Reviewerly/exHarmony huggingface dataset:
Emojilization

Speech To Text (STT) plays a significant role in Voice User Interface (VUI). While preserving necessary semantic information in converted text, STT generally captures no or limited emotional information. In this paper, we present an emojilization tool to automatically attach related emojis to the STT-generated texts by analyzing both textual and acoustic features in speech signals. For a given voice message, the tool selects the most representative emoji from 64 most commonly used emojis. We conducted a pilot study with 34 participants. In our study, 159 utterances were labeled with emojis by our tool. The emotion restoration effect was evaluated. The results indicate that the proposed tool effectively compensates for the emotion loss.
Inferring User Engagement from Interaction Data

This paper presents preliminary results of a study designed to quantify users' engagement levels with interactive media content, through self-reported measures and interaction data. The broad hypothesis of the study is that interaction data can be used to predict the level of engagement felt by the user. The challenge addressed in this work is to explore the effectiveness of interaction data to act as a proxy for engagement levels and reveal what that data shows about engagement with media content. Preliminary results suggest several interesting insights about participants engagement and behaviour. Crucially, temporal statistics support the hypothesis that the participant making use of the controls in the interactive, video-based experience positively correlates with higher engagement.
Deeper Siamese network with multi‐level feature fusion for real‐time visual tracking

In recent years, using Siamese network (SiamN) for visual tracking has witnessed a great success in terms of accuracy and efficiency. Nevertheless, most SiamN-based trackers employ shallow network such as AlexNet to extract the top-layer features as target representation that are less discriminative, usually leading to tracking performance degeneration when suffering from large deformation and similar distractors. A straightforward idea to address this issue is to replace the backbone network of SiamN with deeper ResNet. However, this cannot boost performance much due to the low resolution of high-level feature maps with useful spatial details losing. To address this issue, the authors propose a lightweight yet effective feature agglomeration module (FAM) to adaptively fuse low-level and high-level features for robust tracking. Specifically, they first develop a generalised non-local attention module to enhance the discriminative capability of high-level semantic features. Then, they design an inception-like module to enhance the representative power of low-level features with more spatial details. Both types of features are then adaptively fused in the FAM to complement their characteristics. Extensive evaluations on OTB-2015 and VOT2017 challenge demonstrate that the proposed tracker consistently achieves favourable performance against several state-of-the-art trackers and runs at 50 fps.
Solution of Linear and Non-Linear Boundary Value Problems Using Population-Distributed Parallel Differential Evolution

Abstract Cases where the derivative of a boundary value problem does not exist or is constantly changing, traditional derivative can easily get stuck in the local optima or does not factually represent a constantly changing solution. Hence the need for evolutionary algorithms becomes evident. However, evolutionary algorithms are compute-intensive since they scan the entire solution space for an optimal solution. Larger populations and smaller step sizes allow for improved quality solution but results in an increase in the complexity of the optimization process. In this research a population-distributed implementation for differential evolution algorithm is presented for solving systems of 2 nd -order, 2-point boundary value problems (BVPs). In this technique, the system is formulated as an optimization problem by the direct minimization of the overall individual residual error subject to the given constraint boundary conditions and is then solved using differential evolution in the sense that each of the derivatives is replaced by an appropriate difference quotient approximation. Four benchmark BVPs are solved using the proposed parallel framework for differential evolution to observe the speedup in the execution time. Meanwhile, the statistical analysis is provided to discover the effect of parametric changes such as an increase in population individuals and nodes representing features on the quality and behavior of the solutions found by differential evolution. The numerical results demonstrate that the algorithm is quite accurate and efficient for solving 2 nd -order, 2-point BVPs.
Perception of Volumetric Characters' Eye-Gaze Direction in Head-Mounted Displays

Volumetric capture allows the creation of near-video-quality content that can be explored with six degrees of freedom. Due to limitations in these experiences, such as the content being fixed at the point of filming, an understanding of eye-gaze awareness is critical. A repeated measures experiment was conducted that explored users' ability to evaluate where a volumetrically captured avatar (VCA) was looking. Wearing one of two head-mounted displays (HMDs), 36 participants rotated a VCA to look at a target. The HMD resolution, target position, and VCA's eye-gaze direction were varied. Results did not show a difference in accuracy between HMD resolutions, while the task became significantly harder for target locations further away from the user. In contrast to real-world studies, participants consistently misjudged eye-gaze direction based on target location, but not based on the avatar's head turn direction. Implications are discussed, as results for VCAs viewed in HMDs appear to differ from face-to-face scenarios.
Internet of Things innovation in rural water supply in sub-Saharan Africa: a critical assessment of emerging ICT

