Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-linear Distortion against Hearing Loss

Today, an increasing problem is hearing loss caused by increasingly high Sound Pressure Levels (SPL). Extremely loud noises and sounds originating from our environment often cause damage to our ears and result in noise-induced hearing loss (NIHL). There are procedures to increase loudness without increasing the physical, measurable Sound Pressure Level (SPL). Procedures using equal loudness sensitivity curves amplify at different frequencies. It was our intention to create a device that will control volume and clarity to a suitable level without inflicting any damage to the ear since the sound pressure level remains at a lower level. Additionally, our aim was to design a device, that, in contrast with current procedures today, does not weaken or masquerade the sound, does not distort the intricacies of music or alter tonality. Environmental conscious thinking, especially in energy saving, has become increasingly important today. When amplifying sound, we can achieve significant energy savings from utilising a lower Sound Pressure Level (SPL). Our patented method imitates the human ear’s distortion and non-linear behaviour. Any non-linear distorting causes a sense of loudness growth, but the modified sound will become realistic if we come closer to the human ear’s distortion. In our investigations, we conclude that, by setting (or "imitating") an overtone range similar to the human ear’s distortion, we can achieve an increase in loudness in the entire audible frequency spectrum without loss of sound quality. Our prototype was constructed with the ability to carry out a subjective comparative loudness test. The data was collected individually by questionnaire, and later calculated using the arithmetical mean of the answers, with the results given in decibels. An average increment of 2.7 dB was measured electronically based on the 66 completed questionnaires used with the Triode Proof of Concept Model. The results demonstrated that an increase of loudness level can be achieved not only with the currently used Fletcher-Munson curves, but with the help of the procedure found in our study. In the implementation of this unique method, an increase in the sound pressure level can be avoided, and, so, without SPL increases, the subjective loudness of the sound can be increased, ensuring the ear will not be exposed to the adverse consequences of the higher sound pressure level. Our unique equipment and methodology can help reduce hearing damage without the discomfort caused by coloured tones. While further studies and improvements are required, the usability of this method demonstrates its practical applicability. These may include various electronic sound amplifiers in which the method can be used as a sound effect module. However, the most important field now using this method is huge number of media players, as young people commonly use these audio tools. The greatest benefit of this unique approach is therefore a reduction of hearing damage caused by the widespread and reckless use of headphones.
A scalable and efﬁcient commit algorithm for the JVSTM ∗

Most of the modern and top-performing Software Transactional Memory implementations use blocking designs, relying on locks to ensure an atomic commit operation. This approach revealed better in practice, in part due to its simplicity. Yet, this approach may have scalability problems when we move into many-core computers. In this paper we present and discuss the implementation of a new scalable and efﬁcient commit algorithm for the Java Versioned Software Transactional Memory (JVSTM), a multi-version STM implementation that uses a global lock to commit write transactions. The new commit algorithm allows commits to proceed in parallel during the validation phase, and reduces the critical region of a commit to a minimum by resorting to helping from threads that would otherwise be waiting to commit. Instead they help in the write-back of previous committing transactions. We evaluate the new algorithm with three benchmarking applications, and show it to scale well up to 172 cores, the maximum number of physical cores available on the machine used for the tests. We analyze also how the new commit algorithm affects the overall performance of the applications.
PRIVACY-PRESERVING SPEECH EMOTION RECOGNITION

Due to the lack of very large training sets and machine learning tools, it is often effective to outsource the inferring of datasets to foreign parties that hold accurate predictive models. This, however, raises major privacy concerns. In this work, we base our-selves mainly on the employment of privacy-preserving schemes in a speech emotion recognition task, as a proof of concept that could be extended to other speech analytics tasks. Our aim is to prove that the implementation of privacy-preserving speech mining schemes in challenging tasks involving paralinguistic features is not only feasible, but also efﬁcient and accurate. Using distance-preserving hashing techniques in a ﬁrst approach, and homomorphic encryption in a second approach, with crucial modiﬁcations to state-of-the-art clas-siﬁers, we successfully protect sensitive data with little to no degradation costs regarding the accuracy of predicative model.
ReNew: A Semi-Supervised Self-Learning Framework for Sentiment Flow Analysis(Preliminary Report)

