Reviewerly/exHarmony huggingface dataset:
Exploring Application Opportunities for Smart Vehicles in the Continuous Interaction Space Inside and Outside the Vehicle

We describe applications that implement interactions between the driver and their smart vehicle in a continuous interaction space characterized by the physical distance to the vehicle and by the smart devices that implement those interactions. Specifically, we demonstrate the principles of smart vehicle proxemics with smart rings, smartwatches, smartphones, and other devices employed to interact with the in-vehicle infotainment system while the driver traverses five distinctly identifiable zones, from inside the vehicle to the personal, proximal, distant, and covert zone outside the vehicle. We present engineering details of our applications that capitalize on standardized web technology (HTML, CSS, JavaScript), communication protocols (WebSocket), and data formats (JSON) and, thus, enable straightforward extension to accommodate other smart devices for new interactions with smart vehicles. We also point to future opportunities for designing interactions from a distance and function of the distance between the driver and their vehicle.
An Optimization of Feature Selection for Classification Using Modified Bat Algorithm

Data mining is the action of searching the large existing database in order to get new and best information. It plays a major and vital role nowadays in all sorts of fields like Medical, Engineering, Banking, Education, and Fraud detection. In this paper, Feature selection which is a part of Data mining is performed to do classification. The role of feature selection is in the context of deep learning and how it is related to feature engineering. Feature selection is a preprocessing technique which selects the appropriate features from the data set to get the accurate result and outcome for the classification. Nature-inspired Optimization algorithms like Ant colony, Firefly, Cuckoo Search, and Harmony Search showed better performance by giving the best accuracy rate with less number of features selected and also fine f-Measure value is noted. These algorithms are used to perform classification that accurately predicts the target class for each case in the data set. We proposed two different techniques from Wrapper-based feature selection methods namely Forward/Backward Elimination and Exhaustive Search to find best optimal solution to perform classification using different Nature-Inspired Algorithms out of which Modified Bat Algorithm is proposed in this paper. We applied new and recent advanced optimized algorithm named Modified Bat algorithm on UCI datasets that showed comparatively equal results with best performed existing firefly but with less number of features selected. The work implemented using JAVA and the Medical dataset (UCI) has been used. These datasets were chosen due to nominal class features. The number of attributes, instances, and classes varies from chosen dataset to represent different combinations. Classification is done using J48 classifier in WEKA tool. We demonstrate the comparative results of the presently used algorithms with the existing algorithms thoroughly.
A live environment for inspection and refactoring of software systems

Refactoring helps to improve the design of software systems, making them more readable, maintainable, cleaner, and easy to expand. Most of the tools that already exist on this concept allow developers to select and execute the best refactoring techniques for a particular programming context. However, they aren’t interactive and prompt enough, providing a poor programming experience. In this gap, we can introduce and combine the topic of liveness with refactoring methods. Live Refactoring allows to know continuously, while programming, the blocks of code that we should refactor and why they were classified as problematic. Therefore, it shortens the time needed to create high-quality systems, due to early and continuous refactoring feedback, support, and guidance. This paper presents our research project based on a live refactoring environment. This environment is focused on a refactoring tool that aims to explore the concept of Live Refactoring and its main components --- recommendation, visualization, and application.
Smart Vehicle Proxemics: A Conceptual Framework Operationalizing Proxemics in the Context of Outside-the-Vehicle Interactions

We introduce smart vehicle proxemics, a conceptual framework for interactive vehicular applications that operationalizes proxemics to outside-the-vehicle interactions. We identify four zones around the vehicle affording different kinds of interactions and discuss the corresponding conceptual space along three dimensions (physical distance, interaction paradigm, and goal). We study the dimensions of this framework and synthesize our findings regarding drivers' preferences for (i) information to obtain from their vehicles at a distance, (ii) system functions of their vehicles to control remotely, and (iii) devices (e.g., smartphones, smartglasses, smart key fobs) for interactions outside the vehicle. We discuss the positioning of smart vehicle proxemics in the context of proxemic interactions more generally, and expand on the dichotomy and complementarity of outside-the-vehicle and inside-the-vehicle interactions for new applications enabled by smart vehicle proxemics.
Novel View Video Prediction using a Dual Representation

