Reviewerly/exHarmony huggingface dataset:
Neuro-Spectral Audio Synthesis: Exploiting Characteristics of the Discrete Fourier Transform in the Real-Time Simulation of Musical Instruments Using Parallel Neural Networks

Two main approaches are currently prevalent in the digital emulation of musical instruments: manipulation of pre-recorded samples and techniques of real-time synthesis, generally based on physical models with varying degrees of accuracy. Concerning the first, while the processing power of present-day computers enables their use in real-time, many restrictions arising from this sample-based design persist; the huge on disk space requirements and the stiffness of musical articulations being the most prominent. On the other side of the spectrum, pure synthesis approaches, while offering greater flexibility, fail to capture and reproduce certain nuances central to the verisimilitude of the generated sound, offering a dry, synthetic output, at a high computational cost. We propose a method where ensembles of lightweight neural networks working in parallel are learned, from crafted frequency-domain features of an instrument sound spectra, an arbitrary instrument’s voice and articulations realistically and efficiently. We find that our method, while retaining perceptual sound quality on par with sampled approaches, exhibits 1/10 of latency times of industry standard real-time synthesis algorithms, and 1/100 of the disk space requirements of industry standard sample-based digital musical instruments. This method can, therefore, serve as a basis for more efficient implementations in dedicated devices, such as keyboards and electronic drumkits and in general purpose platforms, like desktops and tablets or open-source hardware like Arduino and Raspberry Pi. From a conceptual point of view, this work highlights the advantages of a closer integration of machine learning with other subjects, especially in the endeavor of new product development. Exploiting the synergy between neural networks, digital signal processing techniques and physical modelling, we illustrate the proposed method via the implementation of two virtual instruments: a conventional grand piano and a hibrid stringed instrument.
Estimation of Speed and Tracking of Vehicles using Radar Duet

Automotive Radar sensors determine the tracking history of vehicle(s) in two and/or three-dimensional space. These make use of Frequency Modulated Continuous Wave (FMCW) and multiple sensor techniques. This paper presents a method to estimate the average speed and track of vehicle(s) using a pair of radar sensors installed at road-side or on a car, mutually separated by some known distance and working in concert. The target vehicle is identified based on the estimated speed, range and the range-to-track target association. Both the single and multiple vehicle scenarios are examined and the study is extended up to 4-lane (one way) road configuration.
Research and Application of Parallel Computing of PVS Algorithm Based on Amazon Human-Machine Game

Because Amazon chess is very complicated, so it is very suitable for the study of machine games. For the Amazon chess human-machine game system, incorporating transposition tables and historical heuristics based on traditional PVS algorithms, optimized the traditional PVS algorithm, improve its pruning efficiency. After that, using the OpenMP standard, parallel design of the above game algorithm. Through experiments, Parallel computing greatly increases CPU utilization, effectively improve the efficiency of the PVS algorithm, enable the algorithm to search for deeper layers at the same time, thus a substantial increase in playing strength.
Opportunity Cost Estimation Using Clustering and Association Rule Mining

Information mining strategies are most appropriate for the classification, useful patterns extraction and predications which are imperative for business support and decision making. However, an efficient method for evaluating the penalty cost has not been proposed. In this article, considering the cross-selling effect, a quantitative approach to estimate the opportunity cost based on association rules in each cluster is proposed. This article helps in better decision making for improving sales, services and quality, which is useful mechanism for business support, investment, and surveillance. A numerical illustration is utilized to clarify the new approach. Further, to understand the effect of above approach in the real scenario, experiments are conducted on a real-world dataset.
Wormhole Learning

Typically, to enlarge the operating domain of an object detector, more labeled training data is required. We describe a method called wormhole learning, which allows to extend the operating domain without additional data, but only with temporary access to an auxiliary sensor with certain invariance properties. We describe the instantiation of this principle with a regular visible-light RGB camera as the main sensor, and an infrared sensor as the temporary sensor. We start with a pre-trained RGB detector; then we train the infrared detector based on the RGB-inferred labels; finally we re-train the RGB detector based on the infrared-inferred labels. After these two transfer-learning steps, the RGB detector has enlarged its operating domain by inheriting part of the invariance to illumination of the infrared sensor; in particular, the RGB detector is now able to see much better at night. We analyze the wormhole learning phenomenon by bounding the possible gain in accuracy using mutual information properties of the two sensors and considered operating domain.
The Most Related Knowledge First: A Progressive Domain Adaptation Method

