Miracle-dz/UniFAR-FTUs huggingface dataset:
PROTOTYPE ULTRASONIC WAYFINDER WITH HAPTIC FEEDBACK FOR AN IOT ENVIRONMENT

Pervasive computing and the Internet of Things (IoT) have stimulated the development of many new assistive devices. It is possible to incorporate sensors such as acoustic, inductive, capacitive, temperature, humidity, pressure, location, and many more. Haptic feedback provides a person with sensory information through the skin using vibration or force-feedback responses. Commercial organizations have moved very quickly into this design space, particularly Sunu (smart-watch), HandSight (cameras on glove), and others. Arduino and Raspberry Pi are examples of the computing platforms currently in use. Sonar or ultrasonic transducers enable the production of lighter equipment with improved functionalities. Sonar as a means of assistive navigation has been used extensively in maritime environments to detect animals (D'Amico and Pittenger, 2009, Evans and Awbrey, 1988). As an assistive technology, there are projects for the blind which upgrade their walking sticks with an ultrasonic sensor (Amemiya and Sugiyama, 2010). Similar projects have been undertaken worldwide and most devices can only provide one or two designated functions. The size of the completed device is small enough to embed on a shoe, a walking stick, or on a wheelchair. A sonar sensor can detect something less than a meter from an individual user. This study uses a glove to attach a sonar sensor on a Raspberry Pi 0, whereas the Tacit glove (Hoefer, 2011) carries two sonar sensors with an Arduino controller actuating vibrating motors on a glove.
Co-evolution of language and mindreading: a computational exploration

Language relies on mindreading: in order to use it successfully we need to be able to entertain and recognise communicative intentions. Mindreading abilities in turn profit from language, as language provides a means for expressing mental states explicitly, and for transmitting our knowledge of mental states to others. Given this interdependence, it has been hypothesised that language and mindreading have co-evolved. In this thesis I formalise the relationship between language and mindreading in a computational model, in order to explore under what circumstances a co-evolutionary dynamic between the two skills could have gotten off the ground. In Chapter 3 I present an agent-based model which combines referential signalling with perspective-taking, where perspective-taking instantiates a very simple form of mindreading. In this model, agents’ communicative behaviour is probabilistically determined by an interplay between their language and their perspective on the world. The literal variant of these agents (explored in Chapters 3 and 4) consists of speakers who produce utterances purely based on their own language and perspective, and listeners who interpret these utterances using what they’ve learned about the speaker’s perspective through interaction. The pragmatic variant of these agents in contrast (explored in Chapters 5 and 6) consists of speakers who optimise their utterances by maximising the probability that the listener will interpret them correctly (assuming the listener shares their perspective), and listeners who interpret these utterances by reasoning about such a speaker, again using what they’ve learned about the speaker’s perspective through interaction. Learning is not straightforward however, because agents’ languages and perspectives are private (i.e. not directly observable to other agents). Instead, the Bayesian learners in this model only get to observe a speaker’s utterances in context, from which they have to simultaneously infer the speaker’s language and perspective. Simulation results show that learners can overcome this joint inference problem by bootstrapping one
Simulating dynamic node movements for MANET in NS-2

MANETs are self-configuring infrastructure-less characterized by changing topology and intermittent connectivity. In these networks, nodes are able to move freely and change their locations due to dynamic nature. Because of mobility, nodes can be controlled and paused at any time based on the change of connections in the network. This report proposes two strategies: Following Nodes and Controlling the Random Waypoint Model. It also intends to show the effects of controlled mobility by simulating a variety of scenarios for MANET in the network simulator NS-2. Performance comparison is also conducted over many cases. Results illustrate that the performance of the node movement varies across different node densities, node distances, and node statuses.
Multi-Level Time-Sensitive Networking (TSN) Using the Data Distribution Services (DDS) for Synchronized Three-Phase Measurement Data Transfer