We address the problem of novel view video prediction; given a set of input video clips from a single/multiple views, our network is able to predict the video from a novel view. The proposed approach does not require any priors and is able to predict the video from wider angular distances, upto 45 degree, as compared to the recent studies predicting small variations in viewpoint. Moreover, our method relies only on RGB frames to learn a dual representation which is used to generate the video from a novel viewpoint. The dual representation encompasses a view-dependent and a global representation which incorporates complementary details to enable novel view video prediction. We demonstrate the effectiveness of our framework on two real world datasets: NTU- RGB+D and CMU Panoptic. A comparison with the State-of-the-art novel view video prediction methods shows an improvement of 26.1% in SSIM, 13.6% in PSNR, and 60% in FVD scores without using explicit priors from target views.
Examining Factors Impacting the Effectiveness of Anti-Phishing Trainings

Approximately 65% of the organizations in the United States have fallen victim to a successful phishing attack. Many organizations offer anti-phishing training to their employees to defend against phishing attacks. The purpose of this study is to examine factors impacting the effectiveness of anti-phishing training and study the relationship between personality traits and phishing susceptibility. Participants filled out pre- and post-training surveys that included questions on identifying phishing and legitimate URLs and questions to determine DISC (Dominant, Influence, Steadiness, and Conscientiousness) personality traits. An analysis of the survey data shows that the participants' average accuracy in detecting phishing URLs increased 8% (t = 2.144, p-value = 0.0374) and their confidence in their answer choices increased 6% (t = 2.032, p-value = 0.0464) from pre-training to post-training surveys. Before and after training, participants with the Influence personality trait had the lowest susceptibility while both Dominant and Steadiness personalities had the highest susceptibility before and after training respectively.
Developing a framework to overcome communication challenges in agile distributed teams – Case study of a Mauritian-based IT service delivery centre

Nowadays, many organisations have combined Agile Methodology with global software development due to the numerous benefits they offer such as faster time to market, cheaper and skilled labour, 24-hour development and better project control. However, agile methods prefer team members to be co-located to promote effective communication between team members and this lack of face-to-face communication in a distributed setting results in various communication challenges due to distance, time and cultural differences. This research proposes a framework to overcome communication challenges such as language barriers, time-zone differences and trust issues. A mixed-method research choice combining both qualitative and quantitative design was opted for this research where for the initial stage of the study, a systematic literature review was performed. The analysis of the survey data collected demonstrated that the majority of respondents have rated the framework components as effective, hence making the latter viable for adoption by distributed agile projects.
Multi-Scale Modeling of Neural Structure in X-Ray Imagery

Methods for resolving the brain’s microstructure are rapidly improving, allowing us to image large brain volumes at high resolutions. As a result, the interrogation of samples spanning multiple diversified brain regions is becoming increasingly common. Understanding these samples often requires multi-scale processing: segmentation of the detailed microstructure and large-scale modelling of the macrostructure. Current brain mapping algorithms often analyze data only at a single scale, and optimization for each scale occurs independently, potentially limiting the consistency, performance, and interpretability. In this work we introduce a deep learning framework for segmentation of brain structure at multiple scales. We leverage a modified U-Net architecture with a multi-task learning objective and unsupervised pre-training to simultaneously model both the micro and macro architecture of the brain. We successfully apply our methods to a heterogeneous, three-dimensional, X-ray micro-CT dataset spanning multiple regions in the mouse brain, and show that our approach consistently outperforms another multi-task architecture, and is competitive with strong single-task baselines at both scales.
A constructive approach to data-driven randomized learning for feedforward neural networks

There is an issue with the way in which feedforward neural networks with random hidden nodes generate random parameters in order to obtain a good projection space. Typically, random weights and biases are both drawn from the same interval, which is misguided as they have different functions. Recently, more sophisticated methods of random parameters generation have been developed, such as the data-driven approach, where the sigmoids are placed in randomly selected regions of the input space and then their slopes are adjusted to the local fluctuations of the target function. In this work, we propose a new constructive data-driven method that builds iteratively the network architecture. This method successively generates new candidate hidden nodes and accepts them when the training error falls significantly. The threshold of acceptance is adapted throughout training, accepting at the beginning of the training process only those nodes which lead to the largest reductions in error. In the next stages, the threshold is successively reduced to accept only those nodes which model the target function details more accurately. This leads to a more compact network architecture, as it includes only ”significant” nodes. It is worth noting that redundant, random nodes, which are usually generated by existing randomized learning methods, are not accepted by the proposed method. We empirically compared our approach with several alternative methods, including its predecessor, competitive randomized learning solutions, a gradient-based network and a generalized additive model. We found that our proposed approach outperformed its competitors in terms of fitting accuracy.
Multi-stream online transfer learning for software effort estimation: is it necessary?