Internet of Things (IoT) technologies are beginning to transform rural water supply in sub-Saharan Africa.Such Information Communication Technologies (ICTs) can facilitate change away from current unsustainable approaches that fail communities.Fast-moving developments in this area are under-researched, and sustainability of the innovations themselves and their place in the complex operating system require fuller consideration and presentation to the practitioner community.First, rural water supply in sub-Saharan Africa is critically contextualised as a 'wicked problem'.Second, specific challenges to rural water supply in Tanzania are quantitatively assessed using expert interviews.Analysis of these coupled with academic and practitioner-oriented literature demonstrates the need to move towards a 'service delivery approach'.Third, existing novel ICT and IoT technologies are categorised and critically evaluated, presenting the landscape of innovation to practitioners within the above context.Current research gaps are outlined.With a focus on research in the context of rapid technological innovation, the paper shows policy makers and practitioners how IoT innovations will support a service delivery approach.Longer-term planning using the enhanced data collection, and more integrated collection-to-use information flows, will advance service delivery further and increase sustainability.Practitioners must contextualise this with an appreciation of the complex operating system.
Semantic region of interest and species classification in the deep neural network feature domain

In this paper, we focus on animal object detection and species classification in camera-trap images collected in highly cluttered natural scenes. Using a deep neural network (DNN) model training for animal- background image classification, we analyze the input camera-trap images to generate a multi-level visual representation of the input image. We detect semantic regions of interest for animals from this representation using k-mean clustering and graph cut in the DNN feature domain. These animal regions are then classified into animal species using multi-class deep neural network model. According the experimental results, our method achieves 99.75% accuracy for classifying animals and background and 90.89% accuracy for classifying 26 animal species on the Snapshot Serengeti dataset, outperforming existing image classification methods.
Formant dynamics of Spanish vocalic sequences in related speakers: A forensic-voice-comparison investigation

This study investigates the dynamic acoustic properties of 19 vocalic sequences of Standard Peninsular Spanish, showing their potential for forensic voice comparison. Parametric curves (polynomials and discrete cosine transform) were fitted to the formant trajectories of the 19 Spanish vocalic sequences of 54 male speakers, comprising monozygotic (MZ) and dizygotic (DZ) twin pairs, non-twin brothers and unrelated speakers. Using the curve-fitting estimated coefficients as input to a multivariate-kernel-density formula, cross-validated likelihood ratios were calculated to express the probability of obtaining the observed difference between two speech samples under the hypothesis that the samples were produced by the same speaker and under the hypothesis that they were produced by a different speaker. The results show that the best-performing system is one that fuses the 19 vocalic sequences with a geometric-mean fusion method. When challenging the system with related speakers, the results show that MZ twin pairs affect performance but, more importantly, that non-twin sibling pairs can deteriorate performance too. This suggests that more investigations are necessary into a range of similar-sounding speakers beyond MZ twins. Several nurture aspects are highlighted as explanatory factors for the strikingly high similarity of a specific non-twin sibling pair.
Adversarial and counter-adversarial support vector machines

A support vector machine (SVM) is a simple but yet powerful classification technique widely used in various applications, such as handwritten digits classification and face recognition. However, as any linear classification algorithm, it is vulnerable to adversarial attacks on test/training data. The majority of machine learning attacks aim to corrupt online data to affect automated classification in a number of business applications. This work formulates a multistage game between an SVM and adversary. The SVM aims to maximize classification accuracy on a test dataset and includes a procedure for validating a training dataset, whereas the adversary aims to minimize the accuracy on the test dataset by perturbing the training dataset. At each stage, a new training data batch arrives and the SVM’s validation procedure yields a training set that includes only those batches whose distributions are sufficiently close to that of a validation dataset, which is not known to the adversary. The procedure uses a modified multivariate Cramér test. However, the SVM has access to the test dataset only in the very end, whereas the adversary knows it from the very beginning. SVM’s strategy is a sequence of thresholds used in the validation procedure, whereas adversary’s strategy is a sequence of upper bounds on perturbation norm. The adversary’s optimization problem at each stage is non-convex and two ways for solving it approximately are suggested. The proposed game is applied in malware detection. Among several considered SVM’s strategies, the one that increases the threshold when SVM performance on the validation set improves is the most efficient one.
Generalized Stress–Strain Curves for IBII Tests on Isotropic and Orthotropic Materials

