Reviewerly/exHarmony huggingface dataset:
Big Data ETL Process and Its Impact on Text Mining Analysis for Employees’ Reviews

Big data analysis is challenging in the current context for enterprises that would like to apply these capabilities in the human resource sector. This paper will show how an organization can take advantage of the current or former employees’ reviews that are provided on a constant basis on different sites, so that the management can adjust or change business decisions based on employees’ wishes, dissatisfaction or needs. Considering the previously mentioned challenge on big data analysis, this research will first provide the best practice for the collection and transformation of the data proposed for analysis. The second part of this paper presents the extraction of two datasets containing employee reviews using data scraping techniques, the analysis of data by using text mining techniques to retrieve business insights and the comparison of the results for these algorithms. Experimental results with Naïve Bayes, Logistic Regression, K-Nearest Neighbor and Support Vector Machine for employee sentiment prediction showed much better performances for Logistic Regression. Three out of the four analyzed algorithms performed better for the second, triple-size dataset. The final aim of the paper is to provide an end-to-end solution with high performance and reduced costs.
Bow and twist deformation measurement system of fuel assembly with underwater dual-line structured light

To realize the non-contact on-line measurement of the crucial dimension for the fuel assembly and assist the nuclear power plant to carry out regular maintenance works in the core, we design an underwater fuel assembly deformation measurement system (UFADMS). Based on the principle of dual-optical line laser triangulation, the deformation measurement system integrates two sets of optical units. During the measurement, each optical unit projects two beams of laser to the surface and captures the relevant image for further process, respectively. According to the point clouds obtained by underwater calibration, three-dimensional (3D) reconstruction, and global calibration, we can calculate the relative relationship between the two optical units, and normalize the corresponding measurement data to calculate the bow and twist deformation of the fuel assembly. Benefitting from the novel waterproof design, the dual-optical line laser triangulation principle, and the underwater single-layer refractive geometry, the measurement system has strong environmental adaptability, high measurement accuracy, and short measurement time. Through the underwater field measurement and validation of standard blocks and fuel assembly, the bow deformation measurement accuracy is within 0.4 mm, and the twist deformation measurement accuracy is within 0.15°. What’s more, the proposed system allows transient measurement at a certain depth of water, which provides a guarantee for on-line measurement of fuel assembly underwater.
Linguistic Profiling of Text Genres: An Exploration of Fictional vs. Non-Fictional Texts

Texts are composed for multiple audiences and for numerous purposes. Each form of text follows a set of guidelines and structure to serve the purpose of writing. A common way of grouping texts is into text types. Describing these text types in terms of their linguistic characteristics is called ‘linguistic profiling of texts’. In this paper, we highlight the linguistic features that characterize a text type. The findings of the present study highlight the importance of parts of speech distribution and tenses as the most important microscopic linguistic characteristics of the text. Additionally, we demonstrate the importance of other linguistic characteristics of texts and their relative importance (top 25th, 50th and 75th percentile) in linguistic profiling. The results are discussed with the use case of genre and subgenre classifications with classification accuracies of 89 and 73 percentile, respectively.
Evaluating TEI

chapter Share on Evaluating TEI Authors: Brygg Ullmer Clemson University Clemson UniversitySearch about this author , Orit Shaer Wellesley College Wellesley CollegeSearch about this author , Ali Mazalek Toronto Metropolitan University Toronto Metropolitan UniversitySearch about this author , Caroline Hummels Search about this author Authors Info & Claims Weaving Fire into Form: Aspirations for Tangible and Embodied InteractionJuly 2022https://doi.org/10.1145/3544564.3544576Published:26 July 2022Publication History 0citation3DownloadsMetricsTotal Citations0Total Downloads3Last 12 Months3Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Bug report summarization using multi-view multi-objective optimization framework

Existing bug text reports are widely used by software engineers to assist them in understanding important components of individual defects and adjustments done to resolve the fault. However, bug reports are typically long and require significant effort to comprehend. Summarization of bug reports appears to be beneficial in this regard, covering essential and diverse information. We frame the Bug report summarization problem as a clustering-based optimization problem and solve it using a multi-view multi-objective optimization framework. To represent the bug reports, semantic and syntactic representations, which are regarded as separate views, are taken into account. Several cluster quality measures computed on partitionings obtained using distinct views are optimized simultaneously using a multi-objective optimization-based approach known as archived multi-objective simulated annealing. To determine the consensus between the partitionings generated using different views, an agreement index is computed, which is also optimized simultaneously along with other cluster quality measures. The proposed methodology automatically determines the number of clusters. The experiments are carried out using the two benchmark datasets (SDS and ADS) and evaluated using the well-known ROUGE, Precision, Recall, and F-measure evaluation metrics. The obtained results show that the proposed methodology outperforms state-of-the-art methods.
Virtual contrast enhancement for CT scans of abdomen and pelvis

