Reviewerly/exHarmony huggingface dataset:
PRED: A Parallel Network for Handling Multiple Degradations via Single Model in Single Image Super-Resolution

Existing SISR (single image super-resolution) methods mostly assume that a low-resolution (LR) image is bicubicly down-sampled from its high-resolution (HR) counterpart, which inevitably give rise to poor performance when the degradation is out of assumption. To address this issue, we propose a framework PRED (parallel residual and encoder-decoder network) with an innovative training strategy to enhance the robustness to multiple degradations. Consequently, the network can handle spatially variant degradations, which significantly improves the practicability of the proposed method. Extensive experimental results on real LR images show that the proposed method can not only produce favorable results on multiple degradations, but also reconstruct visually plausible HR images.
Kiswahili and Shingazidja

Abstract This chapter is a detailed discussion of ellipsis in Kiswahili and Shingazidja, two languages belonging to the G.40 group of Bantu languages. Building upon various arguments, including the use of tests established by Ngonyani (1996, 1998) and Greenberg (2005) and the examination of prosodic patterns, it is shown that several kinds of ellipsis are possible in these languages: clausal ellipses (gapping, stripping, argument cluster coordination), sluicing and fragments, comparative ellipsis, N-deletion, etc. Special attention is given to gapping and V-stranding VP-ellipsis, a structure that has previously been discussed in the literature on Bantu languages. The differences between the two languages, such as the fact that right-node raising is only possible in Shingazidja, are also addressed. The final section discusses the absence of two types of predicate ellipsis in these languages, namely English-like VP-ellipsis and English-like pseudogapping.
Necessary Precautions in Cognitive Tutoring System

Systems thatVora,Kevin enhance the cognitive capability ofShah,Shashvat the user can be used for the educational purposeHarsoda,Harshad. Physiological and psychological data are the key to figure out the human cognitive statesSheth,Jeel. Using non invasive electroencephalography (EEG), heart rate and webcam a cognitive tutoring system is proposedThakkar,Ankit in this paper with an objective to give feedback to the users to pay attention if the user is not attentive while video session is being played. Necessary precautions are suggested in the paper by analyzing feedback received from the user that helps to understand various aspects to be considered while designing such a system.
Learning to Remember Past to Predict Future for Visual Tracking

Fast and reliable adaptability to appearance variations of any target object has been the holy grail of visual tracking. Recently, Siamese-based trackers have demonstrated outstanding speed, however at the cost of adaptability and accuracy. We propose to model a temporal evolution of appearance features, allowing for adaptability without online training. Specifically, we introduce a memory-augmented convolutional recurrent neural network (RNN), named Past-to-Future (P2FNet), that takes appearance features as an input at each frame and predicts the next-frame features. RNN allows for fast adaptability to dynamically varying appearance, while the memory provides the generalization capability over longer sequences via template management. For reliability, we propose a new augmentation to train RNN to disregard corrupted features. A novel visualization method illustrates the reliable template management of the memory. The experimental results on benchmarks demonstrate the tracker shows competitive performance among real-time state-of-the-art trackers.
Symmetric Cryptography and Hardware Chip Implementation on FPGA

Cryptography is the process of creating the data secretly to provide secure communication in the existence of third parties called adversaries. Cryptography changes data into a format that is not readable for an illegal user. The paper provides the hardware chip-based solution for cryptographic encryption and decryption using the XOR cipher technique. The chip design is done in Xilinx ISE 14.2 with the help of VHDL language and simulation is carried for 8-bit to 256-bit encryption/decryption in Modelsim 10.0 software. The FPGA parameters such as hardware and timing are also synthesized for SPARTAN 3E, 3s500efg320-5 device.
Color Face Hallucination Using Neighbor Locality Representation and Inter-Channel Correlation

Recently, the locality-constrained linear coding (LLC) based techniques have been widely exploited for face hallucination. However, for the color face image, the conventional LLC model ignores the neighbor self-similarity prior as well as the relevance of different color channels, resulting in unsatisfactory representations. This paper presents a novel Neighbor locality Representation and inter-Channel Correlation (NRCC) model for color face hallucination. Compared with conventional LLC, NRCC makes full use of neighbor self-similarity prior and takes advantage of the co-manifold structure among RGB channels. The neighbor self-similarity prior and co-manifold structure can make the reconstruction results of eyes and lips generated from the proposed method better than those from other methods. The experimental results in some public face databases indicated the superiority of the proposed method over the prior art face hallucination methods.
Fidelity or Quality? A Region-Aware Framework for Enhanced Image Decoding via Hybrid Neural Networks

