Miracle-dz/UniFAR-FTUs huggingface dataset:
Inception-ResNet-v2 with Leakyrelu and Averagepooling for More Reliable and Accurate Classification of Chest X-ray Images

Pneumonia is one of the most commonly seen illnesses in the world and its diagnosis needs some expertise. Computer aided diagnosis methods are used extensively in a lot of fields like health care. This study uses Inception-ResNet-v2 deep learning architecture. Classification is done by using this architecture. ReLU activation function seen in network architecture is changed with LeakyReLU activation function and classification task is done. After that, all of the maxpooling layers seen in network architecture is changed with avepooling layers and again classification task is done. Lastly, this seperate changes done in network architecture is combined in one network and again classification task is done with new network architecture. Four experiments are done in total and their results are compared. The best case with a sensitivity value of 93.16% and with a specificity value of 93.59% is obtained in Inception Resnet V2 with together application of LeakyReLU and Averagepooling.
Speech Emotion Recognition on Indonesian YouTube Web Series Using Deep Learning Approach

These days, human-computer interactions develop in an alarmingly fast rate. To keep up with this development, one of many things to be advanced is machine's capability of recognizing human emotions through speech, or simply put, Speech Emotion Recognition (SER). Various studies regarding SER have been carried out using varying data modalities, such as TV shows, movies, and actor voice recordings. While the result may be proven satisfying, to collect these data of TV and actor recordings can be quite difficult and may require some costs. On the other hand, YouTube is an open and free platform for data gathering, and retrieving data from YouTube is effortless as well. Despite that, almost none of SER studies have tried this method of data collecting. This paper presents SER in Indonesian language, using Indonesian YouTube Web Series dataset with 4 labels of emotions. In the beginning, several experiments were carried out to determine which deep learning approach trained with which specific combination of features would yield out the most favorable result. The initial stage of the experiments showed that the Convolutional Neural Network (CNN) using a feature combination of MFCC, Contrast, and Tonnetz, gives better performance than other deep learning approach that we use. After tuning parameter process, we obtain that CNN with the combination of MFCC, Contrast, and Tonnetz gives 62.30% of F1 - Score.
A Machine-Learning based Approach to Support Academic Decision-Making at Higher Educational Institutions

Taking appropriate decisions in the academic processes at a university has a great impact on improving the quality of education and can have an important benefit for students, faculty members, and the entire academic community. In this paper, we propose a decision support solution providing accurate analysis, better decision support, and reporting and planning capability to assist decision-makers in order to enhance the quality of educational processes. To achieve this goal, a set of machine learning is used. Experiments are conducted on real data describing the College of Computer Science and Engineering (CCSE) at Taibah University in Saudi Arabia. Results show that we can predict graduation rates in a real case study to support decision-making. In addition, a comparison between four techniques of machine learning namely Support Vector Machine, Naïve Bayes, Decision Tree, and Random Forest is held using accuracy, recall, precision, and F-measure.
Cats Are Not Fish: Deep Learning Testing Calls for Out-Of-Distribution Awareness

As Deep Learning (DL) is continuously adopted in many industrial applications, its quality and reliability start to raise concerns. Similar to the traditional software development process, testing the DL software to uncover its defects at an early stage is an effective way to reduce risks after deployment. According to the fundamental assumption of deep learning, the DL software does not provide statistical guarantee and has limited capability in handling data that falls outside of its learned distribution, i.e., out-of-distribution (OOD) data. Although recent progress has been made in designing novel testing techniques for DL software, which can detect thousands of errors, the current state-of-the-art DL testing techniques usually do not take the distribution of generated test data into consideration. It is therefore hard to judge whether the “identified errors” are indeed meaningful errors to the DL application (i.e., due to quality issues of the model) or outliers that cannot be handled by the current model (i.e., due to the lack of training data). Tofill this gap, we take thefi rst step and conduct a large scale empirical study, with a total of 451 experiment configurations, 42 deep neural networks (DNNs) and 1.2 million test data instances, to investigate and characterize the impact of OOD-awareness on DL testing. We further analyze the consequences when DL systems go into production by evaluating the effectiveness of adversarial retraining with distribution-aware errors. The results confirm that introducing data distribution awareness in both testing and enhancement phases outperforms distribution unaware retraining by up to 21.5%.
Interfacial Tension and Hydrogen-Ion Concentration

(1) Long chain carboxylic acids dissolved in benzene show regular changes in interfacial tension against aqueous "buffered" solutions as the hydrion concentration of these is altered. A fall in interfacial tension starts at p h 5·5 and extends over the range of 4·0 p h 9·3 approximately, tending to vanish at this point. The curve is not identical with a dissociation curve, though it extends over the same range of p h . For a given p h the results are identical for phosphate and glycine "buffered" solutions, and for all acids investigated, except capric acid(C 10 ), which shows an abnormality for phosphate. (2) Hexadecylamine shows similar changes, in the opposite sense between approximately the same p h range, which follow the dissociation curve of a weak base rather closely
Design of Post-stroke Upper Limb Rehabilitation Game using Functional Electrical Stimulation for Hemiplegic Patient

