Reviewerly/exHarmony huggingface dataset:
Implementing text-to-speech tools for community radio in remote regions of Romania

The use of text-to-speech (TTS) technology to generate radio content is largely unexplored, despite the importance of radio, in particular in remote parts of the world where TTS offers a robust means of transforming existing data into media for low-literate audiences and those without regular internet access. Is synthetic speech able to meet the expectations of radio listeners and add value to community radio stations in remote areas? We present a preliminary analysis of the design and use of TTS applications in the context of two emerging community radio stations in rural Romania. We find that while the applications developed so far are generally perceived as useful for the running of the station, future work should focus on identifying additional use cases that add value beyond that of 'filling time' or simply replacing the need for a human voice.
Backward Simulation for Sets of Trajectories

This paper presents a solution for recovering full trajectory information, via the calculation of the posterior of the set of trajectories, from a sequence of multitarget (unlabelled) filtering densities and the multitarget dynamic model. Importantly, the proposed solution opens an avenue of trajectory estimation possibilities for multitarget filters that do not explicitly estimate trajectories. In this paper, we first derive a general multitrajectory forward-backward smoothing equation based on sets of trajectories and the random finite set framework. Then we show how to sample sets of trajectories using backward simulation when the multitarget filtering densities are multi-Bernoulli processes. The proposed approach is demonstrated in a simulation study.
Understanding and Handling Blockchain Uncertainties

Blockchain is an immutable and transparent distributed ledger hosted by a Peer-to-Peer (P2P) network. Blockchain provides immutability, transparency, and data integrity to the applications built on top of it.
An Improved Algorithm for Universal Sensor Registration

We revise the ideas presented in a previous paper and propose an improved method for absolute sensor registration in target tracking applications. The approach uses targets of opportunity and, without making assumptions on their dynamical models, allows simultaneous calibration of multiple three- and two-dimensional sensors. The idea is representing the sensor angular misalignments as rotations of the actual position vectors by some rotation matrices. We formulate the registration task as a Maximum Likelihood (ML) estimation problem where the parameters to be estimated as the unknown rotation matrices as well as the unknown ground truth positions. Whereas for two-sensor scenarios only relative registration is possible, in practical cases with three or more sensors unambiguous absolute calibration may be achieved. The derived algorithm, as opposed to its previous version, is ensured to converge for three-dimensional scenarios. The derived algorithms are straightforward to implement and do not require tuning of parameters. The performance of the algorithms is tested in a numerical study.
Super-resolution Reconstruction of Dual Regular Items Infrared Image Based on Deep Plug-and-Play

Infrared image has lower resolution, unclear gray level, more concentrated gray distribution and less obvious texture, which is far from meeting the needs of human eyes for resolution, this paper proposes a Super-resolution reconstruction algorithm of dual regular items infrared image based on deep plug-and-play (DMSR). First of all, considering that most algorithms do not estimate the blur kernels accurately in the process of image degradation, the latest deep plug-and-play network (DPSR) is introduced into the algorithm, which proposed a new quality degradation model, DPSR can effectively estimate the blur kernels that leads to image degradation. Secondly, the infrared image texture is not obvious, so another regular term is added to the regular part of the algorithm to enhance the image details. In the experiment, many kinds of noise are used to reduce the quality of HR image to obtain LR image. The results of LR image reconstruction show that the proposed algorithm is more effective than the original algorithm, and compared with the results of other popular algorithms, DMSR algorithm has more advantages in deblurring and detail enhancement.
Service Composition Optimization Based on the Fireworks Algorithm Mixing Hybrid Mutation and Tournament Strategy

With web service's standards continuously proposed and the amount of web services increasing, the single-function web service is difficult to solve the problem of complex and changing real-time. In order to acquire the complex services through composing multiple simple Web service, an improved fireworks algorithm that mixing hybrid mutation and tournament strategies are proposed in this paper. Firstly, the hybrid mutation is used to optimize the fireworks mutation process, so as to avoid the algorithm falling into local optimum and improve its global search ability. Then the tournament selection strategy is adopted to pick out the next generation of fireworks population and improve the speed of algorithm convergence. Experimental results verify that the algorithm is effective and stable for Web service composition problems.
Fall simulator for supporting supervised Machine Learning techniques in wearable devices

Falls are the predominant cause of injury for older people. How to detect them is being in the last decade the focus of attention of many projects and researchers. This paper presents a simulator for recreating triaxial accelerometer measures of people falling in two circumstances: as a consequence of a loss of conscience or due to bumping into an obstacle. The objective of the simulator is to generate falling instances to train Machine Learning algorithms that can be incorporated into wearable devices. The developed simulator can generate triaxial accelerometer measures that exhibit similar patterns compared to falls recorded with real people and mannequins using a commercial device.
A Dual Scale Matching Model for Long-Term Association