This article presents a particular use of the Virtual Fields Method to exploit the results of Image-Based Inertial Impact (IBII) tests. This test consists on an edge-on impact of a free-standing thin flat rectangular coupon. The specimen response is recorded using an ultra-high speed camera filming the deformation of a grid pattern printed at its surface. From these images, displacement fields are derived, from which strain and acceleration can be obtained. The Virtual Fields Method makes use of the acceleration fields to derive stress information. Until now, a very simple ‘stress-gauge’ approach was used that could only provide relevant stress-strain information if the test was predominantly uniaxial. The alternative was to use the full inverse approach with the Virtual Fields Method but this would not allow the same degree of data understanding as the ‘stress-gauge’ approach. This article proposes an extension to this ‘stress-gauge’ approach for fully multiaxial tests. The equations are first derived and then validated using simulated and experimental IBII test data on isotropic and orthotropic materials.
An Empirical Study of Exception Handling Bugs and Fixes

Modern programming languages often provide a mechanism for programmers to handle exceptions, i.e., unexpected errors occurring while a software system is running. Failing to catch and handle exceptions properly often leads to serious issues such as crashing, resource leaking, or causing negative end-user experiences. However, learning exception handling patterns is often challenging due to the insufficiency of API documentation and source code examples. To understand the nature of exception-related bugs and how developers fix them, in this paper, we performed an empirical study on nearly 300 exception-related bugs and fixes from 10 mobile apps. We report five findings which provide insights about those exception bugs and fixes.
GISCA: Gradient-Inductive Segmentation Network With Contextual Attention for Scene Text Detection

Scene text detection (STD) is an irreplaceable step in a scene text reading system. It remains a more challenging task than general object detection since text objects are of arbitrary orientations and varying sizes. Generally, segmentation methods that use U-Net or hourglass-like networks are the mainstream approaches in multi-oriented text detection tasks. However, experience has shown that text-like objects in the complex background have high response values on the output feature map of U-Net, which leads to the severe false positive detection rate and degrades the STD performance. To tackle this issue, an adaptive soft attention mechanism called contextual attention module (CAM) is devised to integrate into U-Net to highlight salient areas and meanwhile retains more detail information. Besides, the gradient vanishing and exploding problems make U-Net more difficult to train because of the nonlinear deconvolution layer used in the up-sampling process. To facilitate the training process, a gradient-inductive module (GIM) is carefully designed to provide a linear bypass to make the gradient back-propagation process more stable. Accordingly, an end-to-end trainable Gradient-Inductive Segmentation network with Contextual Attention is proposed (GISCA). The experimental results on three public benchmarks have demonstrated that the proposed GISCA achieves the state-of-the-art results in terms of f-measure: 92.1%, 87.3%, and 81.4% for ICDAR 2013, ICDAR 2015, and MSRA TD500, respectively.
Interactive planning and sensing in unknown static environments with task-driven sensor placement

We address planar path-planning for a mobile vehicle to traverse a planar workspace with minimum exposure to a spatially varying scalar field called the threat field. The threat field is unknown, and is estimated by a finite number of sensors that take pointwise noisy measurements. We propose an iterative sensor placement and path-planning algorithm. At each iteration, regions of interest in the workspace are identified near the path with minimum estimated cost, and the next set of sensor locations is determined to improve the confidence of threat field estimates in these regions of interest. This paper breaks new ground in dynamic data-driven autonomy, in that an explicit bidirectional interaction between a path-planning algorithm and a sensor network separate from the actor vehicle is established. We provide theoretical results and corroborative numerical simulation results on the convergence and optimality of the proposed iterative algorithm. We show that the proposed algorithm provides significant improvements over existing approaches, including information-maximizing sensor placement methods.
Improved Whale Optimization Algorithm and Its Application to UCAV Path Planning Problem

This study proposes an improved whale optimization algorithm (WOA), termed improved WOA, by proposing a new judgment criterion for selecting the process of encircling prey or searching for prey in the WOA. The new judgment criterion is a self-tuning parameter that is based on the quality of agent's fitness instead of a random value used in the original WOA. The agent with higher fitness, i.e., superior agent, updates its position towards the best agent found so far. On the contrary, the agent with lower fitness, i.e., inferior agent, updates its position toward a reference agent which is selected randomly from the population. The performance of the proposed WOA is examined by testing six benchmark functions on low, medium, and high dimensions. Furthermore, the proposed WOA is applied to the path planning of unmanned combat aerial vehicle (UCAV). The computed results of flight path and optimal cost obtained using the improved WOA will be compared with those obtained using the original WOA.
Implementing mixed reality in automotive life cycle engineering: A visual analytics based approach

