Reviewerly/exHarmony huggingface dataset:
Geophysical interpretation of the geology of the Stanthorpe region using aeromagnetic, gravity and radiometric data

This study provides an in-depth geophysical interpretation of the geology of the Stanthorpe region with a particular focus on hydrothermal alteration. The radiometric ternary map outlines the main lithological divisions in the region, which together with Th and Th/K ratio maps distinguish various granitic suites. Magnetic signatures delineate a non-magnetic zone within the low to moderately magnetic Stanthorpe Granite. The non-magnetic section, a north–northwest trending belt with a high density of faults and dykes is interpreted as a hydrothermal alteration zone that is narrower in the north, widens southwards, and is much more extensive at depth than is suggested by the surface alteration pattern. Modelling of gravity data reveals a massive low-density granitic intrusion extending to a depth of 10–15 km. The low-gravity section with modelled rock densities between 2450 and 2670 kg/m3 coincides with the low magnetic susceptibility zone in the magnetic model. High K, low Th and low Th/K also characterise the sections of the Stanthorpe Granite that have both low gravity and magnetic responses. Also conspicuous in the geophysical signatures within the non-magnetic zone is an ovoid feature of low magnetic response, high uranium content with moderate density, interpreted as a granitic pluton. Thus, decrease in magnetisation, changes in K concentration and corresponding low gravity with an associated high density of faults, fractures and dykes in the Stanthorpe region are the main features of the quartz–sericite–chlorite–muscovite hydrothermal alteration.
Preempting Catastrophic Forgetting in Continual Learning Models by Anticipatory Regularization

Neural networks trained on tasks sequentially tend to degrade in performance, on the average, the more tasks they see, as the representations learned for one task get progressively modified while learning subsequent tasks. This phenomenon- known as catastrophic forgetting-is a major obstacle on the road toward designing agents that can continually learn new concepts and tasks the way, say, humans do. A common approach to containing catastrophic forgetting is to use regularization to slow down learning on weights deemed important to previously learned tasks. We argue in this paper that, on their own, such post hoc measures to safeguard what has been learned can, even in their more sophisticated variants, paralyze the network and degrade its capacity to learn and counter forgetting as the number of tasks learned increases. We propose instead- or possibly in conjunction-that, in anticipation of future tasks, regularization be applied to drive the optimization of network weights toward reusable solutions. We show that one way to achieve this is through an auxiliary unsupervised reconstruction loss that encourages the learned representations not only to be useful for solving, say, the current classification task, but also to reflect the content of the data being processed-content that is generally richer than it is discriminative for any one task. We compare our approach to the recent elastic weight consolidation regularization approach, and show that, although we do not explicitly try to preserve important weights or pass on any information about the data distribution of learned tasks, our model is comparable in performance, and in some cases better.
A Systematic Literature Review on Autonomous Agile Teams

Organizations are needed of flexible structures to achieve their aims. In the software arena, Self-organization is the one of the principles of the agile methodologies as defined in the agile manifesto. Autonomous agile teams are a way to conform self-organization. The main objective of this paper is to know about the how autonomous agile teams organize themselves and what are the benefits and the challenges faced by these teams. A systematic literature review was conducted to find answers to these questions. 23 relevant papers were identified as primary sources to define and study autonomous and self-organized agile team. From the review, it was found that there are many benefits and challenges of autonomous agile teams and the various strategies to overcome the challenges.
Crowd Counting with respect to Age and Gender by using Faster R-CNN based Detection

Masses gather on a daily basis in events like hajjs, shows, marathons, anniversaries, memorials, festivities, political events, protests and shopping malls. Visual understanding of crowd places is an emerging field in computer vision. Counting the people, based on gender, and estimation of their age in crowd is a tricky job but it has extensive applicability in surveillance, management and planning. Crowd counting is also vital to measure political worth of protests and other unwanted gatherings. In this work, we propose an innovative technique to solve the challenges of crowd counting, gender recolonization, age estimation and localization of people in visual frames. Faster R-CNN is trained to solve these four problems simultaneously. As the task of localization involves the annotations of high-quality frames, we introduce CVML-OCC dataset <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> that incapacitates the limitations of already available datasets. Alongside, we provide evaluation procedures and comprehensive comparison with existing approaches. Our method expressively performs better than state-of-the-art modules with the training set of CVML-OCC dataset, which is a comprehensive dataset having precise annotations in diverse scenarios.
A multi-server oblivious dynamic searchable encryption framework