Multi-object tracking can be characterized as a data association problem. The advantage of RNN in processing temporal dependence makes it an ideal selection in data association. When factors such as scene congestion and weak illumination cause detection failure especially long intervals, association is often very difficult and eventually leads to tracking failure. To solve this problem, Dual Scale Matching model (DSM) containing a Motion Trend Match Network (MTMNet) and an Appearance History Memory Network (AHMNet) is proposed. DSM is a long-term optimization method based on multiple hypothesis tracking. MTMNet aims to learn a similarity metric matching function between tracklets leveraging the motion feature. AHMNet is designed to provide optimal pruning strategies leveraging long period appearance feature. Our method is effective on MOT17 benchmark and it shows that we achieve considerable competitive results with current state-of-the-art trackers.
Unscented Particle Filtering based on Particle Selection and Weight Optimization for Nonlinear Estimation

Particle filtering has the phenomenon of particle degradation. In order to reduce the impact of this phenomenon, this paper studies the basic unscented particle filter and resampling methods. However, the resampling techniques will bring about the particle depletion. Aiming at this problem, this paper proposes an improved unscented particle filtering algorithm to enhance the filtering estimation performance, which improves the resampling method based on particle selection and weight optimization to increase the diversity of particles. Simultaneously, the proposed algorithm is verified by simulations of the non-linear systems. The simulation results indicate that the proposed algorithm has a higher filtering accuracy than the traditional unscented particle filter.
Completely Self-supervised Crowd Counting via Distribution Matching

Dense crowd counting is a challenging task that demands millions of head annotations for training models. Though existing self-supervised approaches could learn good representations, they require some labeled data to map these features to the end task of density estimation. We mitigate this issue with the proposed paradigm of complete self-supervision, which does not need even a single labeled image. The only input required to train, apart from a large set of unlabeled crowd images, is the approximate upper limit of the crowd count for the given dataset. Our method dwells on the idea that natural crowds follow a power law distribution, which could be leveraged to yield error signals for backpropagation. A density regressor is first pretrained with self-supervision and then the distribution of predictions is matched to the prior. Experiments show that this results in effective learning of crowd features and delivers significant counting performance.
Multiple Basic Proposal Distributions Model Based Sampling Particle Filter

A hybrid sampling strategy is considered in multimode sampling based particle filter to alleviate the degeneracy as one of the most typical problems in the particle filter. However, to achieve high accuracy, expensive computation cost is inevitable when generating the hybrid distribution. To overcome this problem, a novel framework of particle filter is proposed in this paper with an improved hybrid sampling strategy. The main novelty is that this framework can simplify the generation of the hybrid distribution and makes the selection of particles more reasonable, in which the likelihood of particle is used to select the particles and determine the weights of multiple basic proposal distributions. Two simulation examples are implemented to test performances of the proposed filter algorithm. The obtained results show that the proposed framework has several superior performances in comparison with the standard particle filter, the unscented particle filter and the multimode sampling based particle filter.
Medical Entity Extraction from Health Insurance Documents

The task of named entity recognition is to identify certain types of entities with special meanings from the text. It is a basic task in natural language processing and the foundation of higher-level tasks such as relation extraction, knowledge graph, and question answering system. The correctness of the entity recognition has a huge influence on the effectiveness of the upper layer application. This paper mainly studies the problem of Chinese named entity recognition in the medical field. By extracting the information about the disease in the insurance text and labeling the entity of disease, treatment, and symptom, the data set for entity recognition is established. On the basis of the BILSTM-CRF model, we use different methods to improve the recognition effectiveness of the model. By incorporating word boundary information and adding attention mechanism in the BiLSTM layer, the effectiveness of entity recognition is further improved.
A Lightweight Permission-Based Blockchain for IoT Environments

The Internet of Things (IoT) is a rapidly evolving field. As it continues to create new applications in life, industry, agriculture and infrastructure, new challenges surface with them. The application of IoT networks range from medical devices to industrial robots. Such application dynamics present several new networking challenges. IoT networks are inherently heterogeneous in terms of device capabilities, data volume, velocity and variety. This paper explores the possibility and effectiveness of combining IoT with the original concepts of blockchain technology in maintaining a verifiable ledger of polymorphic transactions to enable secure communication between devices. We present a practical implementation of a flexible blockchain system as a lightweight attachment to IoT devices. This system embeds a policy manager which defines and regulates the permissions required for authorised transactions; only a single device is permitted to amend this policy. We then demonstrate this system inside a reference topology by simulating activity between multiple homogeneous IoT devices communicating over the blockchain. We include devices of varying capabilities to demonstrate how those with more resources can support the chain whilst weaker devices consume from it.
Level of Digital Literacies Among Austrian College Students Assessed with an Online Survey

