Miracle-dz/UniFAR-FTUs huggingface dataset:
LP-MLTSVM: Laplacian Multi-Label Twin Support Vector Machine for Semi-Supervised Classification

In the machine learning jargon, multi-label classification refers to a task where multiple mutually non-exclusive class labels are assigned to a single instance. Generally, the lack of sufficient labeled training data demanded by a classification task is met by an approach known as semi-supervised learning. This type of learning extracts the decision rules of classification by utilizing both labeled and unlabeled data. Regarding multi-label data, however, current semi-supervised learning methods are unable to classify them accurately. Therefore, with the goal of generalizing the state-of-the-art semi-supervised approaches to multi-label data, this paper proposes a novel two-stage method for multi-label semi-supervised classification. The first stage determines the label(s) of the unlabeled training data by means of a smooth graph constructed using the manifold regularization. In the second stage, thanks to the capability of the twin support vector machine to relax the requirement that hyperplanes should be parallel in classical SVM, we employ it to establish a multi-label classifier called LP-MLTSVM. In the experiments, this classifier is applied on benchmark datasets. The simulation results substantiate that compared to the existing multi-label classification algorithms, LP-MLTSVM shows superior performance in terms of the Hamming loss, average precision, coverage, ranking loss, and one-error metrics.
Computational and Spectral Discussion of Some Substituted Chalcone Derivatives

The substituted chalcone derivatives 1–7 have been synthesized, and spectroscopic characterization were done as done using the experimental FT-IR, UV-Vis, GC-MS, 1D NMR spectroscopy. The favored conformation of substituted chalcone 3 was predicted theoretically by geometry optimization structure selected geometrical parameters and molecular properties such as NBO, AIM, HOMO-LUMO, MEP surface, and atomic charges were derived from optimized structures. The 1H and 13C NMR spectral data had been additionally computed using the Gaussian-09 package and compared with the experimental values. The antibacterial and antifungal activity was derived by the disc diffusion method.
Developing interactive oral assessments to foster graduate attributes in higher education

Abstract Interactive oral assessments demonstrate potential to develop graduate attributes such as critical thinking, professional communication and collaborative skills in students through authentic simulation of workplace scenarios. This study captured the design, delivery and evaluation of interactive oral assessments across three programmes – occupational therapy, nursing and air transport management – in one university. A four-step Model to Build Authentic Assessment was used to design the assessments. Quantitative and qualitative data collected from 158 students, five assessors and three module leaders were analysed for a basic evaluation of the fidelity of interactive oral assessment implementation across different contexts and to explore the experiences of faculty and students undertaking the assessments. Fidelity was considered in terms of: (i) whether students could express their individualised responses, (ii) if they perceived the assessment to be authentic, and (iii) if it helped them gain professional knowledge and skills. In all the developed assessments, each of the three aspects were somewhat evident, evident or clearly evident. Qualitative findings suggested the interactive oral assessments reflected real-world expectations, promoted deep learning and offered a quality learning experience. From these results, practical suggestions for how faculty can self-assess their readiness for implementing interactive oral assessments are offered.
Higher thinking and digital literacy: Empowering technology for pre-service mathematics teacher

Teachers play a more significant role as a symbol of teaching success. Teachers who hold a more potential digital literacy in education teach mathematics with patterns and procedures, leading to practical teaching mathematics. Influential digital literacy and higher thinking are not possible when teachers do not use technology to understand the concept of mathematics. Therefore, teachers are required to improve their technical abilities and higher thinking in mathematics learning. This research used a cross-sectional quantitative research. This research uses statistical analysis of the Kaiser Meyer-Oikin Test (KMO) and the Barlett's Test to obtain exploratory factor analysis. Subsequently, the analyzed using confirmatory factor analysis and structural equation model with Analysis of Moment Structure (AMOS). The purpose of this research is to provide an overview of the relationship between higher thinking skills and digital literacy. The review discoveries show that teachers' digital literacy has a more significant impact on the use of technology in the classroom, and teachers' higher thinking in the role of technology will encourage teachers to use more technology in their practical teaching. Teachers need to broaden their perspectives on the ability and quality of mathematics teaching that is more easily changed by changing practical instruction in the classroom. One of the reasons mathematics teachers still apply traditional teachings is the lower thinking and low level of digital literacy. Teachers are not capable of teaching the concept of mathematics with technology to students.
What Affects Income in Sub-Saharan Africa?

