi-level feature extraction for prostate segmentation of MR images.
Human-Machine Interface for a Smart Wheelchair

The paper describes the integration of hardware and software with sensor technology and computer processing to develop the next generation intelligent wheelchair. The focus is a computer cluster design to test high performance computing for smart wheelchair operation and human interaction. The LabVIEW cluster is developed for real-time autonomous path planning and sensor data processing. Four small form factor computers are connected over a Gigabit Ethernet local area network to form the computer cluster. Autonomous programs are distributed across the cluster for increased task parallelism to improve processing time performance . The distributed programs operating frequency for path planning and motion control is 50Hz and 12.3Hz for 0.3 megapixel robot vision system. To monitor the operation and control of the distributed LabVIEW code, network automation is integrated into the cluster software along with a performance monitor. A link between the computer motion control program and the wheelchair joystick control of the drive train is developed for the computer control interface. A perception sensor array and control circuitry is integrated with the computer system to detect and respond to the wheelchair environment. Multiple cameras are used for image processing and scanning laser rangefinder sensors for obstacle avoidance in the cluster program. A centralized power system is integrated to power the smart wheelchair along with the cluster and sensor feedback system. The on board computer system is evaluated for cluster processing performance for the smart wheelchair, incorporating camera machine vision and LiDAR perception for terrain obstacle detection, operating in urban scenarios.
A Spatially Constrained Multi-Autoencoder approach for multivariate geochemical anomaly recognition

The spatial heterogeneity of geochemical background is often ignored in geochemical anomaly recognition, leading to ineffective recognition of valuable anomalies for geochemical prospecting. In this paper, a Spatially Constrained Multi-Autoencoder (SCMA) approach is proposed to deal with such an issue in multivariate geochemical anomaly recognition, which includes two unique steps: (1) with the consideration of both chemical similarity and spatial continuity of geochemical samples, a region is divided into multiple sub-domains to discriminate the various backgrounds over space, through multivariate clustering, spatial filtering, and spatial fusion; and (2) the geochemical background of each sub-domain is learned and reconstructed by a multi-autoencoder structure, which is designed to reduce the effects of random initialization of weights in an autoencoder neural network. Finally, the anomaly score is calculated as the difference between the observed geochemical features and the reconstructed features. The performance of SCMA was demonstrated by a case study involving Cu, Mn, Pb, Zn and Fe2O3 in stream sediment samples from the Chinese National Geochemical Mapping Project, in the southwestern Fujian province of China. The results showed that the spatial domain constraining greatly improved the quality of anomaly recognition, and SCMA outperformed several existing methods in all aspects. In particular, the anomalies from SCMA were the most consistent with the known Fe deposits in the area, achieving an AUC of 0.89.
Hardening Database Padding for Searchable Encryption

Searchable encryption (SE) is a practical crypto-graphic primitive to build encrypted databases. Recently there has been much attention in leakage-abuse attacks against SE. Among others, attacks based on inference of keyword frequency can easily identify query keywords from the access pattern, i.e., query results. To mitigate these attacks, database padding is considered as a conceptually simple yet effective counter-measure. Unfortunately, none of the existing studies formally understand the relationship between padding security strength and its overhead. Also, how to craft padding is not restricted in current countermeasures, where bogus files are likely to be distinguishable from real ones. In this paper, we propose an information theory based framework to analyse the security strength under certain padding overhead. First, we leverage relative entropy to measure the “closeness” between the distributions of the original dataset and padded dataset. Second, we quantity the attack efforts against padding countermeasures by entropy analysis. Apart from theoretical findings, we further devise an algorithm via outlier detection for padding generation, which considers both the padded dataset distribution and the similarity between real and bogus files. Evaluations on a real-world dataset confirm our theoretical results and demonstrate the efficiency and effectiveness of our proposed padding generation algorithm.
Can Hyperparameter Tuning Improve the Performance of a Super Learner?

