Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting Multilabel Sentiment and Emotions from Bangla YouTube Comments

Sentiment analysis has become a key research area in natural language processing due to its wide range of practical applications that include opinion mining, emotions extraction, trends predictions in social media, etc. Though the sentiment analysis in English language has been extensively studied in recent years, a little research has been done in the context of Bangla language, one of the most spoken languages in the world. In this paper, we present a comprehensive set of techniques to identify sentiment and extract emotions from Bangla texts. We build deep learning based models to classify a Bangla sentence with a three-class (positive, negative, neutral) and a five-class (strongly positive, positive, neutral, negative, strongly negative) sentiment label. We also build models to extract the emotion of a Bangla sentence as any one of the six basic emotions (anger, disgust, fear, joy, sadness and surprise). We evaluate the performance of our model using a new dataset of Bangla, English and Romanized Bangla comments from different types of YouTube videos. Our proposed approach shows 65.97% and 54.24% accuracy in three and five labels sentiment, respectively. We also show that the performance of our model is better for domain and language specific texts.
IT-Consumerization: Domain Control, (Reversed) Presenteeism, and Stress

Today, many employees use privately owned digital solutions for professional and non-professional purposes, a phenomenon known as IT-consumerization. Apart from its positive outcomes, IT-consumerization entails a loss of control over the integration of the work-and non-work domain, which causes domain conflict. Domain conflict appears when work interferes with non-work (i.e., ‘presenteeism’) and vice versa (i.e., ‘ reversed presenteeism’). This last direction lacks research, although studies suggest that bo th directions induce similarly grave adverse effects. Building upon boundary theory and the person-environment fit model, we propose an overarching perspective to simultaneously assess how IT-consumerization affects presenteeism and reversed presenteeism, how these affect each other, and how they affect perceptions of strain. The results of our pre-study provide first evidence on the relationships between IT-consumerization, presenteeism, reversed presenteeism, and stress. Furthermore, they uncover the need for further investigation to understand the interrelationship between (reversed) presenteeism and individual - and organizational factors.
Extensive Experimental Evaluation of Self-Organizing Maps for Automatic Classification of a Multi-Class Multi-Label Corpus

This paper aims at bridging the gap between feature selection and feature space size by utilizing both square and non-square self-organizing maps under different configuration scenarios for classifying a multi-class multi-label corpus, the Reuters Mod Apte’ split. The selection of non-square maps is based on a heuristic process for finding the suitable size for the self-organizing map. Vector construction is based on a simple, yet effective procedure aiming at transforming the vectors from multi-label to uni-label. The proposed solution improves classification efficiency not only in terms of accuracy but also in computational resources needed and time for training. Extensive experiments were conducted, using different configurations regarding map and vector sizes, and training cycles, also utilizing context words, to assess their impact in the classifier’s performance. Furthermore, an intelligent algorithm for label selection is being proposed, aiming to show that the neighboring nodes on the map affect the selection of labels for a specific node. According to the experiments conducted, our approach achieves 10% increase in Macro-Average F1 scores, <inline-formula> <tex-math notation="LaTeX">$30\times $ </tex-math></inline-formula> decrease in vector dimensionality, and approximately <inline-formula> <tex-math notation="LaTeX">$34\times $ </tex-math></inline-formula> smaller maps when compared to the baseline scenario.
Discovering Molecular Functional Groups Using Graph Convolutional Neural Networks