This paper presents the design and implementation of a Multi-level Time Sensitive Networking (TSN) protocol based on a real-time communication platform utilizing Data Distribution Service (DDS) middleware for data transfer of synchronous three phase measurement data. To transfer ultra-high three phase measurement samples, the DDS open-source protocol is exploited to shape the network’s data traffic according to specific Quality of Service (QoS) profiles, leading to low packet loss and low latency by synchronizing and prioritizing the data in the network. Meanwhile the TSN protocol enables time-synchronization of the measured data by providing a common time reference to all the measurement devices in the network, making the system less expensive, more secure and enabling time-synchronization where acquiring GPS signals is a challenge. A software library was developed and used as a central Quality of Service (QoS) profile for the TSN implementation. The proposed design and implemented real-time simulation prototype presented in this paper takes in consideration diverse scenarios at multiple levels of prioritization including publishers, subscribers, and data packets. This allows granular control and monitoring of the data for traffic shaping, scheduling, and prioritization. The major strength of this protocol lies in the fact that it’s not only in real time but it’s time-critical too. The simulation prototype implementation was performed using the Real Time Innovation (RTI) Connext connectivity framework, custom-built MATLAB classes and DDS Simulink blocks. Simulation results show that the proposed protocol achieves low latency and high throughput, which makes it a desired option for various communication systems involved in microgrids, smart cities, military applications and potentially other time-critical applications, where GPS signals become vulnerable and data transfer needs to be prioritized.
Loaded DiCE: Trading off Bias and Variance in Any-Order Score Function Estimators for Reinforcement Learning

Gradient-based methods for optimisation of objectives in stochastic settings with unknown or intractable dynamics require estimators of derivatives. We derive an objective that, under automatic differentiation, produces low-variance unbiased estimators of derivatives at any order. Our objective is compatible with arbitrary advantage estimators, which allows the control of the bias and variance of any-order derivatives when using function approximation. Furthermore, we propose a method to trade off bias and variance of higher order derivatives by discounting the impact of more distant causal dependencies. We demonstrate the correctness and utility of our estimator in analytically tractable MDPs and in meta-reinforcement-learning for continuous control.
Low-Power meets High-Performance: Dynamic Voltage Scaling on the CARMEL Signal Processor

Reducing the power consumption is one of the primary design goals for many digital circuits, systems and applications such as mobile devices and high-performance computing systems. Power efficiency is also a major concern for digital signal processors (DSP) since they often combine the requirements of mobile and high-performance computing devices. In this paper we demonstrate the effect of low-power design strategies on the CARMEL DSP core from Infineon Technologies. We focus on reducing the energy consumption by dynamic voltage scaling (DVS), i.e., the supply voltage and clock frequency of the DSP core is adjusted at runtime. Simulation results show that the energy consumption can be reduced to a quarter compared to the standard implementation.
Analysis and Design Optimization of a Permanent Magnet Synchronous Motor for a Campus Patrol Electric Vehicle

This work presents the analysis, design and optimization of a permanent magnet synchronous motor (PMSM) for an electric vehicle (EV) used for campus patrol with a specific drive cycle. Firstly, based on the collected data like the parameters and speed from a test EV on the campus road, the dynamic calculation of the EV is conducted to decide the rated power and speed range of the drive PMSM. Secondly, according to these requirements, an initial design and some basic design parameters are obtained. Thirdly, optimization process is implemented to improve the performance of the designed PMSM. The permanent magnet (PM) structure, airgap length and stator core geometry are optimized respectively in this step. Different optimization processes are proposed to meet multiple optimization objectives simultaneously. Based on the finite element analysis (FEA) method, it is found that the harmonic of the optimized PMSM is lower than that of the initial design, and the torque ripple is reduced by 24%. The effectiveness of optimization on the core loss and PM eddy loss is validated and the temperature rise is suppressed effectively. Finally, a prototype is fabricated for the optimized PMSM and an experimental platform is developed. The test results verify that the optimized PMSM meets the requirements of the specific campus patrol EV well.
Structured Graph Optimization for Unsupervised Feature Selection

