Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Speech Emotion Recognition Using CNN+LSTM Based on Stochastic Fractal Search Optimization Algorithm

One of the main challenges facing the current approaches of speech emotion recognition is the lack of a dataset large enough to train the currently available deep learning models properly. Therefore, this paper proposes a new data augmentation algorithm to enrich the speech emotions dataset with more samples through a careful addition of noise fractions. In addition, the hyperparameters of the currently available deep learning models are either handcrafted or adjusted during the training process. However, this approach does not guarantee finding the best settings for these parameters. Therefore, we propose an optimized deep learning model in which the hyperparameters are optimized to find their best settings and thus achieve more recognition results. This deep learning model consists of a convolutional neural network (CNN) composed of four local feature-learning blocks and a long short-term memory (LSTM) layer for learning local and long-term correlations in the log Mel-spectrogram of the input speech samples. To improve the performance of this deep network, the learning rate and label smoothing regularization factor are optimized using the recently emerged stochastic fractal search (SFS)-guided whale optimization algorithm (WOA). The strength of this algorithm is the ability to balance between the exploration and exploitation of the search agents’ positions to guarantee to reach the optimal global solution. To prove the effectiveness of the proposed approach, four speech emotion datasets, namely, IEMOCAP, Emo-DB, RAVDESS, and SAVEE, are incorporated in the conducted experiments. Experimental results confirmed the superiority of the proposed approach when compared with state-of-the-art approaches. Based on the four datasets, the achieved recognition accuracies are 98.13%, 99.76%, 99.47%, and 99.50%, respectively. Moreover, a statistical analysis of the achieved results is provided to emphasize the stability of the proposed approach.
A Lightweight Method for Modelling Technology-Enhanced Assessment Processes

: Conducting assessments is one of the core processes in educational institutions. It needs careful planning that can be supported by appropriate process models. Many existing assessment process models take a technical perspective and are not necessarily suitable for communication among educators or other people concerned with assessment organization. The paper reports on an alternative and more lightweight modelling approach and provides two sample process models for technology-enhanced assessments to illustrate its usage. Positive results from an evaluation of the modelling method in a workshop with eight participants demonstrate its suitability from the educator’s perspective.
Biotechnological Evolution of siRNA Molecules: From Bench Tool to the Refined Drug

The depth and versatility of siRNA technologies enable their use in disease targets that are undruggable by small molecules or that seek to achieve a refined turn-off of the genes for any therapeutic area. Major extracellular barriers are enzymatic degradation of siRNAs by serum endonucleases and RNAases, renal clearance of the siRNA delivery system, the impermeability of biological membranes for siRNA, activation of the immune system, plasma protein sequestration, and capillary endothelium crossing. To overcome the intrinsic difficulties of the use of siRNA molecules, therapeutic applications require nanometric delivery carriers aiming to protect double-strands and deliver molecules to target cells. This review discusses the history of siRNAs, siRNA design, and delivery strategies, with a focus on progress made regarding siRNA molecules in clinical trials and how siRNA has become a valuable asset for biopharmaceutical companies.
SenseQ: Creating relationships between objects to answer questions of humans by using Social Networks

Social Networks are a source of information very use to know what happen around us. While, the Internet of Things is a world that tries to measure everything by gathering data from sensors and interconnect different objects. Why not combine both? In this paper, we propose the creation of a system that gathers information from users' sensors, processes it and exposes this information to Social Networks through questions that users ask to the system. Users will be able to ask for data available in distributed sensors using a Social Network and our system will answer to the users also through the Social Network. In order to gather the data from sensors and combine them, we propose three different type of relationship between sensors: neighboring relationship, familiar relationship and work relationship.
Containment Control Of Multi-Agent Systems Subject To Denial Of Service Attacks