Background: Super learning is an ensemble machine learning approach used increasingly as an alternative to classical prediction techniques. When implementing super learning, however, not tuning the hyperparameters of the algorithms in it may adversely affect the performance of the super learner. Methods: In this case study, we used data from a Canadian electronic prescribing system to predict when primary care physicians prescribed antidepressants for indications other than depression. The analysis included 73,576 antidepressant prescriptions and 373 candidate predictors. We derived two super learners: one using tuned hyperparameter values for each machine learning algorithm identified through an iterative grid search procedure and the other using the default values. We compared the performance of the tuned super learner to that of the super learner using default values (“untuned”) and a carefully constructed logistic regression model from a previous analysis. Results: The tuned super learner had a scaled Brier score ( R 2 ) of 0.322 (95% [confidence interval] CI = 0.267, 0.362). In comparison, the untuned super learner had a scaled Brier score of 0.309 (95% CI = 0.256, 0.353), corresponding to an efficiency loss of 4% (relative efficiency 0.96; 95% CI = 0.93, 0.99). The previously-derived logistic regression model had a scaled Brier score of 0.307 (95% CI = 0.245, 0.360), corresponding to an efficiency loss of 5% relative to the tuned super learner (relative efficiency 0.95; 95% CI = 0.88, 1.01). Conclusions: In this case study, hyperparameter tuning produced a super learner that performed slightly better than an untuned super learner. Tuning the hyperparameters of individual algorithms in a super learner may help optimize performance.
Attention-based deep residual learning network for entity relation extraction in Chinese EMRs

Electronic medical records (EMRs) contain a variety of valuable medical concepts and relations. The ability to recognize relations between medical concepts described in EMRs enables the automatic processing of clinical texts, resulting in an improved quality of health-related data analysis. Driven by the 2010 i2b2/VA Challenge Evaluation, the relation recognition problem in EMRs has been studied by many researchers to address this important aspect of EMR information extraction. This paper proposes an Attention-Based Deep Residual Network (ResNet) model to recognize medical concept relations in Chinese EMRs. Our model achieves F1-score of 77.80% on the manually annotated Chinese EMRs corpus and outperforms the state-of-the-art approaches. The residual network-based model can reduce the negative impact of corpus noise to parameter learning, and the combination of character position attention mechanism will enhance the identification features of different type of entities.
How Do I Share My IoT Forensic Experience With the Broader Community? An Automated Knowledge Sharing IoT Forensic Platform

It is challenging for digital forensic practitioners to maintain skillset currency, for example knowing where and how to extract digital artifacts relevant to investigations from newer, emerging devices (e.g., due to the increased variety of data storage schemas across manufacturers and constantly changing models). This paper presents a knowledge sharing platform, developed and validated using an Internet of Things dataset released in the DFRWS 2017-2018 forensic challenge. Specifically, we present an automated knowledge-sharing forensic platform that automatically suggests forensic artifact schemas, derived from case data, but does not include any sensitive data in the final (shared) schema. Such artifact schemas are then stored in a schema pool and the platform presents candidate schemas for use in new cases based on the data presented. In this way, investigators need not learn the forensic profile of a new device from scratch, nor do they have to manually anonymize and share forensic knowledge obtained during the course of an investigation.
An Innovative Study of E-Payment Systems Adoption in Higher Education: Theoretical Constructs and Empirical Analysis

Examining the adoption of e-payment systems is not a new research topic. Nevertheless, studying the factors affecting the adoption of e-payment systems in higher educational institutions is a new research trend. Thus, this study is considered one of the few that attempts to investigate the factors affecting the e-payment systems adoption in six different universities in the United Arab of Emirates (UAE). A total number of 289 students took part in the study. This study proposed a new research model in which the students’ intention to use the e-payment systems are affected by five different factors including perceived benefit, performance expectancy, perceived risk, perceived security/privacy, and trust. The partial least squares-structural equation modeling (PLS-SEM) approach was used to validate the research model. The empirical results suggested that perceived benefit and performance expectancy have a significant positive relationship with the students’ intention to use e-payment systems, whereas perceived security/privacy and perceived risk exhibited a significant negative relationship. However, the results triggered out that trust has an insignificant relationship with the students’ intention to use e-payment systems. The results acquired from this research provide a fresh and an up-to-date information on the e-payment systems adoption in the higher educational institutions.
Location Embeddings for Next Trip Recommendation