The sentiment contained in opinionated text provides interesting and valuable information for building and improving social-based services. However, due to the complexity and diversity of linguistic representations, it is challenging to build a framework that can accurately detect and process the sentiment expressed in opinionated text. We motivate sentiment flow analysis as capturing how sentiment changes within a document. We propose a semi-supervised framework for sentiment flow analysis that exhibits an iterative learning structure. To capture the idea of sentiment flow, instead of inferring the polarity at the word level, our framework focuses on the segment level. Experiments show that our framework performs well in sentiment classification on a review dataset.
Unsupervised Representation Learning for Robust Speech Recognition

The performance of the state-of-the-art automatic speech recognition (ASR) systems remain fragile in high levels of noise and reverberation. The noise robustness can be partly addressed by multi-condition training (utilizing noisy training data from multiple environments) [1]. In spite of this training, the performance difference between multi-condition train-test and the clean train-test of ASR is pronounced, which warrants the need for attaining noise robustness either at speech representation stage or the training stage. In this work, various unsupervised speech representation learning approaches are explored, proposed and compared for robust speech recognition system. The work can be broadly categorized in two parts for representation learning: robust representation learning (processing the spectrogram representation) and representation learning from raw waveform.
Systems for Breast Tumor Detection Using Microwave Imaging

Breast cancer is the most prevailing form of cancer among women. However, conventional preliminary diagnoses are harmful, painful, time consuming and money costly for mass population screening. These drawbacks motivated the research for alternatives for early detection of breast cancer. This paper proposes and demonstrates a breast cancer detection system based on microwave radiation. The paper includes two main correlated topics: de-embedding of dielectric properties of biological tissue samples, and microwave imaging. The first topic is based on the open-ended coaxial cable method. We numerically studied its sensitivity with well-known characterized samples, and experimentally measured the permittivity of animal tissues and some liquid mixtures that mimic the breast. Two of them have similar properties of tumor and fat, which constitute our breast phantoms for the second part of the work. The second topic concerns microwave imaging to extract the tumor response from the breast phantoms. To this end, a modified broadband Vivaldi antenna was designed and tested, both alone and in a circular array configuration. The experimental results corroborate the numerical results, which show an image improvement when using multiple antennas and selecting specific links between them. In order to ease the equipment handling, an user-friendly graphical interface was also developed to automate the measurement procedure. Also a switching circuit was designed and fabricated to allow multiple antenna management.
Mimic Expression System for iCub

Emotions are an important part of interaction between humans. This thesis addresses the problem of emotion recognition and emotion generation in robots. The goal is to make robots express emotions in a more human-like way to improve human-robot interaction by making their communication more natural and to be able to integrate robots in social situations. To achieve this, a system that mimics the human’s face features is purposed as the first step to reach a more natural emotional expression. This work was done with the robot iCub, using its eyebrows and mouth to create different facial expressions. The system extracts the human’s facial features and changes the robot’s facial expression according to those features. It was tested using two different approaches. The first approach uses an ad hoc method to map the human’s facial features into the robot’s features and the second approach uses an automatic method based on Decision Tree Learning algorithms. During experiments, participants were asked to express the seven basic emotions in front of the iCub and then classify the emotion recognition from the iCub’s facial expression and the similarity between the iCub’s and the human’s expressions. Results showed that both methods had a similar accuracy between them. The emotion that was more easily recognized was Happiness, while emotions like Disgust and Fear were difficult to be recognized from the robot’s facial expression.
Gabriella: An Online System for Real-Time Activity Detection in Untrimmed Surveillance Videos

—Activity detection in surveillance videos is a difﬁcult problem due to multiple factors such as large ﬁeld of view, presence of multiple activities, varying scales and viewpoints, and its untrimmed nature. The existing research in activity detection is mainly focused on datasets, such as UCF-101, JHMDB, THUMOS, and AVA, which partially address these issues. The requirement of processing the surveillance videos in real-time makes this even more challenging. In this work we propose Gabriella, a real-time online system to perform activity detection on untrimmed surveillance videos. The proposed method consists of three stages: tubelet extraction, activity classiﬁcation, and online tubelet merging. For tubelet extraction, we propose a localization network which takes a video clip as input and spatio-temporally detects potential foreground regions at multiple scales to generate action tubelets. We propose a novel Patch-Dice loss to handle large variations in actor size. Our online processing of videos at a clip level drastically reduces the computation time in detecting activities. The detected tubelets are assigned activity class scores by the classiﬁcation network and merged together using our proposed Tubelet-Merge Action-Split (TMAS) algorithm to form the ﬁnal action detections. The TMAS algorithm efﬁciently connects the tubelets in an online fashion to generate action detections which are robust against varying length activities. We perform our experiments on the VIRAT and MEVA (Multiview Extended Video with Activities) datasets and demonstrate the effectiveness of the proposed approach in terms of speed ( ∼ 100 fps) and performance with state-of-the-art
Effects of Music on Mood During Basketball Play in Junior High School Physical Education

