Reviewerly/exHarmony huggingface dataset:
Single Image Intrinsic Decomposition with Discriminative Feature Encoding

Intrinsic image decomposition is an important and long-standing computer vision problem. Given a single input image, recovering the physical scene properties is ill-posed. In this work, we take the advantage of deep learning, which is proven to be highly efficient in solving the challenging computer vision problems including intrinsic image decomposition. Our focus lies in the feature encoding phase to extract discriminative features for different intrinsic layers from a single input image. To achieve this goal, we explore the distinctive characteristics between different intrinsic components in the high dimensional feature embedding space. We propose a feature divergence loss to force their high-dimensional embedding feature vectors to be separated efficiently. The feature distributions are also constrained to fit the real ones. In addition, we provide an approach to remove the data inconsistency in the MPI Sintel dataset, making it more proper for intrinsic image decomposition. Experimental results indicate that the proposed network structure is able to outperform the state-of-the-art methods.
UXmood—A Sentiment Analysis and Information Visualization Tool to Support the Evaluation of Usability and User Experience

This paper presents UXmood, a tool that provides quantitative and qualitative information to assist researchers and practitioners in the evaluation of user experience and usability. The tool uses and combines data from video, audio, interaction logs and eye trackers, presenting them in a configurable dashboard on the web. The UXmood works analogously to a media player, in which evaluators can review the entire user interaction process, fast-forwarding irrelevant sections and rewinding specific interactions to repeat them if necessary. Besides, sentiment analysis techniques are applied to video, audio and transcribed text content to obtain insights on the user experience of participants. The main motivations to develop UXmood are to support joint analysis of usability and user experience, to use sentiment analysis for supporting qualitative analysis, to synchronize different types of data in the same dashboard and to allow the analysis of user interactions from any device with a web browser. We conducted a user study to assess the data communication efficiency of the visualizations, which provided insights on how to improve the dashboard.
Person Head Detection Based Deep Model for People Counting in Sports Videos

People counting in sports venues is emerging as a new domain in the field of video surveillance. People counting in these venues faces many key challenges, such as severe occlusions, few pixels per head, and significant variations in person's head sizes due to wide sport areas. We propose a deep model based method, which works as a head detector and takes into consideration the scale variations of heads in videos. Our method is based on the notion that head is the most visible part in the sports venues where large number of people are gathered. To cope with the problem of different scales, we generate scale aware head proposals based on scale map. Scale aware proposals are then fed to the Convolutional Neural Network (CNN) and it provides a response matrix containing the presence probabilities of people observed across scene scales. We then use non-maximal suppression to get the accurate head positions. For the performance evaluation, we carry out extensive experiments on two standard datasets and compare the results with state-of-the-art (SoA) methods. The results in terms of Average Precision (AvP), Average Recall (AvR), and Average F1-Score (AvF-Score) show that our method is better than SoA methods.
UAV Localization Using Panoramic Thermal Cameras

Drone detection and localization became a real challenge of many companies over the past years and for the years to come. Several technologies of different kind have given some results. Among them, thermal sensors, particularly panoramic thermal imager can provide good results. In this paper, we will address two subjects. First, we will introduce the characteristics that panoramic thermal imaging systems should reach to prove their efficiency in a C-UAV system. Then, in a second part, we will present the use of data captured from multiple 360° cameras together in order to localize targets in a 3D environment and the benefits that flow from it: distance, altitude, GPS coordinates, speed, physical dimensions can then be estimated.
Comparison of Data-Driven Techniques for Nowcasting Applied to an Industrial-Scale Photovoltaic Plant

The inherently non-dispatchable nature of renewable sources, such as solar photovoltaic, is regarded as one of the main challenges hindering their massive integration in existing electric grids. Accurate forecasting of the power output of the solar plant might therefore play a key role towards this goal. In this paper, we compare several machine learning and deep learning algorithms for intra-hour forecasting of the output power of a 1 MW photovoltaic plant, using meteorological data acquired in the field. With the best performing algorithms, our data-driven workflow provided prediction performance that compares well with the present state of the art and could be applied in an industrial setting.
Algorithms and Interoperability between Drama and Artificial Intelligence

Computation has played a notable role in contemporary performance, a trend that has paired with the emersion of intermediality. A look at a few historical examples along with an evaluation of more recent research suggests that the notion of algorithm may provide a new approach to the dichotomy between media and performance. From the systems that aim to facilitate the interoperability of content in performance to the seminal distinction between content structures and control structures, it is possible to foresee a new perspective on the relation between algorithms and drama.
Efficient Portable Camera Based Text to Speech Converter for Blind Person