The generative deep learning models such as the generative adversarial networks (GAN) have been shown to efficiently generate visually appealing images by learning the natural scene statistics. However, the signal fidelity, instead of the visual quality, has been largely ignored in the generation process, especially for the highly structural regions. In this paper, we introduce a region-aware visual signal restoration scheme to achieve a good balance between visual quality and fidelity. As a specific example of this framework, we develop an enhanced decoding scheme with hybrid neural networks, such that the base fidelity layer and texture quality enhancement layer are combined adaptively to restore the compressed images. The efficiency of the proposed framework is demonstrated with extensive experimental results, which show favorable performance against the state-of-the-art methods.
CloseUp—A Community-Driven Live Online Search Engine

Search engines are still the most common way of finding information on the Web. However, they are largely unable to provide satisfactory answers to time- and location-specific queries. Such queries can best and often only be answered by humans that are currently on-site. Although online platforms for community question answering are very popular, very few exceptions consider the notion of users’ current physical locations. In this article, we present CloseUp, our prototype for the seamless integration of community-driven live search into a Google-like search experience. Our efforts focus on overcoming the defining differences between traditional Web search and community question answering, namely the formulation of search requests (keyword-based queries vs. well-formed questions) and the expected response times (milliseconds vs. minutes/hours). To this end, the system features a deep learning pipeline to analyze submitted queries and translate relevant queries into questions. Searching users can submit suggested questions to a community of mobile users. CloseUp provides a stand-alone mobile application for submitting, browsing, and replying to questions. Replies from mobile users are presented as live results in the search interface. Using a field study, we evaluated the feasibility and practicability of our approach.
Learning Prediction of Emotional Change on Behaviors

We are interested in the topic about the prediction of emotional change caused by other people's behaviors. Although information of other people's behaviors is intuitive and important for arousing emotional change, existing works on prediction of emotional change rarely take it into consideration. In this work, we propose a multimodal deep network, called Emotional Change Prediction Network (ECPNet), for predicting emotional changes caused by behaviors of other people. ECPNet can learn useful cues from visual and acoustic information and extract both local and global spatiotemporal multimodal features of behaviors. Furthermore, the proposed network can learn the temporal relations of the behaviors in videos which help us to explore the behaviors' cumulative effects on other people's emotions. In order to quantify our model, we establish the first dataset on emotional change prediction by behaviors. Experiment results show that our approach can effectively model emotional change and perform better than other baselines.
Photo Shot-Type Disambiguation by Multi-Classifier Semi-Supervised Learning

The previous studies showed that classifying photo shot-types into either narrow-type or wide-type was non-trivial because of ambiguous photos. To disambiguate such data, we define this problem as learning an ambiguity distribution instead of the conventional binary label, and then propose a multi-classifier semi-supervised learning framework (MCSSL) to tackle two issues: (1) Commonly, the ambiguity distribution of such photos are unavailable in practice. By introducing semi-supervised learning to multiple different classifiers (DNNs), MCSSL can mimic the inconsistent recognitions among multiple sources to determine the ambiguous degrees automatically for a photo. (2) The widely accepted Cross-Entropy loss is unfeasible for ambiguity learning. Instead, MCSSL applies the Kullback-Leibler divergence for DNNs to measure the similarity between the predicted distribution and the one given by multiple sources. The experiments on 122K photos demonstrate that MCSSL raises the classification accuracy of all DNNs, and improve the recognition consistency among them from substantial to almost perfect, which is our goal of data disambiguation.
Motion Planning Framework for Autonomous Vehicles: A Time Scaled Collision Cone Interleaved Model Predictive Control Approach

Planning frameworks for autonomous vehicles must be robust and computationally efficient for real time realization. At the same time, they should accommodate the unpredictable behavior of the other participants and produce safe trajectories. In this paper, we present a computationally efficient hierarchical planning framework for autonomous vehicles that can generate safe trajectories in complex driving scenarios, which are commonly encountered in urban traffic settings. The first level of the proposed framework constructs a Model Predictive Control(MPC)routine using an efficient difference of convex programmingapproach, that generates smooth and collision-free trajectories. The constraints on curvature and road boundaries are seamlessly integrated into this optimization routine. The second layer is mainly responsible to handle the unpredictable behaviors that are typically exhibited by the other participants of traffic. It is built along the lines of time scaled collision cone(TSCC)which optimize for the velocities along the trajectory to handle such disturbances. We additionally show that our framework maintains optimal balance between temporal and path deviations while executing safe trajectories. To demonstrate the efficacy of the presented framework we validated it in extensive simulations in different driving scenarios like over taking, lane merging and jaywalking among many dynamic and static obstacles.
Adaptive Fusion-Based 3D Keypoint Detection for RGB Point Clouds

