Reviewerly/exHarmony huggingface dataset:
TRANS-DOCK

This paper introduces TRANS-DOCK, a docking system for pin-based shape displays that enhances their interaction capabilities for both the output and input. By simply interchanging the transducer module, composed of passive mechanical structures, to be docked on a shape display, users can selectively switch between different configurations including display sizes, resolutions, and even motion modalities to allow pins moving in a linear motion to rotate, bend and inflate. We introduce a design space consisting of several mechanical elements and enabled interaction capabilities. We then explain the implementation of the docking system and transducer design components. Our implementation includes providing the limitations and characteristics of each motion transmission method as design guidelines. A number of transducer examples are then shown to demonstrate the range of interactivity and application space achieved with the approach of TRANS-DOCK. Potential use cases to take advantage of the interchangeability of our approach are discussed. Through this paper we intend to expand expressibility, adaptability and customizability of a single shape display for dynamic physical interaction. By converting arrays of linear motion to several types of dynamic motion in an adaptable and flexible manner, we advance shape displays to enable versatile embodied interactions.
GPU acceleration of NL-means, BM3D and VBM3D

Denoising is an essential part of any image- or video-processing pipeline. Unfortunately, due to time-processing constraints, many pipelines do not consider the use of modern denoisers. These algorithms have only CPU implementations or suboptimal GPU implementations. We propose a new efficient GPU implementation of NL-means and BM3D, and, to our knowledge, the first GPU implementation of the video-denoising algorithm VBM3D. The performance of these implementations enable their use in real-time scenarios.
Adaptation-Oriented Feature Projection for One-shot Action Recognition

One-shot action recognition aims at recognizing actions in unseen classes in cases where only one training video is provided. Compared with one-shot image recognition, one-shot learning on videos is more difficult due to the fact that the temporal dimension of video may lead to greater variation. To handle this variation, it is important to conduct further adaptation in the one-shot training process, despite the scarcity of the training data. While meta-learning is an option for facilitating this adaptation, it cannot be directly applied for two reasons: first, deep networks for action recognition can make current meta-learning methods infeasible to run because of their high computational complexity; second, due to the greater variation in actions, the adapted performance may not be higher than the un-adapted one, making it difficult to train the model by means of meta-learning. To address these problems and facilitate the adaptation, we propose the Adaptation-Oriented Feature (AOF) projection for one-shot action recognition. We first pre-train the base network on seen classes. The output of the network is projected to the adaptation-oriented feature space by fusing the important feature dimensions that are sensitive to adaptation. Subsequently, a small dataset (a.k.a. task) is sampled from seen classes to simulate the unseen-class training and testing settings. The feature adaptation is performed on the training data of this task to integrate the distribution information of the adapted feature. In order to reduce over-fitting, the triplet loss is applied to handle temporal variation with fewer parameters during the adaptation. On the testing data of this task, the losses on both adapted and un-adapted features are calculated to train the projection matrix. This sampling-adaptation-training procedure is then repeated on seen classes until convergence. Extensive experimental results on two challenging one-shot action recognition datasets demonstrate that our proposed method outperforms state-of-the-art methods.
Role of Requirement Prioritization Technique to Improve the Quality of Highly-Configurable Systems

Highly-configurable systems are such systems which are not developed for single scenario.However, perhaps they have variable functionality and they are developed for hybrid scenarios.Producing a good highly-configurable system within time and with customer satisfaction is not easy.Handling requirements effectively in such a way that it take least time to market, is one of the most difficult tasks in highly-configurable system.In this paper, a quantitative requirement prioritization technique for highlyconfigurable systems is proposed.This technique involves all stakeholders and can be used primarily for large scale software projects.The proposed technique is evaluated by taking a case study of the highlyconfigurable point of sale for automotive industry.The result shows that the proposed technique provides promising results and can be enhanced with more future work.
Predicting next Word using RNN and LSTM cells: Stastical Language Modeling

Language Modeling is defined as the operation of predicting next word. It is considered as one of the basic tasks of Natural Language Processing(NLP) and Language Modeling has several applications. In this research paper, the assorted potentialities for the efficient utilization of language models in structured document retrieval are mentioned. A tree-based generative language model for ranking documents and parts has been used here. Nodes within the tree correspond to different document parts like titles, paragraphs and sections. At every node within the document tree, there's a well-defined language model. The language model for a leaf node is predictable directly from the text within the document part related to the node. Inner nodes within the tree are predictable employing a linear interpolation among the various youngster nodes. The paper additionally describes how some common structural queries would be satisfactorily described inside this model.
Security Countermeasures in Web Based Application