The amount of information available in social media and specialized blogs has become useful for a user to plan a trip. However, the user is quickly overwhelmed by the list of possibilities offered to him, making his search complex and time-consuming. Recommender systems aim to provide personalized suggestions to users by leveraging different type of information, thus assisting them in their decision-making process. Recently, the use of neural networks and knowledge graphs have proven to be efficient for items recommendation. In our work, we propose an approach that leverages contextual, collaborative and content information in order to recommend personalized destinations to travelers. We compare our approach with a set of state of the art collaborative filtering methods and deep learning based recommender systems.
Key word extraction for short text via word2vec, doc2vec, and textrank

Day by day huge amounts data are produced, and evaluation of these data becomes more difficult. The data obtained should provide meaningful, correct, and accurate information. Therefore, all data must be separated into clusters correctly, and the right information from these clusters must be obtained. Having the correct clusters depends on the clustering algorithm that is used. There are many clustering algorithms. The density-based methods are very important among the groups of clustering methods, as they can find arbitrary shapes. An advanced model of the density-based spatial clustering of applications with noise (DBSCAN) algorithm, called fuzzy neighborhood DBSCAN Gaussian means (FN-DBSCAN-GM), is offered in this study. The main contribution of FN-DBSCAN-GM is to find the parameters automatically and to divide the data into clusters robustly. The effectiveness of FN-DBSCAN-GM has been demonstrated on overlapping datasets (six artificial and two real-life datasets). The performances of these datasets are compared with the percentage of correct classification and validity index. Our experiments showed that this new algorithm was a preferable and robust algorithm.
Life after Speech Recognition: Fuzzing Semantic Misinterpretation for Voice Assistant Applications

Popular Voice Assistant (VA) services such as Amazon Alexa and Google Assistant are now rapidly appifying their platforms to allow more flexible and diverse voice-controlled service experience.However, the ubiquitous deployment of VA devices and the increasing number of third-party applications have raised security and privacy concerns.While previous works such as hidden voice attacks mostly examine the problems of VA services' default Automatic Speech Recognition (ASR) component, our work analyzes and evaluates the security of the succeeding component after ASR, i.e., Natural Language Understanding (NLU), which performs semantic interpretation (i.e., text-to-intent) after ASR's acousticto-text processing.In particular, we focus on NLU's Intent Classifier which is used in customizing machine understanding for third-party VA Applications (or vApps).We find that the semantic inconsistency caused by the improper semantic interpretation of an Intent Classifier can create the opportunity of breaching the integrity of vApp processing when attackers delicately leverage some common spoken errors.In this paper, we design the first linguistic-modelguided fuzzing tool, named LipFuzzer, to assess the security of Intent Classifier and systematically discover potential misinterpretation-prone spoken errors based on vApps' voice command templates.To guide the fuzzing, we construct adversarial linguistic models with the help of Statistical Relational Learning (SRL) and emerging Natural Language Processing (NLP) techniques.In evaluation, we have successfully verified the effectiveness and accuracy of LipFuzzer.We also use LipFuzzer to evaluate both Amazon Alexa and Google Assistant vApp platforms.We have identified that a large portion of real-world vApps are vulnerable based on our fuzzing result.
HealthyBroker: A Trustworthy Blockchain-Based Multi-Cloud Broker for Patient-Centered eHealth Services