Contrast agents are commonly used to highlight blood vessels, organs, and other structures in magnetic resonance imaging (MRI) and computed tomography (CT) scans. However, these agents may cause allergic reactions or nephrotoxicity, limiting their use in patients with kidney dysfunctions. In this paper, we propose a generative adversarial network (GAN) based framework to automatically synthesize contrast-enhanced CTs directly from the non-contrast CTs in the abdomen and pelvis region. The respiratory and peristaltic motion can affect the pixel-level mapping of contrast-enhanced learning, which makes this task more challenging than other body parts. A perceptual loss is introduced to compare high-level semantic differences of the enhancement areas between the virtual contrast-enhanced and actual contrast-enhanced CT images. Furthermore, to accurately synthesize the intensity details as well as remain texture structures of CT images, a dual-path training schema is proposed to learn the texture and structure features simultaneously. Experiment results on three contrast phases (i.e. arterial, portal, and delayed phase) show the potential to synthesize virtual contrast-enhanced CTs directly from non-contrast CTs of the abdomen and pelvis for clinical evaluation.
Practical attacks on Login CSRF in OAuth

OAuth 2.0 is an important and well studied protocol. However, despite the presence of guidelines and best practices, the current implementations are still vulnerable and error-prone. This research mainly focused on the Cross-Site Request Forgery (CSRF) attack. This attack is one of the dangerous vulnerabilities in OAuth protocol, which has been mitigated through state parameter. However, despite the presence of this parameter in the OAuth deployment, many websites are still vulnerable to the OAuth-CSRF (OCSRF) attack. We studied one of the most recurrent type of OCSRF attack through a variety range of novel attack strategies based on different possible implementation weaknesses and the state of the victim’s browser at the time of the attack. In order to validate them, we designed a repeatable methodology and conducted a large-scale analysis on 395 high-ranked sites to assess the prevalence of OCSRF vulnerabilities. Our automated crawler discovered about 36% of targeted sites are still vulnerable and detected about 20% more well-hidden vulnerable sites utilizing the novel attack strategies. Based on our experiment, there was a significant rise in the number of OCSRF protection compared to the past scale analyses and yet over 25% of sites are exploitable to at least one proposed attack strategy. Despite a standard countermeasure exists to mitigate the OCSRF, our study shows that lack of awareness about implementation mistakes is an important reason for a significant number of vulnerable sites.
Where Does Bias in Common Sense Knowledge Models Come From?

Common Sense knowledge bases and models have been shown to embed bias. We investigate the source of such bias in a knowledge model called common sense transformer (COMET) by training it on various combinations of language models and knowledge bases. We experiment with three language models of different sizes and architectures, and two knowledge bases with different modeling principles. We use sentiment and regard as proxy measures of bias and analyze bias using three methods: overgeneralization and disparity, keyword outliers, and relational dimensions. Our results show that larger models tend to be more nuanced in their biases but are more biased than smaller models in certain categories (e.g., utility of religions), which can be attributed to the larger knowledge accumulated during pretraining. We also observe that training on a larger set of common sense knowledge typically leads to more bias, and that models generally have stronger negative regard than positive.
Principled Machine Learning

We introduce the underlying concepts which give rise to some of the commonly used machine learning methods, excluding deep-learning machines and neural networks. We point to their advantages, limitations and potential use in various areas of photonics. The main methods covered include parametric and non-parametric regression and classification techniques, kernel-based methods and support vector machines, decision trees, probabilistic models, Bayesian graphs, mixture models, Gaussian processes, message passing methods and visual informatics.
Vision talks: Visual relationship-enhanced transformer for video-guided machine translation

Video-guided machine translation is a promising task which aims to translate a source language description into a target language utilizing the video information as supplementary context. The majority of existing work utilize the whole video as the auxiliary information to enhance the translation performance. However, visual information, as a heterogeneous modal with text, introduces noise instead. Toward this end, we propose a novel visual relationship-enhanced transformer by constructing a semantic-visual relational graph as a corss-modal bridge. Specifically, the visual information is regarded as the structured conceptual representation, which builds a bridge between two modalities. Thereafter, graph convolutional network is deployed to capture the relationship among visual semantics. In this way, a transformer with structured multi-modal fusion strategy is allowed to explore the correlations. Finally, the proposed framework is optimized under the scheme of Kullback–Leibler divergence with label smoothing. Extensive experiments demonstrate the rationality and effectiveness of our proposed method as compared to other state-of-the-art solutions. • Structured conceptual representation contributes a bridge in multi-modal fusion. • Objects’ semantics and relationships reflect the visual content effectively. • Use graph convolutional network to explore relationships among visual semantics.
Two-stream interactive network based on local and global information for No-Reference Stereoscopic Image Quality Assessment

