Miracle-dz/UniFAR-FTUs huggingface dataset:
The Future Can’t Help Fix The Past: Assessing Program Repair In The Wild

Automated program repair (APR) has been gaining ground with substantial effort devoted to the area, opening up many challenges and opportunities. One such challenge is that the state-of-the-art repair techniques often resort to incomplete specifications, e.g., test cases that witness buggy behavior, to generate repairs. In practice, bug-exposing test cases are often available when: (1) developers, at the same time of (or after) submitting bug fixes, create the tests to assure the correctness of the fixes, or (2) regression errors occur. The former case – a scenario commonly used for creating popular bug datasets – however, may not be suitable to assess how APR performs in the wild. Since developers already know where and how to fix the bugs, tests created in this case may encapsulate knowledge gained only after bugs are fixed. Thus, more effort is needed to create datasets for more realistically evaluating APR.We address this challenge by creating a dataset focusing on bugs identified via continuous integration (CI) failures – a special case of regression errors – wherein bugs happen when the program after being changed is re-executed on the existing test suite. We argue that CI failures, wherein bug-exposing tests are created before bug fixes and thus assume no prior knowledge of developers on the bugs to be involved, are more realistic for evaluating APR. Toward this end, we curated 102 CI failures from 40 popular real-world software on GitHub. We demonstrate various features and the usefulness of the dataset via an evaluation of five well-known APR techniques, namely GenProg, Kali, Cardumen, RsRepair and Arja. We subsequently discuss several findings and implications for future APR studies. Overall, experiment results show that our dataset is complementary to existing datasets such as Defect4J in realistic evaluations of APR.
Abnormal Respiratory Sound Identification Using Audio-Spectrogram Vision Transformer

Respiratory disease, the third leading cause of deaths globally, is considered a high-priority ailment requiring significant research on identification and treatment. Stethoscope-recorded lung sounds and artificial intelligence-powered devices have been used to identify lung disorders and aid specialists in making accurate diagnoses. In this study, audio-spectrogram vision transformer (AS-ViT), a new approach for identifying abnormal respiration sounds, was developed. The sounds of the lungs are converted into visual representations called spectrograms using a technique called short-time Fourier transform (STFT). These images are then analyzed using a model called vision transformer to identify different types of respiratory sounds. The classification was carried out using the ICBHI 2017 database, which includes various types of lung sounds with different frequencies, noise levels, and backgrounds. The proposed AS-ViT method was evaluated using three metrics and achieved 79.1% and 59.8% for 60:40 split ratio and 86.4% and 69.3% for 80:20 split ratio in terms of unweighted average recall and overall scores respectively for respiratory sound detection, surpassing previous state-of-the-art results.
Towards the definition of Microstates of the Cortical Brain-Heart Axis

Brain microstates are defined as states with quasi-stable scalp activity topography and have been widely studied in literature. Whether those states are brain-specific or extend to the body level is unknown yet. We investigate the extension of cortical microstates to the peripheral autonomic nerve, specifically at the brain-heart axis level as a functional state of the central autonomic network. To achieve this, we combined Electroencephalographic (EEG) and heart rate variability (HRV) series from 36 healthy volunteers undergoing a cognitive workload elicitation after a resting state. Our results showed the existence of microstates at the functional brain-heart axis with spatio-temporal and quasi-stable states that exclusively pertained to the efferent direction from the brain to the heart. Some of the identified microstates are specific for neural or cardiovascular frequency bands, while others topographies are recurrent over the EEG and HRV spectra. Furthermore, some of the identified brain-heart microstates were associated with specific experimental conditions, while others were nonspecific to tasks. Our findings support the hypothesis that EEG microstates extend to the brain-heart axis level and may be exploited in future neuroscience and clinical research.
Detecting Changes in Functional State: A Comparative Analysis Using Wearable Sensors and a Sensorized Tip

Gait analysis can provide relevant information about the physical and neurological conditions of individuals. For this reason, several studies have recently been carried out in an attempt to monitor people's gait and automatically detect gait anomalies. Among the various monitoring systems available for gait analysis, wearable sensors are considered the gold standard due to their wide capture range and low cost. However, in the case of people that require assistive devices for walking, some studies have proposed the use of sensorized devices in order to minimize invasiveness. Nevertheless, there is still a lack of comparative works that evaluate the performance of sensorized assistive devices for walking with widely used wearable sensors. Hence, this paper presents a comparison between the performance of accelerometer-based wearable sensors and a sensorized tip developed by the authors to detect gait anomalies. The comparative study has been carried out in a controlled environment with five healthy subjects, in which three different physical states have been simulated. A machine-learning based anomaly detection approach has been implemented based on the data captured by a set of wearable sensors and the sensorized tip, and the overall performance of both monitoring systems has been evaluated. Results show that even if both devices can provide an average accuracy of more than 80% in gait anomaly detection, the sensorized tip provides better performance.
Test Rig for the Fault Diagnosis of 3-Phase Small Scale Induction Motor