Stroke is a cerebrovascular disease which could lead to physiological impairment, which affects one’s ability to do activities of daily living. Several previous studies state that only small number of patients regain useful motoric abilities. This is due the fact of which patients’ psychological needs are often neglected in a conventional rehabilitation. Hence, the development of rehabilitation methods which ease patient’s psychological burden is necessary. The system used functional electrical stimulation with PID control, combined with repetitive game scenario. Seven normal subjects participated in this study. Average impaired hand movement error was 5.66° from target and range-of motion of 87.86% maximum flexion. Game object control accuracy reached 100%. Moreover, subjects showed improved accuracy by 37.18% at the end of the rehabilitation sessions. Impaired hand trajectory also follows control hand movement, proving the system feasibility for rehabilitation use. Future studies with real hemiplegic patient may provide further validation to the system effectiveness.
toon2real: Translating Cartoon Images to Realistic Images

In terms of Image-to-image translation, Generative Adversarial Networks (GANs) has achieved great success even when it is used in the unsupervised dataset. In this work, we aim to translate cartoon images to photo-realistic images using GAN. We apply several state-of-the-art models to perform this task; however, they fail to perform good quality translations. We observe that the shallow difference between these two domains causes this issue. Based on this idea, we propose a method based on CycleGAN model for image translation from cartoon domain to photo-realistic domain. To make our model efficient, we implemented Spectral Normalization which added stability in our model. We demonstrate our experimental results and show that our proposed model has achieved the lowest Fréchet Inception Distance score and better results compared to another state-of-the-art technique, UNIT.
Incremental Deployment of Hybrid IP/SDN Network with Optimized Traffic Engineering

The recent introduction of Software Defined Networks (SDN) into the traditional networking paradigm to create hybrid SDN networks brings with it several economical, technical and organizational challenges which must be addressed. In deploying hybrid SDN networks, the maintenance of numerous factors is taken into consideration such as throughput, network traffic, load balancing and fast failure recovery. One strategy that has been suggested is the incremental deployment of SDN controllers alongside legacy networking systems to reap the benefits of both paradigms while minimizing disruptions to networking services and maintaining network performance from the perspective of traffic engineering. In this paper, we seek to explore an optimal incremental deployment sequence of legacy networking devices to programmable SDN switches based on traffic engineering measures, namely minimizing maximum link utilization, thus determining the most suitable devices to migrate. A combination of two metaheuristics algorithms (Particle Swarm Optimization and Ant Colony Optimization) are implemented to identify this optimal sequence in terms of the locations of routers to be migrated along with the optimal weight setting and flow split ratios at each stage of migration. The deployment sequence is simulated and compared with static migration algorithms for evaluation.
Driver’s Visual Input Processing of Information Displayed on the Windshield

Heads-up displays (HUDs) that present information on the windshields of vehicles have increasingly attracted attention. However, the process by which information is obtained from HUDs while driving remains unclear; this includes the critical parameters that determine how the input process is performed. Therefore, it is necessary to identify critical parameters and explore visual information processing to enhance the user-friendly design of HUDs. In this study, we focused on differences in the projection method between the virtual image and the real image as a critical parameter of the input process. We used eye tracking to evaluate the time taken for the input process, namely the process of shifting gaze from the environment to the HUD image. The results showed that the real image had shorter input processing time than the virtual image when the display distance was short. Moreover, processing time became shorter as the display distance increased in the virtual image display, while there was no such change in the real image display. This suggests that the input process has at least two critical parameters, namely the display distance and the ease of spatial orientation.
Roadrunner: Autonomous Intersection Management with Dynamic Lane Assignment

The advanced controls of Connected Autonomous Vehicles (CAVs) have enabled complex traffic management for higher efficiency and safety, such as dynamic lane assignment and non-signalized autonomous intersection management (AIM). Unlike traditional intersections, non-signalized AIM does not rely on the settings of signal phases or traffic-light cycles for controlling the traffic, and the measurements of lane congestion can no longer be based on these two settings. Moreover, in the CAV environment, lane use can be more dynamic in response to real-time traffic demands and the policy of AIM. Therefore, we proposed a new AIM system (named Roadrunner) that combines dynamic lane assignment and autonomous intersection management. In Roadrunner, lane use is not limited by turns, and lanes are dynamically assigned to CAVs, based on our lane-assignment policy and a novel approach of measuring lane congestion levels over non-signalized intersection controls. We implemented Roadrunner in a SUMO simulator and compared the performance with different intersection management systems, where lane use is predefined. The result shows that Roadrunner increases the intersection capacity by more than 11%, and CAVs have lower average traveling delay.
A new Evaluation Approach for Deep Learning-based Monocular Depth Estimation Methods

