Cross-Language Speech Emotion Recognition Using Multimodal Dual Attention Transformers

Despite the recent progress in speech emotion recognition (SER), state-of-the-art systems are unable to achieve improved performance in cross-language settings. In this paper, we propose a Multimodal Dual Attention Transformer (MDAT) model to improve cross-language SER. Our model utilises pre-trained models for multimodal feature extraction and is equipped with a dual attention mechanism including graph attention and co-attention to capture complex dependencies across different modalities and achieve improved cross-language SER results using minimal target language data. In addition, our model also exploits a transformer encoder layer for high-level feature representation to improve emotion classification accuracy. In this way, MDAT performs refinement of feature representation at various stages and provides emotional salient features to the classification layer. This novel approach also ensures the preservation of modality-specific emotional information while enhancing cross-modality and cross-language interactions. We assess our model's performance on four publicly available SER datasets and establish its superior effectiveness compared to recent approaches and baseline models.Comment: Under Review IEEE TM

The Allusions of Behavioral Finance

The deliberation in theoretical finance among the Efficient Market Hypothesis (EMH) and the subject of the behavioral finance is of immense interest. from the time when its emerge, the EMH has been the most significant theory which describes the behavior of the diverse agents in the financial markets and overlooks more or less any prospective impact of human behavior in the investment method. From the end of 1970s and the commencement of 1980s, a rising number of researchers and scholars showed the irregularity of this theory. The anomalies of the recent portfolio models and theories have provoked the development of behavioral finance. Behavioral finance assimilates psychology and economics in finance theory and has its heredity in theground-breaking work of great psychologists Tversky and Daniel Kahneman (1979). The rationale of this study is to present a synthesis of the behavioral finance literature over the last two decades. Keywords: Efficient Market Hypothesis, Financial Market, arbitrage, Cognitive dissonance, Regret avoidance Type: Literature Revie

Using technology acceptance model to measure the use of social media for collaborative learning in Ghana

In this Digital era, thousands of teens in the universities use social network sites; it has become a way of life. Social Media Usage has recently received numerous debates in its impact on academics, with its advent, communities have become link to each other, but the lecture room still remains quite isolated, from other teachers, students, and a host of others who could potentially enhance learning. This study aimed at investigating the impact of social media usage on students’ academic performance through collaborative learning among university students in Ghana. Based Davis, Bagozzi & Warshaw (1989), Technology Acceptance Model (TAM), a conceptual framework was adopted for the study. To achieve the objectives, a quantitative data analysis method was employed. A total of 200 students were randomly surveyed for the study. Regression analysis revealed that, Interaction with peers, perceived ease of use and perceived usefulness had a significant positive relationship with collaborative learning. Furthermore, results suggested that there exist a significant mediation effects on the relationship between social media usage dimensions and academic performance. TAM does not take into account environment or economic factors that may influence a person’s intention to perform a behavior. The study recommends a clear mobile learning methodologies, rules and policies for integrating student activities on social media into their final gradesPeer Reviewe

Fetal Anomalies in Ultrasonographically Detected Polyhydramnios

Background: To determine the frequency and types of fetal anomalies in cases of polyhydramnios detected on ultrasonography and to compare maternal age and parity of these subjects with fetal anomalies and those without fetal anomalies. Methods: In this cross sectional study, using colour and power Doppler ultrasound machine, one hundred diagnosed patients with ultrasonographically detected polyhydramnios were included . Sonographic examination was conducted between 12 to 40 weeks of gestation and fetal anomalies were examined. Results: Out of 100 patients, 35 fetal anomalies were found in 30(30%) patients. The age of the patients included in the study ranged from 18 to 40 years. Majority of the anomalies (73%) were found between age group 30 – 40 years and in multigravida (83%). Central Nervous System was the commonest site with fetal anomalies (46%) followed by gastrointestinal tract (20%) Conclusion: Prenatal detection of fetal anomalies has a decisive effect on the outcome of pregnancy and helps the obstetrician in planning the intrapartum management and for post delivery resuscitative measures, if require

Analysis of Machine Learning Based Imputation of Missing Data

Data analysis and classification can be affected by the availability of missing data in datasets. To deal with missing data, either deletion-based or imputation-based methods are used that results in the reduction of data records or wrong predicted value imputed by means/median respectively. A significant improvement can be done if missing values are imputed more accurately with less computation cost. In this work, a flow for analysis of machine learning-based algorithms for missing data imputation is proposed. The K-nearest neighbors (KNN) and Sequential KNN (SKNN) algorithms are used to impute missing values in datasets using machine learning. Missing values handled using statistical deletion approach (List-wise Deletion) and ML-based imputation methods (KNN and SKNN) is then tested and compared using different ML classifiers (Support Vector Machine and Decision Tree) to evaluate effectiveness of imputed data. The used algorithms are compared in terms of accuracy, and results yielded that the ML-based imputation method (SKNN) outperforms LD-based approach and KNN method in terms of effectiveness of handling missing data in almost every dataset with both classification algorithms (SVM and DT)

(E)-3-(2-Chloro-6-methyl-3-quinol­yl)-1-(2,3-dihydro-1,4-benzodioxin-6-yl)prop-2-en-1-one