This paper closely examines how selected macroeconomic variables affect income in Sub-Saharan Africa (SSA). The study employs a more recent dataset and uses fixed and random effects models to characterise the nature and direction of impact evidenced from the data. The analysis further incorporates a monetary policy element by introducing money market interest rate and examining its effects. In general, the paper reveals that income in SSA is significantly affected by trade. Additionally, money market interest rate is found to have no major impact on income as may be perceived, suggesting that this aspect of monetary policy has not played a major role in affecting income levels in SSA. The findings of this study can serve as a guide for policy makers within the region when considering policy actions in relation to income.
A Proposal for Multimodal Emotion Recognition Using Aural Transformers and Action Units on RAVDESS Dataset

Emotion recognition is attracting the attention of the research community due to its multiple applications in different fields, such as medicine or autonomous driving. In this paper, we proposed an automatic emotion recognizer system that consisted of a speech emotion recognizer (SER) and a facial emotion recognizer (FER). For the SER, we evaluated a pre-trained xlsr-Wav2Vec2.0 transformer using two transfer-learning techniques: embedding extraction and fine-tuning. The best accuracy results were achieved when we fine-tuned the whole model by appending a multilayer perceptron on top of it, confirming that the training was more robust when it did not start from scratch and the previous knowledge of the network was similar to the task to adapt. Regarding the facial emotion recognizer, we extracted the Action Units of the videos and compared the performance between employing static models against sequential models. Results showed that sequential models beat static models by a narrow difference. Error analysis reported that the visual systems could improve with a detector of high-emotional load frames, which opened a new line of research to discover new ways to learn from videos. Finally, combining these two modalities with a late fusion strategy, we achieved 86.70% accuracy on the RAVDESS dataset on a subject-wise 5-CV evaluation, classifying eight emotions. Results demonstrated that these modalities carried relevant information to detect users’ emotional state and their combination allowed to improve the final system performance.
Generative Deep Learning to Detect Cyberattacks for the IoT-23 Dataset

The rapid growth of Internet of Things (IoT) is expected to add billions of IoT devices connected to the Internet. These devices represent a vast attack surface for cyberattacks. For example, these IoT devices can be infected with botnets to enable Distributed Denial of Service (DDoS) attacks. Signature-based intrusion detection systems are traditional countermeasures for such attacks. However, these methods rely on human experts and are time-consuming in terms of updates and may not exhaust all attack types especially zero-day attacks. Deep learning has shown some promise in intrusion detection. This paper shows that it is possible to use generative deep learning methods like Adversarial Autoencoders (AAE) and Bidirectional Generative Adversarial Networks (BiGAN) to detect intruders based on an analysis of the network data. The recently posted full IoT-23 dataset based on Somfy door lock, Philips Hue and Amazon Echo devices was used to train generative deep learning models to detect a variety of attacks like DDoS, and various botnets like Mirai, Okiruk and Torii. Over 1.8 million network flows were used to train the various models. The resulting generative models outperform traditional machine learning techniques like Random Forests. Both AAE and BiGAN-based models were able to achieve an F1-Score of 0.99. A BiGAN to detect unknown attacks was also trained to detect novel zero-day attacks with an F1-Score from 0.85 to 1.
Fusion of Depth and Thermal Imaging for People Detection

|The methodology presented in this paper covers the topic of automatic detection of humans based on two types of images that do not rely on the visible light spectrum, namely on thermal and depth images. Various scenarios are considered with the use of deep neural networks being extensions of Faster R-CNN models. Apart from detecting people, independently, with the use of depth and thermal images, we proposed two data fusion methods. The (cid:12)rst approach is the early fusion method with a 2-channel compound input. As it turned out, its performance surpassed that of all other meth-ods tested. However, this approach requires that the model be trained on a dataset containing both types of spatially and temporally synchronized imaging sources. If such a training environment cannot be setup or if the speci(cid:12)ed dataset is not su(cid:14)ciently large, we recommend the late fusion scenario, i.e. the other approach explored in this paper. Late fusion models can be trained with single-source data. We introduce the dual-NMS method for fusing the depth and thermal imaging approaches, as its results are better than those achieved by the common NMS.
Economic Growth at the Expense of Environmental Degradation: Evidence from the Philippines

This study presents an empirical analysis of the impacts of three macroeconomic variables namely, Gross Domestic Product, Foreign Direct Investment, and Urban Population on the emissions of CO2 in the Philippines from the period of 1970 to 2018. The results reveal that Gross Domestic Product and Foreign Direct Investments exhibit a statistically significant relationship with CO2 emissions. The findings of this study suggest that the Philippines’ reliance on high-polluting industries as drivers of economic growth will only worsen its environmental quality. Moreover, its weak environmental laws provide foreign investors the opportunity to exploit the environment in exchange for FDI inflows to the country. Furthermore, the results of this study support the scale effect in the Environmental Kuznets Curve hypothesis, as well as the Pollution Haven Hypothesis.
Modeling and Evaluating Energy Performance of Smartphones

