Reviewerly/exHarmony huggingface dataset:
Automatic recommendation to appropriate log levels

Summary A log statement is one of the key tactics for a developer to record and monitor important run‐time behaviors of our system in a development phase and a maintenance phase. It composes of a message for stating log contents, and a log level (eg, debug or warn ) to denote the severity of a message and controlling its visibility at run time. In spite of its usefulness, a developer does not tend to deeply consider which log level is appropriate in writing source code, which causes the system to be unmaintainable. To address this issue, this paper proposes an automatic approach to validating the appropriateness of the log level in consideration of the semantic and syntactic features and recommending a proper alternative log level. We first build the semantic feature vector to quantify the semantic similarity among application log messages using the word vector space, and the syntactic feature vector to capture the application context that surrounds the log statement. Based on the feature vectors and machine learning techniques, the log level is automatically validated, and an alternative log level is recommended if the log level is invalid. For the evaluation, we collected 22 open‐source projects from three application domains, and obtained the 77% of precision and 75% of recall in validating the log levels. Also, our approach showed 6% higher accuracy than that of the developer group who has 7 to 8 years of work experience, and 72% of the developers accepted our recommendation.
Trajectory Planning of the Unmanned Aerial Vehicles with Adaptive Convex Optimization Method

In this paper, a constrained unmanned aerial vehicles trajectory optimization problem is formulated. We present an iterative convex programming algorithm to solve the optimal control problem, which consists of both nonlinear dynamics and path constraints. A hp-adaptive pseudospectral method is introduced to transform the original problem into nonlinear programming. Furthermore, in order to improve the computational ability, nonlinear dynamics and path constraints are convexified by convexification techniques. The trajectory planning problem is formulated as a convex programming problem. We evaluate the proposed strategy by some numerical results. The results indicate that the algorithm can get better performance compared with other typical optimal control solvers.
A Comparative Study on Adversarial Noise Generation for Single Image Classification

With the rise of neural network-based classifiers, it is evident that these algorithms are here to stay. Even though various algorithms have been developed, these classifiers still remain vulnerable to misclassification attacks. This article outlines a new noise layer attack based on adversarial learning and compares the proposed method to other such attacking methodologies like Fast Gradient Sign Method, Jacobian-Based Saliency Map Algorithm and DeepFool. This work deals with comparing these algorithms for the use case of single image classification and provides a detailed analysis of how each algorithm compares to each other.
Automatic QoE Evaluation of DASH Streaming using ITU-T Standard P.1203 and Google Puppeteer

This document presents a framework for the evaluation of DASH (Dynamic Adaptive Streaming over HTTP) systems, which allows performance measurements to be carried out in an automated and systematic way. We have used Puppeteer, the Node.js library developed by Google, which provides a high level API. This API allows us to automate actions on Chrome Devtools Protocol, such as: start playback, make bandwidth changes and save results of quality change processes, timestamps, stalls and so on. From this data, a processing is made to allow the reconstruction of the visualized video, as well as the extraction of quality metrics and the automated evaluation of the QoE (Quality of Experience) using the ITU-T P.1203 standard.
A Feasible Parking Algorithm in Form of Path Planning and Following

Automatic parking technology is the research trend in auto area in recent years. The parking algorithm is usually decomposed into two parts: path planning with geometrical method and path following. Actually, many papers have contributed on improving the performance of automatic parking algorithm. Among the existing algorithms, the jerks of curvature at some reference path points are hard to be avoided. The problem increases the difficulty of tracking. In path tracking, the simple proportion-integration-differential(PID) controller can not meet the requirements on high precision and stability. Therefore, this paper aims at generating smooth trajectories and tracking them precisely and stably. Firstly, the proposed algorithm utilizes circular arcs to obtain initial reference parking paths in view of passable constraints like slots' boundaries and road boundaries. Secondly, the algorithm chooses appropriate Bezier curves to ease the jerks at path joints. Finally, the controller based on model predictive control(MPC) is designed for path tracking. The automatic parking algorithm is verified in MATLAB and vehicle dynamics simulation software PreScan. Simulation results show that the proposed algorithm is feasible and efficient.
New formulation of the logistic-Gaussian process to analyze trajectory tracking data