We propose a novel keypoint detector for 3D RGB Point Clouds (PCs). The proposed keypoint detector exploits both the 3D structure and the RGB information of the PC data. Keypoint candidates are generated by computing the eigenvalues of the covariance matrix of the PC structure information. Additionally, from the RGB information, we estimate the salient points by an efficient adaptive difference of Gaussian-based operator. Finally, we fuse the resulting two sets of salient points to improve the repeatability of the 3D keypoint detector. The proposed algorithm is compared against the state-of-the-art algorithms on two benchmark datasets. The experimental results show that the proposed scheme outperforms the best existing method by 5.35% and 60.98 points on the SHOT-Kinect dataset and by 5.45% and 145.54 points on the SHOT-SpaceTime dataset in terms of relative and absolute repeatability, respectively.
Evaluating System Sufficiency in a Multimodal, Multiuser Sensemaking Environment Designed for Intelligence Analysis

This paper discusses a prototype brainstorming tool designed for sensemaking in intelligence analysis and integrates multimodal input in an immersive environment that supports multiple users. This digital tool has been designed using structured analytic techniques already in use through analog methods in the intelligence analysis domain. We discuss an ongoing user study that presents preliminary findings for tool, and suggestions for future development.
Design and Detection Algorithm of White-Light Markers in Close-Range Photogrammetry

In the process of close-range photogrammetry, it is hard to stick markers directly on the surface of the object to be measured at times. Projecting optical markers is a good solution. However, there are some problems for the existing optical markers such as difficulty in automatic matching and splicing. Therefore, this paper proposes the design and detection algorithm of white-light markers. White-light marker is composed of collar coded targets and circular non-coded targets. Collar coded targets are designed in the middle of the white-light marker, which can enhance the brightness of the coded strip and improve the recognition accuracy. Circular non-coded targets are designed around the collar coded targets, which can realize matching of non-coded targets among multiple images. Based on Canny operator, feature edges are extracted from the images. According to the criterions of scale, form, etc., the edges of markers are screened out. Markers containing coded strip is decoded to determine the identity. Center of gravity method is used to make the center position of white-light makers come true. The designed white-light marker is applied to the deformation detection experiment of crane girder. Its deformation curve is basically similar with the curve of sticking markers, which verifies the feasibility of the deformation detection of crane girder based on white-light markers.
User perspective and higher cognitive task-loads influence movement and performance in immersive training environments

In virtual reality (VR) applications such as games, virtual training, and interactive neurorehabilitation, one can employ either the first-person user perspective or the third-person perspective to perceive the virtual environment; however, applications rarely offer both perspectives for the same task. We used a targeted-reaching task in a large-scale virtual reality environment (N=30 healthy volunteers) to evaluate the effects of user perspective on the head and upper extremity movements, and on user performance. We further evaluated how different cognitive challenges would modulate these effects. Finally, we obtained the user-reported engagement level under the different perspectives.We found that first-person perspective resulted in larger head movements (3.52±1.3m) than the third-person perspective (2.41±0.7m). First-person perspective also resulted in more upper-extremity movement (30.08±7.28m compared to 26.66±4.86m) and longer completion times (61.3±16.4s compared to 53±10.4s) for more challenging tasks such as the "flipped mode", in which moving one arm causes the opposite virtual arm to move. We observed no significant effect of user perspective alone on the success rate. Subjects reported experiencing roughly the same level of engagement in both first-person and third-person perspectives (F(1.58)=0.9,P=.445).User perspective and its interaction with higher-cognitive load tasks influences the extent of movement and user performance in a virtual theater environment, and may influence the choice of the interface type (first or third person) in immersive training depending on the user conditions and exercise requirements.
Research on Constructing Sentiment Dictionary of Online Course Reviews based on Multi-source Combination

The construction of sentiment dictionary is an important task in text sentiment analysis. Using the sentiment words of specific fields to establish a domain-oriented sentiment dictionary can significantly improve the effect on sentiment recognition and classification in the specific field. A method of constructing a sentiment dictionary for online course reviews was proposed, which based on multi-source combination. In the first place, the sentiment words were identified and extracted using TextRank and the word2vec model, which combined with the general sentiment dictionary and the online course reviews corpus. Then, the label propagation algorithm was applied to discriminate the sentiment polarity of sentiment words, thus constructing a sentiment dictionary for online course reviews. Through the accuracy experiment on the determination of the sentiment polarity of words and the experiment on sentiment classification based on different dictionaries, the accuracy rate, the recall rate and the F value are calculated. The experimental results show that the proposed method was an accuracy and effective way to achieve the sentiment classification of online course reviews.
Vehicle Detection based on Deep Learning Heatmap Estimation