The induction motor (IM) holds a crucial role in industrial machinery operations. As a consequence, any malfunction occurring in induction motors can directly result in substantial system downtime and disrupt overall production processes. In order to effectively diagnose faults, particularly broken rotor bar faults, in small-scale three-phase induction motors, a combination of vibration analysis (VA) and motor current signature analysis (MCSA) has been employed. This approach ensures cross-validation of the results. To facilitate the identification of issues, the time domain signals of the stator current and vibration of the IM have been subjected to fast Fourier transform (FFT) analysis. By comparing the frequency spectra of both healthy and faulty motor current and vibration signals, the distinctive patterns associated with each state can be discerned. This process aids in accurately pinpointing the presence of broken rotor bar faults and distinguishing them from normal operational conditions.
Design and Motion Guidelines for Quadrupedal Locomotion of Maximum Speed or Efficiency with Serial and Parallel Legs

Analytical expressions are derived for actuator demands in quadrupedal locomotion of constant speed and height by using a reduction from a trot/ pace 6-bar model to a single-legged model and employing two widely used two-segmented leg architectures, the serial and the parallel. A method is developed that outputs optimal gait characteristics and leg designs for a robot to move with maximum efficiency or speed. Also, generic guidelines are presented, which answer questions such as: which speed should be selected for maximum efficiency, or which is the optimal leg architecture (serial/ parallel) and leg length for maximum efficiency or speed.
Dr-MUSIC: An Effective Device for Investigating Multisensory Mechanisms during Development with EEG recordings *

From birth, we are continuously exposed to multisensory stimuli that we learn to select and integrate during development to perceive a coherent world. To date, there are no optimal solutions to investigate how auditory, visual and tactile signals are integrated during EEG recording in infants and children. The present work aims to introduce Dr-MUSIC, a novel multisensory device with EEG-compatible timing and an attractive design for children. It is composed of audio, visual, and tactile stimulators arranged in the form of a couple of chubby dragons that can simultaneously provide selectable uni-, bi-, or tri-modal information. We first validated the system’s EEG compatibility in 8 adults by implementing an audio-tactile oddball task during a high-density EEG recording. Then, we replicated the same task in a couple of toddlers to validate the device’s usability for young children. The results suggest that the system can be effectively used for setting new experimental protocols to understand the neural basis of multisensory integration in the first years of life.Clinical Relevance— The amusing design and the possibility of changing the stimulation’s characteristics (i.e., light, sound, and vibrotactile features) make it attractive in children with and without sensory impairments. Therefore, Dr-MUSIC could be used to investigate multisensory development and related neural correlates in typical and atypical children to design new early rehabilitation protocols.
Time-Frequency Fragment Selection for Disease Detection from Imbalanced Phonocardiogram Data

Cardiovascular disease (CVD) has become the most concerning disease worldwide. A Phonocardiogram (PCG), the graphical representation of heart sound, is a non-invasive method that helps to detect CVD by analyzing its characteristics. Several machine learning (ML) approaches have been proposed in the last decade to assist practitioners in interpreting this disease accurately. However, the ML-based method requires a considerable amount of PCG data with a balance between data categories for unbiased performance. Moreover, PCG data in the literature is scarce, and the available database has a strong imbalance between the normal and abnormal categories. This data imbalance causes outcomes to be severely biased towards classes with greater samples. This work proposes a variable-hop fragment selection method with a pre-trained CNN model to counter the issues of data scarcity and imbalance. The proposed framework improves 7.12% of unweighted average recall (UAR) value for assessing an imbalanced PCG dataset compared to the state-of-the-art method and reports an overall UAR of 92.46% on the PhysioNet/CinC Challenge 2016 dataset. The improved performance signifies the clinical relevance of the work providing reliable assistance for heart auscultation and has the potential to screen for heart pathologies in data constraint applications.
SARS-CoV-2 Detection: Radiology based Multi-modal Multi-task Framework