In domain adaptation, how to select and transfer related knowledge is critical for learning. Inspired by the fact that human usually transfer from the more related experience to the less related one, in this paper, we propose a novel progressive domain adaptation (PDA) model, which attempts to transfer source knowledge by considering the transfer order based on relevance. Specifically, PDA transfers source instances iteratively from the most related ones to the least related ones, until all related source instances have been adopted. It is an iterative learning process, source instances adopted in each iteration are determined by a gradually annealed weight such that the later iteration will introduce more source instances. Further, a reverse classification performance is used to set the termination of iteration. Experiments on real datasets demonstrate the competiveness of PDA compared with the state-of-arts.
Seq2seq Model with RNN Attention for Abstractive Summarization

Conventional attention mechanism of seq2seq model is proposed to solve the translation alignment problem, which calculates the attention scores between encoding and decoding states independently for capturing their one-to-one relationship. However, it does not meet the requirements for information compression on abstract summarization. In this work, we propose a novel attention mechanism based on recurrent neural network (RNN), called RNN Attention (RNNA), which is more adaptive to the summary task. At each decoding time step, RNNA will establish a connection in multiple attentions by RNN for forming a many-to-one relationship between source and target, which achieves the ability of global information summarization. We evaluate our model on two benchmark datasets, LCSTS and English Gigaword. The result shows that our model's attention distribution is more extensive than the baseline model to cover more source information. Moreover, its ROUGE points also are improved and outperform the state-of-the-art models.
Human-centered cybersecurity

This paper describes the first edition of the Human-Centered Cybersecurity workshop, held in conjunction with CHItaly 2019. Cyber attacks have been increasing in the recent years. Although users have been identified as one of the major security weaknesses in today's technologies, many cyber-attack preventions focus on systems and technology without addressing user-related issues. The workshop aims at attracting researchers and practitioners whose research addresses human related topics of cybersecurity. This paper also reports a brief description of the accepted contributions.
Research on Image Processing Technology Based on Analog Head Reading

Considerating the widely use and low cost of the mechanical pointer instrument panel, a method for obtaining the dial reading by acquiring the pointer deflection angle and combining with a certain sober algorithm is designed. The method can automatically read the reading of the dial at any time. The image preprocessing algorithm in dial recognition is studied. The edge detection and binarization methods of optimized image processing are introduced. The pointer recognition is realized by Hough transform, and the current position reading of the pointer is obtained by the deflection angle method. The experimental test of the actual analog head readings, the test results show that the proposed image processing method can meet the designer's requirements.
Compare of Machine Learning and Deep Learning Approaches for Human Activity Recognition

Human-centered computing is an emerging research field that aims to understand human behavior and integrate users and their social context with computer systems. This research aimed to find the best algorithm for human activity recognition. We used Logistic Regression, SVM with RBF kernel; CNN, LSTM, Bi-Directional LSTM, and CNNLSTM algorithms for analyzing the data. The data analysis measured and compared the accuracy and training time. The most accuracy belonged to the CNN-LSTM and Bi-Directional LSTM, and the least training time belonged to the SVM with RBF kernel
OCRAM-Assisted Sensitive Data Protection on ARM-Based Platform

On mobile devices, security-sensitive tasks (e.g., mobile payment, one-time password) involve not only sensitive data such as cryptographic keying material, but also sensitive I/O operations such as inputting PIN code via touchscreen and showing the authentication verification code on the display. Therefore, a comprehensive protection of these services should enforce a Trusted User Interface (TUI) to protect the sensitive user inputs and system outputs, in addition to preventing both software attacks and physical memory disclosure attacks. In this paper, we present an On-Chip RAM (OCRAM) assisted sensitive data protection mechanism named Oath on ARM-based platform to protect the sensitive data, particularly, sensitive I/O data, against both software attacks and physical memory disclosure attacks. The basic idea is to store and process the sensitive data in the OCRAM that is only accessible to the TrustZone secure world. After figuring out how to enable TrustZone protection for iRAM, we develop a trusted user interface with an OCRAM allocation mechanism to efficiently share the OCRAM between the secure OS and the rich OS. A prototype implemented on the OP-TEE system shows that Oath works well and has a small system overhead.
Load Balancing in Cloud Computing

Cloud computing is an effective alternative information technology paradigm with its on-demand resource provisioning and high reliability. This technology has the potential to offer virtualized, distributed, and elastic resources as utilities to users. Cloud computing offers numerous types of computing and storage means by connecting to a vast pool of systems. However, because of its large data handling property, the major issue the technology facing is the load balancing problem. Load balancing is the maximum resource utilization with effective management of load imbalance. This chapter shares information about logical and physical resources, load balancing metrics, challenges and techniques, and also gives some suggestions that could be helpful for future studies.
Robustness of ToF and stereo fusion for high‐accuracy depth map