The detection and localization of objects on point cloud provided by LiDAR sensors have various applications, especially for autonomous driving. As methods are being more efficient in terms of performance and execution speed, they operate in an end-to-end fashion, not allowing in case of errors the evaluation of which areas could cause failures. We propose in this paper a simple method that delivers from a simple statistical voxel encoding an intermediate top view heatmap that illustrates the global state of the scene seen by the network. Completely detected vehicles are represented on this map as elliptic blobs. Difficult cases such as occluded cars may not illustrate a complete spot, however their mark may still indicate that a possible hazard, allowing planification algorithms to decide a reduction of the velocity as something may suddenly appear. Furthermore, thanks to the alternative representation of objects, detections in terms of bounding boxes can be extracted even from the intermediate map. In addition to its implementation simplicity, the proposed architecture reaches high level of performance on the KITTI detection benchmark.
Improving the Attribute-Based Active Learning by Clustering the New Items

The issue that recommender system often meets is cold-start problem, where the system does not have any ratings of new items or new users. Thus, it can not provide relevant recommendation for the new users or new items. In previous research, when dealing with item cold-start problem, some scientists combined content information and active learning method, and used factorization machine to model the prediction task. However, a shortcoming in this method is that when using factorization machine model to select users to give ratings to new items, the active users may be selected for too many times, leading to a result that they refuse to give ratings for new items, or randomly give their ratings, which does not exactly show their preferences. In this paper, to solve this issue, we use clustering algorithm to divide new items into different groups and choose one item to represent the group, and only request users giving ratings for the representative items.
Concern Metrics for Modularity-Oriented Modernizations

A known problem in legacy systems is the presence of crosscutting concerns in their architecture hampering and increasing the maintenance costs. A possible solution for that is the conduction of modularity-oriented modernization aiming at restructuring the legacy software system in order to improve its artifacts (source code, database, others) quality. Architecture-Driven Modernization (ADM) is focused on modernizing legacy software systems by using the concepts of model-driven architecture, software reengineering and standard metamodels. Knowledge Discovery Metamodel (KDM) is a standard from ADM that is able to represent software systems by means of KDM instances that are going to be modernized. An intrinsic part of the modernization process is to measure both KDM versions, the legacy and the modernized one. The measurement of legacy KDM instances enables the software engineer to quantify the existing problems while the measurement of the modernized one enables to verify whether the problems have been solved or not. Structured Metrics Metamodel (SMM) is a metamodel that can be used to specify metrics to be applied on KDM instances. However, even though most of the well known metrics are supported by SMM, there is no study that investigate how SMM could be used to specify concern metrics. We present how SMM can be used to specify concern metrics and a tool to support this measurement process, enabling the conduction of modularity-oriented modernizations to help ensuring the quality of the modernization process. Furthermore, we also discuss some challenges to be overcome involving the quality measurement process in ADM blueprint.
Artificial Locust Swarm Routing Algorithm: : An Approach to Consider both Routing via FRA and Applying RAD

A problem of routing in airspace looks both similar and different to the problem of routing on the ground. There are many peculiarities in air routing. However, due to the complexity of the problem and its similarity to 2D routing, there are many applications of a good known routing techniques from graph theory, like Dijkstra, A-Star etc. Nevertheless, these algorithms remain nearly useless or reveal poor efficiency at least in case of implementing specificity of a free routing airspace (FRA) and/or applying some kind of restrictions derived from route availability document (RAD). The results of research presented in current paper give the approach to solve routing problem in airspace with an ability to involve both understanding of routing via FRA and necessity of applying some known restrictions from RAD. The key idea of the developed algorithm is to combine routing in both regular and free airspaces and to apply some permanent requirements from route availability documents within a single route construction procedure. The solution was found in area of so named nature-inspired algorithms. The routing algorithm was considered similar to behavior of a locust swarm according to a few basic principles of movement. Applying some abstraction of a single locust behavior within a swarm allows considering the artificial locust swarm to be a good approach in search of route between departure and destination points for the routing problem in airspace. A couple of algorithm modifications and its areas of applicability were discussed also. Artificial locust swarm routing (ALSR) algorithm can be used as a platform solution for routes construction in airspace and is expected to allow further implementation of some particular routes optimization techniques, like genetic algorithms (GA) and/or other.