Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Speech Recognition Using a Cepstral Minimum-Mean-Square-Error-Motivated

Wepresentanefficientandeffectivenonlinearfeature- domain noise suppression algorithm, motivated by the minimum- mean-square-error (MMSE) optimization criterion, for noise- robust speech recognition. Distinguishing from the log-MMSE spectral amplitude noise suppressor proposed by Ephraim and Malah (E&M), our new algorithm is aimed to minimize the error expressed explicitly for the Mel-frequency cepstra instead of discrete Fourier transform (DFT) spectra, and it operates on the Mel-frequency filter bank's output. As a consequence, the statistics used to estimate the suppression factor become vastly different from those used in the E&M log-MMSE suppressor. Our algorithm is significantly more efficient than the E&M's log-MMSE suppressor since the number of the channels in the Mel-frequency filter bank is much smaller (23 in our case) than the number of bins (256) in DFT. We have conducted extensive speech recognition experiments on the standard Aurora-3 task. The experimental results demonstrate a reduction of the recognition word error rate by 48% over the standard ICSLP02 baseline, 26% over the cepstral mean normalization baseline, and 13% over the popular E&M's log-MMSE noise suppressor. The experiments also show that our new algorithm performs slightly better than the ETSI advanced front end (AFE) on the well-matched and mid-mismatched settings, and has 8% and 10% fewer errors than our earlier SPLICE (stereo-based piecewise linear compensation for environments) system on these settings, respectively.
Meta-surface Structure for Polarization Control Based on Malus Law

Metamaterials and metsurfaces structures in subwavelength scale which is integrated on the different building blocks of optical structures reshapes the design of photonic circuits. These circuits leads to electromagnetic waves control. Huge growth of metasurface nano-fabricated structures helps in numerous optical applications. One of these applications is optical polarizers. Optical polarization has great impact on diffraction, refraction in addition to flat optics and different optical industries in our daily life. Controlling light intensity and phase helps in many applications in different fields. In this article simple 300*300 nm nano Malus-metasurface structure is presented and simulated to show a (45°) phase shift. Detailed Study for transmission and reflection coefficients are introduced for the nano-gold metastructure. Another study for phase dependence of wavelength is presented. This structure enables controlling light phase and intensity according to Malus law. The structure can provide different patterns as grey near field pattern and holographic far field pattern. This implies connection between Pancharatnam-Berry phase shift and continual intensity modification. This design allows to merge between nanooptics, phase manipulation and the classical malus-law. The presented structure is more compatible with many applications due to its dependence on gold rather than any used material. The proposed polarizer can be used in many applications specially in encryption, multiplexing, dual channel display virtual reality, communication, programming, optical fields, biomedical sensing, artificial intelligence and different applications.
Design of Binary Sequences with Low Periodic Autocorrelation Sidelobes

Binary sequences with good periodic autocorrelation properties are essential for phase-modulated continuous-wave radars. Except for a few well-known sequence families with optimal sidelobes, the search for long binary sequences with low sidelobes is difficult because of the very large search space. We propose a search algorithm, valid for any length, that leads to near-optimal sequences in reasonable search time. The sequences that we generate have lower peak sidelobes than state-of-the-art sequences. This enables to create large sequence sets supporting waveform diversity. We provide search results for all lengths smaller than 200 and for some lengths up to 10000.
Novel Anomaly Detection Scheme for Cyber Physical System using C-GAN in Fog assisted IoT Environment

An Internet of Things (IoT) appears to be an innovative technology with great potential for widespread development. There has been a rise in data security issues in recent years as a consequence of various technological developments. Despite the proliferation of security precautions, criminals have learned to circumvent them. We employ a Contingently Generative Adversarial Network in our research to detect harmful online behaviour (CC-GAN). Our proposed system employs CC-GAN, which can identify novel threats, to carry out intrusion detection based on anomalies. An Internet-of-Things layer, a fog layer, as well as a cloud layer make up the whole of our proposed fog-based infrastructure. The IoT layer consists of many connected devices, and it is here that the CC-GAN receives the training data it needs to learn to recognise potential security breaches. A convolutional neural network & encoder are trained on the cloud layer, where additional computing takes place. Adding a fog layer might help decrease the delay. Finally, the attack detection score is evaluated. For this exercise, we use the network simulator version 2.6. (NS 3.26). The simulation result shows that the proposed system achieves a higher detection rate than the state-of-the-art systems.
Adaptive Sensor Scheduling for Federated Learning over 6G in-X Subnetworks

