Reviewerly/exHarmony huggingface dataset:
Privacy-Enhancing Context Authentication from Location-Sensitive Data

This paper proposes a new privacy-enhancing, context-aware user authentication system, ConSec, which uses a transformation of general location-sensitive data, such as GPS location, barometric altitude and noise levels, collected from the user's device, into a representation based on locality-sensitive hashing (LSH). The resulting hashes provide a dimensionality reduction of the underlying data, which we leverage to model users' behaviour for authentication using machine learning. We present how ConSec supports learning from categorical and numerical data, while addressing a number of on-device and network-based threats. ConSec is implemented subsequently for the Android platform and evaluated using data collected from 35 users, which is followed by a security and privacy analysis. We demonstrate that LSH presents a useful approach for context authentication from location-sensitive data without directly utilising plain measurements.
Characteristic Similarity Using Classical CNN Model

Deep neural network such as classical CNN become very popular used in image classification. CIFAR-10 dataset provides the ten image categories dataset with 32x32 pixels of size. Since the similarity became issue when using dataset which is not providing the enough information for classification [2] and labeled subset of tiny image in CIFAR-10 [3], we modify the classical CNN model with several parameters to reach the good similarity result in learning. We find the model can give the high similarity with 90% for representative image in dataset.
An Innovative Approach to Data Management and Curation of Experimental Data Generated Through IR Test Collections

This paper describes the steps that led to the invention, design and development of the Distributed Information Retrieval Evaluation Campaign Tool (DIRECT) system for managing and accessing the data used and produced within experimental evaluation in Information Retrieval (IR). We present the context in which DIRECT was conceived, its conceptual model and its extension to make the data available on the Web as Linked Open Data (LOD) by enabling and enhancing their enrichment, discoverability and re-use. Finally, we discuss possible further evolutions of the system.
Three Dimensional UAV Path Following Using SDRE Guidance

Path following is an essential requirement for unmanned aerial vehicles. Typically, these paths are in three dimensions and the shape of the paths are application dependent. Mapping applications require stationary paths while moving target tracking with a stand-off distance involves moving reference paths. A path following strategy is required that can be used for stationary and moving path following applications and is robust to wind disturbances. In this paper, we propose an adaptive optimal guidance strategy using State Dependent Ricatti Equation (SDRE) approach. Comparison of the guidance law is ascertained and the approach is evaluated under different wind and target motion conditions. The guidance law shows robust performance up to 10 m/s wind speed and can follow different geometric reference paths.
Near-optimal Evasion of Randomized Convex-inducing Classifiers in Adversarial Environments

Classifiers are often used to detect malicious activities in adversarial environments. Sophisticated adversaries would attempt to find information about deployed classifiers in order to strategise different evasion techniques. It is a widely held belief that randomization of decision boundaries/rules of detection systems would introduce further complexities in attempts made by the adversaries for finding minimal adversarial cost (MAC) evading instances. We have extended the results obtained by Nelson et al. [14] and further presented a novel algorithm that can find optimal evading instances in randomized convex-inducing classifiers using polynomial-many queries. Our results have demonstrated that the complexity introduced through randomization only increases the complexity of finding an optimal evading instance by a constant factor and thus the risk of optimal evasion is still present.
Mobile Device Identification via User Behavior Analysis

Modern mobile devices are capable of sensing a large variety of changes, ranging from users’ motions to environmental conditions. Context-aware applications utilize the sensing capability of these devices for various purposes, such as human activity recognition, health coaching or advertising, etc. Identifying devices and authenticating unique users is another application area where mobile device sensors can be utilized to ensure more intelligent, robust and reliable systems. Traditional systems use cookies, hardware or software fingerprinting to identify a user but due to privacy and security vulnerabilities, none of these methods propose a permanent solution, thus sensor fingerprinting not only identifies devices but also makes it possible to create non-erasable fingerprints. In this work, we focus on distinguishing devices via mobile device sensors. To this end, a large dataset, larger than 25 GB, which consists of accelerometer and gyroscope sensor data from 21 distinct devices is utilized. We employ different classification methods on extracted 40 features based on various time windows from mobile sensors. Namely, we use random forest, gradient boosting machine, and generalized linear model classifiers. In conclusion, we obtain the highest accuracy as 97% from various experiments in identifying 21 devices using gradient boosting machine on the data from accelerometer and gyroscope sensors.
Motivation of technical university students and its impact on the effectiveness of the educational process