This article proposes a secure containment control of multi-agent systems under cyber attacks. Multiple Unmanned Aerial Vehicle Systems (UAVs) are considered in this article. The suggested approach considers denial of service (DoS) attacks, multi-UAVs’ containment control and designs a strong steering approach as well as a secure middleware for data sharing and exchange. A combination of graph theory and L1 adaptive control is utilized to ensure efficient steering and cooperation. The data distribution services (DDS) middleware handles data transfer among all the UAVs, which solves the interoperability challenge when interacting with several UAVs from various platforms and can be regarded as an enhanced security strategy depending on its own quality of service (QoS). The L1 controller is used to stabilize the dynamic model of each UAV, while the graph method is used for the containment of multi UAVs. The linear matrix inequalities (LMIs) control is designed as a robust level of security to handle the influence of the DoS attack. Simulation results are utilized to validate and demonstrate the suggested technique’s performance under the criteria mentioned before.
An Appreciation of Diverse Approaches to Learning Design in Higher Education

Bridging the Gap Practical Approaches to Generating Learning Designs . 8 Working on Content . 9 Hierarchical Lists, Concept Maps, Mind Maps . 9 Structured Writing . 9 Accessibility Issues . 9 Delivery Platforms, Bandwidth and Media Choices . 9 Mode of Use . 10 Aims/Publicity . 10 Two Design ‘Classics’ for Educational Content Developers . 10 Working on Activities . 11 Organisational Issues – Encouraging a Systems Approach . 11 Teaching Aims, Learning Outcomes and Assessment Criteria . 11 Journalistic Enquiry Mapped to IMS Learning Design . 12 Online Tutoring and Student Activity Design . 13 Graphical Tools . 13
Endoscopic Ultrasound-Guided Laser Ablation Using a Diffusing Applicator for Locally Advanced Pancreatic Cancer Treatment

Simple Summary Pancreatic cancer (PC) is one of the most lethal cancers; caused by family history, obesity, diabetes, and smoking, it has a 2–9% five-year survival rate. However, patients diagnosed by endoscopic ultrasound (EUS) already have an advanced stage of PC, indicating the difficulty of surgical resection. Recently, laser ablative treatment with a diffusing applicator has been proven to be feasible for treating advanced PC. Despite the potential application for treating PC, further evaluation of acute and chronic tissue responses are essential to determine the efficacy and safety of diffusing applicator under EUS guidance. In this study, EUS-guided diffusing applicator-assisted laser ablation was evaluated to quantify the extent of the tissue response after the delivery of various energy levels. Abstract Endoscopic ultrasound (EUS)-guided cylindrical interstitial laser ablation (CILA) procedures can be used to treat unresectable pancreatic cancer (PC). The aim of this study was to investigate the acute responses of pancreatic tissue after EUS-guided CILA in vivo in porcine models. Eight pigs were tested to compare the effects of different energy levels on pancreatic tissue ablation. A 1064 nm laser system was used to deliver 5 W through a diffusing applicator. The EUS-guided CILA was performed under four different energies: 200, 400, 600, and 800 J. Three days after the experiments, histological analysis was performed. The CILA consistently generated circular coagulated necrosis (CN) in the cross-sectioned pancreatic tissue. The ablation diameter was linearly dependent on the total energy delivery. The area of the CN initially increased with total energy delivery but became saturated at 600 J. The width of the degenerative parenchyma (DP) in the native tissue beyond the CN region increased with the total energy up to 600 J, and then decreased afterward. EUS-guided CILA can be a feasible approach for treating PC. Further animal studies will investigate the chronic responses of the pancreatic tissue to examine the efficacy and safety of the proposed method for clinical translation.
Collaborative governance for forest land use policy implementation and development