Software Effort Estimation (SEE) may suffer from changes in the relationship between features describing software projects and their required effort over time, hindering predictive performance of machine learning models. To cope with that, most machine learning-based SEE approaches rely on receiving a large number of Within-Company (WC) projects for training over time, being prohibitively expensive. The approach Dycom reduces the number of required WC training projects by transferring knowledge from Cross-Company (CC) projects. However, it assumes that CC projects have no chronology and are entirely available before WC projects start being estimated. Given the importance of taking chronology into account to cope with changes, it may be beneficial to also take the chronology of CC projects into account. This paper thus investigates whether and under what circumstances treating CC projects as multiple data streams to be learned over time may be useful for improving SEE. For that, an extension of Dycom called OATES is proposed to enable multi-stream online learning, so that both incoming WC and CC data streams can be learnt over time. OATES is then compared against Dycom and five other approaches on a case study using four different scenarios derived from the ISBSG Repository. The results show that OATES improved predictive performance over the state-of-the-art when the number of CC projects available beforehand was small. Learning CC projects over time as multiple data streams is thus recommended for improving SEE in such scenario. When the number of CC projects available beforehand was large, OATES obtained similar predictive performance to the state-of-the-art. Therefore, CC data streams are unnecessary in this scenario, but are not detrimental either.
A generic Bayesian-based framework for enhancing top-N recommender algorithms

Personalized top-N recommender algorithms have been investigated widely in decades. The core task of different recommender algorithms is to estimate user-item preference scores and then to suggest, for each user, top-N items that have high preference scores. However, little attention was paid to the recommendation of items with low preference scores. In this work, we use the bayesian estimation theory to build the relationship between the estimated preference scores and the actual recommendation accuracy. We then propose a novel metric RNR (Recall-to-Noise Ratio) to recommend items with low estimated preference scores. An interesting counterintuitive phenomenon is found that user-item links with low preference scores may achieve higher accuracy than high preference score ones in the recommendation. Based on RNR, we design a generic framework that could recommend user-item links that have low preference scores without the loss of recommendation accuracy. The effectiveness of the proposed framework is illustrated by both theoretical analysis and empirical experiments.
Organizational implications of agile adoption: a case study from the public sector

While agile software development is increasingly adopted in large organizations, there is still a lack of studies on how traditionally organized enterprises adopt and scale agile forms of organization. This industrial multiple embedded case study explores how the organizational model of a large public sector entity evolved over four years to support the adoption of agile software development methods. Data was collected through semi-structured interviews and document analysis. We describe the change in three phases: pre-transformation, initial transformation, and maturing. Changes in three subcases of organizational units are further described in detail. Moving from an outsourced project-based way-of-working with separate business, IT and vendor organizations, the new organizational design emphasizes internal development capability, cross-functional autonomous teams organized around products and grouped in product areas, and continuous delivery. Starting from the IT department, the transformation expanded to the whole organization, and went beyond software development to the finance and leadership. We describe the target and intermediate organizations employed when adopting agile development methods for the whole organization and three organizational units responsible for different services. Defining suitable product boundaries, achieving alignment across teams, enhancing the competence of product owners, the coexistence of old and new types of systems, processes, and structures, and balancing the teams’ need for autonomy with the organizational needs for coordination and control are remaining challenges.
A hierarchical model for learning to understand head gesture videos

Head gesture videos recorded of a person bear rich information about the individual. Automatically understanding these videos can empower many useful human-centered applications in areas such as smart health, education, work safety and security. To understand a video’s content, low-level head gesture signals carried in the video that capture characteristics of both human postures and motions need to be translated into high-level semantic labels. To meet this aim, we propose a hierarchical model for learning to understand head gesture videos. Given a head gesture video of an arbitrary length, the model first segments the full-length video into multiple short clips for clip-based feature extraction. Multiple base feature extraction procedures are then independently tuned via a set of peripheral learning tasks without consuming any labels of the goal task. These independently derived base features are subsequently aggregated through a multi-task learning framework, coupled with a feature dimensionality reduction module, to optimally learn to accomplish the end video understanding task in an weakly supervised manner, utilizing the limited amount of video labels available of the goal task. Experimental results show that the hierarchical model is superior to multiple state-of-the-art peer methods in tackling versatile video understanding tasks.
Heterogeneous ensemble imputation for software development effort estimation