In the title mol­ecule, C21H16ClNO3, the quinoline and benzene rings are inclined at 56.96 (6)° with respect to each other and the dioxine ring is in a twist-chair conformation. The structure is devoid of any classical hydrogen bonds. Rather weak inter­molecular hydrogen-bonding inter­actions of the types C—H⋯N and C—H⋯O are present, consolidating the crystal structure

Malware Detection in Internet of Things (IoT) Devices Using Deep Learning

Internet of Things (IoT) devices usage is increasing exponentially with the spread of the internet. With the increasing capacity of data on IoT devices, these devices are becoming venerable to malware attacks; therefore, malware detection becomes an important issue in IoT devices. An effective, reliable, and time-efficient mechanism is required for the identification of sophisticated malware. Researchers have proposed multiple methods for malware detection in recent years, however, accurate detection remains a challenge. We propose a deep learning-based ensemble classification method for the detection of malware in IoT devices. It uses a three steps approach; in the first step, data is preprocessed using scaling, normalization, and de-noising, whereas in the second step, features are selected and one hot encoding is applied followed by the ensemble classifier based on CNN and LSTM outputs for detection of malware. We have compared results with the state-of-the-art methods and our proposed method outperforms the existing methods on standard datasets with an average accuracy of 99.5%.publishedVersio

Development and evaluation of scaffold-based nanosponge formulation for controlled drug delivery of naproxen and ibuprofen

Purpose: To develop and evaluate nanosponge (NS) based sustained release formulations of naproxen (NAP) and ibuprofen (IBU).Method: Six formulations of each candidate drug were prepared by emulsion solvent diffusion method, using varying ratios of polymers, i.e., ethyl cellulose and polyvinyl alcohol. The prepared formulations were evaluated for various parameters including production yield, particle size, polydispersity index, actual drug content and entrapment efficiency. Morphological, structural and thermo-analytical evaluations were performed using various techniques. In vitro release studies were performed on selected formulations.Results: Nanosponge (NS) formulations of naproxen and ibuprofen were successfully prepared by emulsion solvent diffusion method. The particle size of naproxen and ibuprofen nanosponge formulations ranged from 347.6 to 1358 nm and 248.7 to 327.6 nm, respectively. Formulations with equal proportion of ethyl cellulose and drug resulted in nanosponges with the desired particle size. Production yield, actual drug content and entrapment efficiency was dependent on the ratio of ethyl cellulose and polyvinyl alcohol. Formulations with equal proportion showed least PDI values (0.09 for NAP and 0.07 for IBU) and highest zeta potential (-27.2 mV for NAP and -28.2 mV for IBU). Morphological, structural and thermo-analytical analysis confirmed the encapsulation of drugs in nanosponge cavities, and exhibited spherical and porous morphology. Nanosponge formulations gave a sustained release pattern, based on Higuchi model. Drug release mechanism was Fickian followed Korsmeyer-Peppas model, due probably to the porosity of the nanosponge.Conclusion: Sustained release nanosponge formulations of naproxen and ibuprofen have successfully been prepared.Keywords: Nanosponge, Naproxen, Ibuprofen, Emulsion solvent diffusion method, Sustained releas

Photocatalytic response in water pollutants with addition of biomedical and anti-leishmanial study of iron oxide nanoparticles

Public health is a major concern globally, owing to the presence of industrial dyes in the effluent. Nanoparticles with green synthesis are an enthralling research field with various applications. This study deals with investigating the photocatalytic potential of Fe-oxide nanoparticles (FeO-NPs) for the degradation of methylene blue dye and their potential biomedical investigations. Biosynthesis using Anthemis tomentosa flower extract showed to be an effective method for the synthesis of FeO-NPs. The freshly prepared FeO-NPs were characterized through UV/Vis spectroscopy showing clear peak at 318 nm. The prepared FeO-NPs were of smaller size and spherical shape having large surface area and porosity with no aggregations. The FeO-NPs were characterized using XRD, FTIR, HRTEM, SEM and EDX. The HRTEM results showed that the particle size of FeO-NPs was 60–90 nm. The antimicrobial properties of FeO-NPs were investigated against two bacterial Staphylococcus aureus 13 (±0.8) and Klebsiella pneumoniae 6(±0.6) and three fungal species Aspergillus Niger, Aspergillus flavus, and Aspergillus fumigatus exhibiting a maximum reduction of 57% 47% and 50%, respectively. Moreover, FeO-NPs exhibited high antioxidant properties evaluated against ascorbic acid. Overall, this study showed high photocatalytic, antimicrobial, and antioxidant properties of FeO-NPs owing to their small size and large surface area. However, the ecotoxicity study of methylene blue degradation products showed potential toxicity to aquatic organisms

(2E)-3-(2-Chloro-6-methyl-3-quinol­yl)-1-(1-naphth­yl)prop-2-en-1-one

In the title mol­ecule, C23H16ClNO, the quinoline and naphthalene ring systems are individually planar, with maximum deviations of 0.020 (2) and 0.033 (2) Å, respectively, and are inclined at a dihedral angle of 30.01 (4)°. Intra­molecular C—H⋯O and C—H⋯Cl inter­actions occur. The crystal structure is devoid of any classical hydrogen bonds, but symmetry-related mol­ecules are linked via weak C—H⋯Cl inter­actions, forming chains propagating in [001]