Reviewerly/exHarmony huggingface dataset:
AUTODEPTH: Single Image Depth Map Estimation via Residual CNN Encoder-Decoder and Stacked Hourglass

We address the task of estimating depth from a single intensity image via a novel convolutional neural network (CNN) encoder-decoder architecture, which learns the depth information using example pairs of color images and their corresponding depth maps. The proposed model integrates residual connections within pooling and up-sampling layers, and hourglass networks which operate on the encoded features, thus processing these at various scales. Furthermore, the model is optimized under the constraints of perceptual as well as the mean squared error loss. The perceptual loss considers the high-level features, thus operating at a different scale of abstraction, which is complementary to the mean squared error loss. The improvements in qualitative and quantitative comparisons with state-of-the-art approaches demonstrate the effectiveness of our approach, even in presence of noise.
Loss Switching Fusion with Similarity Search for Video Classification

From video streaming to security and surveillance applications, video data play an important role in our daily living today. However, managing a large amount of video data and retrieving the most useful information for the user remain a challenging task. In this paper, we propose a novel video classification system that would benefit the scene understanding task. We define our classification problem as classifying background and foreground motions using the same feature representation for outdoor scenes. This means that the feature representation needs to be robust enough and adaptable to different classification tasks. We propose a lightweight Loss Switching Fusion Network (LSFNet) for the fusion of spatiotemporal descriptors and a similarity search scheme with soft voting to boost the classification performance. The proposed system has a variety of potential applications such as content-based video clustering, video filtering, etc. Evaluation results on two private industry datasets show that our system is robust in both classifying different background motions and detecting human motions from these background motions.
Continuous optimisation techniques for optimal aiming strategies in solar power tower plants

Optimising the aiming strategy is crucial for Solar Power Tower plants, in order to maximise the energy generated, whilst also preventing catastrophic damage to receiver components. In this work, a bi-objective optimisation model is developed to find optimal aiming strategies for a Solar Power Tower plant. The primary objective to maximise the radiation captured by the receiver is offset by a secondary objective to minimise the deviation from a desired target distribution, which is designed by solar plant operators to improve plant efficiency. A numerical method is proposed to solve the optimisation problem, and an illustrative example is presented to show functionality of the model and the numerical method. Conclusions are drawn on the model presented, extensions are considered and current work is discussed.
Efficient Dilated-Winograd Convolutional Neural Networks

Dilated convolution is used to achieve wide receptive fields in computer vision algorithms such as image segmentation and denoising. Unlike the strided convolution, dilated convolution maintains the resolution of the output feature map same as the input feature map. Thus, the computational complexity can be increased to configure the convolutional neural network (CNN) architecture with the dilated convolutional layer. However, the complexity accordingly introduces additional computation delay and it is strongly required to have a proper way to lessen the computation delay of the dilated convolution. In this paper, we propose the dilated-Winograd convolution to reduce the computational complexity of the dilated convolution. By using the Winograd transform with a dilation rate, the number of pixels in the tile is effectively reduced. The proposed acceleration methods result in an average speedup of 2.043 and 1.456 with dilation rate of 2 and 4 compared to the state-of-the-art implementation.
Predicting the What and How - a Probabilistic Semi-Supervised Approach to Multi-Task Human Activity Modeling

Video-based prediction of human activity is usually performed on one of two levels: either a model is trained to anticipate high-level action labels or it is trained to predict future trajectories either in skeletal joint space or in image pixel space. This separation of classification and regression tasks implies that models cannot make use of the mutual information between continuous and semantic observations. However, if a model knew that an observed human wants to drink from a nearby glass, the space of possible trajectories would be highly constrained to reaching movements. Likewise, if a model had predicted a reaching trajectory, the inference of future semantic labels would rank "lifting" more likely than "walking". In this work, we propose a semi-supervised generative latent variable model that addresses both of these levels by modeling continuous observations as well as semantic labels. This fusion of signals allows the model to solve several tasks, such as action detection and anticipation as well as motion prediction and synthesis, simultaneously. We demonstrate this ability on the UTKinect-Action3D dataset, which consists of noisy, partially labeled multi-action sequences. The aim of this work is to encourage research within the field of human activity modeling based on mixed categorical and continuous data.
Comparison of Visualization Tools for Matches Analysis of a MOBA Game