The article presents data on the assessment of the impact of motivation on the effectiveness of the educational process. Evaluation of motivation was carried out according to the method of T.I. Ilyina. In 41% of the students surveyed, the main motive is the acquisition of knowledge, in 31% of students - mastering a profession, and in 28% of students - obtaining a diploma. At the same time, the highest average grade point (4.6 out of 5.0) was found among students motivated to master a future profession, which indicates a conscious approach to learning.
A machine learning based automatic folding of dynamically typed languages

The popularity of dynamically typed languages has been growing strongly lately. Elegant syntax of such languages like javascript, python, PHP and ruby pays back when it comes to finding bugs in large codebases. The analysis is hindered by specific capabilities of dynamically typed languages, such as defining methods dynamically and evaluating string expressions. For finding bugs or investigating unfamiliar classes and libraries in modern IDEs and text editors features for folding unimportant code blocks are implemented. In this work, data on user foldings from real projects were collected and two classifiers were trained on their basis. The input to the classifier is a set of parameters describing the structure and syntax of the code block. These classifiers were subsequently used to identify unimportant code fragments. The implemented approach was tested on JavaScript and Python programs and compared with the best existing algorithm for automatic code folding.
A Dataset for Content Error Detection in Web Archives

Archiving modern web pages is challenging, and a clear concept of possible errors is still missing. To further improve current web archiving technology, this paper introduces the concept of content errors, which refers to web pages whose archived versions have unexpected content different from their originals. This paper presents the first large scale analysis of a web crawl of 10.000 pages for content errors-the Webis Web Archive 2017. Using manual inspection and small annotation studies, we identified 5 different classes of content errors, and then annotated the entire crawl for these classes using crowdsourcing: error messages (4.5% of pages), pop-ups (3.9%), pages that largely consist of advertisements (1.1%), CAPTCHAs (0.8%), and loading indicators (0.5%). Combined, about 10% of pages are affected by content errors, which underlines the relevance of the problem.
An Unsupervised Approach of Truth Discovery From Multi-Sourced Text Data

Truth discovery methods aim to identify which piece of information is trustworthy from multi-sourced data. Most existing truth discovery methods, however, are designed for structured data and fail to meet the strong need to extract trustworthy information from raw text data. More specifically, existing methods ignore the semantic information of text answers, i.e., answers may contain multiple factors, the word usages may be diverse, and the answers may be partially correct. In addition, ubiquitous long-tail phenomenon exists in the tasks, i.e., most users provide only a few answers and only a few users provide plenty of answers, which causes the user reliability estimation for small users to be unreasonable. To tackle these challenges, we propose a Graph Convolutional Network (GCN) based truth discovery model to automatically discover trustworthy information from text data. Firstly, Smooth Inverse Frequency (SIF) is utilized to learn real-valued vector representations for answers. Then, we construct undirected graph with these vectors to capture the structural information of answers. Finally, the GCN is utilized to store and update the reliability of these answers, and sums up all the feature vectors of all neighboring answers to improve the accuracy and efficiency of truth discovery. Different from traditional methods, we use vectors to store the reliability of answers which have higher representation capability compared with real numbers, and network is utilized to capture complex relationships among answers rather than simplified functions. The experiment results on real datasets show that though text data structures are complex, our model can still find reliable answers compared with retrieval-based and state-of-the-art truth discovery methods.
GameAAL - an AAL solution based on Gamification and Machine Learning Techniques

GameAAL - Gamification Supporting Active and Assisted Living is an innovative system for monitoring elderly daily life activities and provide neurocognitive stimulation games. It is focused at filling a gap on existing Ambient Assisted Living solutions by promoting social interaction and mobility of its end-users using gamification and machine learning techniques. As long as the goals of user are being achieved, the system unlocks rewards like digital badges, providing the user with the chance of winning free admissions in cultural events provided by community partners. A Proof of Concept is ongoing with 15 real end-users who interact with a TV interface, showing a good acceptability and willing to use the system.
Feature Bundles and their Effect on the Performance of Tree-based Evolutionary Classification and Feature Selection Algorithms

In this paper, we prove the existence of feature bundles in some classification problems. These are a set of features that while in their entirety are relevant to the target variable, any strict subset of them is completely independent of the target. Any machine learning algorithm applied to a strict subset of a feature bundle cannot produce a model that performs better than a feature-less model that always predicts the majority class. We demonstrate and discuss the effect of these feature bundles on the performance of tree-based classification learning and feature selection algorithms.
An Improved Approach based on Balanced Keyword Weight to Traceability Recovery

