Reviewerly/exHarmony huggingface dataset:
Channel-wise Attention in 3D Convolutional Networks for Violence Detection

Aiming at the problems of low computational efficiency and insufficient precision for traditional violent behavior recognition methods, we propose a SELayer-3D Convolutional Neural Network (C3D). Firstly, the C3D model is adopted to extract the spatio-temporal feature information in the video block. Secondly the obtained spatio-temporal features are assigned weights according to the importance degree by SELayer. Finally, the output is predicted by the Softmax classifier. In the test experiment on the CrowdViolence dataset, our method achieves an accuracy of 98.08%, which is 6.08% higher than that of the Deeper 3D Convolutional Neural Network (D3D) model. In the test experiment on the HockeyFight dataset, our method achieves 99.0% accuracy, which is 2.0% higher than that of the FightNet model. The speed can reach 500 FPS or so compared to the artificial feature extraction method of 20-25 FPS. Moreover, experiments show that the proposed method has higher detection accuracy and efficiency.
Word Representation Learning Based on Bidirectional GRUs With Drop Loss for Sentiment Classification

Sentiment classification is a fundamental task in many natural language processing applications. Neural networks have achieved great successes on the sentiment classification task in recent years, since recurrent neural networks and long-short-term memory networks have the ability to deal with sequences of different lengths and to capture contextual semantic information. However, the effectiveness of these methods is limited when used to extract contextual information from relatively long texts. Therefore, in our model, we apply bidirectional gated recurrent units to capture contextual information as far as possible when learning word representations, which may effectively reduce the noise compared to other methods. We also propose a novel loss function namely drop loss (DL) which makes the model focus on the hard examples - examples which are easily classified incorrectly - in order to improve the accuracy of the model. We experiment on four commonly used datasets, and the results show that the proposed method has a good performance on four datasets, and needs fewer parameters compared with recent benchmarks, such as CoVe, ULMFiT, embeddings from language models, and bidirectional encoder representations from transformers. Furthermore, we demonstrate that the classification performance of existing shallow network models can be significantly improved by using DL. In particular, the accuracy of the CNN+LSTM model improves 9% on the IMDB-10 dataset.
Direct Visual and Haptic Volume Rendering of Medical Data Sets for an Immersive Exploration in Virtual Reality

Visual examination of volumetric medical data sets in virtual reality offers an intuitive and immersive experience. To further increase the realism of virtual environments, haptic feedback can be added. Such systems can help students to gain anatomical knowledge or surgeons to prepare for specific interventions. In this work, we present a method for direct visual and haptic rendering of volumetric medical data sets in virtual reality. This method guarantees a continuous force field and does not rely on any mesh or surface generation. Using a transfer function, we mapped computed tomography voxel intensities to color and opacity values and then visualized the anatomical structures using a direct volume rendering approach. A continuous haptic force field was generated based on a conservative potential field computed from the voxel opacities. In a path following experiment, we showed that the deviation from a reference path on the surface of the rendered anatomical structure decreased with the added haptic feedback. This system demonstrates an immersive exploration of anatomy and is a step towards patient-specific surgical planning and simulation.
DBpedia FlexiFusion the Best of Wikipedia &gt; Wikidata &gt; Your Data

The data quality improvement of DBpedia has been in the focus of many publications in the past years with topics covering both knowledge enrichment techniques such as type learning, taxonomy generation, interlinking as well as error detection strategies such as property or value outlier detection, type checking, ontology constraints, or unit-tests, to name just a few. The concrete innovation of the DBpedia FlexiFusion workflow, leveraging the novel DBpedia PreFusion dataset, which we present in this paper, is to massively cut down the engineering workload to apply any of the vast methods available in shorter time and also make it easier to produce customized knowledge graphs or DBpedias. While FlexiFusion is flexible to accommodate other use cases, our main use case in this paper is the generation of richer, language-specific DBpedias for the 20+ DBpedia chapters, which we demonstrate on the Catalan DBpedia. In this paper, we define a set of quality metrics and evaluate them for Wikidata and DBpedia datasets of several language chapters. Moreover, we show that an implementation of FlexiFusion, performed on the proposed PreFusion dataset, increases data size, richness as well as quality in comparison to the source datasets. Stable Databus IRI: https://databus.dbpedia.org/dbpedia/prefusion
The potential of using a combination of storytelling and drama, when teaching young children science