With increase in use of internet, web-based applications are in high demand as they are used for many applications. With increase in its use, it is becoming prone to a number of attacks. Vulnerabilities exist in the application as the application is prone to a number of attacks. In this paper, we discussed about the processing of web applications, need and demand for web security on the network, different security testing methods as well as security mechanisms which helps the web applications to maintain its privacy.
Robust Structural Low-Rank Tracking

Visual object tracking is an essential task for many computer vision applications. It becomes very challenging when the target appearance changes especially in the presence of occlusion, background clutter, and sudden illumination variations. Methods, that incorporate sparse representation and low-rank assumptions on the target particles have achieved promising results. However, because of the lack of structural constraints, these methods show performance degradation when facing the aforementioned challenges. To alleviate these limitations, we propose a new structural low-rank modeling algorithm for robust object tracking in complex scenarios. In the proposed algorithm, we consider spatial and temporal appearance consistency constraints, among the particles in the low-rank subspace, embedded in four different graphs. The resulting objective function encoding these constraints is novel and it is solved using linearized alternating direction method with adaptive penalty both in batch fashion as well as in online fashion. Our proposed objective function jointly learns the spatial and temporal structure of the target particles in consecutive frames and makes the proposed tracker consistent against many complex tracking scenarios. Results on four challenging datasets demonstrate excellent performance of the proposed algorithm as compared to current state-of-the-art methods.
Bidirectional LSTM with Hierarchical Attention for Text Classification

The text classification is a basic task of natural language processing (nlp), which aims to get the corresponding category labels for texts with multiple categories. Nowadays, neural network models have been widely used in the nlp field and has achieved remarkable results in text classification. However, due to the high dimensionality of text data and the complex semantics of natural language, there are still many areas for improvement in the network structure of text classification. In order to cope with the above problems, this paper proposes a new network structure, which includes bidirectional long short-term memory (LSTM) combined with hierarchical attention mechanism. In this network structure, the data is sent to bidirectional LSTM after one-hot encoding, and the output is subjected to hierarchical attention. Finally, the softmax classifier is used to classify the processed context information. The experimental results show that the structure has high accuracy in text classification.
Towards Learning Multi-Agent Negotiations via Self-Play

Making sophisticated, robust, and safe sequential decisions is at the heart of intelligent systems. This is especially critical for planning in complex multi-agent environments, where agents need to anticipate other agents' intentions and possible future actions. Traditional methods formulate the problem as a Markov Decision Process, but the solutions often rely on various assumptions and become brittle when presented with corner cases. In contrast, deep reinforcement learning (Deep RL) has been very effective at finding policies by simultaneously exploring, interacting, and learning from environments. Leveraging the powerful Deep RL paradigm, we demonstrate that an iterative procedure of self-play can create progressively more diverse environments, leading to the learning of sophisticated and robust multi-agent policies. We demonstrate this in a challenging multi-agent simulation of merging traffic, where agents must interact and negotiate with others in order to successfully merge on or off the road. While the environment starts off simple, we increase its complexity by iteratively adding an increasingly diverse set of agents to the agent zoo as training progresses. Qualitatively, we find that through self-play, our policies automatically learn interesting behaviors such as defensive driving, overtaking, yielding, and the use of signal lights to communicate intentions to other agents. In addition, quantitatively, we show a dramatic improvement in the success rate of merging maneuvers from 63% to over 98%.
SafeSec Tropos: Joint security and safety requirements elicitation

Abstract The growing convergence of information technology with operational technology and the accordant proliferation of interconnected cyber-physical systems (CPSs) has given rise to several security and safety challenges. One of these refers to systematically identifying coherent, consistent, and non-conflicting security and safety requirements. This paper proposes an integrated method for safety and security requirements engineering for CPSs at the design stage of the system lifecycle. The method identifies security and safety objectives, it systematically elicits a comprehensive list of requirements, and it links these requirements to objectives, thus facilitating the process of resolving conflicts. To provide insight into the operations of the method, we demonstrate its use to the most vulnerable CPSs on board the Cyber-Enabled Ship (C-ES). By utilizing the proposed method, the safety and security objectives of these systems were defined, and their safety and security requirements were identified.
Accuracy of Commodity Finger Tracking Systems for Virtual Reality Head-Mounted Displays

