Reviewerly/exHarmony huggingface dataset:
Time Present and Time Past: Analyzing the Evolution of JavaScript Code in the Wild

JavaScript is one of the web's key building blocks. It is used by the majority of web sites and it is supported by all modern browsers. We present the first large-scale study of client-side JavaScript code over time. Specifically, we have collected and analyzed a dataset containing daily snapshots of JavaScript code coming from Alexa's Top 10000 web sites (~7.5 GB per day) for nine consecutive months, to study different temporal aspects of web client code. We found that scripts change often; typically every few days, indicating a rapid pace in web applications development. We also found that the lifetime of web sites themselves, measured as the time between JavaScript changes, is also short, in the same time scale. We then performed a qualitative analysis to investigate the nature of the changes that take place. We found that apart from standard changes such as the introduction of new functions, many changes are related to online configuration management. In addition, we examined JavaScript code reuse over time and especially the widespread reliance on third-party libraries. Furthermore, we observed how quality issues evolve by employing established static analysis tools to identify potential software bugs, whose evolution we tracked over time. Our results show that quality issues seem to persist over time, while vulnerable libraries tend to decrease.
Time to Go ONLINE! A Modular Framework for Building Internet-based Socially Interactive Agents

Although socially interactive agents have emerged as a new metaphor for human-computer interaction, they are, to date, absent from the Internet. We describe the design choices, implementation, and challenges in building EEVA, the first fully integrated platform-independent framework for deploying realistic 3D web-based social agents: with real-time multimodal perception of, and response to, the user's verbal and non-verbal social cues, EEVA agents are capable of communicating rich customizable content to users in real time, while building and maintaining users' profiles for long-term interactions. The modularity of the EEVA framework enables it to be used as a testbed for agents' social communication model development of increasing performance and sophistication (e.g. building rapport, expressing empathy). We discuss the framework's feasibility by analyzing the response time of the system over the Internet, in the context of a health intervention built using EEVA authoring functionalities.
A Rule-Based Model for Compliance of Medical Devices Applied to the European Market

There is a growing producer and consumer interest in medical devices and the commensurate need for regulatory frameworks to ensure the quality of medical devices marketed locally and globally. This work focuses on formalizing the clauses enacted by Regulation (EU) 2017/745 for risk-based classification and class-based conformity assessment regarding marketability of medical devices. The resulting knowledge base (KB) represents clauses in Positional-Slotted Object-Applicative (PSOA) RuleML by integrating F-logic-like frames with Prolog-like relationships for atoms used as facts and in the conclusions and conditions of rules. Rules can apply polyadic functions, define polyadic relations, and augment conclusions with actions and conditions with events. The PSOA RuleML-implemented Medical Devices Rules KB was tested by querying in the open-source Java-implemented PSOATransRun system, which has provided a feedback loop for refinement and extension. This prototype can contribute to the licensing process of stakeholders and the registration of medical devices with a CE conformity mark.
Micro Aerial Vehicle Navigation with Visual-Inertial Integration Aided by Structured Light

Considering that traditional visual navigation cannot be utilised in low illumination and sparse feature environments, a novel visual-inertial integrated navigation method using a Structured Light Visual (SLV) sensor for Micro Aerial Vehicles (MAVs) is proposed in this paper. First, the measurement model based on an SLV sensor is studied and built. Then, using the state model based on error equations of an Inertial Navigation System (INS), the measurement model based on the error of the relative motion measured by INS and SLV is built. Considering that the measurements in this paper are mainly related to the position and attitude information of the present moment, the state error accumulation in traditional visual-inertial navigation can be avoided. An Adaptive Sage-Husa Kalman Filter (ASHKF) based on multiple weighting factors is proposed and designed to make full use of the SLV measurements. The results of the simulation and the experiment based on real flight data indicate that high accuracy position and attitude estimations can be obtained with the help of the algorithm proposed in this paper.
Using Frugal User Feedback with Closeness Analysis on Code to Improve IR-Based Traceability Recovery