Abstract This study analyses forestry governance by assessing the situation of local and central governments’ land-use activities. Governance networks and regulations on intergovernmental functions support to shape patterns of coordination. Collaborative governance is often cited as a way to develop a plan for the sustainable management of forest land use. However, an empirical approach to collaborative governance is still lacking. This research investigates which factors of collaborative governance practices correspond to forest land use. Hence, understanding their roles is essential to managing networks for forest management activities. The findings of the study demonstrated that collaborative governance has changed away from information sharing and toward forestry governance and policy network. Furthermore, forest land use also emphasizes guidelines for preventing the spread of land-clearing fires, which is an important aspect of fire prevention in general. The fires from 2010 to 2018 affected agricultural lands, plantations, forests, and swamps. The clearing of peatland for oil palm and timber plantations appears to be the main source of smoke-haze and is indicated as the primary driver of land clearing motives in Riau Province, Indonesia. Nvivo-12 was used as a tool to codify each interview result. Observations were coded for discussion, and the work refined the findings from the analysis. The data were then transferred to Gephi to allow networking analysis. The network was combined using the Gephi software. NVivo was also used to explore forest land-use activities by combining network analysis to assess the pattern of collaboration.
Channel catfish and freshwater drum population demographics across four large Midwestern rivers

Abstract Channel catfish (Ictalurus puncatus) and freshwater drum (Aplodinotus grunniens) are two commercially and recreationally important species in large rivers of the Midwestern United States. Understanding their population demographics is essential to managing sustainable populations. In this study, we determined and compared the size structure, individual growth, and mortality estimates of channel catfish and freshwater drum among the Illinois River and sections of the Mississippi, Ohio, and Wabash rivers to provide a current baseline for managing these populations. Results suggest that both fishes differed in size structure among rivers. Compared to all other rivers, the Mississippi River freshwater drum growth rate was the highest and the theoretical maximum length was the lowest, and the Ohio River annual mortality was lowest. Channel catfish growth did not differ among rivers, but annual mortality was significantly higher in the Mississippi River compared to the Wabash River. Given the importance of these two fishes, better understanding of their population demographics in these systems is essential to improving current and future fisheries management programs.
A New Hybrid Ant Colony Optimization Based PID of the Direct Torque Control for a Doubly Fed Induction Motor

Due to its advantages, the Proportional Integral Derivative (PID) controller has been the most widely used controller in the industrial sector. It allows linear systems to have good performance, but if the system is subjected to physical variation conditions, the system’s behavior becomes non-linear, in which case the PID controller is insufficient. The use of the PID controller for speed control in rotating machines, such as the doubly fed induction motor (DFIM) is widely used, but the non-linearity of the machine parameters allows for undesirable behaviors, resulting in overshoots and torque ripples. For this reason, several techniques have been adopted to increase the DTC’s robustness. One finds the integration of artificial intelligence as optimization algorithms. These algorithms are used to generate gains close to the optimum, converging the behavior of the DFIM to its optimum. In this work, an Ant Colony Optimization (ACO) algorithm was proposed to adjust the PID controller gains of the DTC control to control the DFIM, using a combined weighting cost function, to obtain efficient torque and speed control. This paper presents a new hybrid structure resulting from the intelligent ACO-DTC control implemented on Matlab-Simulink. The performance results extracted from the simulation showed the effectiveness of the intelligent ACO-DTC control, which provides satisfactory performance in terms of rapidity, stability, precision, and torque ripples compared to the conventional DTC.
THE CONCEPT OF ACADEMIC INTEGRITY PERCEIVED THROUGH THE EYES OF STUDENTS AT ONE HIGHER EDUCATIONAL INSTITUTION IN KAZAKHSTAN