The phenomenon of eSports has been increasing over the years, including the interest in online video games, by players, coaches and analysts. One of the most popular genres of these games is the Multiplayer Online Battle Arena (MOBA) and games like League of Legend (LoL) have increased its player base and event viewership. Similarly to other sports, players and coaches analyze the games and all the games events, such as players' movements and spatial, temporal events in which they participate, to understand how they play and to define new strategies to help them progress. This paper presents the VisuaLeague II tool that helps players and coaches to analyze LoL game data using animated maps and other visualizations; and presents a comparative study between VisuaLeague II and other two tools (LoL replay system and OP.GG) to understand if the proposed visualization techniques are adequate for analyzing player's performance. The results suggest that players are capable of visualizing game-related data through the use of animated maps, to solve common analytic tasks and the comparison suggests that participants prefer to visualize spatio-temporal data dynamically, i.e., using animated maps or the replay system.
Unpaired Cross-Spectral Pedestrian Detection Via Adversarial Feature Learning

Even though there exist significant advances in recent studies, existing methods for pedestrian detection still have shown limited performances under challenging illumination conditions especially at nighttime. To address this, cross-spectral pedestrian detection methods have been presented using color and thermal, and shown substantial performance gains on the challenging circumstances. However, their paired cross-spectral settings have limited applicability in real-world scenarios. To overcome this, we propose a novel learning framework for cross-spectral pedestrian detection in an unpaired setting. Based on an assumption that features from color and thermal images share their characteristics in a common feature space to benefit their complement information, we design the separate feature embedding networks for color and thermal images followed by sharing detection networks. To further improve the cross-spectral feature representation, we apply an adversarial learning scheme to intermediate features of the color and thermal images. Experiments demonstrate the outstanding performance of the proposed method on the KAIST multi-spectral benchmark in comparison to the state-of-the-art methods.
Two-Stream Refinement Network for RGB-D Saliency Detection

In this paper, we propose a two-stream refinement network for RGB-D saliency detection. A fusion refinement module is designed to fuse output features from different resolution and modals. The structure information from depth helps distinguish between foreground and background and the lower level features with higher resolution can be adopted to refine the boundary of detected targets. The proposed model predicts high-resolution saliency map and then use a propagation-based module to further refine object boundary. Experimental results demonstrate that the proposed method performs well against to the state of the art methods on the recent RGB-D salient object detection dataset.
Gragnostics: Fast, Interpretable Features for Comparing Graphs

Many analytical tasks, such as social network analysis, depend on comparing graphs. Existing methods are slow, or can be difficult to understand. To address these challenges, this paper proposes gragnostics, a set of 10 fast, layperson-understandable graph-level features. Each can be computed in linear time. To evaluate the ability of these features to discriminate different topologies and types of graphs, this paper compares a machine learning classifier using gragnostics to alternative classifiers, and the evaluation finds that the gragnostics classifier achieves higher performance. To evaluate gragnostics' utility in interactive visualization tools, this paper presents Chiron, a graph visualization tool that enables users to explore the subgraphs of a larger graph. Example usage scenarios of Chiron demonstrate that using gragnostics in a rank-by-feature framework can be effective for finding interesting subgraphs.
Covert and Phantom Features in Annotations

The annotation-based variability of the C preprocessor (CPP) has a bad reputation regarding comprehensibility and maintainability of software systems, but is widely adopted in practice. To assess the complexity of such systems' variability, several analysis techniques and metrics have been proposed in scientific communities. While most metrics seem reasonable at first glance, they do not generalize over all possible usages of C preprocessor variability that appear in practice. Consequently, some analyses may neglect the actual complexity of variability in these systems and may not properly reflect the real situation. In this paper, we investigate two types of variation points, namely negating and #else directives, to which we refer to as corner cases, as they are seldom explicitly considered in research. To investigate these directives, we rely on three commonly used metrics: lines of feature code, scattering degree, and tangling degree. We (1) describe how the considered directives impact these metrics, (2) unveil the resulting differences within 19 systems, and (3) propose how to address the arising issues. The results show that the corner cases appear regularly in variable feature code and can heavily change the results obtained with established metrics. We argue that we need to refine metrics and improve variability analysis techniques to provide more precise results, but we also need to reason about the meaning of corner cases and metrics.
A Framework for Traffic Light Detection and Recognition using Deep Learning and Grassmann Manifolds

