With the emergence of the Internet of Things (IoT), investigation of different diseases in healthcare improved, and cloud computing helped to centralize the data and to access patient records throughout the world. In this way, the electrocardiogram (ECG) is used to diagnose heart diseases or abnormalities. The machine learning techniques have been used previously but are feature-based and not as accurate as transfer learning; the proposed development and validation of embedded device prove ECG arrhythmia by using the transfer learning (DVEEA-TL) model. This model is the combination of hardware, software, and two datasets that are augmented and fused and further finds the accuracy results in high proportion as compared to the previous work and research. In the proposed model, a new dataset is made by the combination of the Kaggle dataset and the other, which is made by taking the real-time healthy and unhealthy datasets, and later, the AlexNet transfer learning approach is applied to get a more accurate reading in terms of ECG signals. In this proposed research, the DVEEA-TL model diagnoses the heart abnormality in respect of accuracy during the training and validation stages as 99.9% and 99.8%, respectively, which is the best and more reliable approach as compared to the previous research in this field.

Mild Cognitive Impairment (MCI) is a condition characterized by a decline in cognitive abilities, specifically in memory, language, and attention, that is beyond what is expected due to normal aging. Detection of MCI is crucial for providing appropriate interventions and slowing down the progression of dementia. There are several automated predictive algorithms for prediction using time-to-event data, but it is not clear which is best to predict the time to conversion to MCI. There is also confusion if algorithms with fewer training weights are less accurate. We compared three algorithms, from smaller to large numbers of training weights: a statistical predictive model (Cox proportional hazards model, CoxPH), a machine learning model (Random Survival Forest, RSF), and a deep learning model (DeepSurv). To compare the algorithms under different scenarios, we created a simulated dataset based on the Alzheimer NACC dataset. We found that the CoxPH model was among the best-performing models, in all simulated scenarios. In a larger sample size (n = 6,000), the deep learning algorithm (DeepSurv) exhibited comparable accuracy (73.1%) to the CoxPH model (73%). In the past, ignoring heterogeneity in the CoxPH model led to the conclusion that deep learning methods are superior. We found that when using the CoxPH model with heterogeneity, its accuracy is comparable to that of DeepSurv and RSF. Furthermore, when unobserved heterogeneity is present, such as missing features in the training, all three models showed a similar drop in accuracy. This simulation study suggests that in some applications an algorithm with a smaller number of training weights is not disadvantaged in terms of accuracy. Since algorithms with fewer weights are inherently easier to explain, this study can help artificial intelligence research develop a principled approach to comparing statistical, machine learning, and deep learning algorithms for time-to-event predictions.

The Affective Audio Dataset (AAD) is a new and novel dataset of non-musical, non-anthropomorphic sounds intended for use in affective research. Sounds are annotated for their affective qualities by sets of human participants. The dataset was created in response to a lack of suitable datasets within the domain of audio emotion recognition. A total of 780 sounds are selected from the BBC Sounds Library. Participants are recruited online and asked to rate a subset of sounds based on how they make them feel. Each sound is rated for arousal and valence. It was found that while evenly distributed, there was bias towards the low-valence, high-arousal quadrant, and displayed a greater range of ratings in comparison to others. The AAD is compared with existing datasets to check its consistency and validity, with differences in data collection methods and intended use-cases highlighted. Using a subset of the data, the online ratings were validated against an in-person data collection experiment with findings strongly correlating. The AAD is used to train a basic affect-prediction model and results are discussed. Uses of this dataset include, human-emotion research, cultural studies, other affect-based research, and industry use such as audio post-production, gaming, and user-interface design.