Unsupervised feature selection has attracted more and more attention due to the rapid growth of the large amount of unlabelled and high-dimensional data. The performance of traditional spectral-based unsupervised methods always depends on the quality of constructed similarity matrix. However, real world data always contain a large number of noise samples and features that make the similarity matrix created by original data cannot be fully relied. We propose an unsupervised feature selection method which conducts feature selection and local structure learning simultaneously. Moreover, we add an important constraint on the similarity matrix to allow it to capture more accurate information of the data structure. To perform feature selection, orthogonal constraint and <inline-formula><tex-math notation="LaTeX">$\ell _{2,p}$</tex-math><alternatives><mml:math><mml:msub><mml:mi>ℓ</mml:mi><mml:mrow><mml:mn>2</mml:mn><mml:mo>,</mml:mo><mml:mi>p</mml:mi></mml:mrow></mml:msub></mml:math><inline-graphic xlink:href="wei-ieq1-2937924.gif"/></alternatives></inline-formula>-norm are adopted on the projection matrix. An efficient and simple algorithm is derived to tackle the problem. We conduct comprehensive experiments on various benchmark data sets, including handwritten digit, face image, and biomedical data, to validate the effectiveness of the proposed approach.
Factored Convolutional Neural Network for Amharic Character Image Recognition

In this paper we propose a novel CNN based approach for Amharic character image recognition. The proposed method is designed by leveraging the structure of Amharic graphemes. Amharic characters could be decomposed in to a consonant and a vowel. As a result of this consonant-vowel combination structure, Amharic characters lie within a matrix structure called ’Fidel Gebeta’. The rows and columns of ’Fidel Gebeta’ correspond to a character’s consonant and the vowel components, respectively. The proposed method has a CNN architecture with two classifiers that detect the row/consonant and column/vowel components of a character. The two classifiers share a common feature space before they fork-out at their last layers. The method achieves state-of-the-art result on a synthetically generated dataset. The proposed method achieves 94.97% overall character recognition accuracy.
Software Implementation of Iris Recognition System using MATLAB

The software implementation of iris recognition system introduces in this paper. This system intends to apply for high security required areas. The demand on security is increasing greatly in these years and biometric recognition gradually becomes a hot field of research. Iris recognition is a branch of biometric recognition method. In thesis, Iris recognition system consists of localization of the iris region and generation of data set of iris images followed by iris pattern recognition. In thesis, a fast algorithm is proposed for the localization of the inner and outer boundaries of the iris region. Located iris is extracted from an eye image, and, after normalization and enhancement, it is represented by a data set. Using this data set a Neural Network (NN) is used for the classification of iris patterns. The adaptive learning strategy is applied for training of the NN. The implementation of the system is developed with MATLAB. The results of simulations illustrate the effectiveness of the neural system in personal identification. Finally, the accuracy of iris recognition system is tested and evaluated with different iris images.
Examining the Conflict Between User Experience and Software Development in Industry

In this age the modern consumer expects a software product to be profusely technically functional with an elegant and intuitive user interface. To accomplish this goal, it has become necessary for software development teams and user experience teams to collaborate on software projects. These two complementary teams often come from different backgrounds, with different technical knowledge, processes, management structures, and deadlines. As these teams continue to collaborate more and more the chance of encountering a conflict also increases. In this research we examine the conflict that can occur between software development and user experience teams and the possible effects on the product quality. This is done by surveying software developers and user experience designers from industry. We collected responses from volunteer participants using an online questionnaire. We examine factors that may make teams more prone to conflict as well as the effect conflict can have on a project. Based on our results we make some suggestions of practices that may lower the likelihood of conflict occurring, and ameliorate conflict that has already occurred.
Scalable Coding Framework for a View-Dependent Streaming of Digital Holograms