We propose a novel, real-time, camera-based framework for traffic light detection and recognition from a moving vehicle. For increasing safe driving, automated detection and recognition of traffic lights plays an important role in intelligent vehicles and Advanced Driver-Assistance Systems (ADAS). However, automated detection and recognition of the traffic lights is challenging because of their small size, colors (red, yellow and green) that may be similar to the background, illumination variation, environmental conditions, etc. In our proposed framework, we detect the traffic lights using Faster Region-based Convolutional Neural Network (R-CNN) and recognise the traffic lights based on Grassmann manifold learning. We use transfer learning on VGG16 to extract features from the detected traffic lights and use these features for creating subspaces for each traffic light (red, yellow and green). These subspaces lie on a Grassmann manifold and encompass the variations in different instances of the same traffic light and the uncertainties during detection. We use discriminant analysis on the manifold for recognising them in real-time. Our experiments on multiple publicly available datasets and comparison with several state-of-the-art methods show that our proposed framework has a high degree of accuracy and is robust.
Mapping ERD to Knowledge Graph

Knowledge representation is considered one of the main challenges in knowledge-based system. Currently, knowledge graph is the most modern knowledge representation model, which has been utilized in critical knowledge based systems. On the other hand, the Entity Relationship Diagram (ERD) for relational database is the most used model in information systems. In this paper, an efficient approach for mapping ERD to knowledge graph is presented with a case study for demonstrating the mapping process. First, relational tables have been represented by knowledge graph, then reification has been implemented to convert the knowledge graph to knowledge base. Eventually, extracting knowledge from these knowledge bases is processed by Prolog. Our proposed model has been empirically evaluated by comparing the Prolog results with the conventional SQL results.
Sinogram Image Completion for Limited Angle Tomography With Generative Adversarial Networks

In this paper, we present a novel approach based on deep neural network for solving the limited angle tomography problem. The limited angle views in tomography cause severe artifacts in the tomographic reconstruction. We use deep convolutional generative adversarial networks (DCGAN) to fill in the missing information in the sino-gram domain. By using the continuity loss and the two-ends method, the image completion in the sinogram domain is done effectively, resulting in high quality reconstructions with fewer artifacts. The sinogram completion method can be applied to different problems such as ring artifact removal and truncated tomography problems.
Fake Data Analysis and Detection Using Ensembled Hybrid Algorithm

Fake data detection is the most important problem to be addressed in the recent years, there is lot of research going on in this field. Because of its serious impacts on the readers. researchers, government and private agencies working together to solve the issue. This paper represents a hybrid approach for fake data detection using the multinomial voting algorithm. This algorithm was tested with multiple fake news dataset which resulted in an accuracy score of 94 percent which is a benchmark in the machine learning field where the other algorithms are at a range of 82 to 88 percent. The list of algorithms that have been used here is as follows Naive Bayes, Random Forest, Decision Tree, Support Vector Machine, K Nearest Neighbors. All these algorithms use training data as the bag of words model which was created using Count Vectorizer. Experimental data has collected from the Kaggle data world. Python is used as a language to verify and validate the results. Tableau is used as a visualization tool. Implementation is carried out using default algorithm values.
Arabic Dialect Identification with Deep Learning and Hybrid Frequency Based Features

Studies on Dialectical Arabic are growing more important by the day as it becomes the primary written and spoken form of Arabic online in informal settings. Among the important problems that should be explored is that of dialect identification. This paper reports different techniques that can be applied towards such goal and reports their performance on the Multi Arabic Dialect Applications and Resources (MADAR) Arabic Dialect Corpora. Our results show that improving on traditional systems using frequency based features and non deep learning classifiers is a challenging task. We propose different models based on different word and document representations. Our top model is able to achieve an F1 macro averaged score of 65.66 on MADAR’s small-scale parallel corpus of 25 dialects and Modern Standard Arabic (MSA).
Agile Autonomous Teams in Complex Organizations

In complex organizations, the effective functioning of autonomous teams is challenged by the need to coordinate and align work with multiple experts and other units in the organization. We report on the challenges experienced in an agile program consisting of cross-functional teams set up with resources from both the IT and business development side of the organization, while team members simultaneously remain in their line organization. Through an empirical case study of the agile program, we find that the production structure (i.e. the distribution of operational tasks) and the control structure (i.e. managing activities related to the operational task) influence agile team autonomy. We contribute by pushing past describing dependencies in terms of coordination challenges and mechanisms. To do this, we use modern sociotechnical theory to discuss how a production structure with many dependencies cause challenges and how a misaligned control structure is time-consuming and reduces team autonomy.
Deep Blind Synthesized Image Quality Assessment with Contextual Multi-Level Feature Pooling