Traceability recovery allows developers to extract and comprehend the trace links among software artifacts (e.g., requirements and code). These trace links can provide important support to software maintenance and evolution tasks. Information Retrieval (IR) is now widely accepted as the key technique of semi-automatic tools to recover candidate trace links based on textual similarities among artifacts. However, the vocabulary mismatch problem between different artifacts hinders the performance of these IR-based approaches. Thus, a growing body of enhancing strategies were proposed based on user feedback. They allow to adjust the textual similarities of candidate links after users accept or reject part of these links. Recently, several approaches successfully used this strategy to improve the performance of IR-based traceability recovery. However, these approaches require a large amount of user feedback, which is infeasible in practice. In this paper, we propose to improve IR-based traceability recovery by introducing only a small amount of user feedback into the closeness analysis on call and data dependencies in code. Specifically, our approach iteratively asks users to verify a chosen candidate link based on the quantified functional similarity for each code dependency (called closeness) and the generated IR values. The verified link is then used as the input to re-rank the unverified candidate links. An empirical evaluation based on five real-world systems shows that our approach can outperform four baseline approaches by using only a small amount of user feedback.
Collaborative filtering recommendation system based on trust-aware and domain experts

Collaborative filtering is a popular tool for recommendation systems. However, collaborative filtering technologies often suffer from high time complexity, the cold-start problem, and low coverage. Recent research shows that social networks and trust-aware methods can effectively solve these proble ms. Therefore, we propose a Trust Domain Expert Collaborative Filtering recommendation system. First, we divide the user item rating matrix into multiple sub-matrices based on the domain attributes of each item. For each sub-matrix, we then use domain experts to construct a user–expert trust matrix. Finally, combined with the target user’s domain of interest, we predict their missing ratings. Experimental results show that this method not only improves the accuracy and recommended coverage of collaborative filtering-based methods, but also reduces the computation time.
Evolution of recommender paradigm optimization over time

In the past few decades recommender system has reshaped the way of information filtering between websites and the users. It helps in identifying user interest and generates product suggestions for the active users. This paper presents an enlightening analysis of various recommender system such as content-based, collaborative-based and hybrid recommendation techniques along with few optimization models that has been applied to improvise the parameters being considered by the aforementioned techniques. We explored 125 articles published from 1992 to 2019 in order to discuss the problems associated with the existing models. Various advantages and disadvantages of each recommendation model including the input methods has been elaborated. Critical review on research problems based on the explored techniques and future directions has also been covered.
UHH-LT at SemEval-2019 Task 6: Supervised vs. Unsupervised Transfer Learning for Offensive Language Detection

We present a neural network based approach of transfer learning for offensive language detection.For our system, we compare two types of knowledge transfer: supervised and unsupervised pre-training.Supervised pre-training of our bidirectional GRU-3-CNN architecture is performed as multi-task learning of parallel training of five different tasks.The selected tasks are supervised classification problems from public NLP resources with some overlap to offensive language such as sentiment detection, emoji classification, and aggressive language classification.Unsupervised transfer learning is performed with a thematic clustering of 40M unlabeled tweets via LDA.Based on this dataset, pre-training is performed by predicting the main topic of a tweet.Results indicate that unsupervised transfer from large datasets performs slightly better than supervised training on small 'near target category' datasets.In the SemEval Task, our system ranks 14 out of 103 participants.
Performance enhanced Boosted SVM for Imbalanced datasets

In recent decades, researchers have proposed different boosting algorithms to improve the learning abilities of classification techniques for imbalanced data. Especially for the SVM, Wang et al. (2010) proposed a cost-sensitive boosting algorithm, named Wang’s Boosted SVM (WBSVM). In this study we made two major modifications to WBSVM algorithm to improve the classification performance without increasing its existing time complexity (O(n3)): (i) a distance-based weight-update rule (ii) a sign based classifier removal technique to obtain a performance enhanced classifier ensemble. We used 43 imbalanced benchmark datasets to assess the performance. The results revealed that both the modifications made a significant improvement in the boosting performance. Also, we compared our modified Boosted SVM (MBSVM) performance with K-Nearest Neighbour, Decision Tree, Artificial Neural Network and one of the deep learning techniques – Deep Neural Network performances. We also developed a framework to construct Receiver operating characteristic (ROC) curve for Boosted SVM. We compared both the boosted SVMs with Area under the ROC curve (AUC) values and also in terms of running time of algorithms. Furthermore, MBSVM was applied to multi-criteria inventory classification data since it is imbalanced in nature. The inventory classification outcomes of four benchmark techniques were used as class labels. The performance of MBSVM was compared with those of WBSVM algorithm as well as the traditional SVM technique.
Transition to Electrical Vehicles Based on Multi-Attribute Decision Making