With advances in hardware miniaturization and wireless communication technologies even small portable wireless devices have much communication bandwidth and computing power. These devices include smartphones, tablet computers, and personal digital assistants. Users of these devices expect to run software applications that they usually have on their desktop computers as well as the new applications that are being developed for mobile devices. Web browsing, social networking, gaming, online multimedia playing, global positioning system based navigation, and accessing emails are examples of a few popular applications. Mobile versions of thousands of desktop applications are already available in mobile application markets, and consequently, the expected operational time of smartphones is rising rapidly. At the same time, the complexity of these applications is growing in terms of computation and communication needs, and there is a growing demand for energy in smartphones. However, unlike the exponential growth in computing and communication technologies, in terms of speed and packaging density, battery technology has not kept pace with the rapidly growing energy demand of these devices. Therefore, designers are faced with the need to enhance the battery life of smartphones. Knowledge of how energy is used and lost in the system components of the devices is vital to this end. With this view, we focus on modeling and evaluating the energy performance of smartphones in this thesis. We also propose techniques for enhancing the energy efficiency and functionality of smartphones. The detailed contributions of the thesis are as follows: (i) we present a finite state machine based model to estimate the energy cost of an application running on a smartphone, and provide practical approaches to extract model parameters; (ii) the concept of energy cost profile is introduced to assess the impact of design decisions on energy cost at an early stage of software design; (iii) a generic architecture is proposed and implemented for enhancing the capabilities of smartphones by sharing resources; (iv) we have analyzed the Internet traffic of smartphones to observe the energy saving potentials, and have studied the implications on the existing energy saving techniques; and finally, (v) we have provided a methodology to select user level test cases for performing energy cost evaluation of applications. All of our concepts and proposed methodology have been validated with extensive measurements on a real test bench. Our work contributes to both theoretical understanding of energy efficiency of software applications and practical methodologies for evaluating energy efficiency. In summary, the results of this work can be used by application developers to make implementation level decisions that affect the energy efficiency of software applications on smartphones. In addition, this work leads to the design and implementation of energy efficient smartphones.
The Effect of Structured Exercise Compared with Education on Neuropathic Signs and Symptoms in People at Risk of Neuropathic Diabetic Foot Ulcers: A Randomized Clinical Trial

Background and Objectives: Lifestyle interventions such as exercise prescription and education may play a role in the management of peripheral neuropathy in people with diabetes. The aim of this study was to determine the effect of undertaking an exercise program in comparison with an education program on the signs and symptoms of peripheral neuropathy in people with diabetes at risk of neuropathic foot ulceration. Materials and Methods: Twenty-four adult participants with diabetes and peripheral neuropathy were enrolled in this parallel-group, assessor blinded, randomised clinical trial. Participants were randomly allocated to one of two 8-week lifestyle interventions, exercise or education. The primary outcome measures were the two-part Michigan Neuropathy Screening Instrument (MNSI) and vibratory perception threshold (VPT). Secondary outcome measures included aerobic fitness, balance and lower limb muscular endurance. Results: Participants in both lifestyle interventions significantly improved over time for MNSI clinical signs (MD: −1.04, 95% CI: −1.68 to −0.40), MNSI symptoms (MD: −1.11, 95% CI: −1.89 to −0.33) and VPT (MD: −4.22, 95% CI: −8.04 to −0.40). Although the interaction effects did not reach significance, changes in values from pre to post intervention favoured exercise in comparison to control for MNSI clinical signs (MD −0.42, 95% CI −1.72 to 0.90), MNSI clinical symptoms (MD −0.38, 95% CI −1.96 to 1.2) and VPT (MD −4.22, 95% CI −12.09 to 3.65). Conclusions: Eight weeks of exercise training or lifestyle education can improve neuropathic signs and symptoms in people with diabetes and peripheral neuropathy. These findings support a role for lifestyle interventions in the management of peripheral neuropathy.
Integration of Three-Phase LLC Resonant Converter and Full-Bridge Converter for Hybrid Modulated Multioutput Topology

A multioutput dc–dc topology based on hybrid modulation of pulse frequency modulation and the phase shift is proposed in this article. The proposed hybrid modulated multioutput converter is derived from the integration of a three-phase LLC resonant converter and the full-bridge converter. With the hybrid modulation, the multioutput is controlled independently free from cross-regulation and isolated from each other. With the three-phase interleaving operation, the resonant currents can be reduced, and thus, the efficiency will be improved. Furthermore, the output current ripple of the main output voltage is reduced; as a consequence, the lifetime of the output filter capacitor is extended, and the reliability is reinforced. What is more, the number of the power switches is reduced, and the zero-voltage switching of the power switches can be achieved within the entire load range by the proposed integrated topology. All the abovementioned features of the proposed converter will lead to a compact, efficient, and cost-effective design. Finally, a 1.4-kW triple-output laboratory prototype is built and tested to validate the feasibility and effectiveness of the proposed converter.
Accelerated Life Testing of Electronic Circuit Boards with Applications in Lead-Free Design

