Reviewerly/exHarmony huggingface dataset:
Robust Test Selection for Deep Neural Networks

Deep Neural Networks (DNNs) have been widely used in various domains, such as computer vision and software engineering. Although many DNNs have been deployed to assist various tasks in the real world, similar to traditional software, they also suffer from defects that may lead to severe outcomes. DNN testing is one of the most widely used methods to ensure the quality of DNNs. Such method needs rich test inputs with oracle information (expected output) to reveal the incorrect behaviors of a DNN model. However, manually labeling all the collected test inputs is a labor-intensive task, which delays the quality assurance process. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Test selection</i> tackles this problem by carefully selecting a small, more suspicious set of test inputs to label, enabling the failure detection of a DNN model with reduced effort. Researchers have proposed different test selection methods, including neuron-coverage-based and uncertainty-based methods, where the uncertainty-based method is arguably the most popular technique. Unfortunately, existing uncertainty-based selection methods meet the performance bottleneck due to one or several limitations: 1) they ignore noisy data in real scenarios; 2) they wrongly exclude many <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">failure-revealing test inputs</i> but rather include many <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">successful test inputs</i> (referring to those test inputs that are correctly predicted by the model); 3) they ignore the diversity of the selected test set. In this paper, we propose RTS, a Robust Test Selection method for deep neural networks to overcome the limitations mentioned above. First, RTS divides all unlabeled candidate test inputs into noise set, successful set, and suspicious set and assigns different selection prioritization to divided sets, which effectively alleviates the impact of noise and improves the ability to identify suspect test inputs. Subsequently, RTS leverages a probability-tier-matrix-based test metric for prioritizing the test inputs in each divided set (i.e., suspicious, successful, and noise set). As a result, RTS can select more suspicious test inputs within a limited selection size. We evaluate RTS by comparing it with 14 baseline methods under 5 widely-used DNN models and 6 widely-used datasets. The experimental results demonstrate that RTS can significantly outperform all test selection methods in failure detection capability and the test suites selected by RTS have the best model optimization capability. For example, when selecting 2.5% test input, RTS achieves an improvement of 9.37%-176.75% over baseline methods in terms of failure detection.
PatchDiscovery: Patch Presence Test for Identifying Binary Vulnerabilities Based on Key Basic Blocks

Software vulnerabilities are easily propagated through code reuses, which pose dire threats to software system security. Automatic patch presence test offers an effective way to detect whether vulnerabilities have been patched, which is significant for large-scale software system maintenance. However, most existing approaches cannot handle binary codes. They suffer from low accuracy and poor efficiency. None of them are resilient to version gap, function size, and patch size. To tackle the above problems, we propose <i>PatchDiscovery</i> , a patch presence test approach to identify binary vulnerabilities by extracting key basic blocks of patch and vulnerability as their signatures for patch discovery. We propose an efficient and accurate basic block matching method over the normalized and simplified control flow graphs (CFGs) of a vulnerable function (VF) and its patched function (PF) to precisely locate a vulnerability and a patch. Then, we conduct fine-grained patch-level analysis on the patch and the vulnerability to gain their key basic blocks as the signatures of PF and VF for patch presence test. Concretely, the key basic blocks of PF and VF are separately searched in a target function (TF) to identify whether the TF is more similar to PF or VF, i.e., patched or not. Extensive experiments based on two real-world binary datasets that contain 524 common vulnerabilities and exposures (CVEs) with 11607 target functions reveal that <i>PatchDiscovery</i> is very effective and efficient. It achieves <inline-formula><tex-math notation="LaTeX">$92.2\%$</tex-math></inline-formula> F-measure and takes only 0.091s on average to test a target function. It is also resilient to version gap, patch size, and function size to a good extent. Moreover, it is outperforming the state-of-the-art works and has a much faster testing speed for large-scale patch detection. Moreover, <i>PatchDiscovery</i> achieves good performance in firmware vulnerability discovery scenario.
Analysis of Formant Correlations in Emotional Speech – Do Formants Correlate?