Text Reader for Blind Person using camera module ensuring portability is the prototype made using the Raspberry Pi 3b and Python to read the text from the handheld objects of the blind person. This paper proposes a better approach for text localization and extraction for detection of text areas in the images. The text size is an important factor whose dimension should be properly elected to make the method more general and insensitive to various font shapes and sizes. The proposed method involves four steps detection of an object, localization of the text, extraction of the text and text to speech conversion. The Region of Interest is extracted from the cluttered background and then the text localization algorithm is applied to locate and extract the text. After extracting the text from the ROI, it is converted it into speech. It works more efficiently with Optical Character Recognition. Convolutional recurrent neural network is proposed for training the words separately. The experiment and training are performed on Synth 90k word dataset. Finally using OCR and CRNN a proposed model has been developed.
Recent trends in applying TPM to cloud computing

Abstract Trusted platform modules (TPM) have become important safe‐guards against variety of software‐based attacks. By providing a limited set of cryptographic services through a well‐defined interface, separated from the software itself, TPM can serve as a root of trust and as a building block for higher‐level security measures. This article surveys the literature for applications of TPM in the cloud‐computing environment, with publication dates comprised between 2013 and 2018. It identifies the current trends and objectives of this technology in the cloud, and the type of threats that it mitigates. Toward the end, the main research gaps are pinpointed and discussed. Since integrity measurement is one of the main usages of TPM, special attention is paid to the assessment of run time phases and software layers it is applied to.
Multithreaded Layer-wise Training of Sparse Deep Neural Networks using Compressed Sparse Column

Training a sparse Deep Neural Network (DNN) is inherently less memory-intensive and processor-intensive compared to training a dense (fully-connected) DNN. In this paper, we utilize Sparse Matrix-Matrix Multiplication (SpMM) to train sparsely-connected DNNs as opposed to dense matrix-matrix multiplication used for training dense DNNs. In our C/C++ implementation, we extensively use in-memory Compressed Sparse Column (CSC) data structures to store and traverse the neural network layers. Also, we train the neural network layer by layer, and within each layer we use 1D-Column partitioning to divide the computation required for training among threads. To speedup the computation, we apply the bias and activation functions while executing SpMM operations. We tested our implementation using benchmarks provided by MIT/IEEE/Amazon HPEC graph challenge [1]. Based on our results, our single thread (1 core) and multithreaded (12 cores) implementations are up to 22×, and 150× faster than the serial Matlab results provided by the challenge. We believe this speedup is due to the 1D-Column partitioning that we use to balance the computation of SpMM operations among computing threads, the efficient mechanism that we use for memory (re)allocation of sparse matrices, and the overlapping of the accumulation of SpMM results with the application of the bias and activation functions.
Combining it all

Control mechanisms of stream processing applications (SPAs) that ensure latency bounds at minimal runtime cost mostly target a specific infrastructure, e.g., homogeneous nodes. With the growing popularity of the Internet of Things, fog, and edge computing, SPAs are more often distributed on heterogeneous infrastructures, triggering the need for a holistic SPA-control that still considers heterogeneity. We therefore combine individual control mechanisms via the latency-distribution problem that seeks to distribute latency budgets to individually managed components of distributed SPAs for a lightweight yet effective end-to-end control. To this end, we introduce a hierarchical control architecture, give a formal definition of the latency-distribution problem, and provide both an ILP formulation to find an optimal solution as well as a heuristic approach, thereby enabling the combination of individual control mechanisms into one SPA while ensuring global cost minimality. Our evaluations show that both solutions are effective---while the heuristic approach is only slightly more costly than the optimal ILP solution, it significantly reduces runtime and communication overhead.
Improved JPEG Phase-Aware Steganalysis Features Using Multiple Filter Sizes and Difference Images