In the upcoming 6G era, various end devices with data and computing resources can collaborate through federated learning (FL) to achieve the goal of ubiquitous intelligence. Inside any of those ‘X’ (i.e. everything) devices, however, a6G in-X subnetwork that wirelessly uploads training data from onboard sensors to an embedded access point (AP) may become a communication bottleneck during the process of FL. To support communication-efficient FL over in-X subnetworks, this paper proposes an adaptive sensor scheduling algorithm that jointly solves a queue stability problem and an age of information (AoI) optimization problem, based on Lyapunov optimization and MaxRatio scheduling methods. Simulations show that the proposed algorithm achieves higher FL accuracy, while guaranteeing queue stability at APs and better data freshness (quality) for data uploading over in-X subnetworks.
Potassium Currents Affect the Rotor Dynamics in a Fractional-order Model of Atrial Fibrillation

Atrial fibrillation (AF) is characterized by chaotic electrical activity in atrial tissue. Spiral propagating waves known as rotors are hypothesized as the drivers of the arrhythmia. Thus, investigating the underlying mechanisms of rotors and their true role during AF may lead to therapeutic solutions. It is known that potassium currents affect the rotor dynamics. However, potassium currents alteration may be the result of complex interactions between electrical and atria structural factors. In this study, a computational model of atrial fibrillation is implemented to study the rotor dynamics under electrical and structural modulations of potassium currents. The model adopts fractional derivative operators for representing the atrial structural heterogeneities. In addition, electrical alterations of the potassium currents are implemented. Under such AF configurations, rotors are simulated and characterized through the singularity trajectory. The results indicate that structural alterations, represented by the fractional derivative order, yield a spatial heterogeneous distribution of the peak value of potassium currents. Furthermore, several patterns for the rotor trajectory can be generated by varying the fractional order. The hierarchical clustering technique is used for assessing similarities between rotor trajectories generated by the structural and electrical modulations of potassium. The resulting hierarchical dendrogram reveals similarities and dissimilarities in the rotor dynamics generated by the distinct AF configurations.
On the Existence of Negative Capacitance: Examining Ferroelectric-Dielectric Stack Experiments Using the NLS and LK Models

The Landau–Khalatnikov (LK) model of ferroelectric switching includes an inherent region of negative capacitance (NC) in its lossless charge versus voltage description and allows the possibility of stabilization of the ferroelectric in this region to achieve quasi-static NC (QSNC). On the other hand, the nucleation-limited switching (NLS) model, which is another model used to describe ferroelectric switching, precludes QSNC and offers an alternative explanation for the appearance of an NC region in recent voltage-pulse experiments performed on ferroelectric-dielectric (FE-DE) stacks. As such, we investigate such experiments that probe the existence of an NC region using both the LK and NLS models. While the LK model can reproduce all experimental results seen in prior literature, we find that the NLS model is incapable of properly reproducing results under a multitude of investigation metrics. Hence, we conclude that the use of the NLS model to exclude the existence of QSNC is problematic.
Facial Expression Recognition in the Wild Using Face Graph and Attention

Facial expression recognition (FER) in the wild from various viewpoints, lighting conditions, face poses, scales, and occlusions is an extremely challenging field of research. In this study, we construct a face graph by selecting action units that play an important role in changing facial expressions, and we propose an algorithm for recognizing facial expressions using a graph convolutional network (GCN). We first generated an attention map that can highlight action units to extract important facial expression features from faces in the wild. After feature extraction, a face graph is constructed by combining the attention map with face patches, and changes in expression in the wild are recognized using a GCN. Through comparative experiments conducted using both lab-controlled and wild datasets, we prove that the proposed method is the most suitable FER approach for use with image datasets captured in the wild and those under well-controlled indoor conditions.
Deep Learning for Cerebral Hemorrhage Detection and Classification in Head CT Scans Using CNN

The classification of cerebral hemorrhages in Computed Tomography (CT) scans using Convolutional Neural Networks (CNNs) is a rapidly evolving field in medical imaging. Brain hemorrhages are a critical condition that can result in serious health consequences and death. Recently, deep neural networks have been employed for image identification and classification, producing encouraging outcomes in medical image analysis. The objective of this study is to utilize deep learning methods and CNNs to identify brain hemorrhages in CT images. The inspiration for this research stems from the challenges faced by physicians in accurately recognizing brain hemorrhages, especially in the early stages when misdiagnosis is more likely. Through a series of CT experiments, two pretrained CNNs (VGG16 and VGG19) were developed and evaluated for image categorization as either hemorrhage or non-hemorrhage. The VGG16 pre-trained model showed exceptional accuracy compared to the VGG19 model. The VGG16 model also achieved the highest accuracy of 99.10% compared to all reference studies.
Joint Beamforming and Channel Reconfiguration for RIS-Assisted Millimeter Wave Massive MIMO-OFDM Systems