Improved communication systems, shrinking battery sizes and the price drop of tracking devices have led to an increasing availability of trajectory tracking data.These data are often analyzed to understand animal behavior.In this work, we propose a new model for interpreting the animal movent as a mixture of characteristic patterns, that we interpret as different behaviors.The probability that the animal is behaving according to a specific pattern, at each time instant, is nonparametrically estimated using the Logistic-Gaussian process.Owing to a new formalization and the way we specify the coregionalization matrix of the associated multivariate Gaussian process, our model is invariant with respect to the choice of the reference element and of the ordering of the probability vector components.We fit the model under a Bayesian framework, and show that the Markov chain Monte Carlo algorithm we propose is straightforward to implement.We perform a simulation study with the aim of showing the ability of the estimation procedure to retrieve the model parameters.We also test the performance of the information criterion we used to select the number of behaviors.The model is then applied to a real dataset where a wolf has been observed before and after procreation.The results are easy to interpret, and clear differences emerge in the two phases.
SAT: Single-Shot Adversarial Tracker

Deep learning-based tracking methods have shown favorable performance on multiple benchmarks. However, most of these methods are not designed for real-time video surveillance systems due to the complex online optimization process. In this article, we propose a single-shot adversarial tracker (SAT) to efficiently locate objects of interest in surveillance videos. Specifically, we propose a lightweight convolutional neural network-based generator, which fuses multilayer feature maps to accurately generate the target probability map (TPM) for tracking. To more effectively train the generator, an adversarial learning framework is presented. During the online tracking stage, the learned TPM generator can be directly employed to generate the target probability map corresponding to the searching region in a single shot. The proposed SAT can lead to the average tracking speed of 212 FPS on a single GPU, while still achieving the favorable performance on several popular benchmarks. Furthermore, we also present a variant of SAT by considering both scale estimation and online updating in SAT, which achieves better accuracy than SAT while still maintaining very fast tracking speed (i.e., exceeding 100 FPS).
Vietnamese- English Cross-Lingual Paraphrase Identification Using Siamese Recurrent Architectures

The paraphrase identification task is one of the important problems that affect the quality of many natural language processing tasks such as querying information, text summarization, plagiarism detection, etc. Especially in the present time, with the development of machine translation tools, the task of paraphrase identification also has to be considered in the case of pairs of texts in two different languages. In this paper, we propose to use the Siamese Recurrent architectures to identify the Vietnamese- English cross-lingual paraphrase cases. In addition, we also use methods such as mapping bilingual word embedding, adding POS vector to word embedding and adjusting the POS tagging label between Vietnamese text and English text. The experimental results in the English- Vietnamese paraphrase corpus with 44,652 sentence pairs show that the use of these methods help to improve the accuracy of the model from 86.86% to 89.61%.
Web-Based Ordering Information System on Food Store

Abstract The purpose of this research is to develop a system that can help food shop owners and customers to make transactions on the website without having to come to the store. The method used in this research was a descriptive method which presents a complete illustration of the current situation, collecting primary and secondary data with interviews and observations about ordering systems in food stores, for the method of approaching the system using object-oriented and developing the system using a prototype. The results of this research are to increase the effectiveness and make it easier for customers to order food and the admin can provide maximum service to customers via the web. This research was conducted by discussing food ordering by customers, payment systems, and order reports.
Expanding the Playtest Method for UX Research Beyond Gaming

The playtest method is known in the gaming community; however, it can be adapted for use across user experience (UX) researchers. By using the playtest method, one can use the laboratory environment and push beyond usability to understand discoverability, enjoyment, and additional user preferences that aren’t otherwise feasible in usability testing due to the low sample size. Additionally, a small-sample playtest method allows a product team to observe participants organically interacting with a product or feature which can be invaluable for building empathy.
Blockchain-Enabled Participatory Incentives for Crowdsourced Mesh Networks

Crowdsourced mesh networks are built, maintained and used by several participants that cooperate to provide and consume connectivity. Providers of infrastructure want to get compensation for their investments and earn tokens; users or consumers want the network to expand for improving the coverage of connectivity and stability. How do we collect funds from consumers and distribute them to providers, guaranteeing satisfaction of every participant? For that, we need of a system that coordinates the flow of economic value in mesh networks in a way that is not only transparent, automated, decentralized and secure, but also beneficial to all. We designed a new economic protocol called Fair to compensate providers for their investments. The key point of our model is that each provider will be paid with different prices for the forwarded traffic: the more devices a provider has, the higher its price/MB forwarded is, up to a certain limit. We implemented the model using MeshDApp, a local blockchain platform for mesh networks. Simulations show how our proposal ensures a win-win situation where the network grows and the providers are compensated for their investment. Also, continuous growth is incentivized while centralization due to few large providers controlling the network is avoided.
Content Based Image Retrieval via Sparse Representation and Feature Fusion