Unlike conventional images and videos, digital holograms contain large amounts of data with very low redundancy. Consequently, current communication networks may be not able to meet the bandwidth requirements for hologram transmission in reasonable time. To enable practical streaming of holographic contents, we propose a progressive coding method that combines quality scalability with viewpoint scalability. From a Gabor wavelets decomposition of the hologram, the server starts by selecting the coefficients corresponding to the user’s viewpoint. Then, the selected coefficients are encoded progressively according to their importance for the reconstructed view. Experimental results reveal that our approach outperforms conventional scalable codecs and enables the streaming of holographic data with a better quality of experience.
Unconventional GVNS for Solving the Garbage Collection Problem with Time Windows

GVNS, which stands for General Variable Neighborhood Search, is an established and commonly used metaheuristic for the expeditious solution of optimization problems that belong to the NP-hard class. This paper introduces an expansion of the standard GVNS that borrows principles from quantum computing during the shaking stage. The Traveling Salesman Problem with Time Windows (TSP-TW) is a characteristic NP-hard variation in the standard Traveling Salesman Problem. One can utilize TSP-TW as the basis of Global Positioning System (GPS) modeling and routing. The focus of this work is the study of the possible advantages that the proposed unconventional GVNS may offer to the case of garbage collector trucks GPS. We provide an in-depth presentation of our method accompanied with comprehensive experimental results. The experimental information gathered on a multitude of TSP-TW cases, which are contained in a series of tables, enable us to deduce that the novel GVNS approached introduced here can serve as an effective solution for this sort of geographical problems.
Stanowisko w sprawie stosowania niepegylowanej liposomalnej doksorubicyny u chorych na chłoniaki ze współistniejącymi chorobami sercowo-naczyniowymi

nych rodzajach chłoniaków nieziarniczych oraz protokołów chemioterapii ABVD i BEACOPP zalecanych (zależnie od stopnia zaawansowania choroby w chwili rozpoznania) w leczeniu chłoniaków ziarniczych. Istotnym ograniczeniem terapii doksorubicyną jest kardiotoksyczność, która stwarza problemy zwłaszcza u chorych z już istniejącymi problemami kardiologicznymi, ale usunięcie doksorubicyny ze schematu chemioterapii znacznie zmniejsza skuteczność przeciwnowotworową. W tej sytuacji celowe jest zastępowanie preparatu konwencjonalnego preparatem liposomalnym, który ma mniejszą kardiotoksyczność. Zastosowanie doksorubicyny liposomalnej niepegylowanej (Myocet) zamiast konwencjonalnej doksorubicyny jest wskazane w leczeniu chłoniaków (ICD10: C81–C85) w przypadku współwystępowania istotnych czynników ryzyka poważnych zdarzeń sercowo-naczyniowych, takich jak: 1. choroba wieńcowa 2. łagodna dysfunkcja skurczowa lewej komory serca z EF: 45–50% 3. cukrzyca insulinozależna 4. przewlekłe migotanie przedsionków 5. arytmia komorowa 6. umiarkowane zwężenie zastawki aortalnej 7. nadciśnienie tętnicze z powikłaniami 8. przebyta w przeszłości terapia doksorubicyną konwencjonalną z wykorzystaniem dawki łącznej ≥ 200 mg/m2.
Planning in entropy-regularized Markov decision processes and games

We propose SmoothCruiser, a new planning algorithm for estimating the value function in entropy-regularized Markov decision processes and two-player games, given a generative model of the SmoothCruiser. SmoothCruiser makes use of the smoothness of the Bellman operator promoted by the regularization to achieve problem-independent sample complexity of order $\tilde{\mathcal{O}}(1/\epsilon^4)$ for a desired accuracy $\epsilon$, whereas for non-regularized settings there are no known algorithms with guaranteed polynomial sample complexity in the worst case.
An Effective Classifier for Predicting Churn in Telecommunication

