Reviewerly/exHarmony huggingface dataset:
Of Portals and Orbs

A lot of virtual reality (VR) experiences require switching between different environments or scenes. To achieve a plausible and perspicuous transition between those scenes, transition techniques can be used. These techniques visualize the change and should support continuity in storytelling and a high degree of presence while respecting the interactive nature of VR. We implemented six different transition techniques. Three of them are inspired by techniques which are used in movies since decades: Cut, Fade, and Dissolve. Furthermore, we added three techniques that leverage the peculiarities of VR: Portal, Orb, and Transformation. The techniques were compared in a user study regarding presence, continuity, usability, and preference. The results showed significant differences for all variables. In general, Orb and Portal received highest ratings.
Latent factor models and aggregation operators for collaborative filtering in reciprocal recommender systems

Online dating platforms help to connect people who might potentially be a good match for each other. They have exerted a significant societal impact over the last decade, such that about one third of new relationships in the US are now started online, for instance. Recommender Systems are widely utilized in online platforms that connect people to people in e.g. online dating and recruitment sites. These recommender approaches are fundamentally different from traditional user-item approaches (such as those operating on movie and shopping sites), in that they must consider the interests of both parties jointly. Latent factor models have been notably successful in the area of user-item recommendation, however they have not been investigated within user-to-user domains as of yet. In this study, we present a novel method for reciprocal recommendation using latent factor models. We also provide a first analysis of the use of different preference aggregation strategies, thereby demonstrating that the aggregation function used to combine user preference scores has a significant impact on the outcome of the recommender system. Our evaluation results report significant improvements over previous nearest-neighbour and content-based methods for reciprocal recommendation, and show that the latent factor model can be used effectively on much larger datasets than previous state-of-the-art reciprocal recommender systems.
Let there be IMU data

Recent advances in Machine Learning, in particular Deep Learning have been driving rapid progress in fields such as computer vision and natural language processing. Human activity recognition (HAR) using wearable sensors, which has been a thriving research field for the last 20 years, has benefited much less from such advances. This is largely due to the lack of adequate amounts of labeled training data. In this paper we propose a method to mitigate the labeled data problem in wearable HAR by generating wearable motion data from monocular RGB videos, which can be collected from popular video platforms such as YouTube. Our method works by extracting 2D poses from video frames and then using a regression model to map them to sensor signals. We have validated it on fitness exercises as the domain for which activity recognition is trained and shown that we can improve a HAR recognition model using data that was produced from a YouTube video.
Two-dimensional stitching interferometry for self-calibration of high-order additive systematic errors

Stitching interferometry is performed by collecting interferometric data from overlapped sub-apertures and stitching these data together to provide a full surface map. The propagation of the systematic error in the measured subset data is one of the main error sources in stitching interferometry for accurate reconstruction of the surface topography. In this work, we propose, using the redundancy of the captured subset data, two types of two-dimensional (2D) self-calibration stitching algorithms to overcome this issue by in situ estimating the repeatable high-order additive systematic errors, especially for the application of measuring X-ray mirrors. The first algorithm, called CS short for "Calibrate, and then Stitch", calibrates the high-order terms of the reference by minimizing the de-tilted discrepancies of the overlapped subsets and then stitches the reference-subtracted subsets. The second algorithm, called SC short for "Stitch, and then Calibrate", stitches a temporarily result and then calibrates the reference from the de-tilted discrepancies of the measured subsets and the temporarily stitched result. In the implementation of 2D scans in x- and y-directions, step randomization is introduced to generate nonuniformly spaced subsets which can diminish the periodic stitching errors commonly observed in evenly spaced subsets. The regularization on low-order terms enables a highly flexible option to add the curvature and twist acquired by another system. Both numerical simulations and experiments are carried out to verify the proposed method. All the results indicate that 2D high-order repeatable additive systematic errors can be retrieved from the 2D redundant overlapped data in stitching interferometry.
On the choice of similarity measures for type-2 fuzzy sets

Similarity measures are among the most common methods of comparing type-2 fuzzy sets and have been used in numerous applications. However, deciding how to measure similarity and choosing which existing measure to use can be difficult. Whilst some measures give results that highly correlate with each other, others give considerably different results. We evaluate all of the current similarity measures on type-2 fuzzy sets to discover which measures have common properties of similarity and, for those that do not, we discuss why the properties are different, demonstrate whether and what effect this has in applications, and discuss how a measure can avoid missing a property that is required. We analyse existing measures in the context of computing with words using a comprehensive collection of data-driven fuzzy sets. Specifically, we highlight and demonstrate how each method performs at clustering words of similar meaning.
Pedestrian detection based on improved LeNet-5 convolutional neural network