Representing users’ hands and fingers in virtual reality is crucial for many tasks. Recently, virtual reality head-mounted displays, capable of camera-based inside-out tracking and finger and hand tracking, are becoming popular and complement add-on solutions, such as Leap Motion.However, interacting with physical objects requires an accurate grounded positioning of the virtual reality coordinate system relative to relevant objects, and a good spatial positioning of the user’s fingers and hands.In order to get a better understanding of the capabilities of Virtual Reality headset finger tracking solutions for interacting with physical objects, we ran a controlled experiment (n =24) comparing two commodity hand and finger tracking systems (HTC Vive and Leap Motion) and report on the accuracy of commodity hand tracking systems.
Classification methods performance on human activity recognition

Abstract There are around 650 million people from all over the world who lived with disabilities. One of the fundamental rights of people with disabilities is the existence of a companion to supervise his activity. Meanwhile, the use of mobile phones for monitoring the activities of people with disabilities has been widely carried out. The human activity monitoring mobile application requires human activity recognition methods that provide high accuracy, precision, and recall to reduce the error rate of the activity estimation. Some researches use machine learning algorithms like K-Nearest Neighbour (k-NN) algorithm, Artificial Neural Networks (ANN), Support Vector Machine (SVM), and Random Forest for human activity recognition methods. However, the results of these studies have not been compared apples to apples. Therefore, this study presents a performance comparison of SVM, KNN, and Random Forest machine learning methods. Based on our findings, the SVM method with Support Vector Classifier (SVC) and Radial Basis Function (RBF) kernels can achieve the highest precision and recall, 87% and 85% respectively. The fastest processing time is obtained using the SVM method with the Stochastic Gradient Descent. However, in general, the best performance is shown by Random Forest. The Random Forest method with a depth of 100 and 300 trees can reach an accuracy of 96% within 0.45 minutes.
Pedestrian detection in underground mines via parallel feature transfer network

Pedestrian detection has been one of the key technologies in computer vision for autonomous driving in underground mines. However, such pedestrian detection is easily affected by complex environmental factors, such as uneven light, dense dust and cable interference. Recently, the problem of pedestrian detection is solved as an object detection task, which has achieved significant advances with the framework of deep neural networks. In this paper, we propose a novel parallel feature transfer network based detector called PftNet that achieves better efficiency than one-stage methods and maintains comparable accuracy of two-stage methods. PftNet consists of two interconnected modules, i.e., the pedestrian identification module and the pedestrian location module. The former aims to roughly adjust the location and size of the anchor box, filter out the negative anchor box, and provide better initialization for the regression. The latter enables PftNet to adapt to different scales and aspect ratios of objects and further improves the regression accuracy. Meanwhile, a feature transfer block compromising gated units is well designed to transmit the pedestrian characteristics between two modules. Extensive experiments on self-annotated underground dataset as well as INRIA and ETH datasets show that PftNet achieves state-of-the-art detection efficiency with high accuracy, which is significant to realizing unmanned driving systems in mines.
A knowledge-light approach to personalised and open-ended human activity recognition

Human Activity Recognition (HAR) is a core component of clinical decision support systems that rely on activity monitoring for self-management of chronic conditions such as Musculoskeletal Disorders. Deployment success of such applications in part depend on their ability to adapt to individual variations in human movement and to facilitate a range of human activity classes. Research in personalised HAR aims to learn models that are sensitive to the subtle nuances in human movement whilst Open-ended HAR learns models that can recognise activity classes out of the pre-defined set available at training. Current approaches to personalised HAR impose a data collection burden on the end user; whilst Open-ended HAR algorithms are heavily reliant on intermediary-level class descriptions. Instead of these "knowledge-intensive" HAR algorithms; in this article, we propose a "knowledge-light" method. Specifically, we show how by using a few seconds of raw sensor data, obtained through micro-interactions with the end-user, we can effectively personalise HAR models and transfer recognition functionality to new activities with zero re-training of the model after deployment. We introduce a Personalised Open-ended HAR algorithm, MN Z, a user context aware Matching Network architecture and evaluate on 3 HAR data sources. Performance results show up to 48.9% improvement with personalisation and up to 18.3% improvement compared to the most common "knowledge-intensive" Open-ended HAR algorithms.
Recommendation Analysis on Item-based and User-Based Collaborative Filtering

Providing recommendations to users in every facet of technology is of prior importance for all online and offline web platforms. There are several types of recommender systems, like content-based, context-based, collaborative-based, etc. Collaborative filtering can be classified into User-Based and Item-Based both of these techniques have different advantages and disadvantages. In this paper, we have analyzed the two techniques on a benchmark MovieLens dataset and provided the results stating the performance of each algorithm and analyzing which algorithm provides better results.
Handwriting Text Recognition Based on Faster R-CNN