Based on a sociocultural perspective, this study explores the outcome of using a model that combines storytelling and drama to teach young children science. The research question is: How is children's learning affected when using a combination of storytelling and drama to explain a complex scientific concept?. Two preschools and one primary school were visited. Altogether 25 children aged 4–8 years participated. Each group listened to a story about The Rhinovirus Rita. No pictures were shown during storytelling. After the story was told, a play was performed with the children, telling the same story they just had listened to, and the children also made drawings. At a second visit to the schools, each child was interviewed individually and their drawings were used to stimulate recall. The results show that many of the children had learnt the names of immune system cells and how they work when someone has a cold. Moreover, they had also learnt that viruses cause colds. There were also a small number of children who did not show any learning development related to this specific content. Still, we argue that the combination of storytelling and drama is an instructional strategy that has positive potential when it comes to teaching children science.
A combined group decision making based IFCM and SERVQUAL approach for strategic analysis of airline service quality

Transportation becomes increasingly important for global trade, mobility, and economies. Passengers expect speed and quality from transportation services. As a result, the demand for air transportation is steadily rising. Higher demand for flights brings along new expectations and competition. In p articular, airline companies are focused on service quality, knowing that it is a necessity to survive in a competitive market by meeting customer expectations. Hence, it is aimed that to analyze Airline Service Quality (ASQ) in Turkey. A SERVQUAL based model is developed and the criteria of the model is analyzed using group decision making (GDM) based intuitionistic fuzzy cognitive map (IFCM) approach. A real case is conducted in Turkey airline industry. By considering the hesitation, uncertainty and intuition of decision-making processes and the opinions of the decision makers, classical FCM approach is expanded to IF environment. Decision makers who are experts in Turkey airline industry, evaluate the relationship between the criteria. The importance ratings of the criteria are determined. Thanks to dynamic structure of IFCM, scenario analysis is performed to make strategic decisions. Originality of the paper is that it is the first study dealing with GDM based IFCM and SERVQUAL approach for ASQ in Turkey to the best of our knowledge.
Noise-Resilient Training Method for Face Landmark Generation From Speech

Visual cues such as lip movements, when available, play an important role in speech communication. They are especially helpful for the hearing impaired population or in noisy environments. When not available, having a system to automatically generate talking faces in sync with input speech would enhance speech communication and enable many novel applications. In this article, we present a new system that can generate 3D talking face landmarks from speech in an online fashion. We employ a neural network that accepts the raw waveform as an input. The network contains convolutional layers with 1D kernels and outputs the active shape model (ASM) coefficients of face landmarks. To promote smoother transitions between video frames, we present a variant of the model that has the same architecture but also accepts the previous frame's ASM coefficients as an additional input. To cope with background noise, we propose a new training method to incorporate speech enhancement ideas at the feature level. Objective evaluations on landmark prediction show that the proposed system yields statistically significantly smaller errors than two state-of-the-art baseline methods on both a single-speaker dataset and a multi-speaker dataset. Experiments on noisy speech input with five types of non-stationary unseen noise show statistically significant improvements of the system performance thanks to the noise-resilient training method. Finally, subjective evaluations show that the generated talking faces have a significantly more convincing match with the input audio, achieving a similarly convincing level of realism as the ground-truth landmarks.
A Predictive Workload Balancing Algorithm in Cloud Services

Performance of dynamic clouds depends on the efficiency of its load balancing and resource allocation. This paper is an exploratory study on the predictive approach for dynamic resource distribution of cloud services. Efficient cloud resource management can be achieved by simulating cloud services based on the predictions of incoming workloads, which can be more efficient than static allocation methods. This paper introduces a rule-based workload-balancing algorithm based on the predictions of an end-to-end system called Cicada. A simulation of cloud services can be achieved by a cloud service simulator called CloudSim and it will be used to achieve an algorithm with lower computational demand and a faster workload balancing. The final result will demonstrate the effectiveness of a predictive workload balancing approach that can achieve faster workload balancing with a lower computational power usage.
Plan-Driven Approaches Are Alive and Kicking in Agile Global Software Development