Choosing the appropriate Missing Data (MD) imputation technique for a given Software development effort estimation (SDEE) technique is not a trivial task. In fact, the impact of the MD imputation on the estimation output depends on the dataset and the SDEE technique used and there is no best imputation technique in all contexts. Thus, an attractive solution is to use more than one single imputation technique and combine their results for a final imputation outcome. This concept is called ensemble imputation and can help to significantly improve the estimation accuracy. This paper develops and evaluates a heterogeneous ensemble imputation whose members were the four single imputation techniques: K-Nearest Neighbors (KNN), Expectation Maximization (EM), Support Vector Regression (SVR), and Decision Trees (DT). The impact of the ensemble imputation was evaluated and compared with those of the four single imputation techniques on the accuracy measured in terms of the standardized accuracy criterion of four SDEE techniques: Case Based Reasoning (CBR), Multi-Layers Perceptron (MLP), Support Vector Regression (SVR) and Reduced Error Pruning Tree (REPTree). The Wilcoxon statistical test was also performed in order to assess whether the results are significant. All the empirical evaluations were carried out over the six datasets, namely, ISBSG, China, COCOMO81, Desharnais, Kemerer, and Miyazaki. Results show that the use of heterogeneous ensemble-based imputation instead single imputation significantly improved the accuracy of the four SDEE techniques. Indeed, the ensemble imputation technique was ranked either first or second in all contexts.
A Multi-view Convolutional Neural Network Approach for Image Data Classification

Multi-view learning promises to enhance the classical machine learning algorithm performance with the optimal setting. Currently, the Convolution Neural networks (CNN) is a widely utilized algorithm for image data for feature extraction and classification. The applicability of multi-view learning to enhance the CNN may be analyzed. Therefore, this research has focused to develop a novel approach, called Multi-view convolutional neural network (MvCNN) that enhances the classical CNN performance. The applicability has been analyzed by experiments over nine standard image datasets, where MvCNN performance has compared with single-view learning of CNN (SvCNN) successfully. The comparative analysis with classification accuracy and non-parametric statistical analysis shows that the performance of MvCNN is better than SvCNN.
Detecting Product Review Spammers Using Principles of Big Data

The growing consumerism has led to the importance of online reviews on the Internet. Opinions voiced by these reviews are taken into consideration by many consumers for making financial decisions online. This has led to the development of opinion spamming for profitable motives or otherwise. This work has been done to tackle the challenge of identifying such spammers, but the scale of the real-world review systems demands this problem to be tackled as a big data challenge. So, an effort has been made to detect online review spammers using the principle of big data. In this article, a rating-based model has been studied under the light of large-scale datasets (more than 80 million reviews by 20 million reviewers) using the Hadoop and Spark frameworks. Scale effects have been identified and mitigated to provide better context to large review systems. An improved computational framework has been presented to compute the overall spamcity of reviewers using exponential smoothing. The value of the smoothing factor was set empirically. Finally, future directions have been discussed.
Revisiting Binary Local Image Description for Resource Limited Devices

The advent of a panoply of resource limited devices opens up new challenges in the design of computer vision algorithms with a clear compromise between accuracy and computational requirements. In this letter we present new binary image descriptors that emerge from the application of triplet ranking loss, hard negative mining and anchor swapping to traditional features based on pixel differences and image gradients. These descriptors, BAD (Box Average Difference) and HashSIFT, establish new operating points in the state-of-the-art's accuracy vs. resources trade-off curve. In our experiments we evaluate the accuracy, execution time and energy consumption of the proposed descriptors. We show that BAD bears the fastest descriptor implementation in the literature while HashSIFT approaches in accuracy that of the top deep learning-based descriptors, being computationally more efficient. We have made the source code public <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Adapting, modifying and applying cinematography and editing concepts and techniques to cinematic virtual reality film production

Virtual reality (VR) filmmaking presents a unique cinematic experience requiring new and innovative ways to conceptualise and practice specific aspects of the production process. This article integrates a range of adapted and modified filmmaking thinking, approaches and components into the cinematic virtual reality (CVR) language and grammar in the two critical areas of cinematography and editing. This focus provides a range of possible strategies and tools for would-be VR film directors to engage more efficiently and effectively in VR film production. The article utilises an extended case study of the VR feature film Calling to present the director/editor’s observations and experiences using transmedia journaling and three-dimensional (3D) CVR previsualisation as a simulation tool to create this dynamic, interactive CVR film.