Handwriting text recognition is one of the emphases in computer vision. The traditional Optical Character Recognition (OCR) technology requires the text writing neatly and handwriting clearly, but in fact, the handwriting text always fail to meet such states. In this paper, a novel handwriting text recognition algorithm based on deep learning is presented to improve the problems. In this paper, the method based on an object detection algorithm (Faster R-CNN) finds a new dimension to study the problem. The algorithm sets two steps: First, preprocessing the handwriting character based on Faster RCNN, second, character recognition based on the Convolutional Neural Networks. The correctness of this method is better than the traditional OCR by the testing data. Experimental results show that the recognition algorithm in this paper is effective and illuminating.
RL-ABE: A Revocable Lattice Attribute Based Encryption Scheme Based on R-LWE Problem in Cloud Storage

In this article, we propose a revocable lattice-based CP-ABE (Ciphertext-Policy Attribute-Based Encryption) scheme (RL-ABE), which is suitable to be applied in the cloud storage. The RL-ABE scheme can resist quantum algorithm attack and ensure fine-grained access control to the users’ rights in achieving shared data. In addition, our scheme can realize attribute revocation, which can expediently renew users’ attributes to grant or revoke their access rights. Then, we formally prove the security of our scheme based on the hardness of Ring Learning with Error problem(R-LWE) to resist quantum algorithm attack, and prove our scheme can solve security threatens to withstand collusion attacks. Finally, the performance analysis shows the high efficiency of our scheme compared with other related schemes.
Regularized Bayesian transfer learning for population-level etiological distributions

Computer-coded verbal autopsy (CCVA) algorithms predict cause of death from high-dimensional family questionnaire data (verbal autopsy) of a deceased individual, which are then aggregated to generate national and regional estimates of cause-specific mortality fractions. These estimates may be inaccurate if CCVA is trained on non-local training data different from the local population of interest. This problem is a special case of transfer learning, i.e., improving classification within a target domain (e.g., a particular population) with the classifier trained in a source-domain. Most transfer learning approaches concern individual-level (e.g., a person's) classification. Social and health scientists such as epidemiologists are often more interested with understanding etiological distributions at the population-level. The sample sizes of their data sets are typically orders of magnitude smaller than those used for common transfer learning applications like image classification, document identification, etc. We present a parsimonious hierarchical Bayesian transfer learning framework to directly estimate population-level class probabilities in a target domain, using any baseline classifier trained on source-domain, and a small labeled target-domain dataset. To address small sample sizes, we introduce a novel shrinkage prior for the transfer error rates guaranteeing that, in absence of any labeled target-domain data or when the baseline classifier is perfectly accurate, our transfer learning agrees with direct aggregation of predictions from the baseline classifier, thereby subsuming the default practice as a special case. We then extend our approach to use an ensemble of baseline classifiers producing an unified estimate. Theoretical and empirical results demonstrate how the ensemble model favors the most accurate baseline classifier. We present data analyses demonstrating the utility of our approach.
Structured-light-field 3D imaging without phase unwrapping

Directly using wrapped phases may cause a problem of phase ambiguity in fringe-projection-based three-dimensional (3D) imaging. In our previous work, the phase ambiguity was addressed by introducing light-field imaging to associate a wrapped phase-encoded field in the image space with several candidates of spatial points in the object space to identify the correct fringe orders for absolute phase unwrapping. The candidate spatial points with absolute phases can be reconstructed by satisfying the mandatory requirements of system fixation and pre-calibration, which may lack flexibility and applicability. Since we also illustrated that the correspondence cue in an unwrapped phase-encoded field could be used for accurate light-field depth estimation, an instructive question is whether or not depth cues of the light-field imaging can provide constraint conditions to deal with the phase ambiguity without these systematic requirements. To this end, a method for structured-light-field 3D imaging is proposed. The wrapped phase-encoded field was processed to be locally continuous with respect to the phases in specific angular coordinates. After that, the correspondence cue focusing on the local angular variance of the processed wrapped phase-encoded field was used to estimate depths correctly without the requirement of system pre-calibration. Furthermore, comparative experiments under different measurement conditions, including the changes of the fringe period of projected patterns, the focal length of the projector, and the relative orientation among system components, produced the consistent measurement results. Consequently, the proposed method is proved to adapt to the changes in the measurement environment and be a flexible way for structured-light-field 3D imaging without the need for phase unwrapping.