Functional groups (FGs) are molecular substructures that are served as a foundation for analyzing and predicting chemical properties of molecules. Automatic discovery of FGs will impact various fields of research, including medicinal chemistry and material sciences, by reducing the amount of lab experiments required for discovery or synthesis of new molecules. In this paper, we investigate methods based on graph convolutional neural networks (GCNNs) for localizing FGs that contribute to specific chemical properties of interest. In our framework, molecules are modeled as undirected relational graphs with atoms as nodes and bonds as edges. Using this relational graph structure, we trained GCNNs in a supervised way on experimentally-validated molecular training sets to predict specific chemical properties, e.g., toxicity. Upon learning a GCNN, we analyzed its activation patterns to automatically identify FGs using four different explainability methods that we have developed: gradient-based saliency maps, Class Activation Mapping (CAM), gradient-weighted CAM (Grad-CAM), and Excitation Back-Propagation. Although these methods are originally derived for convolutional neural networks (CNNs), we adapt them to develop the corresponding suitable versions for GCNNs. We evaluated the contrastive power of these methods with respect to the specificity of the identified molecular substructures and their relevance for chemical functions. Grad-CAM had the highest contrastive power and generated qualitatively the best FGs. This work paves the way for automatic analysis and design of new molecules.
Closed-Form Formulas of Input Impedance and Axial Ratio of a Circular Patch With Perturbation Segments

We present a simple and useful closed-form expression to compute antenna input impedance and the axial ratio of a circular patch antenna with perturbation segments. The closed-form expression is obtained by applying the cavity perturbation theory on antenna equivalent circuits that represent the resonant behaviors of two degenerated modes in the antenna. In particular, we exclude the assumption that equal resistance occurs between two modes and include the perturbation shape parameters in the proposed formulas, which results in a more accurate prediction of the antenna input impedance and axial ratio. The validity of the proposed equations is demonstrated by comparing the analytical results to full-wave simulations and measurements.
Time-Variant Visual Attention in 360-Degree Video Playback

The user visual attention has been playing an imperative role in visual Quality of Experience (QoE) modeling. In recent years, researchers have developed numerous visual attention models for images and videos. However, existing studies on visual attention generally perform on spatial domain (i.e. pictures) without consideration on time-variant user attention. Besides, the user attention on immersive video playback has not been well investigated in Virtual Reality (VR) environment. In this paper, we design subjective experiments to acquire the user attention during 360-degree video playback, in order to characterize the visual attention changes on time axis. We compare and model the temporal attentions in two scenarios: 360-degree video and conventional 2D video display. It is observed in video display, the user attention needs a short period (~ 30 seconds) to concentrate to a relatively stable level. Within this period, the user attention to 360-degree video is significantly lower than that to traditional 2D videos, which shows that the users are relatively difficult to adapt to VR display. Therefore, short videos less than 30 seconds, especially short 360-degree videos, are not preferable in subjective video quality assessment, although they have been tested in some scenarios. We hope this conclusion would conduce to QoE modeling during video display of VR environment.
Off-Grid Commercial LED Driver Optimization using GaN Transistors

In the past few years, wide-bandgap (WBG) semiconductors based on gallium nitride (GaN) have appeared as a solution to enable the evolution of power electronics. These devices allow for higher operating frequencies without sacrificing performance, especially in hard-switching topologies. Their use in commercial products is, however, rather modest specially due to their higher cost when compared directly to its silicon (Si) counterparts. This paper presents the optimization process of a commercial low-voltage 100 W LED driver by replacing Si-based semiconductors by their GaN counterparts. The original boost-based converter was redesigned using a synchronous boost topology. We replaced both switches by GaN transistors and two different switching frequencies were tested, 350 kHz and 1 MHz. We present performance and economical analysis of both converters. The proposed driver at 350 kHz exhibited higher efficiency, with a maximum of 97.6%. At 1 MHz, we obtained little improvement in performance. The power density of both GaN converters is 12x higher than the original product. This volume reduction directly impacts system cost as less thermal material is needed to cool the circuit and provide its waterproof rating. These results prove that despite being more expensive than Si MOSFETs in a direct comparison, GaN transistors allow for a lower overall cost.
Contact and Collaboration Context Model