This dissertation presents methods for addressing research problems that currently can only adequately be solved using Quality Reliability Engineering (QRE) approaches especially accelerated life testing (ALT) of electronic printed wiring boards with applications to avionics circuit boards. The methods presented in this research are generally applicable to circuit boards, but the data generated and their analysis is for high performance avionics. Avionics equipment typically requires 20 years expected life by aircraft equipment manufacturers and therefore ALT is the only practical way of performing life test estimates. Both thermal and vibration ALT induced failure are performed and analyzed to resolve industry questions relating to the introduction of lead-free solder product and processes into high reliability avionics. In chapter 2, thermal ALT using an industry standard failure machine implementing Interconnect Stress Test (IST) that simulates circuit board life data is compared to real production failure data by likelihood ratio tests to arrive at a mechanical theory. This mechanical theory results in a statistically equivalent energy bound such that failure distributions below a specific energy level are considered to be from the same distribution thus allowing testers to quantify parameter setting in IST prior to life testing. In chapter 3, vibration ALT comparing tin-lead and lead-free circuit board solder designs involves the use of the likelihood ratio (LR) test to assess both complete failure data and S-N curves to present methods for analyzing data. Failure data is analyzed using Regression and two-way analysis of variance (ANOVA) and reconciled with the LR test results that indicating that a costly aging pre-process may be eliminated in certain cases. In chapter 4, vibration ALT for side-by-side tin-lead and lead-free solder black box designs are life tested. Commercial models from strain data do not exist at the low levels associated with life testing and need to be developed because testing performed and presented here indicate that both tin-lead and lead-free solders are similar. In addition, earlier failures due to vibration like connector failure modes will occur before solder interconnect failures.
The Interactions among Isolates of Lactiplantibacillus plantarum and Dairy Yeast Contaminants: Towards Biocontrol Applications

Yeast diversity in the cheese manufacturing process and in the cheeses themselves includes indispensable species for the production of specific cheeses and undesired species that cause cheese defects and spoilage. The control of yeast contaminants is problematic due to limitations in sanitation methods and chemicals used in the food industry. The utilisation of lactic acid bacteria and their antifungal products is intensively studied. Lactiplantibacillus plantarum is one of the most frequently studied species producing a wide spectrum of bioactive by-products. In the present study, twenty strains of L. plantarum from four sources were tested against 25 species of yeast isolated from cheeses, brines, and dairy environments. The functional traits of L. plantarum strains, such as the presence of class 2a bacteriocin and chitinase genes and in vitro production of organic acids, were evaluated. The extracellular production of bioactive peptides and proteins was tested using proteomic methods. Antifungal activity against yeast was screened using in vitro tests. Testing of antifungal activity on artificial media and reconstituted milk showed significant variability within the strains of L. plantarum and its group of origin. Strains from sourdoughs (CCDM 3018, K19-3) and raw cheese (L12, L24, L32) strongly inhibited the highest number of yeast strains on medium with reconstituted milk. These strains showed a consistent spectrum of genes belonging to class 2a bacteriocins, the gene of chitinase and its extracellular product 9 LACO Chitin-binding protein. Strain CCDM 3018 with the spectrum of class 2a bacteriocin gene, chitinase and significant production of lactic acid in all media performed significant antifungal effects in artificial and reconstituted milk-based media.
Materials for Dentoalveolar Bioprinting: Current State of the Art

Although current treatments can successfully address a wide range of complications in the dentoalveolar region, they often still suffer from drawbacks and limitations, resulting in sub-optimal treatments for specific problems. In recent decades, significant progress has been made in the field of tissue engineering, aiming at restoring damaged tissues via a regenerative approach. Yet, the translation into a clinical product is still challenging. Novel technologies such as bioprinting have been developed to solve some of the shortcomings faced in traditional tissue engineering approaches. Using automated bioprinting techniques allows for precise placement of cells and biological molecules and for geometrical patient-specific design of produced biological scaffolds. Recently, bioprinting has also been introduced into the field of dentoalveolar tissue engineering. However, the choice of a suitable material to encapsulate cells in the development of so-called bioinks for bioprinting dentoalveolar tissues is still a challenge, considering the heterogeneity of these tissues and the range of properties they possess. This review, therefore, aims to provide an overview of the current state of the art by discussing the progress of the research on materials used for dentoalveolar bioprinting, highlighting the advantages and shortcomings of current approaches and considering opportunities for further research.