Reviewerly/exHarmony huggingface dataset:
The Significance of Improvisation in the Age of Technology

While there is a sense in which technological advancements enhance our experience of the world, there is also a sense in which technology obscures the world. One such instance of technology's ability to (potentially) obscure the world is the way in which makes works of art (I focus on music) so readily accessible. While in many ways the accessibility and democratisation of music offered by digital streaming, for example, is seen to be convenient, we may also notice how this accessibility can efface the significance of the musical work – works that were once only encountered in the concert hall as "works of art" now also function as background music while we drive our cars, or do the dishes, for instance. In this paper, I argue that improvisation, such as that which is demonstrated by improvising musicians, offers us a "way in" to (re)experience music as works of art, qua art, in the age of technology.
Democratic Reflection

Nudges are increasingly adopted by governments to promote social welfare, but there is an open debate on the ethics of nudges and their application in highly contested domains. We present a tool for nudging citizens? democratic engagement with political election debates. Democratic Reflection is a moment-by-moment second screen interaction technology for capturing audience feedback to time-based stimuli like speeches, TV debates, or video replays. While viewing the stimuli, users select from a matrix of icons, each describing a reflective nudge and instant audience reaction. Initial insights from the applications of this technology in the 2015, 2017 and 2019 UK elections, suggest that the reflective nudges enabled by Democratic Reflection can promote active engagement with politics, and may increase the willingness of people to be involved in political processes in the future.
Position Attention-Guided Learning for Infrared-Visible Person Re-identification

Infrared-Visible person re-identification is a challenging and fundamental task of associating the same person across visible and thermal cameras. Most of the studies focus on improving the global features to address the cross-modality issue, thus, some discriminative local and salient features are ignored by the deep models. A novel deep architecture named Dual-path Local Information Structure (DLIS) with Position Attention-guided Learning Module (PALM) is proposed to address the cross-modality issue for Infrared-Visible PReID task. The DLIS has two individual branches which contains a visible stream and an infrared stream to extract modality sharable features. The PALM can capture long-range dependencies and enhance the discriminative local feature representations to form the final feature descriptors. To supervise the network extracting discriminative features to shrink the margin of different modalities, the proposed model is conducted the joint supervision of cross-entropy loss function and hetero-center loss function. Compared with the recent studies, the proposed methods achieve the state-of-the-art on the two benchmark datasets including SYSU-MM01 and RegDB dataset.
HPM: A Hybrid Model for User’s Behavior Prediction Based on N-Gram Parsing and Access Logs

The continuous growth of the World Wide Web has led to the problem of long access delays. To reduce this delay, prefetching techniques have been used to predict the users’ browsing behavior to fetch the web pages before the user explicitly demands that web page. To make near accurate predictions for users’ search behavior is a complex task faced by researchers for many years. For this, various web mining techniques have been used. However, it is observed that either of the methods has its own set of drawbacks. In this paper, a novel approach has been proposed to make a hybrid prediction model that integrates usage mining and content mining techniques to tackle the individual challenges of both these approaches. The proposed method uses N-gram parsing along with the click count of the queries to capture more contextual information as an effort to improve the prediction of web pages. Evaluation of the proposed hybrid approach has been done by using AOL search logs, which shows a 26% increase in precision of prediction and a 10% increase in hit ratio on average as compared to other mining techniques.
Improved Particle Swarm optimization Base on the Combination of Linear Decreasing and Chaotic Inertia Weights

Particle swarm optimization (PSO) is one of the uncomplicated optimization algorithm, but it also has its disadvantages, such as premature convergence, it is difficult to get the globally optimal solution and it easily falls into local extremes. In this paper, a new PSO is proposed by combining two types of inertia weights. In order to find a solution for above mentioned disadvantages, the linear decreasing inertia weight is combined with the chaotic inertia weight. The control factor is introduced as an exponential function. The following benchmark functions: Ackley Function, Rastrigin Function, Schwefel Function, Cigar function, Sphere Function, and Booth Function are being used to validate the effectiveness of the improved PSO and the simulation results show that the proposed PSO can achieve promising performance.
Proving Non-inclusion of Büchi Automata Based on Monte Carlo Sampling