The digital age offers opportunities for the development of online information and communications technology (ICT) systems that emphasize the important role of digital literacy, including communication between people and the values necessary for working in an online environment, which greatly influences the processes and practices of learning and teaching. Especially in the field of mobile learning, digital and data-evaluating skills are of great importance, as this type of technologically extended learning is based on a comprehensive use of cloud and internet-based services. The purpose of this study was to evaluate the German version of a validated 54-item questionnaire and to assess the level of digital literacies and digital readiness of undergraduate and graduate students at the Carinthia University of Applied Sciences. An online questionnaire was developed based on the items designed by Kurtz and Peled [1]. The questionnaire included 54 items divided into seven areas: data research and retrieval; data validation; data management; data processing; teamwork; integrity awareness; and social responsibility. The items were translated into German language and their validity was evaluated with Cronbach’s Alpha in a pre-study on 155 students. After improvement of some items, all students from the Carinthia University of Applied Sciences were asked to complete the questionnaire. The first key outcome of this study is the validation of the German version of a validated 54-item questionnaire. The second key outcome is the level of digital literacies and digital readiness of undergraduate and graduate students at the Carinthia University of Applied Sciences. Finally, we want to find out if there are differences in the digital competencies between the various degree programs of the Carinthia University of Applied Sciences. Our findings should enable faculty and educational policymakers to identify the strengths and weaknesses of digital literacies of students from the Carinthia University of Applied Sciences.
Eye Tracking in Driving Environment Based on Multichannel Convolutional Neural Network

Gaze is the most important way for human to obtain information from the outside world, and it is the most direct and significant cue to analysis human behavior and intention. In driving environment, eye tracking is usually applied to model driver's fixations and gaze allocations, which is important in advanced driver assistance system (ADAS). In this paper, we have proposed a new eye tracking method in driving environment, which is based on multichannel convolutional neural network. Firstly, we establish the dataset for driver's eye tracking, which includes the left eye region image, the right eye region image and the face region image. After that, the multi-channel convolutional neural network is training using the dataset. Finally, the driver's gaze zone will be estimated using the pre-trained network. Experimental results show that the accuracy of the proposed method is 94.60% for seven gaze zone estimation, and it can be used in ADAS to analysis the driver's behavior and detect driver distraction.
An Intelligent Topology Optimization Strategy Toward the Robust Onion-like Structure

The Internet of Things (IoT) is susceptible to threats from natural disasters and human damage, thereby affecting the communication efficiency and security of the network. The scale-free IoT topology can resist the impact of random attacks, but is particularly vulnerable to malicious attacks against critical nodes. To tackle this problem, this paper proposes an intelligent topology optimization strategy combined with convolutional neural network, which evolves the initial IoT topology into a robust onion-like structure against malicious attacks. In addition, we introduce a method to convert topology information into a sequence model suitable for feature extraction. The experimental results show that this strategy can efficiently optimize the network topology into an onion-like structure.
Minding the Gap: Conceptualizing “Perceptualized” Timbre in Music Analysis

In the past decade, a growing music-analytic practice has emerged around timbre, a parameter long considered either irrelevant to musical structure or too unwieldy to tackle. This new practice centers on an understanding of timbre as a perceptual rather than physical (acoustical) attribute and privileges timbre as a bearer of musical meaning. Through a focused survey of scholarship on timbre from the 1980s to present, this article considers theoretical commitments and challenges that have attended the shift toward this subjective, “perceptualized” conception of timbre, particularly in light of music theory's objectivist and structuralist disciplinary leanings.
Parallel K-Prototypes Clustering with High Efficiency and Accuracy

Big data is often characterized by a huge volume and mixed types of data including numeric and categorical. The k-prototypes is one of the best-known clustering methods for mixed data. Despite this, it is not suitable to deal with huge volume of data. Several methods have attempted to solve the efficiency problem of the k-prototypes using parallel frameworks. However, none of the existing clustering methods for mixed data, satisfy both accuracy and efficiency. To deal with this issue, we propose a novel parallel k-prototypes clustering method that improves both efficiency and accuracy. The proposed method is based on integrating a parallel approach through Spark framework and implementing a new centers initialization strategy using sampling. Experiments were performed on simulated and real datasets show that the proposed method is scalable and improves both the efficiency and accuracy of the existing k-prototypes methods.
A Brief Review of Linear Regression Estimation in Quantum Tomography