A depth map can be used in many applications such as robotic navigation, driverless, video production and 3D reconstruction. Both passive stereo and time‐of‐flight (ToF) cameras can provide the depth map for the captured real scenes, but they both have innate limitations. Since ToF cameras and passive stereo are intrinsically complementary for certain scenes, it is desirable to appropriately leverage all the available information by ToF cameras and passive stereo. As a result, this study proposes an approach to integrate ToF cameras and passive stereo to obtain high‐accuracy depth maps. The main contributions are: the first step is to design an energy cost function to utilise the depth map from ToF cameras to guide the stereo matching of passive stereo and the second step is to design their weight function for depth maps pixel‐level fusion. The experiments show that the proposed approach achieves the improved results with high accuracy and robustness.
A comparative evaluation of techniques for time series visualizations of emotions

The growing availability of social media and other online information sources has increased interest in sentiment analysis to understand the emotional responses of users. Being able to visualize users' emotions could help stakeholders to better understand the underlying trends behind events or stories. Various techniques have been used to generate time series visualizations of emotions; however, there is neither a prevalent method nor any guidelines for the design of visualizations that depict emotions and their evolution over time. We report on a controlled user study that compared four common visualization techniques. User performance and preferences were measured under a formal task taxonomy, using Twitter data about real-world events. The results, although highly task-dependent, show both an overall performance advantage and a higher level of user preference for the line chart, and suggest that the radar chart, despite its popularity in the literature, may not be the best choice to depict such data.
Compositional Feature-Oriented Systems

Feature-oriented systems describe system variants through features as first-class abstractions of optional or incremental units of systems functionality. The choice how to treat modularity and composition in feature-oriented systems strongly influences their design and behavioral modeling. Popular paradigms for the composition of features are superimposition and parallel composition. We approach both in a unified formal way for programs in guarded command language by introducing compositional feature-oriented systems (CFOSs). We show how both compositions relate to each other by providing transformations that preserve the behaviors of system variants. Family models of feature-oriented systems encapsulate all behaviors of system variants in a single model, prominently used in family-based analysis approaches. We introduce family-ready CFOSs that admit a family model and show by an annotative approach that every CFOS can be transformed into a family-ready one that has the same modularity and behaviors.
Makerspaces and virtual reality

As part of the wider EU MakEY project on ‘enhancing digital literacy and creativity’ the study reported on in this chapter explored how makerspace-style workshops can provide the opportunity for children to play and make in relation to emerging virtual reality (VR) technologies and software. The literature review outlines two areas, firstly, what is known about VR in children’s lives, and secondly how making can be used as a means of knowledge generation. These two areas set the context for our study that explored children’s engagement with a variety of experimental ideas created to allow them to make and play with physical and virtual materials and spaces. The findings provide insight into how children learnt about the affordances of VR and also how the experience of making with physical materials provided to be as much of an immersive experience as that of virtual reality. The outcomes of the project provide a contribution to the very limited amount of research that has been undertaken about children’s use of VR for entertainment, play and creativity.
Refocusable Gigapixel Panoramas for Immersive VR Experiences

There have been significant advances in capturing gigapixel panoramas (GPP). However, solutions for viewing GPPs on head-mounted displays (HMDs) are lagging: an immersive experience requires ultra-fast rendering while directly loading a GPP onto the GPU is infeasible due to limited texture memory capacity. In this paper, we present a novel out-of-core rendering technique that supports not only classic panning, tilting, and zooming but also dynamic refocusing for viewing a GPP on HMD. Inspired by the network package transmission mechanisms in distributed visualization, our approach employs hierarchical image tiling and on-demand data updates across the main and the GPU memory. We further present a multi-resolution rendering scheme and a refocused light field rendering technique based on RGBD GPPs with minimal memory overhead. Comprehensive experiments demonstrate that our technique is highly efficient and reliable, able to achieve ultra-high frame rates ( $> 50$ > 50 fps) even on low-end GPUs. With an embedded gaze tracker, our technique enables immersive panorama viewing experiences with unprecedented resolutions, field-of-view, and focus variations while maintaining smooth spatial, angular, and focal transitions.
Towards a Better Understanding of Negotiation in Group Recommender Systems