The incorporation of music in the physical education (PE) environment during physical activity has been shown to be beneficial for participants. Karageorghis et al. (1999) created a conceptual framework focusing on asynchronous music, identifying four factors important to a given piece of music: rhythm response, musicality, cultural impact, and association. The purpose of this study was to investigate the effect of two conditions, with and without the incorporation of music, in the PE environment on student moods in 948 junior high school students (501 males, 447 females). The conditions were measured using the Profile of Mood States (POMS) Short Form. Significant differences were observed in the mean scores of POMS between preintervention (without music) and postintervention (with music) for total mood disturbance, tension, anger, fatigue, depression, and confusion (all p values < 0.0001), as well as significantly higher mean scores for esteem-related affect and vigor (p values < 0.0001). Results from this study, and others, provide an impetus for PE teachers and PE teacher education to incorporate music during games/activities for the purpose of improving student moods and subsequent activity levels.
ConEE: Contextual Entity Embeddings for Tail Disambiguation

Named entity disambiguation (NED)—the task of linking ambiguous mentions in natural language to speciﬁc entities in the world—is a critical component of any system that must understand people, places, or things. While existing NED systems can accurately resolve mentions to popular entities with many textual occurrences, it is the long tail of rare entities that poses considerable challenge owing to limited or no textual information for these entities. The goal of this work is to “tackle the tail” by understanding how enhancing textual context with intensional signals from type hierarchies and knowledge graphs improves tail disambiguation. We achieve this by building a self-supervised NED system, ConEE , that represents entities as a combination of learned contextualized embeddings and pretrained embeddings from types and knowledge graphs to learn disambiguation patterns. We show that ConEE improves performance by up to 28.8 micro F1 points on tail entities while remaining competitive with state-of-the-art methods on a sentence-level AIDA CoNLL disambiguation benchmark.
Tool-path Generation for a Multiple Independent Print Head System for Fused Deposition Modeling

The additive manufacturing is a growing industry with applications on a wide variety of areas. Unfortunately, the systems that implement FDM technology have small buildareas due to the time required to manufacture the parts. One possible solution to increase the build-area dimensions, without increasing proportionality the time of fabrication, is to use multiple deposition systems simultaneously. This work proposes a new approach for a tool-path generation algorithm for a multiple independent print head system, in order reduce the fabrication time of a large part using FDM technology. The algorithm generates fabrication scenarios, composed by the combination of the part position, rotation and number of print heads. The scenarios are generated randomly, using Monte Carlo simulations or heuristically centering the part along the build areas assigned in each scenario, for simulation and optimization purpose respectively. A multiple tool-path algorithm generates the deposition paths for each print head, at the same time, avoiding collisions between them. The algorithm with branch & bound method optimizes the fabrication time. The system was performed with four different model parts applying the developed strategies for the generation of fabrication scenarios. Its performance was evaluated comparing the fabrication time using multiple deposition systems with the fabrication time using a single one. The results show an effective reduction of the fabrication time, as expected. It is also shown that the efficiency of the algorithm is related, not only with the number of deposition heads used, but it also depends on the proximity of the printing heads assigned to the fabrication of a model.
Performance and Energy-Efficiency Modelling for Multi-Core Processors

In the last years, the increasing computational needs of modern applications brought an increase in the complexity of multi-core processor architectures. Hence, deeply understanding the factors that have a major impact on the performance, power consumption and efficiency on those platforms has become a difficult task. Thus, it is not trivial to guarantee the best execution efficiency for applications in multi-core processors. Given this challenge, insightful tools capable of relating the application requirements with the processor capabilities, such as CacheAware Roofline Model and Original Roofline Model, are very valuable for programmers, mostly during the stages of prototyping and design of the applications. However, the simplicity of these tools brings up certain limitations when characterizing the behavior of real-world applications and determining their execution bottlenecks. To address these limitations, this Thesis proposes a set of Cache-Aware Roofline Model extensions to increase model insightfulness and usability, in order to provide more accurate hints regarding application optimization. To validate the proposed extensions and methodologies, a set of applications from standard benchmark suites is characterized in Intel Skylake 6700K, correlating their behavior with the different computational capabilities of the processor and providing primary hints about their main bottlenecks. Besides, the insights derived from the models proposed in this Thesis allowed to increase the performance of an application kernel for up to 6.43×, when compared to its unoptimized version, demonstrating the model usability when optimizing the execution of real applications.
The Impact of Audible Feedback on EMG-to-Speech Conversion