Data privacy is one of the main concerns for data outsourcing on the cloud. Although standard encryption can provide confidentiality, it prevents the client from searching/retrieving meaningful information on the outsourced data thereby, degrading the benefits of using cloud services. To address th is data utilization versus privacy dilemma, Dynamic Searchable Symmetric Encryption (DSSE) has been proposed. DSSE enables encrypted search and update functionality over the encrypted data via a secure index. However, the state-of-the-art DSSE constructions leak information from the access pattern, making them vulnerable against various attacks. While generic Oblivious Random Access Machine (ORAM) can hide the access pattern, it incurs a heavy communication overhead, which was shown costly to be directly used in the DSSE setting. In this article, by exploiting the multi-cloud infrastructure, we develop a comprehensive Oblivious Distributed DSSE (ODSE) framework that allows oblivious search and updates on the encrypted index with high security and improved efficiency over the use of generic ORAM. Our framework contains a series of ODSE schemes each featuring different levels of performance and security required by various types of real-life applications. ODSE offers desirable security guarantees such as information-theoretic security and robustness in the presence of a malicious adversary. We fully implemented ODSE framework and evaluated its performance in a real cloud environment (Amazon EC2). Our experiments showed that ODSE schemes are 3×-57× faster than using generic ORAMs on a DSSE encrypted index under real network settings.
Tangible Around-Device Interaction Using Rotatory Gestures with a Magnetic Ring

The majority of mobile applications use built-in touchscreens and/or accelerometers to provide direct ways for user inputs. Yet, the need to manipulate the device itself (e.g. touch, tilt) poses usability issues such as occlusion and inaccuracy. To address these issues, research proposed using the built-in magnetometer and magnets to facilitate around-device interactions. However, there is little evaluation in how this technique impacts performance and user experience beyond simple docking tasks. To fill this gap, we explored the mobile gameplay context by implementing an interface that uses rotatory gestures from a magnetic ring as input, and compared two control mappings (angular and linear) with touch and tilt in a usability study using a mobile game. We found that rotatory gestures with the ring, when mapped to angular controls, were on par with touch and superior over tilt, and engendered greater gameplay experience and sense of mapping. Based on our findings, we discuss implications of using this technique for gameplay, as well as other applications.
Dairy Cow Tiny Face Recognition Based on Convolutional Neural Networks

In practical applications of cow face recognition, the accuracy is often lower than expected because of the influence of camera's low resolution and position. In this paper, we aim to develop and pilot a method for improving recognition accuracy and recovering identity information for generating cow faces closed to the real identity. Specifically, our network architecture consists of two parts: a super-resolution network for recovering a high-resolution cow face from a low-resolution one, and a face recognition network. The super-resolution network is cascaded with the recognition network. An alternately training strategy was introduced to ensure the stability of the training process. The cow face dataset was collected by us, which contains 85200 dairy cow face images from 1000 subjects. Experimental evaluations demonstrate the superiority of the proposed method. Our method has achieved 94.92% recognition accuracy on the small size (12 × 14) cow face.
The Influence of Hand Size on Touch Accuracy

Touch accuracy is not just dependent on the performance of the touch sensor itself. Instead, aspects like phone grip or occlusion of the screen have been shown to also have an influence on accuracy. Yet, these are all dependent on one underlying factor: the size and proportions of the user's hand. To better understand touch input, we investigate how 11 hand features influence accuracy. We find that thumb length in particular correlates significantly with touch accuracy and accounts for about 12% of touch error variance. Furthermore, we show that measures of some higher level interactions also correlate with hand size.
Classification of Mobile Application User Reviews for Generating Tickets on Issue Tracking System

Mobile application development has now been in the mainstream and a lot of mobile applications have been released to Apple App Store and GooglePlay Store. As there are many applications in the same category and the competition is very high in the market, mobile development teams need to get user feedback on their applications so that they can improve quality of the applications. An important source of feedback is user review on App Store and Play Store from which the developers can analyze problems and recommendations for future maintenance and evolution of the applications. Since there might be a large number of user reviews for each release of a mobile application, this paper proposes an automated approach to classifying user reviews as bug reports or feature requests. These classification results are used as a basis for generating tickets for an issue tracking system, i.e. Jira. In user review classification, text classification, natural language processing, sentiment analysis, and review metadata are used with several machine learning algorithms, i.e. Naïve Bayes, Decision Tree, KNN, LinearSVC, Logistic Regression, and Ensemble methods. The best classifiers for both categories of reviews are used further in an implementation of a Jira ticket generating tool. The tool considers semantic similarity of review comments and can filter out duplicate user reviews. When necessary, the tool applies text summarization to the user review to extract the title of a ticket for the corresponding bug report or feature request. The approach can facilitate the mobile development team in their maintenance task as the developers can pick the tickets and work on them to improve the application in a future release.
EOG Glasses