Nowadays, stereoscopic image quality assessment (SIQA) based on convolutional neural network (CNN) has become the mainstream model of image quality assessment (IQA). Compared with the two-dimensional quality evaluation model, stereoscopic image quality evaluation is more challenging due to the effects of depth and parallax information. In this paper, we propose a two-stream interactive network model to perform quality evaluation, which can well simulate the process of human stereo visual perception. Meanwhile, we enhance the extraction of local and global features of images by asymmetric convolution kernel and interactive sub-networks of inter-layers, respectively, which can further optimize our network model. Our proposed algorithm was evaluated on four public databases. The final experimental results show that our proposed algorithm exhibits good performance not only on the whole database but also on each single distortion type.
Comparison of weighted least-squares phase unwrapping algorithms in noise resistance, discontinuity characteristics, convergence speed, and accuracy

In practice, the wrapped phase in interferometry is often affected by noise and discontinuity, and among the various types of phase unwrapping (PU) method, the weighted least-square (WLS) PU algorithm, as a global strategy, is widely utilized. However, the excessive smoothing effect exists within the process of PU. Therefore, it is necessary to conduct a comprehensively analysis of different WLS PU algorithms, which includes noise resistance, discontinuity characteristics, convergence speed, and accuracy. First, different weighting strategies were compared with detail for the WLS approach. Under slight noise condition, the edge detection map (EDM) and the filtering method both obtained relatively accurate and reliable phase information. However, when it comes to global multiplicative noise such as speckle noise, the filtering method showed better anti-noise performance than the EDM method, whereas EDM was more capable of dealing with discontinuous phase. Second, to improve the iterative convergence speed and accuracy, the initial value selection was analyzed in detail, and a new initial value selection method was proposed. Simulation and experiments were carried out and validated the results of the analysis.
A Real-Time Distance Prediction via Deep Learning and Microsoft Kinect

Abstract 3D(Dimension) understanding has become the herald of computer vision and graphics research in the era of technology. It benefits many applications such as autonomous cars, robotics, and medical image processing. The pros and cons of 3D detection bring convenience to the human community instead of 2D detection. The 3D detection consists of RGB (Red, Green and Blue) colour images and depth images which are able to perform better than 2D in real. The current technology is relying on the high costing light detection and ranging (LiDAR). However, the use of Microsoft Kinect has replaced the LiDAR systems for 3D detection gradually. In this project, a Kinect camera is used to extract the depth of image information. From the depth images, the distance can be defined easily. As in the colour scale, the red colour is the nearest and the blue colour is the farthest. The depth image will turn black when reaching the limitation of the Kinect camera measuring range. The depth information collected will be trained with deep learning architecture to perform a real-time distance prediction.
Siamese Adaptive Transformer Network for Real-Time Aerial Tracking

Recent visual object trackers provide strong discriminability towards accurate tracking under challenging scenarios while neglecting the inference efficiency. Those methods handle all inputs with identical computation and fail to reduce intrinsic computational redundancy, which constrains their deployment on Unmanned Aerial Vehicles (UAVs). In this work, we propose a dynamic tracker which selectively activates the individual model components and allocates computation resources on demand during the inference, which allows deep network inference on onboard-CPU at real-time speed. The tracking pipeline is divided into several stages, where each stage consists of a transformer-based encoder that generates a robust target representation by learning pixels interdependence. An adaptive network selection module controls the propagation routing path determining the optimal computational graph according to confidence-based criteria. We further propose a spatial adaptive attention network to avoid computational overhead in the transformer encoder, where the self-attention only aggregates the dependencies information among selected points. Our model achieves a harmonious proportion between accuracy and efficiency for dealing with varying scenarios, leading to notable advantages over static models with a fixed computational cost. Comprehensive experiments on aerial and prevalent tracking benchmarks achieve competitive results while operating at high speed, demonstrating its suitability on UAV-platforms which do not carry a dedicated GPU.
Drone Collision Detection and Classification using Proprioceptive Data