In this article, according to the real-time and accuracy requirements of advanced vehicle-assisted driving in pedestrian detection, an improved LeNet-5 convolutional neural network is proposed. Firstly, the structure of LeNet-5 network model is analyzed, and the structure and parameters of the network are improved and optimized on the basis of this network to get a new LeNet network model, and then it is used to detect pedestrians. Finally, the miss rate of the improved LeNet convolutional neural network is found to be 25% by contrast and analysis. The experiment proves that this method is better than SA-Fast R-CNN and classical LeNet-5 CNN algorithm.
Quantum phase communication channels assisted by non-deterministic noiseless amplifiers

We address quantum M-ary phase-shift keyed communication channels in the presence of phase diffusion and analyze the use of probabilistic noiseless linear amplifiers (NLAs) to enhance performance of coherent signals. We consider both static and dynamical phase diffusion and assess the performances of the channel for ideal and realistic phase receivers. Our results show that NLA employed at the stage of signal preparations is a useful resource, especially in the regime of weak signals. We also discuss the interplay between the use of NLA and the memory effects occurring with dynamical noise in determining the capacity of the channel.
A hybrid CNN-LSTM model based actuator fault diagnosis for six-rotor UAVs

With the development and popularity of multi-rotor UAVs, actuator fault diagnosis in multi-rotor UAVs has become more and more important. This paper proposes a deep-learning-based method to accurately locate actuator faults by using flight data of a real UAV. The proposed method splits the UAV's data into smaller pieces and then extracts features by one-dimensional convolutional neural network (1D-CNN), and explores internal connections of the UAV's time series data by adding the long short-term memory (LSTM). So, a hybrid CNN-LSTM model is developed for the fault diagnosis of actuator faults. Experiments show that the average accuracy of fault diagnosis of the hybrid CNN-LSTM model is 92.74%, which is better than that of other models, such as the CNN model, the LSTM model, and the deep neural network (DNN) model.
Attribute-aware non-linear co-embeddings of graph features

In very sparse recommender data sets, attributes of users such as age, gender and home location and attributes of items such as, in the case of movies, genre, release year, and director can improve the recommendation accuracy, especially for users and items that have few ratings. While most recommendation models can be extended to take attributes of users and items into account, their architectures usually become more complicated. While attributes for items are often easy to be provided, attributes for users are often scarce for reasons of privacy or simply because they are not relevant to the operational process at hand. In this paper, we address these two problems for attribute-aware recommender systems by proposing a simple model that co-embeds users and items into a joint latent space in a similar way as a vanilla matrix factorization, but with non-linear latent features construction that seamlessly can ingest user or item attributes or both (GraphRec). To address the second problem, scarce attributes, the proposed model treats the user-item relation as a bipartite graph and constructs generic user and item attributes via the Laplacian of the user-item co-occurrence graph that requires no further external side information but the mere rating matrix. In experiments on three recommender datasets, we show that GraphRec significantly outperforms existing state-of-the-art attribute-aware and content-aware recommender systems even without using any side information.
Examining Spontaneous Perspective Taking and Fluid Self-to-Data Relationships in Informal Open-Ended Data Exploration

(2020). Examining Spontaneous Perspective Taking and Fluid Self-to-Data Relationships in Informal Open-Ended Data Exploration. Journal of the Learning Sciences: Vol. 29, Situating Data Science: Exploring How Relationships to Data Shape Learning, pp. 32-56.
Frost Filtering Algorithm of SAR Images With Adaptive Windowing and Adaptive Tuning Factor

The traditional Frost filter is improved by the adaptive windowing and adaptive tuning factor for the synthetic aperture radar (SAR) images in this letter. The proposed double-adaptive Frost filter simultaneously makes the window size and the tuning factor adaptively adjusted in terms of the regional characteristics, leading to an effective balance between speckle suppression and edge preservation. The despeckling experiments on the simulated and real SAR images demonstrate that in comparison to the Lee filter, the Gamma maximum a posteriori (MAP) filter, the traditional Frost filter, and the Frost filter only with the adaptive tuning factor, the double-adaptive Frost filter sufficiently suppresses the speckle in homogeneous regions and in edge regions and effectively preserves the edges and fine details.
The correlation framework: Bridging the gap between modeling and analysis for 3D woven composites

A large variety of different descriptors can be employed for describing 3D woven composites. Each of them is targeted to a specific application, ranging from design, weaving, molding, impregnation down to non-destructive testing, imaging and numerical modeling of the actual micro-structure. In order to relate these different representations, it is proposed herewith to rely on the inherent weaving pattern as an intrinsic common feature, and to resort to images as a common language to guaranty the continuity of information. To connect these 3D images (either “real” or synthesized), Digital Volume Correlation (DVC) is called for in order to exploit the conservation of topology. A complete test scenario is devised in which different manufactured woven samples are compared to the theoretical textile arrangement. The results confirm the effectiveness of the method.
Hiring is Broken: What Do Developers Say About Technical Interviews?