In this extended abstract, I describe the topic of my PhD thesis: Evaluating what the impact of audible feedback is on silent speech production and how audible feedback affects the conversion of surface electromyographic data to audible speech. I willl perform recordings using a novel data acquisition protocol and develop a real-time low-latency EMG-to-Speech conversion system and then use and evaluate this system in feedback experiments. A proposed timeline including information on what work has already been completed is presented in the conclusion of this extended abstract.
Interleaved TMS-fMRI in the Investigation of Multisensory Integration

To provide a more reliable percept of our environment, the brain combines information from multiple senses. Cross-modal deactivations are one of the possible types of multisensory influences that can be observed within the human brain. By suppressing potentially distracting neural activity in non-matching sensory areas these deactivations may mediate an increase in the salience of a sensory event. However, the exact mechanisms underlying cross-modal deactivations are still not known. The intraparietal sulcus (IPS) is a multisensory region that has been shown to modulate activity in sensory cortices. Here, we applied TMS to the right IPS during fMRI with the aim at determining the role of this region in the cross-modal deactivations during visual, auditory and audiovisual stimuli presentation. As a prior requirement to the interleaved TMS-fMRI experiment, we designed an experimental paradigm and stimuli that elicited robust activations and deactivations even in the presence of possible TMS confounding effects. Although the results obtained so far are still inconclusive with respect to the neural mechanism of cross-modal deactivations, they have provide important insights to guide future experiments.
Conformists and Mavericks in the Lab: The Structure of Frequency-Dependent Social Learning

Conformity is a type of social learning that has received considerable attention among social psychologists and human evolutionary ecologists, but existing empirical research does not identify conformity cleanly. Conformity is more than just a tendency to follow the majority; it involves an exaggerated tendency to follow the majority. We conducted an experiment to see if players were conformists by separating individual and social learners. Players chose between two technologies repeatedly. Payoffs were random, but one technology had a higher expected payoff. Individual learners knew their realized payoffs after each choice, while social learners only knew the distribution of choices among individual learners. A subset of social learners behaved according to a classic model of conformity. The remaining social learners did not respond to frequency information. Given this heterogeneity in learning strategies, a tendency to conform increased earnings dramatically.
Impact of Web Service Quality on Re-purchase Intention

The purpose of this study is to create a research model that understands the important dimensions of the quality of the website and its impact on the intention to buy. The study also analyzes the impact of customer satisfaction on the acquisition of goals. The results of the actual analysis have confirmed that the quality of the website can be created as a component of the search, ease of understanding, useful information, web design, ease of use, security and confidentiality, customization convenience and personalization. Second, site quality has a positive impact on customer purchases and customer satisfaction. Thirdly, the quality of the web service may affect the buyer's goals by increasing the satisfaction of the facilitator as it has a full impact on the purchase purpose. The research has developed a tool for quality web site servicing in the context of online shopping. The purpose of this study is to develop a research model that understands the important dimensions of the quality of web services and their impact on purchasing intentions. The study also analyzes the impact of customer satisfaction on the acquisition of goals. The results of the actual analysis have confirmed that the quality of the website can be created as a component of the search, ease of understanding, useful information, web design, ease of use, security and confidentiality, customization convenience and personalization. Second, site quality has a positive impact on customer purchases and customer satisfaction. Thirdly, the quality of the web service may affect the buyer's goals by increasing the satisfaction of the facilitator as it has a full impact on the purchase purpose. The research has developed a quality web services scale in the context of online shopping.
Towards a collaborative experience to generate knowledge: Use of gamification in robotics for Good Agricultural Practices

Currently gamification strategies are implemented in the most diverse fields. This paper discusses the design of gamification environment on a collaborative wiki on good agricultural practices. The aim of the project is to present a gamification design is complemented by a robotic interface, aimed at increasing customizing the experience from a cooperative community context. The robot interface also presents the possibility of containing a living organism, linked to the actions of the wiki. Received on 13 February 2020; accepted on 20 February 2020; published on 04 March 2020