This paper proposes an approach for collision detection and obstacle classification based on the physical interaction between a drone and the environment. Our method does not require any special collision-detection sensor, since it uses only proprioceptive data (accelerations). Time-series classification of acceleration data is carried out by a deep neural network, which uses acceleration time-series to classify the drone’s flight mode into three classes: no collision, collision with a soft obstacle, and collision with a hard obstacle. Experimental results showed that the classification achieved accuracy of 98.7% on the testing set. Also, the method was used to perform on-line flight state predictions, when data is applied continuously into the neural network’s input, thus showing the potential of this method for collision detection and classification in several applications.
A 3D Benchmark for UAV Path Planning Algorithms: Missions Complexity, Evaluation and Performance

Unnamed Aerial Vehicles can carry out several missions. It is essential to use path planning algorithms to obtain an efficient trajectory without collision to complete these missions. Several path planning algorithms in the literature are divided into four categories: exact classic, approximate classic, meta heuristic, and machine learning. However, it is not easy to define which is the best for each mission among all path planning algorithms. In this context, an analysis between these categories can facilitate research determining the techniques that obtain better results in each UAV mission. So, in this work, we perform a deep benchmarking of 3D path planning techniques in a simulated and real environment. As a result, classical techniques demonstrate better capacities in dynamic path planning. On the other hand, meta heuristic and machine learning techniques performed the best results for static path planning.
The Co4AIR Marathon – A Matlab Simulated Drone Racing Competition

We describe a UAV competition concept in which a Parrot Mambo drone must race over a sequence of colored markers in minimum time. The competition is implemented in Matlab, using the Simulink Support Package for Parrot Minidrones, and can be organized fully in simulation, although an optional real-drone component is included. Students with either control or computer-science backgrounds are accommodated by providing baseline solution modules for the part outside their expertise. We present the competition design, a baseline solution, and our experience with the first edition, which was held in 2021, including student feedback and lessons learned.
Tangent-Based Method for Collision Detection and Avoidance System for UAVs using ADS-B Transponder and ADS-B Like Solution

This paper discusses a tangent-based collision avoidance method to avoid velocity-dependent ellipsoid geometry for high-density ADS-B traffic areas. A velocity- dependent energy function constrains the possible altitudes based on a predetermined post-maneuver velocity. This method allows the aircraft maneuvers to be constrained to the physical limits of the ownship, optimizing aircraft energy retention. Using velocity-based extrapolation of the ownship and intruder aircraft positions obtained from ADS-B or custom ADS-B solution data, a future collision may be predicted. In a predetermined period before the estimated collision, the positions of the two aircraft are considered to be static, and a corrective trajectory is generated. The corrective trajectory is calculated through a tangent plane on the velocity-dependent ellipsoid region surrounding the intruder. The tangent plane's surface contains the current location of the ownship, as well as the location of the target maneuver position. Then, the closest of the two avoidance waypoints is selected, the angles relative to the aircraft's current heading are calculated, and commands are issued to reach the new specified attitude. The developed algorithm was tested in simulation prior to flight testing. Simulation and flight test results are shown.
Fast search method for moving targets using multiple UAVs cooperative searching

This paper presents the moving targets fast search method with multiple UAVs cooperative searching. In order to decrease the search duration and improve the search efficiency of moving targets, the dynamic and time-sensitive key area model is formulated according to the target probability map by just searching the key areas instead of searching whole mission area. On this basis, these key areas are allocated to different UAVs and the moving targets searching problem is modeled as the dynamic coupled traveling salesman problem (DC-TSP). The coupled solution framework consisting of the task allocation layer and the path planning layer is designed to solve this problem, where the former confirms the number of tasks and execution order for each UAV to sequential search the key areas using an opposition-based genetic algorithm with the elite strategy, and the latter solves the search path for the UAV to execute its search tasks using depth first search with the receding horizon optimization. The numerical simulation results demonstrate that FSM proposed can significantly reduce the mission duration and find the all moving targets in mission area, outperforming than distributed ant colony optimization and genetic algorithm.
A Pressure Washing Hosing-Drone – Mitigating Reaction Forces and Torques

In order to keep workers out of harm’s way and to improve the efficiency of infrastructure cleaning and inspection, a Hosing-Drone is proposed. The main challenge to implementing high pressure spraying with a UAV is compensating for the strong reaction forces and torques. Hose dynamics are explored with a series of experiments involving a free hose under high pressure flow in an infrared tracking testbed, an actuated hose with nozzle controlled via a gantry testbed, and with early field testing of the UAS. Analytic models for the dynamics are proposed and fit to experimental data. A simple control law inspired by the classic "cart-pendulum" problem is presented for eliminating swing in the dangling hose.