This paper presents application of multi-attribute decision making applied in a large public institution, to make strategic choice about new mobility technology. Electrical vehicles are becoming important parts of all vehicles product portfolios. Based on their advantages, many users are making transitions and replacing internal combustion engine vehicles with electrical vehicles. Considering a large number of vehicles, with various features, which are currently available on the market, and specific users' requirements, it is often required to apply a comprehensive decision making model in order to select the best solution. In the project presented here, decision making process and methodology used by the Institution, is highlighted. Final decision is based on the knowledge of the previous solution, already executed by one organization with similar operational requirements, and on inputs from internal departments which placed their preferences on various features of several models of electrical vehicles.
Activity Recognition Using Smartphone Sensors, Robust Features, and Recurrent Neural Network

Human activity recognition is one of the most challenging research areas in human-machine interaction. Activity recognition by wearable sensors produces significant information about a person's functional ability and lifestyle. In this paper, we present an inertial sensors-based approach for activity and postural transition recognition. Efficient features are extracted from the sensor data. Then, the features are normalized using Z-score followed by Generalized Discriminant Analysis (GDA) to make them more robust. Finally, the features are trained with a Recurrent Neural Network (RNN) for activity and postural transition recognition. The proposed method recognizes 12 activities with an overall accuracy of 97%. In the activity recognition system, the multimodal input data is collected from only one accelerometer and one gyroscope sensor embedded in the user's smartphone. The proposed system can contribute in research fields such as human-robot interaction for practical applications such as eldercare in smart homes.
Information Retrieval Optimization Based on Tree of Social Network

Abstract Information source such as the Web is a representation of social activities. Social activities create social structures that can be explored through social network concepts. Social networks not only show the structure of a social community, but prove the existence of its members and community. In that context, within the source of information, evidence of a community can be accessed through a collection of documents whose existence continues to increase, whereby the measurements can be performed using recall and precision. However, the recall and the precision as part of the measurement of information retrieval also requires technology to retrieve the related documents, an information retrieval based on network concepts. To solve it is proposed the concept of a collection of stars from trees and the degree of social actors in social networks, and produced a formula about the measurement of recall and precision with better results.
Prediction of pressure drop and minimum spouting velocity in draft tube conical spouted beds using genetic programming approach

The smart method of genetic programming (GP) is used to predict the operating pressure drop (Δ P s ) and the minimum spouting velocity u ms for conical spouted beds (CSBs) equipped with nonporous draft tubes. Accordingly, six dimensionless variables have been taken as model inputs, including crucial parameters associated with the bed and tube geometric and operating conditions. Two general correlations comprising almost all constitutive and operating variables have been derived for the first time by the GP approach. Both Δ P s and u ms values predicted by the GP technique are in a fair agreement with the values corresponding to the experiments, with average absolute relative errors (AARE) of 18.9 and 19.9 %, respectively. The results of the proposed correlations show that the GP method is a powerful tool to make reasonable estimates.
Virtual prototyping in the design of see-through features in mobile machinery

Limited visibility from a mobile machine cab can decrease task performance and lead to accidents. Therefore, it is important to consider visibility issues already in the design phase. This paper describes the use of virtual prototyping in the evaluation of see-through features of mobile work machines. The goal is to evaluate whether two different machine boom transparency levels have an effect on task performance. In addition, two alternative placements of overlaid information in the operators’ field of view are assessed. A within-subject design was used in this study. Based on the results, there was no significant difference in performance between the transparency levels. However, the test participants preferred a transparency level of 70–80% (where 0% is completely opaque). Similar results were found with the placement of the overlaid information, which had no significant effect on task performance. Both placements, on the windscreen and on the tunnel wall, were equally favoured by the participants. The findings of this study contribute to the design of see-through features for mobile work machines. In addition, the study demonstrates the use of virtual prototyping in the design of novel features in human–machine systems.
Training Supervised Deep Learning Networks

Training supervised deep learning networks involves obtaining model parameters using labeled dataset to allow the network to map an input data to a class label. The labeled dataset consists of training examples, where each example is a pair of an input data and a desired class label. The deep model parameters allow the network to correctly determine the class labels for unseen instances. This requires the model to generalize from the training dataset to unseen instances.
Reconfigurable RISC-V Secure Processor And SoC Integration

In IoT (Internet of Things) applications, security issues are increasingly attracting attention. However, current embedded processors lack cryptographic protection mechanism. In this paper, an austere RISC-V core processor with RV32I subset instruction is deemed as a master device to cooperate with an AES cryptographic engine in an SoC, due to its openness and flexibility. This core contains separate instructions and a data bus connected to a Wishbone crossbar. A Spartan-6 XC6SLX9 board is taken as an architecture protocol verification platform, where the peak operating frequency of the RISC-V core and the encryption SoC is 105MHz and 111.5MHz, respectively. The hardware resource utilization is reduced compared with the MIPS core with identical efforts
S2Net: Preserving Privacy in Smart Home Routers