The paper considers the problems with academic dishonesty at one higher educational institution that is located in East Kazakhstan. Despite the fact the Government has been fought for academic integrity since 2011 and a lot of measures have been taken to establish Academic Honesty, Kazakhstan's educational system is still experiencing plagiarism. The questions that the paper wants to answer are as follows: With what kind of academic dishonesty actions are students familiar with? What is the motive of plagiarism among students? Why is plagiarism still relevant in the current academic context? How should higher education institutions address the issue of academic dishonesty? The method employed in this paper is qualitative research. 10 students( 1 -first-year student, 6 – third-year students, 3 – fourth-year students) from different departments were interviewed.This research reveals some crucial outcomes referring to the knowledge of students, reasons of plagiarism and recommendations of participants to address the current situation in higher educational institutions. According to the findings, despite the fact that there are several types of actions against Academic integrity, the vast majority of students are aware of only plagiarism as the main academic dishonesty action. One of the significant motives of students’ plagiarism is the negligence of educators in terms of verification for reference in students’ papers. Another predominant figure from these findings is that the lack of knowledge in citation plays a significant role as the main reason of academic dishonesty among students. This issue is still relevant in current academic context due to the several factors, such as students’ biased opinion related to the program anti – plagiat, the equal level of plagiarism from students and teachers. To sum up, this research paper concerns the significant problem of Kazakhstani universities, its relevance and its potential solutions.
Firm performance, vertical agency crisis and corporate governance of Indian listed companies

PurposeThis study aims to examine the influence of corporate governance variables on firm performance and also to find out whether the corporate governance mechanism is capable of mitigating the vertical agency crisis. Here the researcher uses corporate governance mechanisms such as board meeting frequency, board independence, percentage of non-executive directors, percentage of woman directors on board and the board size to measure the firm performance and, at the same time, tries to mitigate the agency crisis, which is measured through return on asset and asset turnover ratio.Design/methodology/approachThe present study considers period from 2009 to 2020 with data corresponding to a panel of 271 non-financial firms listed in 500 NSE index, India. The study introduces a panel regression model to analyze the data collected from the sample firms.FindingsThe study detects a positive as well as a statistically significant relationship between board size and vertical agency cost. The study also observes a negative relationship between board independence and agency cost. Further, the study finds a positive relationship between corporate governance variables and firm performance, though it is non-significant.Originality/valueAs the study progresses, the study detects a negative relationship between non-executive directors and agency costs. This study tries to give policy prescription to the corporate policymaker regarding various measures to be taken by the firm for the improvement of firm performance and reduction of owner and manager conflict inside the company. The study fills the literature gap by revealing a significant relationship between corporate governance, vertical agency crisis and firm performance.
Leadership response to COVID-19: a comparative analysis of the education sector in USA and India

ABSTRACT As the COVID-19 pandemic overwhelmed the world, educational leaders implemented crisis management plans to mitigate its impact on their students, faculty, staff, and their families. We studied five cases in the education sector in the world’s largest democracies: the United States of America and India. Through document analysis and semi-structured interviews, we investigated the responses of educational leadership during each stage of the crisis. In the cases we studied, leaders in USA adopted a consultative decision-making approach more often than their Indian counterparts. Moreover, group-based approach was evident only among leaders in USA. Leaders in both countries were creative and diligent in communicating, both to gather information as well as for dissemination. Leaders had to think out of the box to meet the needs of students and faculty, as well as the communities in their immediate areas. Resource identification and distribution, overcoming the digital divide between rural and urban areas, and the acquisition and appropriate allocation of safety equipment are just a few of the challenges addressed by the academic leaders. This study makes an important contribution to crisis leadership by looking at competencies needed to manage a crisis, and the influence of national culture in decision-making behaviours during a crisis.
A Survey on Task Scheduling Algorithms in Cloud Computing for Fast Big Data Processing

— The recent explosion of data of all kinds (persistent and short-lived) have imposed processing speed constraints on big data processing systems (BDPSs). One such constraint on running these systems in Cloud computing environments is to utilize as many parallel processors as required to process data fast. Consequently, the nodes in a Cloud environment encounter highly crowded clusters of computational units. To properly cater for high degree of parallelism to process data fast, efficient task and resource allocation schemes are required. These schemes must distribute tasks on the nodes in a way to yield highest resource utilization as possible. Such scheduling has proved even more complex in the case of processing of short-lived data. Task scheduling is vital not only to handle big data but also to provide fast processing of data to satisfy modern time data processing constraints. To this end, this paper reviews the most recently published (2020-2021) task scheduling schemes and their deployed algorithms from the fast data processing perspective .
Magnetic resonance imaging at 3.0-T in postmenopausal osteoporosis: a prospective study and review of the literature