This study probes new territory and aims to bring detailed evidence for the presence and specificity of formant correlations in speech. It demonstrates that formant correlations do occur in some circumstances and vary depending on the vowel position and emotion. The implication in phonetics is that a new method for speech analysis is established; in medical science, the formant correlation tool may help in diagnosis; in speech technology, it opens a new way of speech data compression.
Coordinated Search and Mapping Using Swarm Robots

Natural disasters can cause widespread devastation, leaving survivors trapped in rubble or buried under debris. The time it takes to identify and rescue survivors is critical, this research seeks to demonstrate how Cooperative behaviors in multi-robot systems can significantly improve the speed and accuracy of victim identification following a natural disaster. This research focuses on the navigation of the robot swarm, mapping, and victim identification. The navigation approach is determined by the particle swarm theory, with the robots serving as the swarm’s agents. It moves to a destination location by avoiding impediments, maintaining group compactness, and using the attraction and repulsion forces typical of swarm particle systems. Here, each agent independently uses YOLO 5 to find the victims. When the agents agree that a victim may be present, a robot is chosen based on the Election by Consensus protocol. The chosen robot then moves around the area and maps it using RTAB, which generates a 3D point cloud and a 2D occupancy grid map of the area.
Personalized Multi-Track Leveling Algorithm

This paper analyzes the importance of music mixing and presents a personalized volume control algorithm. We discuss the constraints of existing multi-track leveling methods and highlight the necessity for a tailored approach that incorporates individual preferences and creative perspectives. The primary objective of this study is to enhance the creative process of mix engineers, musicians and producers, through the application of a customized multi-track scaling algorithm. This method aims to autonomously address the initial phase of the mixing process, namely the loudness control of each track. It achieves this by effectively determining the optimal balance between the various elements of the mix, taking into account the workflow and decision-making processes typically employed by professional mix engineers. The proposed method presents a formulation of the volume leveling as an optimization problem, which uses a customized genetic algorithm that aims to determine the optimal coefficients for the multi-tracks that compose a song.
Classified emotion as implicit recommendation system feedback

the paper proposes the use of 3D convolutional neural network for recognizing user emotions on videos in a recommender system. The data approach aims to use the recognized emotion as important implicit feedback and improve the recommendation result. This is expected to significantly improve the performance of the recommender system.
Software Architecture Evaluation of a Machine Learning Enabled System: A Case Study

Machine learning components are increasingly being included in the so-called machine learning enabled software systems. Machine learning and software engineering communities have agreed in that it is necessary to consider the particular aspects and issues that arise from the development and deployment of such systems, especially when related to software architecture. In relation to software architecture, architecture evaluation is a relatively mature area which is seen as inextricable with any architecture design. However, we believe machine learning and software engineering communities have not given enough attention to the evaluation of software architectures of machine learning enabled software. We propose in this paper a set of aspects that should be attended in order to enact a machine learning enabled software architecture evaluation such as the diversity of stakeholders’ knowledge. We designed, planned, run and report a case study in which the case was the evaluation of a white matter brain lesions segmentation machine learning enabled system using the Decison-Centric Architecture Review (DCAR) method. In this paper we report some of the decisions that were reviewed and changed, along with interesting insights regarding factors that influence software architecture evaluation adoption in this topic. We conclude and suggest in this paper that both machine learning and software architecture research and practitioner communities should start considering the study of software architecture evaluations in the context of machine learning enabled software architectures.
An Application for Research of Object Selection and Tracking in Images

The work is devoted to the process of research and development of an object selection and tracking in images. The article offers a solution to the problem of crossing the bounding frames that arises in the operation of YOLO and R-CNN type neural networks by developing a method for estimating the optimal size of the search area, which will allow finding the appropriate object and its bounding frame, and offer a generalized approach to the visualization of the research process, which will allow to visually represent the overlap of the bounding frames and will facilitate the selection of the optimal object. To confirm the effectiveness of the proposed method, there conduct experiments on the appropriate data set.
Improved Robot Path Planning with A* and DWA Fusion

The traditional A* algorithm is known to have limitations such as excessive search path length, a high number of turning nodes, lack of smoothness in the generated path, and inability to effectively avoid random obstacles. This paper proposes a novel path planning algorithm that addresses these issues by combining the A* algorithm with the DWA algorithm. The fusion algorithm reduces the number of searches by incorporating parent node influence, optimizing the cost function, expanding the single neighborhood search range, using interpolation functions to make the path smoother and using the local path planning DWA algorithm to go to the key points on the path in turn, and completing the robot path planning for obstacle avoidance. The simulation results show that the improved A* algorithm is smoother than the traditional A* algorithm, and the integration of the DWA algorithm reduces the path length by 9.5% while achieving obstacle avoidance.
Enhancing Sensor-based Capabilities in Low-cost Drone for Surveying and Inspection Applications