The search for a proof of correctness and the search for counterexamples (bugs) are complementary aspects of verification. In order to maximize the practical use of verification tools it is better to pursue them at the same time. While this is well-understood in the termination analysis of programs, this is not the case for the language inclusion analysis of Büchi automata, where research mainly focused on improving algorithms for proving language inclusion, with the search for counterexamples left to the expensive complementation operation.In this paper, we present \(\mathsf {IMC}^{2}\), a specific algorithm for proving Büchi automata non-inclusion \(\mathcal {L}(\mathcal {A}) \not \subseteq \mathcal {L}(\mathcal {B})\), based on Grosu and Smolka’s algorithm \(\mathsf {MC}^{2}\) developed for Monte Carlo model checking against LTL formulas. The algorithm we propose takes \(M = \lceil \ln \delta /\ln (1-\varepsilon ) \rceil \) random lasso-shaped samples from \(\mathcal {A}\) to decide whether to reject the hypothesis \(\mathcal {L}(\mathcal {A}) \not \subseteq \mathcal {L}(\mathcal {B})\), for given error probability \(\varepsilon \) and confidence level \(1 - \delta \). With such a number of samples, \(\mathsf {IMC}^{2}\) ensures that the probability of witnessing \(\mathcal {L}(\mathcal {A}) \not \subseteq \mathcal {L}(\mathcal {B})\) via further sampling is less than \(\delta \), under the assumption that the probability of finding a lasso counterexample is larger than \(\varepsilon \). Extensive experimental evaluation shows that \(\mathsf {IMC}^{2}\) is a fast and reliable way to find counterexamples to Büchi automata inclusion.
A New Mode of Army Equipment Support

Abstract Firstly, it points out the problems in the mode of providing equipment for the Army, then analyzes the characteristics and advantages of cloud computing, constructs a cloud platform technology structure suitable for the Army, and combines the leading ideas of military and civilian integration to improve the supply system in a timely and efficient manner. It provides ideas for improving the construction of Army equipment and equipment support.
Learning Ability Community for Personalized Knowledge Tracing

Knowledge tracing is an essential task that estimates students’ knowledge state as they engage in the online learning platform. Several models have been proposed to predict the state of students’ learning process to improve their learning efficiencies, such as Bayesian Knowledge Tracing, Deep Knowledge Tracing, and Dynamic Key-Value Memory Networks. However, these models fail to fully consider the influence of students’ current knowledge state on knowledge growth, and ignore the current knowledge state of students is affected by forgetting mechanisms. Moreover, these models are a unified model that does not consider the use of group learning behavior to guide individual learning. To tackle these problems, in this paper, we first propose a model named Knowledge Tracking based on Learning and Memory Process (LMKT) to solve the effect of students’ current knowledge state on knowledge growth and forgetting mechanisms. Then we propose the definition of learning capacity community and personalized knowledge tracking. Finally, we present a novel method called Learning Ability Community for Personalized Knowledge Tracing (LACPKT), which models students’ learning process according to group dynamics theory. Experimental results on public data sets show that the LMKT model and LACPKT model are effective. Besides, the LACPKT model can trace students’ knowledge state in a personalized way.
Employing Decision Templates to Imbalanced Data Classification

Data difficulties as imbalanced class distribution cause that the methods which can produce reliable predictive models remain a focus of intense research. This work attempts employing the concept of Decision Templates for the mentioned classification task. Additionally, a modification to the original method is introduced, which uses many decision templates for each class instead of one per class. The usefulness of the algorithms employing the idea of Decision Template algorithm is evaluated based on extensive experimental study and backed-up with a thorough statistical analysis. We also present an in-depth discussion of both the positive and negative impacts of the proposed approach.
A New Enhanced Recurrent Extreme Learning Machine Based on Feature Fusion with CNN Deep Features for Breast Cancer Detection

The health and life of women’s are seriously threatened by breast cancer. In female diseases, morbidity breast cancer and mortality breast cancer are ranked as first and second. The breast cancer’s mortality can be reduced by effective lump detection in early stages. The early detection, diagnosis and treatment of breast cancer are enabled by a mammogram-based computer-aided diagnosis (CAD) system. But unsatisfied results are produced by available CAD systems. Feature fusion-based breast CAD method is proposed in this work, which uses deep features of convolutional neural network (CNN). Deep feature of CNN-based mass detection is proposed in the first stage. Clustering is performed by enhanced recurrent extreme learning machine (ERELM) method. Loads are forecasted using recurrent extreme learning machine (RELM) and gray wolf optimizer (GWO) is used to optimize the weights. Deep, morphological, density and texture features are extracted in the next stage. The malignant and benign breast masses are classified by developing fused feature set-based ERELM classifier. High value of efficiency and accuracy is produced by a proposed classification technique.
Quantum Secured Internet Transport

Quantum computing represents an emerging threat to the public key infrastructure underlying transport layer security (TLS) widely used in the Internet. This paper describes how QKD symmetric keys can be used with TLS to provide quantum computing resistant security for existing Internet applications. We also implement and test a general hybrid key delivery architecture with QKD over long distance fibers between secure sites, and wireless key distribution over short distance within each site. Finally we show how this same capability can be extended to a TLS cipher scheme with perfect security.
IBUKI: Gesture Input Method Based on Breathing