Abstract Software traceability recovery has become more and more important to the software life cycle recent days with the increasing complexity of software system. However, it is a tedious and error-prone task by manual. Many researchers have devoted themselves in building and predicting traceability links. In this paper, we approach a novel method to calculate text similarity and improve our results by a learning to rank method. And a couple of experiments are conducted to verify our method.
“Ready Player One”: Enhancing Omnidirectional Treadmills for Use in Virtual Environments

As large-scale immersive virtual environments become more common, technologies develop to allow increasingly naturalistic travel in these spaces. One such technology, is the omnidirectional treadmill. Though the hardware has been realized, it still lacks a concrete software paradigm that ensures comfort and performance for the user. We identified three potentially key settings for omnidirectional treadmill locomotion in virtual reality (VR) environments that may lead to more comfort, increased degrees of freedom, and better performance: movement speed, treadmill sensor sensitivity, and decoupling the head's rotation from that of the body. To integrate these settings, we developed an original first-person movement script and conducted a prestudy to discover if enough variance between individuals existed to necessitate calibration on a per-individual basis. Initial prestudy results suggest that these three values do show enough variance to recommend they be calibrated on a per-individual basis and opens up the opportunity for further research into the potential benefits of these variables on locomotion in environments that use omnidirectional treadmills.
Ontology-based approach to design problem formalization

At the present time ontologies are applied almost everywhere. This is due to the fact of intensive growth of information flow to be processed. Ontologies provide interrelations between elements, hierarchy among domain concepts, data structure and integration of heterogeneous information. All of this allows forming a single information base for different computer applications and programs. The goal of the present work is circuit partitioning problem formalization on the base of ontology. The Protégé 4.2 editor was used for ontology development. Proposed ontology includes a formalized statement of circuit partitioning problem. So, it should be a “frame” of knowledgebase (KB) for user CAD system. This work demonstrates ontology formalized model and its usage in CAD systems. Ontology was developed on the base of formalized circuit partition problem model. The novelty of present work is ontology usage for optimization problem formalization within design stage. The proposed ontology can be extended: new concepts, individuals, attributes and relations can be added. This is the key advantage for examined domain. This is due to the fact of microelectronic manufacturing technology development and appearing of new criteria, constraints, boundary conditions and input data presentation. The experiment of ontology development has proved its efficiency for optimization design problem solving.
Study of different geostatistical methods to model formation porosity (Cast study of Zubair formation in Luhais oil field)

Abstract This study is concerned with making comparison in using different geostatistical methods for porosity distribution of upper shale member - Zubair formation in Luhais oil field which was chosen to study. Kriging, Gaussian random function simulation and sequential Gaussian simulation geostatistical methods were adopted in this study. After preparing all needed data which are contour map, well heads of 12 wells, well tops and porosity from CPI log. Petrel software 2009 was used for porosity distribution of mentioned formation in methods that are showed above. Comparisons were made among these three methods in order to choose the best one, the comparing criteria was according to different statistical information and variograms analysis of entered porosity which was scaled up and modeled. The best method gave porosity distribution in model closest to enter porosity that represent real porosity of Zubair formation. The comparison proved that sequential Gaussian simulation is the best model of porosity distribution followed by Gaussian random function simulation and kriging methods respectively. Based on the results obtained, it was concluded that the distribution accuracy of the porosity or other petrophysics properties is one of the main factors that affect building correct geological model that represents the base of reservoir model and also pore volume (Oil reserve) value was affected by geostatistical methods variation.
Unsupervised Out-of-context Action Understanding

The paper presents an unsupervised out-of-context action (O2CA) paradigm that is based on facilitating understanding by separately presenting both human action and context within a video sequence. As a means of generating an unsupervised label, we comprehensively evaluate responses from action-based (ActionNet) and context-based (ContextNet) convolutional neural networks (CNNs). Additionally, we have created three synthetic databases based on the human action (UCF101, HMDB51) and motion capture (mocap) (SURREAL) datasets. We then conducted experimental comparisons between our approach and conventional approaches. We also compared our unsupervised learning method with supervised learning using an O2CA ground truth given by synthetic data. From the results obtained, we achieved a 96.8 score on Synth-UCF, a 96.8 score on Synth-HMDB, and 89.0 on SURREAL-O2CA with F-score.
OPV: Bias Correction Based Optimal Probabilistic Viewport-Adaptive Streaming for 360-Degree Video