In this work we present the smart eyewear demo setup consisting of the software platform for cognitive and social interaction assessments in the wild, with several application cases and a demonstration of activity recognition in real-time. The platform is designed to work with Jins MEME, smart EOG enabled glasses, The user software is capable data logging, posture tracking and recognition of several activities, such as talking, reading and blinking. In this work we present several applications and studies that the platform has been used for.
Preferred Tools for Agile Development: A Sociocultural Perspective

Tools are of paramount importance in supporting software development methods, Agile ones included. In this paper, we aim to identify the most popular tools used by the Agile developers community, studying whether there are shared opinions or there are discrepancies, that could be related to cultural or geographical differences. The study is based on the DESMET approach, enhanced with some additional considerations. Results show that Agilists are well integrated and tend to use the same tools even if some regional differences exist. Moreover, they prefer well-known and established products; interestingly, planning tools are generally regarded as unsatisfactory. We list, classify, and discuss the most popular tools.
Behavioral fingerprinting of Internet‐of‐Things devices

Abstract Rapid advances in the Internet‐of‐Things (IoT) domain have led to the development of several useful and interesting devices that have enhanced the quality of home living and industrial automation. The vulnerabilities in the IoT devices have rendered them susceptible to compromise and forgery. The problem of device authentication, that is, the question of whether a device's identity is what it claims to be, is still an open problem. Device fingerprinting seems to be a promising authentication mechanism. Device fingerprinting profiles a device based on information available about the device and generate a robust, verifiable and unique identity for the device. Existing approaches for device fingerprinting may not be feasible or cost‐effective for the IoT domain due to the resource constraints and heterogeneity of the IoT devices. Due to resource and cost constraints, behavioral fingerprinting provides promising directions for fingerprinting IoT devices. Behavioral fingerprinting allows security researchers to understand the behavioral profile of a device and to establish some guidelines regarding the device operations. In this article, we discuss existing approaches for behavioral fingerprinting of devices in general and evaluate their applicability for IoT devices. Furthermore, we discuss potential approaches for fingerprinting IoT devices and give an overview of some of the preliminary attempts to fingerprint IoT devices. We conclude by highlighting the future research directions for fingerprinting in the IoT domain. This article is categorized under: Application Areas &gt; Science and Technology Application Areas &gt; Internet Technologies &gt; Machine Learning Application Areas &gt; Industry Specific Applications
Food Safety Event Detection Based on Multi-Feature Fusion

Food safety event detection is a technique used to discover food safety events by monitoring online news. In general, a set of keywords are extracted as features to represent news, and then the news is clustered to generate events. The most popular method for news feature extraction is Term Frequency-Inverse Document Frequency (TF-IDF), however, it has some defects such as being prone to the “dimension disaster”, low computational efficiency, and a lack of semantic information. In addition, Latent Dirichlet Allocation (LDA) is also widely used in news representation. Despite its low dimension, it still suffers from some drawbacks such as the need to set a predefined number of clusters and has difficulty recognizing new events. In this paper, a method based on multi-feature fusion is proposed, which combines the TF-IDF features, the named entity features, and the headline features to represent the news. Based on the representations, the incremental clustering method is used to cluster the news documents and to detect food safety events. Compared with the traditional methods, the proposed method achieved higher Precision, Recall, and F1 scores. The proposed method can help regulatory authorities to make decisions and improve the reputation of the government, whilst reducing social anxiety and economic losses.
A Mechanism for Automatically Summarizing Software Functionality from Source Code