In this paper, we propose a method for gesture input which uses a sensor attached to a mask to detect the user's breathing and trajectory of his or her breath. To prevent the spread of infectious diseases, it is important to keep our hands clean and avoid spreading the virus to our homes. Using a smartphone after touching various objects in public facilities increases the risk of infection. Our research focuses on operating devices such as smartphones using breathing gestures rather than touching the devices directly. The gestures were performed while wearing a commercial mask with a breathing sensor and a 9-DoF IMU attached. The breathing sensor detects inhalation and exhalation patterns, and the 9-DoF IMU estimates the breath direction and movement trajectory by the user's head. The breathing gesture was estimated by combining the information from the two sensors. Through prototyping, we determined that our method was able to estimate the gestures. We also provide an example application of this interface.
Hierarchical Path-Planning for Mobile Robots Using a Skeletonization-Informed Rapidly Exploring Random Tree*

An efficient, hierarchical, two-dimensional (2D) path-planning method for large complex environments is presented in this paper. For mobile robots moving in 2D environments, conventional path-planning algorithms employ single-layered maps; the proposed approach engages in hierarchical inter- and intra-regional searches. A navigable graph of an environment is constructed using segmented local grid maps and safe junction nodes. An inter-regional path is obtained using the navigable graph and a graph-search algorithm. A skeletonization-informed rapidly exploring random tree* (SIRRT*) efficiently computes converged intra-regional paths for each map segment. The sampling process of the proposed hierarchical path-planning algorithm is locally conducted only in the start and goal regions, whereas the conventional path-planning should process the sampling over the entire environment. The entire path from the start position to the goal position can be achieved more quickly and more robustly using the hierarchical approach than the conventional single-layered method. The performance of the hierarchical path-planning is analyzed using a publicly available benchmark environment.
PART-GAN: Privacy-Preserving Time-Series Sharing

In this paper, we provide a practical privacy-preserving generative model for time series data augmentation and sharing, called PART-GAN. Our model enables the local data curator to provide a freely accessible public generative model derived from original data for cloud storage. Compared with existing approaches, PART-GAN has three key advantages: It enables the generation of an unlimited amount of synthetic time series data under the guidance of a given classification label and addresses the incomplete and temporal irregularity issues. It provides a robust privacy guarantee that satisfies differential privacy to time series data augmentation and sharing. It addresses the trade-offs between utility and privacy by applying optimization strategies. We evaluate and report the utility and efficacy of PART-GAN through extensive empirical evaluations of real-world health/medical datasets. Even at a higher level of privacy protection, our method outperforms GAN with ordinary perturbation. It achieves similar performance with GAN without perturbation in terms of inception score, machine learning score similarity, and distance-based evaluations.
Complete Weight Enumerators of a Class of Linear Codes From Weil Sums

By appropriately choosing a defining set, we define a class of linear codes and determine their complete weight enumerators and weight enumerators using Weil sums. They only have two or three nonzero weights, and some of them are optimal with respect to the Griesmer bound or Markus Grassl's code tables. So they are suitable for applications in strongly regular graphs and secret sharing schemes.
Zone-Based Path Planning of a Mobile Robot Using Genetic Algorithm

Path planning is one of the widely studied problems in mobile robotics, which deals in finding an optimal path for a robot. To generate a collision-free path for a robot in an environment by satisfying certain constraints is a complex task. So, path planning is an NP-hard problem. In this paper, we present a new formulation of the path planning problem for a mobile robot by introducing zones which are neighbors to the static obstacles, through which a robot can pass avoiding the collisions. Our proposed model has an advantage of shrinking the search space which in turns reduces the computational complexity. We consider the minimization of the travel distance of a robot as the main objective to find a feasible path. We implement a genetic algorithm (GA) as a solution technique and compare it with two other well-studied meta-heuristic algorithms, viz. Tabu Search and Simulated Annealing. Further, we incorporate a modified mutation operation in all three algorithms to replace a zone from the reduced search space to generate a new potential solution. The simulations for different environments and comparative analysis using obtained results show that GA performs better than the other two approaches.
A Comprehensive Pre-processing Approach for High-Performance Classification of Twitter Data with several Machine Learning Algorithms

Producing an average of five hundred million tweets per date, Twitter has grown as one of the most comprehensive platforms of data interpretation for the researchers. Beforehand, various researches have been conveyed on twitter data i.e., sentimental analysis. Nevertheless, not much research has been performed to classify the tweets in terms of categories so that tweets can be spread as per user preferences. In this research, we started by constructing four comprehensive classes: politics, sports, crime and natural. Next, we implemented our proposed preprocessing model on the raw twitter dataset. After that, we implemented different machine learning techniques (Random Forest, K-Nearest Neighbors, Naive Bayes, Logistic Regression, Decision Tree and Support Vector Machine) to classify the twitter data. Finally, we examined the outcomes with and without preprocessing in terms of sensitivity, specificity, and accuracy. We found that our proposed preprocessing model enhanced the performance of all the machine learning classifiers.
Teaching acronyms to the military: A paper-based DDL approach