The global community is still grappling with the SARS-CoV-2 pandemic, declared by the World Health Organization in March 2020. Radiology is an important screening method for the early detection of SARS-CoV-2. Doctors typically recommend that patients undergo one of the radiology procedures during the early stages of diagnosis. Recent research has focused on developing deep learning-based architectures that use either X-Rays or CT-Scans, but not both. This paper presents a multi-modal, multi-task learning framework that uses either the X-Rays or CT-Scans to identify SARS-CoV-2 patients. The framework employs a shared feature embedding that utilizes common information from both X-Rays and CT-Scans, as well as task-specific feature embeddings that are independent of the type of chest screening. The shared and task-specific embeddings are combined to obtain the final classification results, which have been shown to have an accuracy of 98.23% and 98.83% in detecting SARS-CoV-2 using X-Rays and CT-Scans, respectively.
Radiation Dose Reduction in Digital Breast Tomosynthesis by MTANN with Multi-scale Kernels

Digital breast tomosynthesis (DBT) is an advanced three-dimensional screening modality for the early detection of breast cancer. DBT is able to reduce the problem of tissue overlap in standard two-dimensional mammograms, thus improving the sensitivity and specificity of cancer detection. Although DBT can improve diagnostic accuracy, it leads to higher radiation dose to patients compared to two-dimensional mammography. In this paper, we propose a novel radiation dose reduction technique that introduces multi-scale kernels to our original massive-training artificial neural network (MTANN) to reduce radiation dose substantially, while maintaining high image quality in DBT. After training our new MTANN with low-dose (LD) images and the corresponding "teaching" high-dose (HD) images, we can convert new LD images to "virtual" high-dose (VHD) images where noise and artifact in the LD images are significantly reduced. In VHD images, it is critical to preserve subtle structures and tiny patterns such as microcalcifications (MCs) which are essential for breast cancer diagnosis. We developed anatomical MTANN experts including an MC-specific expert with multi-scale kernels, which are combined by gating layers to generate whole VHD images. Our MTANN scheme was able to achieve a 79% dose reduction while preserving details of MCs. Experimental results demonstrated that our method achieved the highest performance among the best-known noise-reduction techniques and state-of-the-art deep-learning techniques.Clinical Relevance— Our method can decrease the dose radiation dose in DBT and maintain the image quality.
Multiresolution Self-Supervised Feature Integration via Attention Multiple Instance Learning for Histopathology Analysis

Digital histopathology image analysis of tumor tissue sections has seen great research interest for automating standard diagnostic tasks, but also for developing novel prognostic biomarkers. However, research has mainly been focused on developing uniresolution models, capturing either high-resolution cellular features or low-resolution tissue architectural features. In addition, in the patch-based weakly-supervised training of deep learning models, the features which represent the intratumoral heterogeneity are lost. In this study, we propose a multiresolution attention-based multiple instance learning framework that can capture cellular and contextual features from the whole tissue for predicting patient-level outcomes. Several basic mathematical operations were examined for integrating multiresolution features, i.e. addition, mean, multiplication and concatenation. The proposed multiplication-based multiresolution model performed the best (AUC=0.864), while all multiresolution models outperformed the uniresolution baseline models (AUC=0.669, 0.713) for breast-cancer grading. (Implementation: https://github.com/tsikup/multiresolution-clam)
Preliminary Results of Branch Level, Brachial Plexus Peripheral Nerve Stimulation on a Non-Human Primate

Restoring functional hand control is a priority for those suffering from neurological impairments. Functional electrical stimulation (FES) is commonly applied to assist with rehabilitation. However, FES applied intramuscularly typically results in complex surgeries requiring many implants. This paper presents the preliminary findings from a feasibility study focused on evaluating the potential to access the upper extremity peripheral nerves through a single surgical approach (axillary approach). A single Japanese macaque (macaca fuscata) monkey was used to validate the feasibility of this study. Four of the five peripheral nerves which control the upper extremity were exposed, and had multi-contact epineural cuffs implanted: median, radial, ulnar and musculocutaneous. The axillary nerve was not accessible for epineural cuff placement with the current surgical approach used in this study. Electrical stimuli were used to produce movement contraction patterns of muscles relevant to the innervated peripheral nerves. In addition, to assist in quantifying the outcome, evoked potentials were simultaneously recorded from five extrinsic forearm flexors during median nerve stimulation. This feasibility study demonstrated that the axillary approach enables electrode placement to four of the five peripheral nerves required for upper extremity control through a single skin incision.Clinical relevance— This study demonstrated that the electrode placement to most of the peripheral nerves that control the arm and hand can be done by a single surgical approach: axillary approach
Compact Filters on Dielectric Paper Substrate