Supporting engineers with insights on product- or process related environmental impacts requires comprehensive interpretations of complex LCA models and results. Those rely on large amounts of data as well as different engineering models, both within the technosphere and at the interface to the ecosphere. Methods from visual analytics can support the interpretation of LCA results leading to a better integration of environmental constraints during decision-making. Interactivity is an essential element of the visual analytics process. In this regard, mixed reality technologies can increase interactivity and might lead to a better and faster understanding of the knowledge hidden behind the data. A case study applying a mixed reality solution within a visual analytics-based approach is introduced. It addresses the conceptual design stage in the engineering of future vehicle generations. Based on the case study, potentials and barriers of mixed reality in an automotive life cycle engineering context are discussed. Subsequently, an approach for evaluation with respect to capabilities in decision support is proposed.
Forensic analysis of BiP Messenger on android smartphones

Nowadays, social media applications and communication tools are utilized as communication platforms and the vast majority of communication is performed using these tools. The instant messaging applications have been widely used for peer to peer communication worldwide and BiP Messenger (BM) is one of them. BM has been generally used in Turkey. The BM was presented by Turkcell mobile GSM carrier offers many services such as messaging, video chatting, data transfer to smartphone users and approximately 10 million people have been used BM. Hence, it is one of the most encountered applications in forensics examinations. In this study, the BM is analysed using a mobile forensics methodology for the Android phones and this work presents the methodology of what artefacts produced by BM is, how to analyse them and how to show their relations with each other is presented. The proposed analysis methodology shows contact analyses, communication information, message information, deleted messages, group messages, message sending and receiving processes, the chronology of multimedia files and how to reconstruct them. The proposed methodology analysed data structure, communication protocols, permissions, user information, contact information, message information and log analyses of the BM.“wct 2 Comprehensively mobile forensics examinations of BM were presented using these analyses in this paper.
Invited Paper: Beagle: A New Framework for Smart Contracts Taking Account of Law

This paper presents a new Beagle framework for Smart Contracts (SCs) taking account of law. Different from previous SC development or execution frameworks, this framework takes a practical approach to integrate law into SCs. Instead of translating legal contracts into codes directly, this paper proposes to treat SCs as a key component of legal contracts, use SCs to partially automate the executions of legal contracts, and produce legal evidence during the process. Thus, the proposed SC design will be significantly different from previous SC designs, not in programming languages to be used, but in the way SCs are designed and executed. This Beagle framework has five stages: SC template development from domain analysis, formal SC model and code development from templates, verification and validation (V&V), SC execution, and runtime monitoring.
Discriminative Structure Learning of Bayesian Network Classifiers from Training Dataset and Testing Instance

Over recent decades, the rapid growth in data makes ever more urgent the quest for highly scalable Bayesian networks that have better classification performance and expressivity (that is, capacity to respectively describe dependence relationships between attributes in different situations). To reduce the search space of possible attribute orders, k-dependence Bayesian classifier (KDB) simply applies mutual information to sort attributes. This sorting strategy is very efficient but it neglects the conditional dependencies between attributes and is sub-optimal. In this paper, we propose a novel sorting strategy and extend KDB from a single restricted network to unrestricted ensemble networks, i.e., unrestricted Bayesian classifier (UKDB), in terms of Markov blanket analysis and target learning. Target learning is a framework that takes each unlabeled testing instance P as a target and builds a specific Bayesian model Bayesian network classifiers (BNC) P to complement BNC T learned from training data T . UKDB respectively introduced UKDB P and UKDB T to flexibly describe the change in dependence relationships for different testing instances and the robust dependence relationships implicated in training data. They both use UKDB as the base classifier by applying the same learning strategy while modeling different parts of the data space, thus they are complementary in nature. The extensive experimental results on the Wisconsin breast cancer database for case study and other 10 datasets by involving classifiers with different structure complexities, such as Naive Bayes (0-dependence), Tree augmented Naive Bayes (1-dependence) and KDB (arbitrary k-dependence), prove the effectiveness and robustness of the proposed approach.
A self-organizing deep belief network based on information relevance strategy

One of the major obstacles in using deep belief network (DBN) is the structure design. Numerous studies, both empirically and theoretically, show that choosing suitable structure can improve the performance of DBN. In this paper, a self-organizing DBN (S-DBN), based on the information relevance strategy (IRS), was proposed to design the structure of DBN. For this IRS, the maximal information coefficient was designed to measure the input and output information relevance of hidden neurons. Meanwhile, the mutual information was introduced to measure the information relevance among the hidden layers. Then, a novel self-organizing strategy was developed to grow and prune both the hidden neurons and layers during the training process. Moreover, a contrastive divergence algorithm was used to adjust the parameters of S-DBN. Finally, several benchmark problems were used to illustrate the effectiveness of S-DBN. The experimental results demonstrate that the proposed S-DBN owns better performance for classification problems and modeling nonlinear systems than some existing methods.