During the last two decades, content-based image retrieval (CBIR) has been widely studied. The limitations of low-level feature representation of images have been a thorny issue in image retrieval problems. In this paper, a CBIR algorithm based on sparse representation and feature fusion is proposed, in which global features and local features are combined to retrieve the images. Firstly, the GIST features are used to roughly retrieve the images with similar scene information by measuring Canberra distance. Then, a representation of the local features extracted from the rough retrieval results is obtained by sparse coding and feature pooling. Finally, Euclidean distance is used to measure the similarity of the sparse feature vectors to obtain the retrieval results. The proposed method is tested on the Coil20 and Caltech256 image datasets. The experimental results demonstrate that the proposed algorithm can achieve comparable superior retrieval performance to the existing single feature-based image retrieval algorithms.
The Intuitiveness of Gesture Control with a Mixed Reality Device

Mixed reality is a new technology that requires users to control a head-mounted device via gestures with their hands. Users of these devices must learn and remember a new way of interacting. It has been shown that creating gestures that resemble movements used to operate touch screens can help with this new transfer. This study investigates how well people learn to use the out-of-the-box gestures for a mixed reality headset, Microsoft HoloLens, after interacting with it for a very short period of time. Performance with the gestures was measured with novices before and after approximately five minutes of practice game play. Participants showed a significant improvement on the gestures to open and position windows and reported them to be easier to do after the short practice. This information could help to create apps or tutorials that help teach these gestures, as well as identifying which gestures are more intuitive to users.
Photon mapping to accelerate daylight simulation with high-resolution, data-driven fenestration models

Abstract Data-driven modelling provides a general means to represent optically complex fenestration in daylight simulation by its Bidirectional Scattering Distribution Function (BSDF). R adiance employs the tensor tree as a compact data structure to store the BSDF at high directional resolution. The application of such models under sunny sky conditions is, however, computationally demanding, since the density of stochastic backward samples must match the BSDF resolution. The bidirectional Photon Map is proposed to rapidly forward-sample the BSDF, starting from the known sun direction. Its exemplary application shows a potential speed-up of ⩾ 98% when compared to backward ray-tracing.
Learning bipartite Bayesian networks under monotonicity restrictions

Learning parameters of a probabilistic model is a necessary step in machine learning tasks. We present a method to improve learning from small datasets by using monotonicity conditions. Monotonicity simplifies the learning and it is often required by users. We present an algorithm for Bayesian Networks parameter learning. The algorithm and monotonicity conditions are described, and it is shown that with the monotonicity conditions we can better fit underlying data. Our algorithm is tested on artificial and empiric datasets. We use different methods satisfying monotonicity conditions: the proposed gradient descent, isotonic regression EM, and non-linear optimization. We also provide results of unrestricted EM and gradient descent methods. Learned models are compared with respect to their ability to fit data in terms of log-likelihood and their fit of parameters of the generating model. Our proposed method outperforms other methods for small sets, and provides better or comparable results for larger sets.
“A Lot of Flexibility within the Structure”: Academic Library Collection Development &amp; E-Resources Management from an Athletics Perspective

There is an established history of academic libraries collaborating with their institutions’ athletics departments to provide learning and research support to student-athletes in order to achieve positive educational outcomes. It is rarer to find documented the effect that athletics has had on the professional life of an academic librarian. The author details how the skills acquired and lessoned learned being an athlete and collegiate coach directly influenced his approach to providing leadership in various collection development and electronic resources management initiatives at a medium-sized academic library.
A characterization for some type-2 fuzzy strong negations

P. Hernandez et al. in 2014 established the axioms that an operation must fulfill in order to be a negation on a bounded poset (partially ordered set). In this work, we focus on the set L of the membership degrees of the type-2 fuzzy sets which are normal and convex functions in [0,1]. This set has a bounded and complete lattice structure, thank to which negations and strong negations have been constructed by the authors applying the Zadeh’s Extension Principle. In addition, the authors showed new negations on L that are different from the negations presented in 2014 applying the Zadeh’s Extension Principle. In this work, the authors obtain a characterization of the strong negations on L that leave the constant function 1 fixed.
Word Reordering for Translation into Korean Sign Language Using Syntactically-guided Classification