In this paper, we consider the problem of joint active and passive beamforming for reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) multiple-input-multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems. The objective is to maximize the spectral efficiency by jointly optimizing the RIS's reflection coefficients and the hybrid precoder/combiner at the base station (BS)/mobile station (MS). By resorting to classical hybrid precoder/combiner optimization techniques, this joint beamforming problem can be converted into an optimization concerning only the design of the reflection coefficients. Nevertheless, the objective function of the resulting passive beamforming problem does not have an explicit expression of the reflection coefficients. To address this difficulty, the inherent structure of the effective channel is exploited, based on which an approximation of the truncated singular value decomposition (SVD) of the effective channel is obtained, and a favorable propagation environment can be realized by directly manipulating the singular values of the effective channel. Simulation results show that the proposed method can configure a favorable channel with a small condition number. Moreover, the proposed method presents a clear performance advantage over state-of-the-art algorithms, while with a low computational complexity.
Identification of Malicious Activity in Distributed Average Consensus via Non-Concurrent Checking

We consider the problem of average consensus in a network system under a fixed, undirected communication topology, when there are malicious nodes present that may try to influence the average calculation. In the setting considered, the average consensus is performed by the nodes in a distributed fashion using a linear iterative algorithm. We assume malicious nodes can manipulate, in an arbitrary manner, the value of their state in the aforementioned algorithm; the problem is then to check whether or not each node is correctly performing the updates of its state. To address this problem, we propose a distributed algorithm whereby each node is in charge of checking the updates performed by its neighboring nodes based on information that it receives from them and also from the neighbors of its neighbors. The algorithm leverages ideas from non-concurrent error detection schemes and its main advantage is that information from two-hop neighbors is only needed infrequently—a relaxation that significantly reduces the communication overhead associated with the requirement to make such information available.
Insertion and Deletion Correction in Polymer-Based Data Storage

Synthetic polymer-based data storage seems to be a particularly promising candidate that could help to cope with the ever-increasing demand for archival storage requirements. It involves designing molecules of distinct masses to represent the respective bits {0,1}, followed by the synthesis of a polymer of molecular units that reflects the order of bits in the information string. Reading out the stored data requires the use of a tandem mass spectrometer, that fragments the polymer into shorter substrings and provides their corresponding masses, from which the composition, i.e. the number of 1s and 0s in the concerned substring can be inferred. Prior works have dealt with the problem of unique string reconstruction from the set of all possible compositions, called composition multiset. This was accomplished either by determining which string lengths always allow unique reconstruction, or by formulating coding constraints to facilitate the same for all string lengths. Additionally, error-correcting schemes to deal with substitution errors caused by imprecise fragmentation during the readout process, have also been suggested. This work builds on this research by extending previously considered error models, mainly confined to substitution of compositions. To this end, we define new error models that consider insertions of spurious compositions and deletions of existing ones, thereby corrupting the composition multiset. We analyze if the reconstruction codebook proposed by Pattabiraman et al. is indeed robust to such errors, and if not, propose new coding constraints to remedy this.
Explainable Deep Learning for Coffee Leaf Disease Classification in Smart Agriculture: A Visual Approach

In recent years, the application of deep learning techniques for plant disease classification has become increasingly important for smart agriculture. Early classification and treatment of plant diseases are crucial for maintaining the quality and quantity of crops, and deep learning algorithms have the potential to provide accurate and efficient solutions to this problem. In this study, we used two datasets collected from Mendeley for coffee leaf disease classification. The first dataset contained two classes of images, while the second dataset contained the remaining three types of images. The two datasets were combined to create a more robust dataset for training the two-transfer learning and CNN model. We update the pre-trained model MobileNet and RestNet50 by adding a flattened and dense layer to classify the five types of coffee diseases. The model was trained using 100 epochs, and the performance was evaluated using evaluation metrics such as accuracy and loss error for training and validation. The best accuracy was obtained from ResNet50, then MobileNet, and then CNN. The accuracy is 100% for Proposed ResNet50, 98% for CNN, and 99% for MobileNet. To understand the affected areas and help farmers comprehend the reason for the prediction, we used visualization techniques such as Grad-CAM and Grad-CAM++. These techniques generated heat maps of the regions of interest, highlighting the areas that contributed most to the classification decision. Our study highlights the potential of using deep learning techniques for accurate and efficient plant disease detection. By automating the disease classification process, farmers can identify and treat diseases early, leading to better crop yields and quality. The results of our study demonstrate the effectiveness of the proposed method and can be used to develop efficient and cost-effective solutions for plant disease detection.
The Effect of Technology Readiness and Customers' Acceptance on Online Hotel Booking Intention