Blind image quality metrics have achieved significant improvement on traditional 2D image dataset, yet still being insufficient for evaluating synthesized images generated from depth-image-based rendering. The geometric distortions in synthesized image are non-uniform, which is challenging for feature representation and pooling. To address this, we propose an end-to-end deep blind synthesized image quality metric SIQA-CFP. We particularly design a contextual multilevel feature pooling module to encode low- and high-level features, which are extracted by a deep pre-trained ResNet. Experimental results on IRCCyN/IVC DIBR dataset show that our method outperforms state-of-the-art synthesized image quality metrics. Our method also achieves competitive performance on traditional 2D image datasets like LIVE Challenge and TID2013.
PolyResponse: A Rank-based Approach to Task-Oriented Dialogue with Application in Restaurant Search and Booking

We present PolyResponse, a conversational search engine that supports task-oriented dialogue.It is a retrieval-based approach that bypasses the complex multi-component design of traditional task-oriented dialogue systems and the use of explicit semantics in the form of task-specific ontologies.The PolyResponse engine is trained on hundreds of millions of examples extracted from real conversations: it learns what responses are appropriate in different conversational contexts.It then ranks a large index of text and visual responses according to their similarity to the given context, and narrows down the list of relevant entities during the multi-turn conversation.We introduce a restaurant search and booking system powered by the PolyResponse engine, currently available in 8 different languages.
Bayer Pattern Compression with JPEG XS

Image sensors in digital cameras use a technology called "Bayer Patterns" allowing color photography with a planar arrangements of photosensitive elements. Alternating arrangements of red, green and blue filter masks on top of a rectangular grid of such elements allow capturing of color information, but also require a de-mosaicing algorithm to reconstruct a full-resolution color image from the sensor data. In high-speed applications, or applications where the system design requires low-latency, low-complexity compression, the JPEG XS standard of the JPEG committee offers an elegant solution to compress Bayer pattern images close to the sensor, and to transmit the compressed data over a lower bandwidth connection while maintaining visually lossless quality. This paper presents contributions to JPEG XS that are currently under discussion in the JPEG committee (SC29WG1) as parts of an amendment for Bayer pattern compression.
Modeling Long- and Short-Term Temporal Context for Video Object Detection

Video object detection enhances the performance of the still-image based object detection by exploiting temporal context information from neighboring frames. Most of the state-of-the-art video object detectors are non-causal and require lots of preceding and succeeding frames, which makes them impractical for real-time online detection where succeeding frames are not available. In this paper, we propose a causal recurrent flow-based method for online video object detection. The proposed method reads only the current frame and one preceding frame from memory buffer at each time step. Two types of temporal context information are utilized. The short-term temporal context information is utilized by warping the feature map from the nearby preceding frame based on the optical flow. The long-term temporal context information is extracted from the temporal convolutional LSTM, where informative features from distant preceding frames are stored and propagated through the time. By aggregating both long- and short-term temporal context information, our proposed method achieves competitive performance (75.5% mAP) on ImageNet VID dataset at a relatively high speed.
An Approach to Secure Collaborative Recommender System Using Artificial Intelligence, Deep Learning, and Blockchain

This paper aims at highlighting the increasing role of artificial intelligence in business and making familiar its various aspects vis-a-vis its immediate requirement in the present Indian business scenario. The paper takes into account the aspects of blockchain and deep learning components with regard to the business as future of artificial intelligence in business scenario. The study also includes the benefits and challenges of the use of artificial intelligence in business with influence of blockchain and deep learning. The relation between blockchain and deep learning and artificial intelligence has been discussed in this paper. The research collates findings from the use and implementation of components of blockchain and deep learning. The model is recommended in regard to future of artificial intelligence in business. The algorithm is written used for the implementation of artificial intelligence in business. The study concludes with the observations, future, and recommendations with respect to artificial intelligence in business with the implementation of blockchain and deep learning.
A novel online state-based anomaly detection system for process control networks

Industrial control networks are the core part of critical infrastructures such as power grid and oil refinery. In recent years, the number of cyber-attacks to industrial control networks are growing increasingly. Moreover, connecting industrial networks to the public network makes these critical infrastructures more vulnerable to the cyber-attacks. Therefore, improving the security of these networks has attracted much attention nowadays. To protect industrial control networks, the proposed online method is able to detect anomalies with low computational time while do not use prior knowledge about the system and anomalies. This method can adjust the severity of detection in order to efficiently detect changes which lead to anomalies; And also can be adapted to inevitable network changes by updating the anomaly threshold using the latest normal states. The proposed method finds anomalies in the network using high-pass filters and Euclidean distance of the current state with the latest states. To evaluate the efficiency of the proposed approach, a boiler control system is simulated and three test datasets are provided from this simulation. The proposed intrusion detection system was evaluated through these datasets, as well as the SWaT dataset. The results show that the proposed approach not only is highly effective for detecting anomalies, but also is adaptable to the normal variations in the network.