Machine translation aims to break the language barrier that prevents communication with others and increase access to information. Deaf people face huge language barriers in their daily lives, including access to digital and spoken information. There are very few digital resources for sign language processing. In this article, we present a transfer-based machine translation system for translating Korean-to-Korean Sign Language (KSL) glosses, mainly composed of (1) dictionary-based lexical transfer and (2) a hybrid syntactic transfer based on a data-driven model. In particular, we formulate complicated word reordering problems in syntactic transfer as multi-class classification tasks and propose “syntactically guided” data-driven syntactic transfer. The core part of our study is a neural classification model for reordering order-important constituent pairs with a reordering task that is newly designed for Korean-to-KSL translation. The experiment results evaluated on news transcript data show that the proposed system achieves a BLEU score of 0.512 and a RIBES score of 0.425, significantly improving upon the baseline system performance.
Reverse Engineering in Student Mark Recapitulation Application

Abstract This study intends to conduct reverse engineering at mark recapitulation application at the Information Systems Department, Universitas Komputer Indonesia (UNIKOM). This research is the first step of the reengineering process in the mark recapitulation application. Reengineering needed because the application of the mark recapitulation that is currently running does not meet user needs, and there is no documentation of application development. This study used the big bang approach, the reverse engineering stage follows the stages of general software reengineering model by analyzing three things, analysis of application functions, analysis of user activities with applications and database analysis. The results of this research are documentation of application function requirements, documentation of user activities with application and database documentation.
Augmented Reality Technology As One Of The Media In Therapy For Children With Special Needs

Learning is a need that must be fulfilled by each human. However, in its development, each human is facing a difference from one to each other. Say, in real life, there are some children with a growth different from the condition in common. The difference in this growth is called with the condition of children with special needs. The existence of children with special needs requires a special treatment. A variety of the methods are used to help children with special needs to growth optimally. The development of computer technology has provided a new breakthrough in helping the children with special needs in improving their motoric ability. One of the breakthroughs used is the development of Medical Rehabilitation Tool by using the augmented reality technology. This technology is able to give a new experience for its user in that the users can directly interact with the virtual objects so as to be able to increase the perception in understanding a form of an object. In this research, the process of testing on the usability of the application to 30 respondents was performed consisting of medical rehabilitation staffs for the speech therapy and occupational therapy. The test on the usability covered the test of Usefulness, Ease of Use, Ease of Learning and Satisfaction. The results of the test from the usefulness aspect showed the result of 81.06%, the aspect of Ease of Use showed the result of 79.13%, the aspect of Ease of Learning showed the result of 78.70 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> and the aspect of Satisfaction showed the result of 78.40%. With those results obtained, the respondents agreed that the level of usability from the application can help children with special needs to study.
Bacterial Foraging Optimization Algorithm with Dimension by Dimension Improvement

Bacterial Foraging Optimization (BFO) is a new nature-inspired intelligent algorithm. When solving the multidimension function optimization problem, the global update evaluation strategy will deteriorate the convergence speed and the quality of the solution due to the interference between the dimensions. Aiming at the above problems, a dimension by dimension improvement based bacterial foraging optimization algorithm is proposed. In the iterative process of our algorithm, a dimension by dimension update evaluation strategy is adopted for the solution, which combines the updated values of each dimension with those of other dimensions to form a new solution. Experimental results show that the improved strategy can effectively improve the convergence speed of BFO algorithm and improve the quality of solution. Furthermore, compared with classical BFO algorithm, the results show that the improved algorithm is competitive in solving continuous function optimization problems.
Exploiting Blockchain Technology for Attribute Management in Access Control Systems

Access Control systems are a key resource in computer security to properly manage the access to digital resources. Blockchain technology, instead, is a novel technology to decentralise the control and management of a shared state, representing anything from a data repository to a distributed virtual machine. We propose to integrate traditional Access Control systems with blockchain technology to allow the combined system to inherit the desirable properties blockchain technology provides, mainly transparency and, consequently, auditability. Depending on the application scenario considered, for some systems it may not be desirable to employ a fully decentralised approach. As such, in this paper we outline how our proposal can be adapted to allow for the minimal possible integration of blockchain technology in a traditional Access Control system. In particular, we consider the scenario where Attribute Managers only may be managed on chain through smart contracts. We provide a proof of concept implementation based on Ethereum, and show its performance through experimental results.