Background: Agile methods are no longer restricted to small projects and co-located teams. The last decade has seen the spread of agile into large scale, distributed and regulated domains. Many case studies show successful agile adoption in GSD, however, taken as a whole, it remains unclear how widespread this trend is, and what form the agile adoption takes in a global software development (GSD) setting. Aims: Our objective is to gain a deeper understanding of how organisations adopt agile development methods in distributed settings. Specifically we aim to plot the current development process landscape in GSD. Method: We analyse industrial survey data from 33 different countries collected as part of the HELENA project that explored the wider use of hybrid development approaches in software development. We extract and analyse the results of 263 surveys completed by participants involved in globally distributed projects. Results: In our sample, 72% of globally distributed projects implement a mix of both agile and traditional approaches (termed `hybrid'). 25% of GSD projects are predominantly agile, with only very few (2%) opting for traditional approaches. GSD projects that used only agile methods tended to be very large. Conclusions: Globally Distributed Software Development (and project size) is not a barrier to adopting agile practices. Yet, to facilitate project coordination and general project management, many adopt traditional approaches, resulting in a hybrid approach that follows defined rules.
Data journalism’s many futures: Diagrammatic displays and prospective probabilities in data-driven news predictions

This article explores how newsmakers exploit numeric records in order to anticipate the future. As this nascent area of data journalism experiments with predictive analytics, we examine its reports and computer-generated presentations, often infographics and data visualizations, and ask what time frames and topics are covered by these diagrammatic displays. We also interrogate the strategies that are employed in order to modulate the uncertainty involved in calculating for more than one possible outlook. Based on a comprehensive sample of projects, our analysis shows how data journalism seeks accuracy but has to cope with a number of different prospective probabilities and the puzzle of how to address this multiplicity of futures. Despite their predictive ambition, these forecasts are inherently grounded in the past because they are based on archival data. We conclude that this form of quantified premediation limits the range of imaginable future thoughts to one preferred mode, namely extrapolation.
Facial Expression Recognition using Convolutional Neural Network on Graphs

Since convolutional neural network on graph (GCN) can process data with non-Euclidean structure compared with convolutional neural network, this paper constructs GCN network as a classifier of facial expression recognition and proposes a novel method of combining fixed points with random points to construct undirected graph from the face image. Firstly, face image is transformed into undirected graph by the method of combining fixed and random points. Then, we put the undirected graph into trained GCN and get the result of facial expression. The GCN constructed in this paper consists of six graph convolution layers, a fully connection layer and a SoftMax layer. Each graph convolution layer includes a signal filtering layer and a graph coarsening layer. The proposed method is evaluated on CK + and JAFFE datasets. The results show that the recognition rate of facial expression based on the method of combining fixed points with random points is higher than that based on the method of only using fixed points, and higher than that of using convolutional neural network.
Short-term power prediction of photovoltaic power station based on long short-term memory-back-propagation

Accurate prediction of the generation capacity of photovoltaic systems is fundamental to ensuring the stability of the grid and to performing scheduling arrangements correctly. In view of the temporal defect and the local minimum problem of back-propagation neural network, a forecasting method of power generation based on long short-term memory-back-propagation is proposed. On this basis, the traditional prediction data set is improved. According to the three traditional methods listed in this article, we propose a fourth method to improve the traditional photovoltaic power station short-term power generation prediction. Compared with the traditional method, the long short-term memory-back-propagation neural network based on the improved data set has a lower prediction error. At the same time, a horizontal comparison with the multiple linear regression and the support vector machine shows that the long short-term memory-back-propagation method has several advantages. Based on the long short-term memory-back-propagation neural network, the short-term forecasting method proposed in this article for generating capacity of photovoltaic power stations will provide a basis for dispatching plan and optimizing operation of power grid.
Social Robot and Sensor Network in Support of Activity of Daily Living for People with Dementia

In this paper, an eHealth system is described to improve the lifestyle of people with dementia and their caregivers. In the eWare project, a social robot is integrated with a sensor network to measure ADLs with the goal to provide context relevant suggestions to the person with dementia and reduce the caregiver burden. Furthermore, the context relevant messages can remind people with dementia to perform the activities that they want and need to perform, but forgot due to a decline of brain. The developments are based on iterative co-design and in-situ evaluations with people with dementia and their (in)formal carers. The architecture configuration of eWare was tested in laboratory experimental tests to provide a final ecosystem that will be further evaluated during the pilot cases of the project.
SCRSR: An efficient recursive convolutional neural network for fast and accurate image super-resolution

Convolutional neural networks have recently demonstrated high-quality reconstruction for single image super-resolution (SR). These CNN networks effectively recover a high-resolution (HR) image from a low-resolution (LR) image, at the cost of enormous parameters and heavy computational burden. In this work, we propose a recursive efficient deep convolutional network for fast and accurate single-image SR with only 0.28M parameters. A Split-Concatenate-Residual (SCR) block is proposed to reduce computation and parameters. With downsampling block and upsampling block, we significantly reduce computational complexity and enlarge the size of the receptive field. Specifically, two-level recursive learning is proposed which can improve accuracy by increasing depth without adding any weight parameters. We also employ local, semi-global and global residual techniques to train our very deep network steadily and improve its performance. Extensive experiments indicate that our proposed method Split-Concatenate-Residual Super Resolution (SCRSR) yields promising SR performance while maintaining shorter running time and fewer parameters.
The RealEstateCore Ontology