Quantum-dot cellular automata (QCA) is an emerging transistor-less field-coupled nanocomputing (FCN) approach to ultra-scale ‘nanochip’ integration. In QCA, to represent digital circuitry, electrostatic repulsion between electrons and the mechanism of electron tunnelling in quantum dots are used. QCA technology can surpass conventional complementary metal oxide semiconductor (CMOS) technology in terms of clock speed, reduced occupied chip area, and energy efficiency. To develop QCA circuits, irreversible majority gates are typically used as the primary components. Recently, some studies have introduced reversible design techniques, using reversible majority gates as the main building block, to develop ultra-energy-efficient QCA circuits. However, this approach resulted in time delays, an increase in the number of QCA cells used, and an increase in the chip area occupied. This work introduces a novel hybrid design strategy employing irreversible, reversible, and partially reversible QCA gates to establish an optimal balance between power consumption, delay time, and occupied area. This hybrid technique allows the designer to have more control over the circuit characteristics to meet different system needs. A combination of reversible, irreversible, and innovative partially reversible majority gates is used in the proposed hybrid design method. We evaluated the hybrid design method by examining the half-adder circuit as a case study. We developed four hybrid QCA half-adder circuits, each of which simultaneously incorporates various types of majority gates. The QCADesigner-E 2.2 simulation tool was used to simulate the performance and energy efficiency of the half-adders. This tool provides numerical results for the circuit input/output response and heat dissipation at the physical level within a microscopic quantum mechanical model.

Foley artistry is an essential part of the audio post-production process for film, television, games, and animation. By extension, it is as crucial in emergent media such as virtual, mixed, and augmented reality. Footsteps are a core activity that a Foley artist must undertake and convey information about the characters and environment presented on-screen. This study sought to identify if characteristics of age, gender, weight, health, and confidence could be conveyed, using sounds created by a professional Foley artist, in three different 3D humanoid models, following a single walk cycle. An experiment was conducted with human participants (n=100) and found that Foley manipulations could convey all the intended characteristics with varying degrees of contextual success. It was shown that the abstract 3D models were capable of communicating characteristics of age, gender, and weight. A discussion of the literature and inspection of related audio features with the Foley clips suggest signal parameters of frequency, envelope, and novelty may be a subset of markers of those perceived characteristics. The findings are relevant to researchers and practitioners in linear and interactive media and demonstrate mechanisms by which Foley can contribute useful information and concepts about on-screen characters.

Energy efficiency considerations in terms of reduced power dissipation are a significant issue in the design of digital circuits for very large-scale integration (VLSI) systems. Quantum-dot cellular automata (QCA) is an emerging ultralow power dissipation approach, distinct from traditional, complementary metal-oxide semiconductor (CMOS) technology, for building digital computing circuits. Developing fully reversible QCA circuits has the potential to significantly reduce energy dissipation. Multiplexers are fundamental elements in the construction of useful digital circuits. In this paper, a novel, multilayer, fully reversible QCA 8:1 multiplexer circuit with ultralow energy dissipation is introduced. The power dissipation of the proposed multiplexer is simulated using the QCADesigner-E version 2.2 tool, describing the microscopic physical mechanisms underlying the QCA operation. The results show that the proposed reversible QCA 8:1 multiplexer consumes 89% less energy than the most energy-efficient 8:1 multiplexer circuit previously presented in the literature.

As interaction design has advanced, increased attention has been directed to the role that aesthetics play in shaping factors of user experience. Historically stemming from philosophy and the arts, aesthetics in interaction design has gravitated towards visual aspects of interface design thus far, with sonic aesthetics being underrepresented. This article defines and describes key dimensions of sonic aesthetics by drawing upon the literature and the authors’ experiences as practitioners and researchers. A framework is presented for discussion and evaluation, which incorporates aspects of classical and expressive aesthetics. These aspects of aesthetics are linked to low-level audio features, contextual factors, and user- centred experiences. It is intended that this initial framework will serve as a lens for the design, and appraisal, of sounds in interaction scenarios and that it can be iterated upon in the future through experience and empirical research.