Objective To promote advanced research using magnetic resonance imaging (MRI) in the diagnosis of and screening for osteoporosis by looking for correlations among the T-scores measured by dual-energy X-ray absorptiometry (DEXA), the apparent diffusion coefficient (ADC) values on diffusion-weighted imaging (DWI), and the T1-weighted signal intensity values. Materials and Methods This was a prospective study of postmenopausal women with no contraindications to MRI and no history of cancer who underwent DEXA within 30 days before or after the MRI examination. A 3.0-T scanner was used in order to acquire sagittal sequences targeting the lumbar spine. Results Thirteen women underwent DEXA and MRI. In two cases, the MRI was discontinued early. Therefore, the final sample comprised 11 patients. The ADC values and T1-weighted signal intensity were found to be higher in patients with osteoporosis. However, among the patients > 60 years of age with osteoporosis, ADC values were lower and T1-weighted signal intensity was even higher. Conclusion It is unlikely that MRI will soon replace DEXA for the diagnostic workup of osteoporosis. Although DWI and ADC mapping are useful for understanding the pathophysiology of osteoporosis, we believe that T1-weighted sequences are more sensitive than is DWI as a means of performing a qualitative analysis of vertebral alterations.
Constrained Retrievals of Aerosol Optical Properties Using Combined Lidar and Imager Measurements During the FIREX-AQ Campaign

Smoke aerosols arise from a variety of regional sources and fuel types dependent on the properties of the fire, leading to spatial variability in smoke composition and optical properties. After emission, these aerosols age and mix in the atmosphere with other aerosol species, such as sulfates, altering the optical, and microphysical properties of the smoke aerosols over time. Thus, lidar ratio (extinction to backscatter ratio) derived from lidar sensors exhibit spatiotemporal variability for smoke. Traditional backscatter lidar processing algorithms employ a signal loss method that utilizes the reduction of signals below and above cloud layers, enabling simultaneous retrievals of both layer-averaged lidar ratio and particulate extinction, which avoids the need for assigning lidar ratios based on layer type as is typically used for backscatter lidar algorithms. In this study, the signal loss method, which is traditionally designed for cloud property retrievals, is attempted for elevated smoke plume property retrievals using NASA’s Cloud Physics Lidar (CPL) observations from the 2019 Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field campaign. Good agreement (linear correlation coefficient of 0.67) is found between aerosol optical depth (AOD) derived from the signal loss method and the constrained method, utilizing collocated GOES MAGARA AOD values as constraints for lidar ratio retrievals, for the Williams Flats smoke event. Differences in derived lidar ratios from the signal loss method and the constrained method (13.6 and 7.4%) are found to be smaller than the expected signal loss lidar ratio error estimate of ∼17–23%. A good agreement is also found in lidar ratios derived from this study and from using Differential Absorption Lidar-High Spectral Resolution Lidar (DIAL-HSRL) measurements for the Williams Flats Fire. The lidar ratio statistics of smoke plumes presented in this analysis (51 ± 13 sr) also compare favorably with lidar ratio values found in previous studies; however, they remain lower than the assumed smoke lidar ratio of 70 sr (at 532 nm) used by CALIPSO and CPL, and vary with plume transport distance. These findings suggest lidar ratio is likely to be regionally specific and evolve with plume transport. Thus, innovative methods for simultaneous retrieval of lidar ratio and aerosol extinction, such as the signal loss method proposed in this study, are needed for accurate aerosol retrievals from standard backscatter lidars in the future.