Nowadays the world is more immersive than ever, but huge bitrate and large Internet delay impede the wide applications of 360-degree video. Viewport adaptive streaming is emerging to transmit quality-variable videos based on user's viewport. To prevent the playback stalling, it is necessary to prefetch some subsequent videos within user's future viewport by head motion prediction. However, long-term prediction is easier biased, which results in videos quality drop and quality oscillation. To alleviate the bias' ill effect, it has to decide whether to download a new segment, or replace old tiles with more accurate viewing probability. To this end, we propose a bias correction based optimal probabilistic viewport-adaptive approach, which selects the best tiles and bitrates to alleviate the ill effect of prediction bias. Besides, viewport quality, playback stalling, and quality oscillation are jointly considered in the optimization model to maximize the Quality of Experience in the viewport. The experimental results confirm that our proposal outperforms the existing viewport adaptive approaches.
Improving Incremental Planning Performance through Overlapping Replanning and Execution

Deployment of motion planning algorithms in practical applications has lagged due to their slow speed in reacting to disturbances. We believe that the best way to address this is to reuse learned planning and control information across queries. In previous work, we introduced Chekov, a reactive, integrated motion planning and execution system that reuses learned information in the form of an enhanced roadmap. We have previously shown how we can use Chekov to formulate trajectory optimization problems that result in superior performance in static environments. In this work, we show how incremental planning can be incorporated into the formulation of optimized trajectories from roadmap seed trajectories. Further, we show how an incremental planner can be adapted to reduce the overhead incurred for replanning when trajectories become invalid during execution.
Research on Cross-version Software Defect Prediction Based on Evolutionary Information

Abstract Software defect prediction is an important part of the software testing field. According to the characteristics of object-oriented software, this paper considers the evolution information separately in different packages and summarizes the evolution metrics that affect the defect prediction. Existing research on evolutionary information often ignores the impact of newly added and disappearing classes on software defects prediction. Based on these factors, evolution metrics are proposed and applied to defect prediction. Two evolution metrics, transition class ratio and static metric category number, are proposed for object-oriented cross-version defect prediction. Experiments are carried out in the commonly used software defect prediction set. The experimental results show that the proposed metrics have better defect prediction ability than the traditional static metric.
Novel algorithm for software planning &amp; development

A software project has a complete process of developing software from requirement specification to its implementation and maintenance, which is carried out as per the execution methodology to get the desired software product. The scope of a software project is the requirements specified by the user for the results and being a fundamental step of project life cycle if these requirements are not specified accurately, it will lead to the creation of a software product that does not fit the user's need and results in project failure. This research paper presents a fuzzy-based approach for prioritizing and dividing the requirements such that it brings efficiency in software development and contributes to a project success as well as in the improvement of project management. For the development of an efficient project deliverable, the paper presents a model based on fuzzy logic, which helps in assigning weights to the requirements such that they can be serialized or run in parallel, and can reduce the duration of development. The process of stable allocation of resources to the project's module will be efficient towards the effective completion of work. The collaborative efforts made at the initial stage of project life cycle will help in reducing the risk of failure in project development process.
Supporting seamless learners: exploring patterns of multiple device use in an open and distance learning context

Educators need to be aware of not just what their students are learning, but how and why as well. This study investigates how Open and Distance Learning (ODL) students make use of multiple devices for learning, particularly how students use their devices together. This study is situated in the context of ‘seamless learning’, where ODL students learn at different times, in different locations and with the use of different technologies. Understanding the needs and learning practices of students can help to improve the design of learning experiences and support offered to students. Data were collected through semi-structured interviews at two ODL universities, one in Spain and one in South Africa. The results show that while students mainly use one digital device at a time, they sometimes make use of two or more devices together. This usage can be characterised as sequential (moving from one device to another) or simultaneous (using two or more devices together at a time). This article describes the study patterns associated with sequential and simultaneous use of multiple devices, facilitated by the use of different devices and synchronisation tools. A continuum of seamless learners is proposed that can be used to help identify levels of support required by ODL students.
An End-to-End Deep Learning Based Gesture Recognizer for Vehicle Self Parking System

Hand gesture recognition have become versatile in numerous applications. In particular, the automotive industry has benefited from their deployment, and human-machine interface designers are using them to improve driver safety and comfort. In this paper, we investigate expanding the product segment of one of America’s top three automakers through deep learning to provide an increased driver convenience and comfort with the application of dynamic hand gesture recognition for vehicle self parking. We adapt the architecture of the end-to-end solution to expand the state of the art video classifier from a single image as input (fed by monocular camera) to a multiview 360 feed, offered by a six cameras module. Finally, we optimize the proposed solution to work on a limited resource embedded platform that is used by automakers for vehicle-based features, without sacrificing the accuracy robustness and real time functionality of the system.