In terms of feature-based steganalysis for JPEG images, JPEG phase-aware features (e.g., DCTR and GFR) currently provide the best detection performance on modern adaptive steganographic schemes. But in DCTR and GFR, the types of residual images are relatively single. They only use the convolution residuals obtained with the DCT or Gabor filters of fixed size 8 × 8. In this paper, to further improve DCTR and GFR, two strategies are proposed to enrich the features by diversifying residual images, and the corresponding symmetrization rules for the features are also elaborately designed. First, instead of a single filter size of 8 × 8, convolution filters of multiple sizes are adopted to generate different residual images. Second, we also compute JPEG phase-aware features from the difference images between two convolution residuals. Since the features from convolution residuals and difference images are diverse and complementary, the combination of these two kinds of features can significantly improve the detection accuracy. Last but not least, different symmetrization rules are accordingly designed for these features by considering filter types, filter sizes, and residual subtraction to decrease the feature dimension and enhance the feature robustness. The experimental results demonstrate the effectiveness of our proposed features, and we can further boost the performance by incorporating the knowledge of the selection channel and using the accelerated weighted histogram method.
Blockchain-Powered Applications for Smart Transactive Grids

Recently, special attention has been brought to blockchain-based platforms for the development of decentralized Smart Grid applications. The scope of this paper is first to review the state-of-the-art assessing the effectiveness of applying blockchain as a vehicle for the decentralization of the energy market and afterwards, to design a simplified decentralized transactive application and investigate its applicability through simulations, focusing on the Ethereum blockchain protocol. The results support the applicability of blockchain in automating electricity supply contracting under the concept of decentralized markets.
Disparity Estimation Using Stereo Images With Different Focal Lengths

This study proposes a disparity estimation method for images captured by cameras with different focal lengths. Whereas a short focal length camera captures a close scene, a long focal length camera captures a far scene. The proposed method computes full disparity maps for both the close and far scenes. To achieve this, we introduce a stereo rectification method that works directly on images with different focal lengths and parameterizes the zoom levels between the two cameras. To evaluate the proposed method, we set up a stereo system with two cameras with different focal lengths and captured sequences of stereo images. In addition, we used KITTI, EISATS, and SceneFlow stereo datasets to simulate images with different focal lengths. Experimental results show that the proposed disparity estimation method successfully computed disparity maps for both near and far scenes, with significantly better performance than state-of-the-art monocular disparity estimation methods. Source code and experimental results are available online at https://github.com/comvisdinh/disparityestimation.
Securing Blockchain Shards By Using Learning Based Reputation and Verifiable Random Functions

In order to meet the increasing demand of the blockchain, it needs to find a solution to the scalability problem. It has been focused on sharding recently to address the scalability problem. In the sharding method, the blockchain is divided into pieces. Instead of a more extensive network, networks with fewer nodes are created. As a result, it becomes more important that each node in the network is reliable. In this study, studies using sharding method have been investigated, and methods for the assigning nodes to shards are proposed. The use of learning-based adaptive methods for this process will contribute to the safe and reliable use of shards. The probability of the shards to deteriorate and influence the whole blockchain will be reduced.
An Image Segmentation Method Based on Two-Dimensional Entropy and Chaotic Lightning Attachment Procedure Optimization Algorithm

Threshold segmentation has been widely used in recent years due to its simplicity and efficiency. The method of segmenting images by the two-dimensional maximum entropy is a species of the useful technique of threshold segmentation. However, the efficiency and stability of this technique are still not ideal and the traditional search algorithm cannot meet the needs of engineering problems. To mitigate the above problem, swarm intelligent optimization algorithms have been employed in this field for searching the optimal threshold vector. An effective technique of lightning attachment procedure optimization (LAPO) algorithm based on a two-dimensional maximum entropy criterion is offered in this paper, and besides, a chaotic strategy is embedded into LAPO to develop a new algorithm named CLAPO. In order to confirm the benefits of the method proposed in this paper, the other seven kinds of competitive algorithms, such as Ant–lion Optimizer (ALO) and Grasshopper Optimization Algorithm (GOA), are compared. Experiments are conducted on four different kinds of images and the simulation results are presented in several indexes (such as computational time, maximum fitness, average fitness, variance of fitness and other indexes) at different threshold levels for each test image. By scrutinizing the results of the experiment, the superiority of the introduced method is demonstrated, which can meet the needs of image segmentation excellently.
Exploratory study on clustering methods to identify electricity use patterns in building sector

Abstract In this paper, we perform a cluster analysis using smart meter electricity demand data from 656 households in Switzerland, collected during one year. First, we present the silhouette analysis to determine the optimum number of clusters for a k-means clustering approach. Secondly, we try different distance functions used in the k-means clustering to partition the samples into different categories. We find that the choice of distance function has no effect on the clustering performance. Finally, we investigate the “dimensionality curse” and find that low dimensions should be preferred to increase the quality of the clustering outcome.
Enhancing sentient embodied conversational agents with machine learning