Quantum tomography is one of the essential tasks in quantum information technology. Much effort has been devoted to increasing the precision and efficiency of quantum tomography. In this paper, we only focus on a specific tomography method, i.e., the linear regression estimation (LRE). We give a brief review on how to employ LRE for quantum state tomography, adaptive quantum state tomography and quantum Hamiltonian identification.
PerFECt: A Performative Framework to Establish and Sustain Onlife Communities and Its Use to Design a Mobile App to Extend a Digital Storytelling Platform with New Capabilities

This paper presents how the concept of virtual community can be further elaborated to account for the use of modern digital technologies in a way that promotes the establishment of rich social contexts within which human creativity is exercised and learning happens. The new reality brought about by digital technologies is characterized by new qualities, new opportunities for action, new community affordances, that are captures by the term onlife. This term was initially proposed in the Onlife Manifesto and used in this paper to signify certain qualities that are important in understanding modern digital platforms and applications as well as guide the efforts for effectively extending to provide new capabilities to their users. Specific design principles are presented exemplified by the presentation of the design of a mobile app addressing creativity and learning in the field of cultural heritage to showcase how these principles are put in action. This mobile app enables the users of an existing digital storytelling platform inspired by traditional Shadow Theatre, develop their own digital puppets in a way that incorporates the creative process of developing traditional shadow theatre puppets. The use of the mobile app is compared to the need to use external image processing tools and the pros and cons of each approach are presented and linked to the design principles initially presented.
Dynamic Procedural Music Generation from NPC Attributes

Procedural Music Generation in Games (PMGG) can enrich the playing experience by providing both entertainment and communication to the player. We present a system that generates unique procedural thematic music for non-player characters (NPC) based on developer-defined attributes and game state. The system responds in real-time to the dynamic relationship between the player and target “boss” NPC. We create a multiplayer 2D adventure game using and evaluate the music generation system by means of user study. Subjects confront four NPC bosses each with their own uniquely generated dynamic track. Results indicate the generated music is generally pleasing and harmonious, and players are able to detect a relationship between themselves and the NPCs as reflected by the music, even if they can not decipher the exact details.
Association Rule Mining Using Discrete Jaya Algorithm

Data Mining (DM) is defined as the non-trivial extraction of implicit, previously unknown and potentially useful information from data. Association Rule Mining (ARM) is one of the most important and well studied task in DM for discovering the most interesting patterns from the data stored in databases. Existing approaches suffer from problems of redundant computation, high time complexity, and large storage space. Motivated by the success of population-based metaheuristic in spite of large transactional databases, we proposed an ARM approach based on a Discrete JAYA population-based metaheuristic. This paper formulates the problem of ARM based on Discrete Jaya Algorithm (DJaya-ARM). The effectiveness of the proposed approach is tested on a real database of a population with or without Diabetes Mellitus and compared to FP-Growth Algorithm.
Design and Evaluation of a Spark Gap Based EM-fault Injection Setup

The rapid and widespread deployment of electronic devices operating in the field is bringing security issues into the spotlight. Fault injection, for instance, is a class of attacks that allows adversaries to bypass security-related capabilities by tampering with the normal functioning of a device. In this paper we describe a setup capable of faulting integrated circuits by exposing them to a pulsed magnetic field. The magnetic field is generated by discharging a pulse forming network made from a transmission line over an injection probe. The discharge is triggered by a spark gap based switch. We describe the mechanisms behind the different circuit components and evaluate the performance of the setup in practice. To the best of our knowledge, this is the first time a spark gap switch is used to build an electromagnetic (EM) pulse fault injection setup.
SDSK2BERT: Explore the Specific Depth with Specific Knowledge to Compress BERT

The success of a pretraining model like BERT in Natural Language Processing (NLP) puts forward the demand for model compression. Previous works adopting knowledge distillation (KD) to compress BERT are conducted with fixed depth, thus the problem of over-parameterization is not fully explored without answering the appropriate depth for a specific data set. In this work, we take two data sets of Natural Language Inference (NLI) with different difficulty levels as examples to answer the question of layer numbers. During the exploration of depth, we use the learned dataset-specific weights to warm up the networks in the next run, making the model find a better local optimum. With 1%~2% drops on the accuracy, our method reduces the 12-layer BERT model to 6-layer on the MNLI-matched dataset and 2-layer on the DNLI dataset, which not only reduces the parameters to 1/2x and 1/6x respectively but also outperforms the general knowledge distillation framework by about 1% accuracy. What's more, we explain why and when our framework works with the help of visualization.