Working collaboratively is no longer an issue but a reality, what matters today is how to implement collaboration so that it is as successful as possible. It is therefore necessary to consider the criteria to be taken into account to promote its effectiveness. As part of our work, we are interested in taking into account the collaboration context for this purpose and the role that contact can take in this context. We wondered about its definition, representation and exploitation. The latter must be able to be done at different stages of collaboration: before, during and after. In this article, we present the collaboration context model that we have established and show why and how it can be used to establish successful collaboration.
The Automated Heart Disease Detection System with Dry-Electrode Based Outdoor Shirts

The typical method of monitoring arrhythmia is to use a body patch type sensor with a wet electrode. It has several problems caused by wet electrodes for long-term monitoring. Thus, we develop a Smart Outhdoor Shirts equipped with a dry electrode electrocardiogram (ECG) sensor for a cardiac arrhythmia computer aided diagnosis system in this study. The sensor can be inserted in a console close to the chest, charged, used to communicate wirelessly, and connected with a smartphone application. According to experiments, the ECG signals measured by the smart shirt indicated that 97 $5\pm 1\%$ of the signals could be measured in an immobile state and at least 85. $2\pm 2\%$ of the signals could be measured during movement. In addition, we propose a computer aided diagnosis system for detecting cardiac arrhythmia. It was determined through experiments that the system can detect arrhythmia with an accuracy of 98. $2\pm 2 \%$.
Uplink Resource Allocation for Relay-Aided Device-to-Device Communication

The device-to-device (D2D) communications mode has been regarded as an effective technique for solving/relieving the contradiction between the exponentially increased data traffic requirements of mobile customers and the essence of scarcity of radio resources in wireless communication networks. In the presence of unfavorable direct communication links between D2D peers, cooperative relays may play an important role in enhancing both reliability and flexibility of D2D communications. Aiming at maximizing the sum throughout of the network with a low computation complexity, a new scheme, which jointly considers a number of critical aspects, such as power control, interference limit based on the location information, optimal relay selection based on delineated area, and optimal link selection based range division, etc., is proposed in this paper. Numerical results reveal that the proposed scheme is capable of substantially improving the system’s performance compared with either the existing brute-force technique or the area-division scheme. Furthermore, the proposed scheme also exhibits its advantages over the existing techniques in terms of computational complexity.
Investigation of Reliability of Diversity Systems Through Stress-Strength Model Analysis

Paper concerns a problem of reliability indexes calculation of two channel homogeneous and diversity system. Using SSM it is easy to determine the probability of Common Cause Failures (CCF) and other parameters of investigated redundant systems. Here is used a complex approach with Multi-Factor Stress-Strength Model (MFSSM). The obtained analytical formulas are verificated via simulation.
Gaussian State Estimation with Non-Gaussian Measurement Noise

Many sensor data fusion approaches are based on physically motivated models. Some of them include non-Gaussian noise like radar clutter or GNSS multipath. Classical fusion algorithms like Kalman filters assume Gaussian noise processes. Nevertheless, they are widely applied to state estimation problems under non-Gaussian noise conditions. We investigate in this paper how a unimodal measurement noise distribution affects the distribution of the state vector of a linear Kalman filter. The analysis is based on cumulants which reflect the non- Gaussian character of a distribution. The theoretical results are, that the distribution of the state vector gets closer the Gaussian distribution over time during the settlement process. The higher cumulants converge faster than the variance due to higher exponents. The distribution converges to the Gaussian distribution only if there is no system noise present. Otherwise, there will be a remaining deviation of the converged distribution from the Gaussian one. In a simulation, the theoretical results are proven. The higher cumulants decrease over time until they converge not necessarily to zero. Filters which have to deal with high system noise or only few observations may benefit from an improved model of tracking uncertainty based on the cumulants.
Co-Occurrence Context of the Data-Driven Local Differential Excitation Ternary Pattern for Biomedical Image Retrieval