The paper presents microwave filters implemented on a paper substrate with a dielectric constant of 2.85. The paper presents three filters with central frequencies of 10.5 GHz, 20 GHz and 60 GHz. The advantages of the quasi-elliptical filter structure due to the presence of full transmission resonances are demonstrated. It is also possible to reduce the weight and dimensions by 5 times due to the bending of the filter. The possibility of synthesis of a narrowband bandpass filter with a bandwidth 1.8 GHz is shown. The presented filters are made with the use of SIW-technology, which is widely known due to the possibility of reducing the mass-size parameters of the product, as well as relatively low cost and ease of manufacture, while maintaining high quality factor and low insertion loss inherent in conventional rectangular waveguides. Calculation formulas are given and the synthesis process of SIW-filters is demonstrated. Experimental studies of the manufactured filter prototypes were carried out and the amplitude-frequency characteristics of computer modes were compared.
Blockchain-Based Key Management Solution for Clustered Flying Ad-Hoc Network

The Flying ad hoc networks (FANETs) have been recognized as one of the most emergent technologies for ensuring the performance and safety of different systems. However, its security presents a major concern inviting researchers to propose and implement new efficient solutions. In this context, due to the dynamic behavior of FANETs, the Key Management (KM)- based solution is increasingly explored as a security issue. In this paper, we investigate a Blockchain-based Key Management solution for Clustered FANETs (BKM-CF) to effectively perform cluster key dissemination, allow UAVs to update their public/private keys, migrate between clusters, and revoke malicious UAVs. Through security analysis and performance evaluation, our solution can resist against various attacks and provide reduced network energy consumption.
Passives are not hard to interpret but hard to remember: evidence from online and offline studies

ABSTRACT Passive sentences are considered more difficult to comprehend than active sentences. Previous online-only studies cast doubt on this generalisation. The current paper directly compares online and offline processing of passivization and manipulates verb type: state vs. event. Stative passives are temporarily ambiguous (adjectival vs. verbal), eventive passives are not (always verbal). Across 4 experiments (self-paced reading with comprehension questions), passives were consistently read faster than actives. This contradicts the claim that passives are difficult to parse and/or interpret, as argued by main perspectives of passive processing (heuristic, syntactic, frequentist). The reading time facilitation is compatible with broader expectation/surprisal theories. When comprehension targeted theta-role assignment, passives were more errorful, regardless of verb type. Verbal WM measures correlated with the difference in accuracy, but not online measures. The accuracy effect is argued to reflect a post-interpretive difficulty associated with maintaining/manipulating the passive representation as required by specific tasks.
Optimizing the trajectory of deep brain stimulation leads reduces RF heating during MRI at 3 T: Characteristics and clinical translation

Radiofrequency (RF) induced tissue heating around deep brain stimulation (DBS) leads is a well-known safety risk during magnetic resonance imaging (MRI), hindering routine protocols for patients. Known factors that contribute to variations in the magnitude of RF heating across patients include the implanted lead’s trajectory and its orientation with respect to the MRI electric fields. Currently, there are no consistent requirements for surgically implanting the extracranial portion of the DBS lead. Recent studies have shown that incorporating concentric loops in the extracranial trajectory of the lead can reduce RF heating, but the optimal positioning of the loop is unknown. In this study, we evaluated RF heating of 77 unique lead trajectories to determine how different characteristics of the trajectory affect RF heating during MRI at 3 T. We performed phantom experiments with commercial DBS systems from two manufacturers to determine how consistently modifying the lead trajectory mitigates RF heating. We also presented the first surgical implementation of these modified trajectories in patients. Low-heating trajectories included small concentric loops near the surgical burr hole which were readily implemented during the surgical procedure; these trajectories generated nearly a 2-fold reduction in RF heating compared to unmodified trajectories.Clinical Relevance— Surgically modifying the DBS lead trajectory can be a cost-effective strategy for reducing RF-induced heating during MRI at 3 T.
Functional Connectivity Analysis in Multi-channel EEG for Emotion Detection with Phase Locking Value and 3D CNN

The noise-assisted multivariate Empirical mode decomposition (NA-MEMD) is applied to multi-channel EEG signals to obtain narrow-band scale-aligned intrinsic mode functions (IMFs) upon which functional connectivity analysis is performed. The connectivity pattern in relation to inherent functional activity of brain is estimated with the phase locking value (PLV). Instantaneous phase difference among different EEG channels gives PLV that is used to build the functional connectivity map. The connectivity map yields spatial-temporal feature representation which is taken as input of the proposed emotion detection system. The spatial-temporal features can be learned with a 3D convolutional neural network for classifying emotion states. The proposed system is evaluated on two publicly available DEAP and SEED dataset for binary and multi-class emotion classification. On detecting low versus high level in the valence and arousal dimensions, the attained accuracy values are 97.37% and 96.26% respectively. Meanwhile, this system yields 94.78% and 99.54% accuracy on multi-class task on DEAP and SEED, which outperform previously reported systems with other deep learning models and conventional EEG features.