This research investigates the use of Data-driven learning (DDL) tasks in the teaching and learning of acronyms in a specialised corpus. Our target population is professional military staff (n=16). The researchers collected and analysed the Salvage and Rescue of Submarines Corpus (SAR) where the patterning of acronyms, neglected in English for Specific Purposes (ESP), plays a substantial role. Using a mixed-methods methodology, this research looked at the students’ interaction with DDL, as well as at the subsequent interviews with the students. Deductive and inductive paper-based DDL tasks with concordance lines of acronyms were used with two groups of students of different rank. Both groups found the tasks challenging and showed mixed reactions towards concordance lines. While there has been a much-needed emphasis on tools and corpus methods training in DDL, we suggest that conversations with adult, professional students about the nature of instructed language learning and language patterning are absolutely essential to promote a more active learner role in DDL approaches.
Receivers location privacy in avionic crowdsourced networks: Issues and countermeasures

The lack of message encryption characterizing wireless avionic protocols, including Automatic Dependent Surveillance - Broadcast (ADS-B), recently favored the rise of a few communities that, gathering data collected by receivers at the ground or in space, offer advanced services, while at the same time releasing the cited data to the public. In this context, hiding the location of an ADS-B receiver could be useful for several reasons, including military and privacy aspects. Therefore, taking into account these considerations, the data provided by a few antennas in one of the most popular crowdsourcing platforms, Opensky Network, are released removing any information that could lead to their direct location identification. In this manuscript, we investigate the effectiveness of protecting location privacy in avionic crowdsourced networks. As a worst-case scenario, we demonstrate that, when a feasible number of receivers are deployed in the same area of a protected one, due to the nature of involved ADS-B data, standard time-based localization schemes can identify the location of any protected receiver. Our model, applied to real data, can identify the location of a protected receiver with an error ranging from 0.9 km to 2.6 km, depending on the target sensor—while the location uncertainty induced by the anonymization technique was expected to be of approximately 450 km. Our findings, supported by an extensive experimental campaign run over real data, apply to a variety of potentially protected receivers. Moreover, we also provide effective countermeasures to increase receivers’ location privacy. Finally, we discuss the trade-offs implied by the cited countermeasures, showing that it is possible to increase location privacy while not decreasing data utility.
Clustering Centroid Selection using a K-means and Rapid Density Peak Search Fusion Algorithm

In the k-means algorithm, it is difficult to choose the K value and the initial centroids of the clusters. To solve this problem, the K-CFSFDP method, which combines the “clustering by fast search and find of density peaks” (CFSFDP) algorithm and the k-means algorithm, was proposed. In this study, we obtained the optimal value of the hyperparameter dc by using the silhouette coefficient SIL and the error sum of squares SSE to facilitate the selection of dc while testing the cluster centroid determined by the window selection method or the method that first sorts the products of ρi and δi in descending order and then uses the slope change trend on the University of California-Irvine (UCI) dataset. We found that the window selection method was more stable and more effectively enhanced the clustering ability of the proposed k-means and CFSFDP fusion algorithm.
March on Data Imperfections: Domain Division and Domain Generalization for Semantic Segmentation

Significant progress has been made in semantic segmentation by deep neural networks, most of which concentrate on discriminative representation learning. However, model performances suffer from deterioration when the training process is optimized without awareness of data imperfections (e.g., data imbalance and label noise). In contrast to previous works, we present a novel model-agnostic training optimization algorithm which has two prominent components: Domain Division and Domain Generalization. Rather than sampling all pixels uniformly, an uncertainty-based Domain Division method is proposed to deal with data imbalance, which dynamically decomposes the pixels into meta-train and meta-test domains according to whether they lie near the classification boundary. The meta-train domain corresponds to highly-uncertain but more informative pixels and determines the current main update direction. Furthermore, to alleviate the degradation caused by label noise, we propose a Domain Generalization technique with a meta-optimization objective which ensures that update on the meta-train domain should generalize to the meta-test domain. Comprehensive experimental results on three public benchmarks across multi-modalities show that the proposed optimization algorithm is superior to other segmentation optimization methods and significantly outperforms conventional methods without introducing additional model parameters.
Temporal Consistent Motion Parallax for Omnidirectional Stereo Panorama Video

We present a new pipeline to enable head-motion parallax in omnidirectional stereo (ODS) panorama video rendering using a neural depth decoder. While recent ODS panorama cameras record short-baseline horizontal stereo parallax to offer the impression of binocular depth, they do not support the necessary translational degrees-of-freedom (DoF) to also provide for head-motion parallax in virtual reality (VR) applications.