The introduction of sophisticated website features and attributes in the development of hotel booking websites has led to an increase in the site's complexity, making it more difficult to use for customers who are not particularly tech-savvy. Therefore, the purpose of the study is to examine the effect of customers' technology readiness on customers' acceptance of the online reservation system through the hotel website. Subsequently, the study evaluated the connection between customers' acceptance of online booking via hotel websites and their booking intention. The online survey was distributed to 190 respondents in Jakarta. Using a technique known as snowball sampling, the questionnaire was distributed across a variety of online platforms, and users were given the option to share it with other people at any time. The collected data were analyzed using Partial Least Squares - Structural Equation Modeling (PLS-SEM) techniques. The result of the study found that customers' technology readiness positively influences perceived usefulness, perceived ease of use, and perceived enjoyment of hotel reservation websites. This study also revealed that customers' acceptance of online reservation systems through hotel websites positively influences their intention to book a hotel room. The findings of this study imply that customers are drawn to hotel websites mostly because they are simple to navigate. This is because consumers do not require any prior knowledge of these websites to effectively use them.
Is Customer Experience Important to Recommend the Use of Digital Bank Services?

Digital banking services are self-service innovations for customers in getting information and completing banking transactions through electronic media while still paying attention to security aspects. Therefore, this study aims to analyze the behavior of customers who use digital bank services, focusing on security aspects and the perceived service experience. This study targets 210 customers in Indonesia with a purposive sampling technique. Data was collected using a questionnaire distributed online through social media. Data analysis used Partial Least Square-Structural Equation Modeling (PLS-SEM). As a result, all hypotheses are accepted, namely customer experience and perceived technology security affect continuance usage and finally impact the intention to recommend digital banking services. Banking management must focus on security systems and services to create the best customer experience.
Optical Phase Synchronization by Injection of Common Broadband Low-Coherent Light

We demonstrate experimentally and numerically that, by injecting common broadband optical noise into two uncoupled lasers, the phases of the respective laser oscillations can be successfully synchronized. Experimental observation of the resulting phase dynamics is achieved using a heterodyne detection method. The present phase synchronization differs from the synchronization induced by coherent light signals or narrow band optical noise in the sense that it occurs without any frequency locking to the injection light. Moreover, it is revealed that there is an optimal intensity of the injection light that maximizes the quality of the phase synchronization. These results can open new perspectives for understanding and functional utilization of the laser phase dynamics.
Trajectory Learning by Therapists' Demonstrations for an Upper Limb Rehabilitation Exoskeleton

In this work, we propose a method for trajectory implementation based on the Learning by Demonstrations approach to deal with trajectory planning issues in upper-limb rehabilitation exoskeletons. Currently applied path-planning methods use mathematical trajectories or Teach-and-play approaches. The former do not propose human-like movements to patients, which is crucial to induce correct motor relearning. Moreover, they often differ from therapists' expectations of how movements should be executed, reducing the acceptability and use of exoskeletons in hospitals. The latter, using a single filtered trajectory demonstration, better meet therapists' expectations but lack consistency and optimization. In our approach, we employed Hidden Markov Models, still never used for rehabilitation robotics, to study a set of demonstrations and we optimized the results to respect physiological muscular activation patterns. Recorded few repetitions of a movement from the interaction of a therapist with an exoskeleton, our machine-learning-based algorithm returns a ready-to-use trajectory representing the therapist's desires. We tested our method on a 4 degrees-of-freedom exoskeleton to record 5 exercises, interacting with 5 therapists. Comparing our trajectories with those obtained with literature methods, we see that our approach produces better kinematic and human-likeness results, and is better according to the global opinion expressed by the therapists.
Intrinsic Motivation of Thai Students Taking Japanese at Thai-Nichi Institute of Technology

The aims of this study were to identify 1) differences in intrinsic motivation between grades; 2) the factors that make a difference in intrinsic motivation. The sample for this study consists of 742 randomly selected participants. These 742 students were from the Faculty of Engineering, the Faculty of Information Technology, and the Faculty of Business Administration, who took Business Japanese1, Business Japanese3, and Business Japanese5 courses offered in the first semester of academic year 2022 at Thai-Nichi Institute of Technology. A questionnaire consisting of 30 questions measuring intrinsic motivation was used in this study. Survey was distributed to students using Google Forms. ANOVA was used for analysis across the grades of study. Furthermore, factor analysis was conducted on the 30 questions. Factor analysis revealed four factors: Causality, Challenge, Curiosity, and Mastery. Differences were found among grades in Challenge, Curiosity, and Mastery, with first-grade students having higher average values for Challenge and Mastery, and second-grade and third-grade students having higher average values for Curiosity.