Within the area of intelligent User Interfaces, we propose what we call Sentient Embodied Conversational Agents (SECAs): virtual characters able to engage users in complex conversations and to incorporate sentient capabilities similar to the ones humans have. This paper introduces SECAs together with their architecture and a publicly available software library that facilitates their inclusion in applications –such as educational and elder-care– requiring proactive and sensitive agent behaviours. In fact, we illustrate our proposal with a virtual tutor embedded in an educational application for children. The evaluation was performed in two stages: firstly, we tested a version with basic textual processing capabilities; and secondly, we evaluated a SECA with Machine-Learning-enhanced user understanding capabilities. The results show a significant improvement in users' perception of the agent's understanding capability. Indeed, the Response Error Rate decreased from 22.31% to 11.46% using ML techniques. Moreover, 99.33% of the participants consider the global experience of talking with the virtual tutor with sentient capabilities to be satisfactory.
Extended Object Tracking assisted Adaptive Clustering for Radar in Autonomous Driving Applications

Multiple Extended Object Tracking in autonomous driving scenarios must be applicable in highly varying environments such as highway scenarios as well as in urban and rural environments. In this paper, a flexible UKF-based Interacting Multiple Motion (IMM) model extension for the Random Matrix Model (RMM) framework is introduced for nonlinear models. In addition to that, an adaptive clustering method where the provided tracking prior information is invoked to obtain stable clustering and tracking in varying environments with different objects and varying object types is derived. The effectiveness of the filter and clustering method is demonstrated in a real-world scenario.
Improved Distant Supervised Model in Tibetan Relation Extraction Using ELMo and Attention

The task of relation extraction is classifying the relations between two entities in a sentence. Distant supervision relation extraction can automatically align entities in texts based on Knowledge Base without labeled training data. For low-resource language relation extraction, such as Tibetan, the main problem is the lack of labeled training data. In this paper, we propose an improved distant supervised relation extraction model based on Piecewise Convolutional Neural Network (PCNN) to expand the Tibetan corpus. We add self-attention mechanism and soft-label method to decrease wrong labels, and use Embeddings from Language Models (ELMo) to solve the semantic ambiguity problem. Meanwhile, according to the Tibetan characteristics, we combine the word vector and part of speech vector to extract deeply feature of words. Finally, the experimental results show that P@avg value increases by 14.4% than baseline.
Object Detection and Distance Estimation Tool for Blind People Using Convolutional Methods with Stereovision

In this research, a tool that can provide information about object around is made. This tool can also estimate distance of detected object through camera which is combined with glasses, to ease blind people who use it. This tool is certainly can help them to identify object around and improve their skill and ability. This tool use camera as main sensor, which works like human eyes, to provide real time video as visual data. The RGB visual data is processed using Convolutional Neural Network which has 176 × 132 pixels by convoluting 2 times. It produces smaller pixels with size 41 × 33 pixels, so weights is obtained for classification using back propagation and determined dataset. After getting detection result, the next step is a find centroid value as center point for measuring the distance between objects and cameras with Stereo Vision The results is converted into sound form and connected to earphones, so blind people can hear the information. The test results show that this tool can detect predetermined objects, namely humans, tables, chairs, cars, bicycles and motorbikes with an average accuracy of 93.33%. For measurements of distances between 50 cm to 300 cm it has an error of around 6.1%.
TiltWriter

Touch is the predominant method of text entry on mobile devices. However, these devices also facilitate tilt-based input using accelerometers and gyroscopes. This paper presents the design and evaluation of TiltWriter, a non-touch, tilt-based text entry technique. TiltWriter aims to supplement conventional techniques when precise touch is not convenient or possible (e.g., the user lacks sufficient motor skills). Two keyboard layouts were designed and evaluated: Qwerty, and "Custom", a layout inspired by the telephone keypad. Novice participants in a longitudinal study achieved speeds of 12.1 wpm for Qwerty, 10.7 wpm for Custom. Error rate averaged 0.76% for Qwerty, 0.62% for Custom. A post-study extended session yielded 15.2 wpm with Custom, versus 11.5 wpm with Qwerty. Results and participant feedback suggest that a selection dwell time between 700 - 800 ms benefits accuracy.