Recent developments in data analysis and machine learning support novel data-driven operations optimizations in the real estate industry, enabling new services, improved well-being for tenants, and reduced environmental footprints. The real estate industry is, however, fragmented in terms of systems and data formats. This paper introduces RealEstateCore (REC), an OWL 2 ontology which enables data integration for smart buildings. REC is developed by a consortium including some of the largest real estate companies in northern Europe. It is available under the permissive MIT license, is developed and hosted at GitHub, and is seeing adoption among both its creator companies and other product and service companies in the Nordic real estate market. We present and discuss the ontology’s development drivers and process, its structure, deployments within several companies, and the organization and plan for maintaining and evolving REC in the future. Resource Type: Ontology IRI: https://w3id.org/rec/full/3.0/ DOI: https://doi.org/10.5281/zenodo.2628367
HyperCubes: A Playful Introduction to Computational Thinking in Augmented Reality

We present HyperCubes, an Augmented Reality (AR) platform to foster computational literacy. Using paper cubes as AR markers and spatial tracking, the user becomes familiar with sequences of instructions as coding blocks. We leverage spatial cognition as a means to improve understanding of procedural and sequential models. We have performed two pilot studies for an iterative and user centered design of the platform. With a final qualitative user study we address engagement levels and the educational potential of the platform. We argue that by using spatial cognition and the flexibility of the AR medium, a playful introduction to basic computational thinking concepts can be presented in late elementary school and middle school children.
Ciphertext-only attack on optical scanning cryptography

The optical scanning cryptography (OSC) is one of the most utilized optical encryption systems. Our research is inspired from the observation that there is a linear system property within the ciphertext expression equation of OSC. Given the linear system property, it is feasible to recover the plaintext with cipher-only attack (COA). Our proposed COA method converts the problem of COA into a phase retrieval problem with a single intensity measurement. With the frequency domain constraint and the object domain support obtained from the ciphertext of OSC, the hybrid input-output (HIO) algorithm can be used to recover plaintext. Simulation results demonstrate the feasibility and robustness of the attack. This shows for the first time that the OSC scheme is vulnerable to COA attack.
A Hybrid Control Scheme for Adaptive Live Streaming

The live streaming is more challenging than on-demand streaming, because the low latency is also a strong requirement in addition to the trade-off between video quality and jitters in playback. To balance several inherently conflicting performance metrics and improve the overall quality of experience (QoE), many adaptation schemes have been proposed. Bitrate adaptation is one of the major solution for video streaming under time-varying network conditions, which works even better combining with some latency control methods, such as adaptive playback rate control and frame dropping. However, it still remains a challenging problem to design an algorithm to combine these adaptation schemes together. To tackle this problem, we propose a hybrid control scheme for adaptive live streaming, namely HYSA, based on heuristic playback rate control, latency-constrained bitrate control and QoE-oriented adaptive frame dropping. The proposed scheme utilizes Kaufman's Adaptive Moving Average (KAMA) to predict segment bitrates for better rate decisions. Extensive simulations demonstrate that HYSA outperforms most of the existing adaptation schemes on overall QoE.
Semantic-Aware Informative Path Planning for Efficient Object Search Using Mobile Robot

In this article, a novel informative path planning (IPP) framework is proposed for efficient robotic object search. We innovatively reformulate the object search into an IPP problem, which takes account of the knowledge of possible target object locations. To model the target object distribution knowledge, the semantic information of the focused environment is utilized to obtain the probabilities of finding the target object at possible locations. Then, the probability distribution is modeled by Gaussian mixture model (GMM) to generate an information map. Based on the map, a sampling-based IPP method is proposed to minimize the object search cost. It is worth noting that the object search path is planned with a tree structure and evaluated by a utility function that concerns both search information gain and path cost. Moreover, to improve the quality of the search path, a novel informative sampling strategy and a rewire mechanism are conceived. The performance of the proposed object search framework is fully evaluated through both simulation experiments and real-world tests with a mobile robot platform. Results demonstrated that our method can find the target object efficiently and robustly with shorter path length than three comparative methods in the literature and the mobile robot shows human-like behavior when searching for the target object.