When developers search online to find software components to reuse, they usually first need to understand the container projects/libraries, and subsequently identify the required functionality. Several approaches identify and summarize the offerings of projects from their source code, however they often require that the developer has knowledge of the underlying topic modeling techniques; they do not provide a mechanism for tuning the number of topics, and they offer no control over the top terms for each topic. In this work, we use a vectorizer to extract information from variable/method names and comments, and apply Latent Dirichlet Allocation to cluster the source code files of a project into different semantic topics. The number of topics is optimized based on their purity with respect to project packages, while topic categories are constructed to provide further intuition and Stack Exchange tags are used to express the topics in more abstract terms.
Improving network embedding with partially available vertex and edge content

Network embedding aims to learn a low-dimensional representation for each vertex in a network, which has recently shown its power in many graph mining problems such as vertex classification and link prediction. Most existing methods learn such representations according to network structure information, and some methods further consider vertex content in a network. Unlike prior works, we study the problem of network embedding with two distinctive properties: (1) content information exists on both vertices and edges; (2) only a part of vertices and edges have content information. To solve this problem, we propose a novel Partially available Vertex and Edge Content Boosted network embedding method, namely PVECB, which uses available vertex and edge content information to fine-tune structure-only representations through two hand-designed mechanisms respectively. Empirical results on four real-world datasets demonstrate that our method can effectively boost structure-only representations to capture more accurate proximities between vertices.
ST-RNet: A Time-aware Point-of-interest Recommendation Method based on Neural Network

Point-of-interest (POI) recommendation is one of the most important services in the rapid growing location-based social networks (LBSNs). Good POI recommendation can help people explore the locations they haven't visited but are interested in, and help merchants find their target users. Time-aware POI recommendation aims to recommend unvisited POIs for a given user at a specified time in a day. However, previous methods, such as user-based collaborative filtering, lack the mining of the features of POIs and the learning of abstract spatio-temporal interactions. In this paper, we propose a novel time-aware POI recommendation method named ST-RNet (Spatio-Temporal Recommender Network) to address these shortages. ST-RNet works in the following fashion. Firstly, we analyze the crucial features in LBSNs to alleviate data sparsity problem and further measure the similarities between POIs. For subsequent network training, we then construct the embedding matrices with same dimension for users and POIs by POI-based Collaborative Filtering (PCF). Furthermore, the positive and negative check-in records are fed into a novel recommender neural network (RNet) to learn the embedding matrix of times and the abstract interactions between users, POIs and times. Finally, ST-RNet recommends the unvisited POIs most likely to be visited to a given user at a given time. The experimental results on Foursquare real-world dataset show that ST-RNet is effective on time-aware POI recommendation task and is capable of analyzing the hidden patterns behind spatio-temporal interactions.
Image Captioning Based On Sentence-Level And Word-Level Attention

Existing attention models of image captioning typically extract only word-level attention information. i.e., the attention mechanism extracts local attention information from the image to generate the current word. We propose an image captioning approach based on self-attention to utilize image features more effectively. The self-attention mechanism can extract sentence-level attention information with richer visual representation from images. Furthermore, we propose a double attention model. The model combines sentence-level and word-level attention information to better simulate human perception system. We implement supervision and optimization in the intermediate stage of the model to solve over-fitting and information interference problems, and we apply reinforcement learning to two-stage training to optimize the evaluation metrics of the model. Finally, we evaluate our model on MSCOCO dataset. The experimental results show that our approach can generate more accurate and richer captions, and outperforms many state-of-the-art image captioning approaches on various evaluation metrics.
Crater matching algorithm based on feature descriptor

In the field of visual navigation, crater is an ideal navigation landmark on planet surface, because of its universality and significance. During the descent phase of lander, crater needs to be extracted and matched. However, as the landing proceeds, images taken by the on-board camera will change, such as the variation of scale, translation, rotation, illumination which will increase the difficulty of the matching algorithm. This paper proposes a more accurate and faster crater matching algorithm based on feature descriptor. This algorithm is designed for navigation during the descending phase. It has the invariance of scaling, rotation, illumination. First, this algorithm extracts circular arc by Gaussian Pyramid and ELSD algorithm, so that this algorithm has the invariance against scale variation. Then, in order to describe the circular arc, this algorithm determine the direction of the circular arc by the direction histogram. And this algorithm constructs the circular arc band descriptors in the support region to achieve translation, rotation and illumination invariance. At last, matching criteria is the combination of the Nearest Neighbor Distance Ratio (NNDR) and the Euclidean distance constraint. The results are obtained under different image variations. The matching results show that this crater matching algorithm has high crater correct extracting and matching rate and high computationally efficient.