At present, wireless home routers are becoming increasingly smart. While these smart routers provide rich functionalities to users, they also raise security concerns. Although the existing end-to-end encryption techniques can be applied to protect personal data, such rich functionalities become unavailable due to the encrypted payloads. On the other hand, if the smart home routers are allowed to process and store the personal data of users, once compromised, the users' sensitive data will be exposed. As a consequence, users face a difficult trade-off between the benefits of the rich functionalities and potential privacy risks. To deal with this dilemma, we propose a novel system named Secure and Smart Network (S2Net) for home routers. For S2Net, we propose a secure OS that can distinguish and manage multiple sessions belonging to different users. The secure OS and all the router applications are placed in the secure world using the ARM TrustZone technology. In S2Net, we also confine the router applications in sandboxes provided by the proposed secure OS to prevent data leakage. As a result, S2Net can provide rich functionalities for users while preserving strong privacy for home routers. In addition, we develop a crypto-worker model that provides an abstraction layer of cryptographic tasks performed by a heterogeneous multi-core system. The other important role of crypto-worker is to parallelize the computations in order to resolve the high computation cost of cryptographic functions. We report the system design of S2Net and the details of our implementation. Experimental results with benchmarks and real applications demonstrate that our implementation is capable of achieving high performance in terms of throughput while mitigating the overhead of S2Net design.
On the efficiency of data collection for multiple Naïve Bayes classifiers

Many classification problems are solved by aggregating the output of a group of distinct predictors. In this respect, a popular choice is to assume independence and employ a Naïve Bayes classifier. When we have not just one but multiple classification problems at the same time, the question of how to assign the limited pool of available predictors to the individual classification problems arises. Empirical studies show that the policies we use to perform such assignments have a strong impact on the accuracy of the system. However, to date there is little theoretical understanding of this phenomenon. To help rectify this, in this paper we provide the first theoretical explanation of the accuracy gap between the most popular policies: the non-adaptive uniform allocation, and the adaptive allocation schemes based on uncertainty sampling and information gain maximisation. To do so, we propose a novel representation of the data collection process in terms of random walks. Then, we use this tool to derive new lower and upper bounds on the accuracy of the policies. These bounds reveal that the tradeoff between the number of available predictors and the accuracy has a different exponential rate depending on the policy used. By comparing them, we are able to quantify the advantage that the two adaptive policies have over the non-adaptive one for the first time, and prove that the probability of error of the former decays at more than double the exponential rate of the latter. Furthermore, we show in our analysis that this result holds both in the case where we know the accuracy of each individual predictor, and in the case where we only have access to a noisy estimate of it.
The Design of a Mobile Number Portability System on a Permissioned Private Blockchain Platform

This paper discusses the design of a mobile number portability processing system in the telecom domain with a solution proposed using permissioned private blockchain technology. As a first step, the system is designed to be managed by a neutral entity. Subsequently, the design is extended to be supported by a neutral blockchain-based platform. The Hyperledger Fabric blockchain platform is used as the framework to develop the solution. Channels are utilized for different subsets of nodes that interact with each other to enable transactions in the system. Concurrent state transition graph paths are utilized to enable faster processing in the blockchain system. The paper concludes with experimental results and possibilities for future extensions.
Moving Object Detection Using Adaptive Blind Update and RGB-D Camera

A novel background subtraction approach using an RGB-D camera and an adaptive blind updating policy is introduced. This method in the initialization creates a model to store background pixels to compare each pixel of the new frame with the model in the same location to identify background pixels. The background-model update presented in this paper uses regular and blind updates which also has different criteria from existing methods. In particular, blind update frequently changes based on the background changes and the speed of moving object. This will allow the scene model to adapt to the changes in the background, detecting the stationary moving object and reducing the ghost phenomenon. In addition, the proposed bootstrapping segmentation and shadow detection are added to the system to improve the accuracy of the algorithm in shadow and depth camouflage scenarios. The proposed method is compared with the original method and the other state of the art algorithms. The experimental results show significant improvement in those videos that stationary object appears. In addition, the benchmark results also indicate strong and stable results compared to the other state of the art algorithms.