This article presents a novel feature descriptor titled as local differential excitation co-occurrence pattern (LDETCoP) for bio-medical image retrieval. The proposed method aims at extracting texture features present in the medical images using differential excitation and a novel local pattern structure. All canonical pattern based methods are based on gray level difference to exploit local structure information and ignores human perception of pattern. The proposed method incorporates human perception into the pattern computation using differential excitation. Further, the proposed method exploits local structure information using radiusl (8 neighbours) and radius2 (16 neighbours) neighbouring pixels using differential excitation in an instructive way. LDETCoP also encodes co-occurrence of similar ternary edges for feature extraction. The performance of the proposed method is evaluated in terms of classification efficiency on benchmark databases VIA/I-ELCAP and NEMA-CT. The results after investigation show a substantial improvement in terms of their evaluation measures as compared to the state of the art approaches on respective databases.
A Broadband Circularly Polarized Wide-Slot Antenna With a Miniaturized Footprint

This letter presents a novel and simple feeding technique for exciting orthogonal components in a wide-slot antenna. In this technique, a rectangular bracket-shape parasitic strip is placed at the open end of the straight microstrip line to excite the fundamental horizontal and vertical components of the circular polarization (CP). The proposed technique—when employed in conjunction with the asymmetrical geometry of coplanar waveguide and a protruded stub from the ground plane—permits for maintaining axial ratio (AR) below 3 dB within a wide range in the C-band as well as a compact footprint of the antenna. All geometry parameters of the antenna are adjusted through rigorous EM-driven optimization to obtain the best performance in terms of footprint, impedance matching, and AR bandwidth (ARBW). The size of the proposed antenna is only 27 mm × 28.8 mm. The structure features a 62% impedance bandwidth (3.6–6.85 GHz) and ARBW of approximately 49% (3.6–5.93 GHz) with the average realized gain of 3.3 dB within the CP operating band. Numerical results are validated experimentally. A close agreement between simulation and measurement results has been observed.
Wideband Substrate-Integrated Waveguide-Fed Endfire Metasurface Antenna Array

A vertically polarized substrate-integrated waveguide (SIW)-fed endfire metasurface antenna array is proposed for wideband operation. Each of the metasurfaces consists of <inline-formula> <tex-math notation="LaTeX">$3\times 3$ </tex-math></inline-formula> rectangular patches and is printed on the two surfaces of a single-layered substrate with a thickness of <inline-formula> <tex-math notation="LaTeX">$0.16\lambda _{0}$ </tex-math></inline-formula> (where <inline-formula> <tex-math notation="LaTeX">$\lambda _{0}$ </tex-math></inline-formula> is the wavelength at 32.65 GHz in free space). The antenna is fed at one side of metasurface by an open-end SIW for wideband endfire radiation. The proximity-coupled interdigital strips are introduced between the SIW and metasurface for fault-tolerant coupling. The wideband operating mechanism is revealed by studying the propagation mode and multiple resonant modes of metasurface. To verify the proposed antenna, a <inline-formula> <tex-math notation="LaTeX">$1\times 4$ </tex-math></inline-formula> array is presented with uniform excitation including two SIW Y junctions and an SIW T junction. Moreover, the connected SIW Y junction improved the impedance matching at the low frequencies based on the cavity mode analysis. The proposed design shows that the measured impedance bandwidth (10 dB return loss) is 26.6–38.7 GHz (37%) with the achieved gain of 9.1–13.8 dBi.
Compact Wideband CPW-Fed Meandered-Slot Antenna With Slotted Y-Shaped Central Element for Wi-Fi, WiMAX, and 5G Applications