Quantum-dot cellular automata (QCA) is a technological approach to implement digital circuits with exceptionally high integration density, high switching frequency, and low energy dissipation. QCA circuits are a potential solution to the energy dissipation issues created by shrinking microprocessors with ultra-high integration densities. Current QCA circuit designs are irreversible, yet reversible circuits are known to increase energy efficiency. Thus, the development of reversible QCA circuits will further reduce energy dissipation. This paper presents novel reversible and irreversible sequential QCA set/reset (SR), data (D), Jack Kilby (JK), and toggle (T) flip-flop designs based on the majority gate that utilizes the universal, standard, and efficient (USE) clocking scheme, which allows the implementation of feedback paths and easy routing for sequential QCA-based circuits. The simulation results confirm that the proposed reversible QCA USE sequential flip-flop circuits exhibit energy dissipation less than the Landauer energy limit. Irreversible QCA USE flip-flop designs, although having higher energy dissipation, sometimes have floorplan areas and delay times less than those of reversible designs; therefore, they are also explored. The trade-offs between the energy dissipation versus the area cost and delay time for the reversible and irreversible QCA circuits are examined comprehensively.

The presence of instructions within executable programs is what makes the binaries executable. However, attackers leverage on the same to achieve some form of Control Flow Hijacking (CFH). Such code re-use attacks have also been found to lead to Denial of Service (DoS). An example of code re-use attack is Return Oriented Programming (ROP) which is caused by passing input crafted as chained sequences of instructions that are already existing as subroutines in the target program. The instructions are called gadgets and they would normally end with ret. The ret instructions enable the flow of hijacked execution from one set of instruction to another within the attacker’s control. There could however be exceptions depending on the structure of the chained gadgets where the chained gadget fails to run its course due to inability of specific gadgets to replace the value in the return address (ra) register. The dangers of chained gadgets are not a new idea but the possibility for an attacker’s gadget chain to fall into a trap during a ROP attack is not commonly addressed. In addition to this, recent studies have revealed that understanding the behaviours of gadgets would be useful for building information base in training machine learning (ML) models to combat ROP. This study explains the behaviour of certain ROP gadgets showing the possibility of occurrence of a loop in execution during exploitation. A sample program which accesses gadgets from the GNU C library (glibc) is used to demonstrate the findings. Gadgets identified with this possibility are poor for chaining as they do not contain instructions to load or move new values to the ra register and would produce unreliable exploits. This would result in a trap for the chained gadgets instead of arbitrary code execution, and DoS on the path of the user. This implies that the impact that a ROP chain could have on a targeted process does not only rely on the underlying system architecture but also on relies on the structure of the chained gadget. In this paper, the RISC-V architecture is the focus, new gadget finders (scripts are available) are presented, and sample of chained gadgets are analysed on a RISC-V -based binary.

The field of Music Emotion Recognition has become and established research sub-domain of Music Information Retrieval. Less attention has been directed towards the counterpart domain of Audio Emotion Recognition, which focuses upon detection of emotional stimuli resulting from non-musical sound. By better understanding how sounds provoke emotional responses in an audience, it may be possible to enhance the work of sound designers. The work in this paper uses the International Affective Digital Sounds set. A total of 76 features are extracted from the sounds, spanning the time and frequency domains. The features are then subjected to an initial analysis to determine what level of similarity exists between pairs of features measured using Pearson’s r correlation coefficient before being used as inputs to a multiple regression model to determine their weighting and relative importance. The features are then used as the input to two machine learning approaches: regression modelling and artificial neural networks in order to determine their ability to predict the emotional dimensions of arousal and valence. It was found that a small number of strong correlations exist between the features and that a greater number of features contribute significantly to the predictive power of emotional valence, rather than arousal. Shallow neural networks perform significantly better than a range of regression models and the best performing networks were able to account for 64.4% of the variance in prediction of arousal and 65.4% in the case of valence. These findings are a major improvement over those encountered in the literature. Several extensions of this research are discussed, including work related to improving data sets as well as the modelling processes.