Technical interviews-a problem-solving form of interview in which candidates write code-are commonplace in the software industry, and are used by several well-known companies including Facebook, Google, and Microsoft. These interviews are intended to objectively assess candidates and determine fit within the company. But what do developers say about them?To understand developer perceptions about technical interviews, we conducted a qualitative study using the online social news website, Hacker News-a venue for software practitioners. Hacker News posters report several concerns and negative perceptions about interviews, including their lack of real-world relevance, bias towards younger developers, and demanding time commitment. Posters report that these interviews cause unnecessary anxiety and frustration, requiring them to learn arbitrary, implicit, and obscure norms. The findings from our study inform inclusive hiring guidelines for technical interviews, such as collaborative problem-solving sessions.
The Potentials and Impacts of Blockchain Technology in Construction Industry: A Literature Review

Today, the Blockchain Technology has been widely explored and continuously revolutionising many sectors around the world, including construction industry. Construction industry need blockchain technology to improve the current limitation of centralised technology in its various project life-cycle. In this paper, we presented literature review, aimed to identify the potentials of Blockchain applications in construction industry. This paper also reviewed the unique features behind the blockchain technology which trigger its capabilities in construction industry. Paper further discussed the implications of the blockchain application if it were adopted in construction industry. In short, Blockchain technology is still relatively new in construction industry and further deep research is needed to develop real life on-hand application for construction industry in near future.
Enhanced Deep Learning Approach for Predicting Invasive Ductal Carcinoma from Histopathology Images

Breast cancer is the more prevalent form of cancer among women, and the most common type of breast cancer is the invasive ductal carcinoma (IDC). Accurate identification and categorization of breast cancer subtypes have major importance in clinical tasks, and automated approaches are relevant in saving time and reducing error. Deep learning has been applied to several breast cancer detection tasks. It outperformed traditional approaches that include handcrafted features for data representation and machine learning methods for learning task. In this paper, we develop a deep learning architecture for the prediction of IDC. This study trained an improved CNN network and investigated the performance of the model on the IDC patch-based classification task. Experimental results show that our approach yields the best performance on the IDC dataset when compared to other published approaches. Our model achieves f-score of 85.28% and balanced accuracy of 85.41% with increase improvementof11.51% on f-score and 0.86% on balanced accuracy against the latest published deep learning approach on IDC detection.
EVA: Generating Emotional Behavior of Virtual Agents using Expressive Features of Gait and Gaze

We present a novel, real-time algorithm, EVA, for generating virtual agents with various perceived emotions. Our approach is based on using Expressive Features of gaze and gait to convey emotions corresponding to happy, sad, angry, or neutral. We precompute a data-driven mapping between gaits and their perceived emotions. EVA uses this gait emotion association at runtime to generate appropriate walking styles in terms of gaits and gaze. Using the EVA algorithm, we can simulate gaits and gazing behaviors of hundreds of virtual agents in real-time with known emotional characteristics. We have evaluated the benefits in different multi-agent VR simulation environments. Our studies suggest that the use of expressive features corresponding to gait and gaze can considerably increase the sense of presence in scenarios with multiple virtual agents.
Deep Residual Autoencoder with Multiscaling for Semantic Segmentation of Land-Use Images

Semantic segmentation is a fundamental means of extracting information from remotely sensed images at the pixel level. Deep learning has enabled considerable improvements in efficiency and accuracy of semantic segmentation of general images. Typical models range from benchmarks such as fully convolutional networks, U-Net, Micro-Net, and dilated residual networks to the more recently developed DeepLab 3+. However, many of these models were originally developed for segmentation of general or medical images and videos, and are not directly relevant to remotely sensed images. The studies of deep learning for semantic segmentation of remotely sensed images are limited. This paper presents a novel flexible autoencoder-based architecture of deep learning that makes extensive use of residual learning and multiscaling for robust semantic segmentation of remotely sensed land-use images. In this architecture, a deep residual autoencoder is generalized to a fully convolutional network in which residual connections are implemented within and between all encoding and decoding layers. Compared with the concatenated shortcuts in U-Net, these residual connections reduce the number of trainable parameters and improve the learning efficiency by enabling extensive backpropagation of errors. In addition, resizing or atrous spatial pyramid pooling (ASPP) can be leveraged to capture multiscale information from the input images to enhance the robustness to scale variations. The residual learning and multiscaling strategies improve the trained model’s generalizability, as demonstrated in the semantic segmentation of land-use types in two real-world datasets of remotely sensed images. Compared with U-Net, the proposed method improves the Jaccard index (JI) or the mean intersection over union (MIoU) by 4-11% in the training phase and by 3-9% in the validation and testing phases. With its flexible deep learning architecture, the proposed approach can be easily applied for and transferred to semantic segmentation of land-use variables and other surface variables of remotely sensed images.
Twitter Dataset for Hate Speech and Cyberbullying Detection in Indonesian Language