Drones used in modern applications require sophisticated command, control, and communication. Depending on the application, the computing power and communication requirements vary from one application to another, in some applications it requires a processing power that surpasses the capabilities of the onboard systems. As a result, several technology businesses have been developing a variety of drone control systems and offering various functionalities for their drones with a high price tag, making them not feasible option for people with limited resources. The drone system described in this paper proposes an alternative way to control and integrate one of the applications that require high processing power like object detection using a purpose-built drone and a budget companion computer. The paper provides a comprehensive guide to the hardware and assembly of a drone system, the software needed for a basic drone setup, as well as additional software required for controlling the drone and applying significant processing power technology to validate our results. These components are implemented using an additional Raspberry Pi 4 Model B, flight controller, and Raspberry Pi High Quality Camera Module resulting in a cost-effective solution. This study also explores updates regarding the universal asynchronous receiver-transmitter (UART) serial connections between the flight controller and the companion computer.
Dysarthric Speech Recognition: A Comparative Study

Automatic Speech Recognition (ASR) systems are designed to convert spoken words into written text. These systems have the potential to greatly benefit individuals with speech impairments like dysarthria, improving their overall quality of life. However, there are significant challenges that researchers must address in order to develop an effective ASR system for atypical speech, specifically dysarthric speech. Among these challenges are the limited availability of dysarthric speech data and the need for an accurate interpretation model. In this study, we explored the development of dysarthric ASR systems, starting from traditional approaches that were specifically designed to recognize dysarthric speech. We then delve into deep-learning-based ASR systems and modern ASR engines that utilize transformers. Additionally, we highlighted the notable dysarthric speech collections and identified the key factors that impede the performance of dysarthric ASR systems. The findings indicate that although researchers have achieved gradual improvements in performance, further research is required to achieve practical performance levels. Likewise, it is important to establish guidelines and standards to accurately measure and benchmark the performance of dysarthric ASR systems in order to ensure their usefulness and effectiveness in real-world applications.
Synthetic Speaking Children – Why We Need Them and How to Make Them

Contemporary Human-Computer Interaction (HCI) research relies primarily on neural network models for machine vision and speech understanding of a system user. Such models require extensively annotated training datasets for optimal performance and when building interfaces for users from a vulnerable population such as young children, GDPR introduces significant complexities in data collection, management, and processing. Motivated by the training needs of an Edge-AI smart-toy platform this research explores the latest advances in generative neural technologies and provides a working proof-of-concept of a controllable data-generation pipeline for speech-driven facial training data at scale. In this context, we demonstrate how StyleGAN-2 can be fine-tuned to create a gender-balanced dataset of children's faces. This dataset includes a variety of controllable factors such as facial expressions, age variations, facial poses, and even speech-driven animations with realistic lip synchronization. By combining generative text-to-speech models for child voice synthesis and a 3D landmark-based talking heads pipeline, we can generate highly realistic, entirely synthetic, talking child video clips. These video clips can provide valuable, and controllable, synthetic training data for neural network models, bridging the gap when real data is scarce or restricted due to privacy regulations.
Enhanced Reconciliation of Quantum Key Distribution

Quantum Key Distribution (QKD) could share a secret key between two distant nodes Alice and Bob. In the presence of an eavesdropper and noisy channel, Alice’s key may not be the same as Bob’s key. Thus, QKD protocol has to provide not only the distribution in key agreement phase but also the integrity in key verification phase. Error reconciliation is often used in QKD to share identical keys between Alice and Bob in key verification phase. Recently, Kurt et al. introduce a reconciliation algorithm for QKD (RQKD) to prevent error propagation in some previous reconciliation algorithms. Kurt et al.’s RQKD is a block-wise operation, and uses polynomial interpolation for reconciliation. However, if the x-axis values of polynomials are the same the blocks are removed. In this paper, we propose an enhanced RQKD (ERQKD) to deal with this weakness. When using polynomial-interpolation approach for reconciliation, our ERQKD without deleting block improves the efficiency of sharing qubits.
Nonparallel Expressive TTS for Unseen Target Speaker using Style-Controlled Adaptive Layer and Optimized Pitch Embedding

Recent advancements in text-to-speech (TTS) systems have focused on developing style-controlled models that generate speech with desired characteristics such as accent, tone, pitch, and expressiveness. However, controlling the style during the training process remains a challenge, mainly when dealing with nonparallel data (i.e., where text and audio data lack perfect alignment on a one-to-one basis). In this paper, we propose a nonparallel expressive TTS approach by first introducing a novel advanced control style adaptive layer normalization method to improve the controllability and stability of the training process. Second, we present an optimized pitch embedding method that enhances flexibility and control over pitch manipulation, resulting in improved accuracy in pitch generation. Our experimental results demonstrate that our approach achieves state-of-the-art performance in naturalness and similarity to the speaking styles of the synthesized speeches for unseen target speakers.
Advancing Limited Data Text-to-Speech Synthesis: Non-Autoregressive Transformer for High-Quality Parallel Synthesis