Delivering electronic health care (eHealth) services across multi-cloud providers to implement patient-centric care demands a trustworthy brokering architecture. Specifically, such an architecture should aggregate relevant medical information to allow informed decision-making. It should also ensure that this information is complete and authentic and that no one has tampered with it. Brokers deployed in eHealth services may fall short of meeting such criteria due to two key behaviors. The first involves violating international health-data protection laws by allowing user anonymity and limiting user access rights. Second, brokers claiming to provide trustworthy transactions between interested parties usually rely on user feedback, an approach vulnerable to manipulation by malicious users. This paper addresses these data security and trust challenges by proposing HealthyBroker, a novel, trust-building brokering architecture for multiple cloud environments. This architecture is designed specifically for patient-centric cloud eHealth services. It enables care-team members to complete eHealth transactions securely and access relevant patient data on a “need-to-know” basis in compliance with data-protection laws. HealthyBroker also protects against potential malicious behavior by assessing the trust relationship and tracking it using a neutral, tamper-proof, distributed blockchain ledger. Trust is assessed based on two strategies. First, all transactions and user feedback are tracked and audited in a distributed ledger for transparency. Second, only feedback coming from trustworthy parties is taken into consideration. HealthyBroker was tested in a simulated eHealth multi-cloud environment. The test produced better results than a benchmark algorithm in terms of data accuracy, service time, and the reliability of feedback received as measured by three malicious behavior models (naïve, feedback isolated, and feedback collective). These results demonstrate that HealthyBroker can provide care teams with a trustworthy, transparent ecosystem that can facilitate information sharing and well-informed decisions for patient-centric care.
Meta-SSD: Towards Fast Adaptation for Few-Shot Object Detection With Meta-Learning

The state-of-the-art object detection frameworks require the training on large-scale datasets, which is the crux of the present dilemma: overfitting or degrading performance with insufficient samples and time-consuming training process. On the basis of meta-learning, this paper proposes a generalized Few-Shot Detection (FSD) framework to overcome the above drawbacks of the current advances in object detection. The proposed framework consists of a meta-learner and an object detector. It can learn the general knowledge and proper fast adaptation strategies across many tasks. The meta-learner can teach the detector how to learn from few examples in just one updating step. Here, the object detector can be any supervised learning detection models in theory. Specifically, the proposed FSD framework employs Single-Shot MultiBox Detector (SSD) as the object detector in this paper, thus called Meta-SSD. Besides, a novel benchmark is constructed from Pascal VOC dataset for training and evaluation of meta-learning FSD. Experiments show that the Meta-SSD yields a promising result for FSD. Furthermore, the properties of Meta-SSD is analyzed. This paper can serve as a strong baseline and provide some inspiration for meta-learning FSD.
A Compact and Language-Sensitive Multilingual Translation Method

Multilingual neural machine translation (Multi-NMT) with one encoder-decoder model has made remarkable progress due to its simple deployment.However, this multilingual translation paradigm does not make full use of language commonality and parameter sharing between encoder and decoder.Furthermore, this kind of paradigm cannot outperform the individual models trained on bilingual corpus in most cases.In this paper, we propose a compact and language-sensitive method for multilingual translation.To maximize parameter sharing, we first present a universal representor to replace both encoder and decoder models.To make the representor sensitive for specific languages, we further introduce language-sensitive embedding, attention, and discriminator with the ability to enhance model performance.We verify our methods on various translation scenarios, including one-to-many, many-to-many and zero-shot.Extensive experiments demonstrate that our proposed methods remarkably outperform strong standard multilingual translation systems on WMT and IWSLT datasets.Moreover, we find that our model is especially helpful in low-resource and zero-shot translation scenarios.
Automated Writing Evaluation Tools in the Improvement of the Writing Skill

The improvement of the writing skill with the help of modern computer programmes has gained considerable attention in the English as a Foreign Language writing learning process and the better understanding about the contributions of these technological programmes are needed in the field of education.This paper sought to shed light on the effects of implementing free Automated Writing Evaluation tools on the writing performance of the students of an English Teacher Training Programme.Ecuadorian undergraduate learners were encouraged to use Grammark and Grammarly