During the 2019 election period in Indonesia, many hate speech and cyberbullying cases have occurred in social media platforms including Twitter. The government tries to filter every negative content to be spread out during this period. However, to detect hate speech is not an easy task. This paper presents the process of developing a dataset that can be used to build a hate speech detection model. More than 1 million tweets have been successfully collected from using Twitter API. The basic preprocessing and preliminary study using machine learning was implemented. Latent Dirichlet Allocation (LDA) algorithm was used to extract the topic for each tweet to see whether these topics can be associated with debate themes. Pretrained sentiment analysis was also applied to the dataset to generate a polarity score for each tweet. From 83,752 tweets included in the analysis step, the number of positive and negative tweets are almost the same.
Recurrent Tensor Factorization for time-aware service recommendation

With the advent of the era “everything is service ”, the emergence of Web services on the Internet is experiencing an exponential growth trend. How to recommend services to users that utilize sequential historical records has become one of the most challenging research topics in service computing. Tensor Factorization (TF) and Long Short Term Memory (LSTM) networks are two typical application paradigms for sequential service recommendation tasks. However, TF can only learn static short-term dependency patterns between users and services, ignoring the dynamic long-term dependency patterns between users and services. Although LSTM in Deep Leaning can learn dynamic long-term dependency patterns, it often encounters the trouble of vanishing gradient due to its complex gated mechanism. To address these critical challenges, we develop a novel model based on Deep Learning named Recurrent Tensor Factorization (RTF) with three innovations: (1) Three-dimensional interaction tensor of user–service-time was granulated into three fixed-size embedding dense vectors. (2) Personalized Gated Recurrent Unit (PGRU) and Generalized Tensor Factorization (GTF) simultaneously work on shared embedding dense vectors to memorize the long-term and short-term dependency patterns between users and services respectively. (3) Armed with GTF and PGRU, RTF is competent to predict the unknown Quality of Service (QoS) through comprehensive analysis. Experiments are conducted on real-world dataset, and the results indicate that our proposed method obviously outperforms six state-of-the-art time-aware service recommendation methods.
Data Augmentation of Minority Class with Transfer Learning for Classification of Imbalanced Breast Cancer Dataset Using Inception-V3

In this paper, deep learning based experiments are conducted to investigate the effect of data augmentation on the minority class for the imbalanced breast cancer histopathology dataset (BREAKHIS). Two different pre-trained networks are fine-tuned with the minority-augmented dataset. The pre-trained networks were already trained on the well-known ImageNet dataset comprising of millions of high resolution images belonging to multiple object categories. The model so trained is further subjected to transfer learning, to correctly classify cancerous pattern from non-cancerous conditions, in a supervised manner. Our experiments were carried out in two phases. Phase-I investigates the effect of data augmentation applied on minority class for the Inception-v3 and ResNet-50 pre-trained networks. Results of phase-I are further enhanced in phase-II by the transfer learning approach in which features extracted from all layers of Inception-v3 are learnt by the SVM and weighted SVM classifiers. From experimental results, it was found that the pre-trained Inception-v3 model with data augmentation on minority class outperforms other network types. Results also indicate that Inception-v3 with data augmentation of minority class and transfer learning with weighted SVM gives the highest classification accuracies.
Learning Bayesian networks with local structure, mixed variables, and exact algorithms

Modern exact algorithms for structure learning in Bayesian networks first compute an exact local score of every candidate parent set, and then find a network structure by combinatorial optimization so as to maximize the global score. This approach assumes that each local score can be computed fast, which can be problematic when the scarcity of the data calls for structured local models or when there are both continuous and discrete variables, for these cases have lacked efficient-to-compute local scores. To address this challenge, we introduce a local score that is based on a class of classification and regression trees. We show that under modest restrictions on the possible branchings in the tree structure, it is feasible to find a structure that maximizes a Bayes score in a range of moderate-size problem instances. In particular, this enables global optimization of the Bayesian network structure, including the local structure. In addition, we introduce a related model class that extends ordinary conditional probability tables to continuous variables by employing an adaptive discretization approach. The two model classes are compared empirically by learning Bayesian networks from benchmark real-world and synthetic data sets. We discuss the relative strengths of the model classes in terms of their structure learning capability, predictive performance, and running time.