In this communication, a compact and broadband coplanar waveguide-fed meandered-slot antenna is presented. Compact size of <inline-formula> <tex-math notation="LaTeX">$0.3 \times 0.2\,\,\lambda ^{2}$ </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">$30 \times 20$ </tex-math></inline-formula> mm<sup>2</sup> on a 1.6 mm thick FR4 dielectric substrate) is achieved by employing a modified meandered slot, a slotted Y-shaped monopole, and a trident-shaped feed strip. The measurement results show an impedance bandwidth (defined for S<sub>11</sub> ≤ −10 dB) of 41.8% (from 2.33 to 3.56 GHz) and gain larger than 0.6 dBi within the bandwidth of interest, yielding this antenna a valid choice for Wi-Fi, WiMAX, and 5G applications. The use of a <inline-formula> <tex-math notation="LaTeX">$60 \times 60$ </tex-math></inline-formula> mm<sup>2</sup> metallic reflector placed at a distance of 30 mm from the antenna is also demonstrated to effectively enhance the antenna gain to values larger than 3 dBi without significantly affect the impedance bandwidth. Furthermore, a compact <inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula>/4 spaced four-antenna array is prototyped and the measurement results reveal an envelope correlation coefficient lower than 0.03 within the bandwidth, confirming that the proposed antenna is also attractive for compact multiple-input multiple-output array applications.
Resource Unit (RU) based OFDMA Scheduling in IEEE 802.11ax system

IEEE 802.11ax is a revolutionary effort to provide a improvement over current generation of 802.11 and has been approved to deliver the next-generation Wireless Local Area Network (WLAN) technologies. The medium access control protocol is the critical component to enable efficient sharing of the wireless medium whilst at the same time providing Quality of Service (QoS) to diverse applications. Orthogonal Frequency Division Multiple Access (OFDMA) is the access method adopted by IEEE 802.11ax where the subcarriers are divided into Resource Units (RUs) for scheduling. We propose a contention-based RU allocation in OFDMA based IEEE 802.11ax using an adaptation of IEEE 802.11e Enhanced Distributed Channel access (EDCA). We extend EDCA to the OFDMA system where the access point (AP) transmits Physical Protocol Data Units (PPDUs) by determining the RUs to be allocated to its associated stations (STAs). We also determine the RU limit by normalizing the RU requirements using different methods that is provided as input to the scheduling algorithm and also propose a feedback mechanism to throttle the input arrival process.
Multiple Model Adaptive ILC for Human Movement Assistance

A switched multiple model iterative learning control framework is developed which guarantees robust stability and performance bounds under the assumption that the true plant belongs to a plant uncertainty set that is specified by the designer. In addition, the framework automatically adapts the reference trajectory according to the action of an existing internal control loop that is assumed to be embedded in the plant structure. The framework is inspired by the needs of stroke rehabilitation where assistive technology must support the remaining, weak volitional effort of the patient. Exploit- ing the multiple model based switching between models and reference trajectories, the framework is also able to potentially eliminate the need for identification and tuning and hence meet the demanding needs of clinical application.
Privacy Preserving Collaborative Computing: Heterogeneous Privacy Guarantee and Efficient Incentive Mechanism

Collaborative computing uses multiple data servers to jointly complete data analysis, e.g., statistical analysis and inference. One major obstruction for it lies in privacy concern, which is directly associated with nodes’ participation and the fidelity of received data. Existing privacy-preserving paradigms for cloud computing and distributed data aggregation only provide nodes with homogeneous privacy protection without consideration of nodes’ diverse trust degrees to different data servers. We propose a two-phase framework that computes the average value while preserving heterogeneous privacy for nodes’ private data. The new challenge is that in the premise of meeting privacy requirements, we should guarantee the proposed framework has the same computation accuracy with existing privacy-aware solutions. In this paper, nodes obtain heterogeneous privacy protection in the face of different data servers through one-shot noise perturbation. Based on the definition of KL privacy, we derive the analytical expressions of the privacy preserving degrees (PPDs) and quantify the relation between different PPDs. Then, we obtain the closed-form expression of computation accuracy. Furthermore, an efficient incentive mechanism is proposed to achieve optimized computation accuracy when data servers have fixed budgets. Finally, extensive simulations are conducted to verify the obtained theoretical results.