Group recommender systems identify items that fit all group members' preferences. The final step---the group negotiation for finding consensus on the item to choose---is essential for user satisfaction with the system and its outcome. It typically follows the acquisition of information about users' preferences as well as the generation and presentation of recommendations. This paper contributes to a better understanding of requirements for negotiation support in group recommender systems. In particular, we report on preliminary results of our exploratory study on the effect of three different negotiation conditions on user satisfaction.
Cosplay as inspiration for wearables research

Cosplay, where people dress up with special costumes resembling or inspired by an imaginary character, derived e.g. from game or comic genre, is an interesting area of social clothing design. In our on-going research, we consider cosplay as a source of inspiration for wearable computing. We investigate different aspects related to the identity and expression with cosplay costumes, and seek to identify factors that affect the engagement and user satisfaction in participating in the cosplay. We believe that wearable computing research can learn from cosplay, and take elements that help in designing future wearables.
Distributed Access Control with Outsourced Computation in Fog Computing

With the rapid development of Internet of things (IOT) and big data, the number of network terminal devices and big data transmission are increasing rapidly. Traditional cloud computing faces a great challenge in dealing with this massive amount of data. Fog computing which extends the computing at the edge of the network can provide computation and data storage. Attribute based-encryption can effectively achieve the fine-grained access control. However, the computational complexity of the encryption and decryption is growing linearly with the increase of the number of attributes. In order to reduce the computational cost and guarantee the confidentiality of data, distributed access control with outsourced computation in fog computing is proposed in this paper. In our proposed scheme, fog device takes most of computational cost in encryption and decryption phase. The computational cost of the receiver and sender can be reduced. Moreover, the private key of the user is generated by multi-authority which can enhance the security of data. The analysis of security and performance shows that our proposed scheme proves to be effective and secure.
Kernelized Correlation Filter Target Tracking Algorithm Based on Saliency Feature Selection

To address the problem of using fixed feature and single apparent model which is difficult to adapt to the complex scenarios, a Kernelized correlation filter target tracking algorithm based on online saliency feature selection and fusion is proposed. It combined the correlation filter tracking framework and the salient feature model of the target. In the tracking process, the maximum Kernel correlation filter response values of different feature models were calculated respectively, and the response weights were dynamically set according to the saliency of different features. According to the filter response value, the final target position was obtained, which improves the target positioning accuracy. The target model was dynamically updated in an online manner based on the feature saliency measurement results. The experimental results show that the proposed method can effectively utilize the distinctive feature fusion to improve the tracking effect in complex environments.
Text Analysis Using Large High-Resolution Displays

Large high-resolution displays (LHRDs) are entering into our daily life. Today, we already see them in installations where they display tailored applications, e.g. in exhibitions. However, while heavily studied under lab conditions, real-world applications for personal use, which utilize the extended screen space are rarely available. Thus, today's studies of LHRD are particularly designed to embrace the large screen space. In contrast, in this paper, we investigate a real-world application designed for researchers working on large text corpora to support them in deep text understanding. We conducted a study with 14 experts from the humanities and computational linguistics which solved a text analysis task using a standard desktop version on a 24 inch screen and an LHRD version on three 50 inch screens. Surprisingly, the smaller display condition outperformed the LHRD in terms of task completion time and error rate. While participants appreciated the overview provided by the large screen, qualitative feedback also revealed that the need for head movement and the scrolling mechanism decreased the usability of the LHRD condition.
A dynamical analysis on non-local autoregressive model and its application on image reconstruction

Natural images are highly structured, which reflects strong spatial redundancy underlying its pixels. In this paper, a dynamical analysis of the non-local autoregressive (NAR) model is researched for the first time. We prove that under the condition when the spectrum radius of NAR matrix is less than 1, the original image as the global stable fixed point of function system referring to a modified NAR model can be iteratively recovered via attractor reconstruction from an arbitrary start point.
Hybrid Partitioning-Density Algorithm for K-Means Clustering of Distributed Data Utilizing OPTICS

The authors present the first clustering algorithm for use with distributed data that is fast, reliable, and does not make any presumptions in terms of data distribution. The authors' algorithm constructs a global clustering model using small local models received from local clustering statistics. This approach outperforms the classical non-distributed approaches since it does not require downloading all of the data to the central processing unit. The authors' solution is a hybrid algorithm that uses the best partitioning and density-based approach. The proposed algorithm handles uneven data dispersion without a transfer overload of additional data. Experiments were carried out with large datasets and these showed that the proposed solution introduces no loss of quality compared to non-distributed approaches and can achieve even better results, approaching reference clustering. This is an excellent outcome, considering that the algorithm can only build a model from fragmented data where the communication cost between nodes is negligible.