In recent days, telecom industry plays a major role in our daily life. The proliferation of telecommunication industry becomes very difficult for the service providers to survive in the market. To stabilize in this field, the service providers have to be aware of the features that make the customer to churn. The proposed predictive model identifies the traits that highly influence customer churn, with the help of machine learning techniques like KNN, Random Forest and XG Boost. IBM Watson dataset has been analysed to forecast the churn. At last a comparative study has been made among the machine learning algorithm to identify the better algorithm of higher accuracy. The proposed model shows that Fiber Optic customers with greater monthly charges have higher influence for churn.
A multimodal conversational coach for active ageing based on sentient computing and m‐health

As life expectancy increases, it has become more necessary to find ways to support healthy ageing. A number of active ageing initiatives are being developed nowadays to foster healthy habits in the population. This paper presents our contribution to these initiatives in the form of a multimodal conversational coach that acts as a coach for physical activities. The agent can be developed as an Android app running on smartphones and coupled with cheap widely available sport sensors in order to provide meaningful coaching. It can be employed to prepare exercise sessions, provide feedback during the sessions, and discuss the results after the exercise. It incorporates an affective component that informs dynamic user models to produce adaptive interaction strategies.
Information and Technology's role and digital transformation challenges: a systematic literature review

With the emergence and maturity of digital technologies (e.g., social networks, mobile telephony, big data, artificial intelligence), companies in virtually every segment are pursuing a range of initiatives to leverage their benefits. Given the increased competition from globalization and greater importance of customer focus, companies are being pressured to become digital ahead of others to survive and gain a competitive advantage. Going through such a change requires transforming the way the company sees its value proposition, its processes, the profile of its clients and its economic sustainability. Moreover, the functions of the information technology department and their leadership are being questioned. From a systematic examination of the literature, this paper presents an overview about Bimodal IT, the participation of IT in digital transformation and the hurdles of such a change.
Domain-Agnostic Video Prediction from Motion Selective Kernels

Existing conditional video prediction approaches train a network from large databases and generalise to previously unseen data. We take the opposite stance, and introduce a model that learns from the first frames of a given video and extends its content and motion, to, e.g., double its length. To this end, we propose a dual network that can use in a flexible way both dynamic and static convolutional motion kernels, to predict future frames. We demonstrate experimentally the robustness of our approach on challenging videos in-the-wild and show that it is competitive w.r. t related baselines.
Evidence-based wound management

Wound Care.Past Beliefs and Practices.Cells, Tissues and Skin.What is a Wound.The Patient With a Wound.Diagnosing and Assessing Wounds.Documenting Wound Care.Basic Tools for Providing The Optimal Wound Healing environment.Modern Wound Management Technologies.Managing Infected Wounds, classification and Treatment of Different Wound Types.Chronic Wounds, classification and Treatment of Different Wound Types.Acute Wounds.The incidence and Prevalence of Wounds.Case Studies, Professional Guidance for practitioners.Glossary of Terms.References. Index
Quantum Wasserstein Generative Adversarial Networks

The study of quantum generative models is well-motivated, not only because of its importance in quantum machine learning and quantum chemistry but also because of the perspective of its implementation on near-term quantum machines. Inspired by previous studies on the adversarial training of classical and quantum generative models, we propose the first design of quantum Wasserstein Generative Adversarial Networks (WGANs), which has been shown to improve the robustness and the scalability of the adversarial training of quantum generative models even on noisy quantum hardware. Specifically, we propose a definition of the Wasserstein semimetric between quantum data, which inherits a few key theoretical merits of its classical counterpart. We also demonstrate how to turn the quantum Wasserstein semimetric into a concrete design of quantum WGANs that can be efficiently implemented on quantum machines. Our numerical study, via classical simulation of quantum systems, shows the more robust and scalable numerical performance of our quantum WGANs over other quantum GAN proposals. As a surprising application, our quantum WGAN has been used to generate a 3-qubit quantum circuit of ~50 gates that well approximates a 3-qubit 1-d Hamiltonian simulation circuit that requires over 10k gates using standard techniques.