Despite the impressive results achieved by autoregressive generative models like Tacotron2 in end-to-end speech synthesis, their slow inference speed remains a significant drawback. To overcome this limitation, non-autoregressive Text-to-Speech (TTS) models like FastSpeech2 and neural vocoders like AutoVocoder, have emerged as faster alternatives with comparable quality. In this work, we present a novel lightweight Arabic TTS system based on a transformer architecture that utilizes fewer parameters than Tacotron2. Our approach combines convolutional and transformer-based blocks and is fully based on a non-autoregressive training framework. Our system can accurately reproduce the characteristics of natural speech like tone, pitch, timing and word pronunciation with state-of-the-art quality, making it suitable for practical applications like speech synthesis for low-resource languages and conversational agents. Our method is validated by acoustic analysis and subjective listening tests.
Investigation of a Specific Effect of Alcohol on Formants

In this paper, we investigated the influence of alcohol on speech production. Specifically, the effect on formant properties was investigated in terms of individual formants and combinations of formants and vowels in the 2D frequency plane - the so-called vowel triangles. The voices of sixteen individuals from an open-access database were used for this purpose. The data were reformatted, processed and displayed by internal and specially programmed algorithms in MATLAB. The effect of alcohol on formants was investigated for a selected group of vowels /ɑ e i o u/ to maintain a good spatial dispersion. The results show a systematic effect of alcohol on the frequency distribution of individual formants and the spatial properties of triangles in the 2D plane in terms of centroid distance and area differences.
Automated Processes of Preparing and Washing Deceased during Pandemic

In pandemic periods, the deceased are either in large numbers as a natural disaster or a carrier of infectious diseases as in pandemic times. Dealing with the deceased by a human is challenging for high numbers of deceased, as in natural disasters. In addition, it may transmit disease to the people who wash and prepare for those deceased during the pandemic. This paper will introduce a proposed system in pandemic situations and natural disasters to prevent people from contacting the deceased directly. The system includes an automated bathing chamber designed to make a device that cleans, bathes, and packs cadavers or bodies. It is also designed to cater to cheerful COVID-19 corpses, wherein the current guide for Muslim carcasses, the conventional cleaning process for burial procedures, is to be abandoned. The procedure is about the guidelines provided by the government in handling COVID-19 Muslim corpses in 2020. The policy states that if the corpse is a confirmed COVID-19 case, performing the tayammum on the surface of the plastic bag is adequate to eliminate any risk of COVID-19 transmission. Nowadays, this idea is for an Automated Bathing Chamber specializing in cleaning, sanitization, and packing positive COVID-19 cadavers. An initial design for the device can be visualized as a washing machine that fits a body. Furthermore, the proposed system can also provide benefits in managing the deceased who died due to infectious diseases and facilitate the bathing process for homeless cadavers and unclaimed deceased.
Liveness Detection – Automatic Classification of Spontaneous and Pre-recorded Speech for Biometric Applications

Biometric systems based on speaker recognition are susceptible to attacks consisting of an impostor (unauthorized user) presenting a pre-recorded speech sample obtained from a genuine user. This type of liveness detection is called a replay attack. This paper proposes an automatic speaker verification system to classify spontaneous speech (from a legitimate user) and pre-recorded speech samples (leveraged by an unauthorized user) using classical machine learning algorithms and deep neural networks. The system was evaluated on two corpora: ASVspoof 2017 and ASVspoof 2019. These datasets contain genuine and spoofed utterances, recorded in realistic conditions, and pre-split into training, development (dev), and evaluation (eval) subsets. Final performance for the best proposed systems reaches an equal error rate (EER) of 18.9% (dev) and 26.1% (eval) for the ASVspoof 2017 dataset, and 10.2% (dev) and 15.2% (eval) for ASVspoof 2019.
E3D: A Scaled-up Framework for Human Action Recognition

The research represents an E3D architecture created by upscaling 2D EfficientNet architecture into a three-dimensional one. E3D is a simple yet productive network that utilises a custom and novel Depthwise 3D convolution layer on which the entire algorithm depends. A depthwise convolution layer is devised for simultaneously understanding the spatial and temporal information for a large-scale processed frame sequence dataset by leveraging and scaling EfficientNetB0. This homogeneous architecture summarises the 101 distinct actions from two publicly available action classification datasets, HMDB-51 and UCF-101. The manuscript further explains the functionality of E3D and how its trained features outshine the other state-of-the-art classification benchmark in terms of performance, efficiency and deployment characteristics by achieving 98.74% and 86.49% accuracy on the UCF-101 dataset and HMDB-51 dataset, respectively; with this, the proposed architecture is computationally cheaper than the other action recognition architectures.