In smart mobility based road navigation, object detection, depth estimation and tracking are very important tasks for improvement of the environment perception quality. In the recent years, a surge of deep-learning based depth estimation methods for monocular cameras has lead to significant progress in this field. In this paper, we propose an evaluation of state-of-the-art depth estimation algorithms based on single single input on both the KITTI dataset and the recently published NUScenes dataset. The models evaluated in this paper include an unsupervised method (Monodepth2) and a supervised method (BTS). Our work lies in the elaboration of novel depth evaluation protocols, object depth evaluation and distance ranges evaluation. We validated our new protocols on both KITTI and NUScenes datasets, allowing us to get a more comprehensive evaluation for depth estimation, especially for applications in scene understanding for both road and rail environment.
Generative Data Augmentation for Diabetic Retinopathy Classification

A fundamental factor limiting the effectiveness of classification algorithms, especially in the medical imaging domain, has been an insufficient quantity of relevant class-specific data. In particular, positive examples of disease conditions tend to be rare, and represent a common bottleneck in improving model performance. In this paper, we introduce GAN-based generative data augmentation methods with dynamic input sampling, and compare their performance against an image feature transfer technique, towards improving the performance of real-world diabetic retinopathy classification tasks. Results suggest that generative data augmentation has the potential to significantly improve classification performance over the baseline.
A Deep Multitask Learning Approach for Requirements Discovery and Annotation from Open Forum

The ability in rapidly learning and adapting to evolving user needs is key to modern business successes. Existing methods are based on text mining and machine learning techniques to analyze user comments and feedback, and often constrained by heavy reliance on manually codified rules or insufficient training data. Multitask learning (MTL) is an effective approach with many successful applications, with the potential to address these limitations associated with requirements analysis tasks. In this paper, we propose a deep MTL-based approach, DEMAR, to address these limitations when discovering requirements from massive issue reports and annotating the sentences in support of automated requirements analysis. DEMAR consists of three main phases: (1) data augmentation phase, for data preparation and allowing data sharing beyond single task learning; (2) model construction phase, for constructing the MTL-based model for requirements discovery and requirements annotation tasks; and (3) model training phase, enabling eavesdropping by shared loss function between the two related tasks. Evaluation results from eight open-source projects show that, the proposed multitask learning approach outperforms two state-of-the-art approaches (CNC and FRA) and six common machine learning algorithms, with the precision of 91 % and the recall of 83% for requirements discovery task, and the overall accuracy of 83% for requirements annotation task. The proposed approach provides a novel and effective way to jointly learn two related requirements analysis tasks. We believe that it also sheds light on further directions of exploring multitask learning in solving other software engineering problems.
Digestive Ripening of Thiolated Gold Nanoparticles: The Effect of Alkyl Chain Length

Digestive ripening, heating a colloidal suspension at or near the solvent boiling point in the presence of a surface-active ligand, was applied to polydisperse colloidal gold in toluene using a series of alkylthiols, viz., octyl-, decyl-, dodecyl-, and hexadecylthiols. In all the instances, digestive ripening significantly reduced the average particle size and polydispersity. All the colloids remain suspended in solution above 80 °C, but at room temperature the tendency to form 3D superlattices and precipitate increased with declining alkyl chain length. For example, using octanethiol as the ligand makes the colloids aggregate into big 3D superlattices and precipitate; decane- and dodecanethiol also produce precipitated 3D superlattices along with separate particles, while hexadecanethiol-coated particles remain well separated from each other. The optical spectra at room temperature reveal, apart from the gold plasmon band at 530 nm, a large tail above 700 nm for Au−octanethiol and Au−decanethiol cases an.
Promoting Events within Communities in Mauritius: Exploring the Use and Acceptance of NuLendroit Mobile Application

Events are essential constituents of communities whereby providing a number of benefits including connecting people and improving well-being. Event organizers as well as advertising agencies use all kinds of methods to promote events within the society and techniques used range from print media to social media. However, these methods do not always reach the maximum targeted audiences. Also, events are spatio-temporally distributed making searching difficult. Recently, with the widespread proliferation of mobile devices and applications, event management solutions have become common. However, in the context of Mauritius, no such mobile application is available and as such, use and acceptance of mobile-based event promotion application has not been well investigated. As such, this paper investigates this gap by analyzing the use and acceptance of an innovative mobile application called NuLendroit aimed at promoting events within communities in Mauritius. In order to evaluate the use and acceptance of the mobile application, a combination of an extended version of the User Theory of Acceptance and Use of Technology (UTAUT) and Computer System Usability Questionnaire (CSUQ) was used. 63 participants were involved in the study and results revealed insightful information on the most and least positive constructs that are part of the framework.
ER Catcher: A Static Analysis Framework for Accurate and Scalable Event-Race Detection in Android

Android platform provisions a number of sophisticated concurrency mechanisms for the development of apps. The concurrency mechanisms, while powerful, are quite difficult to properly master by mobile developers. In fact, prior studies have shown concurrency issues, such as event-race defects, to be prevalent among real-world Android apps. In this paper, we propose a flow-, context-, and thread-sensitive static analysis framework, called ER Catcher, for detection of event-race defects in Android apps. ER Catcher introduces a new type of summary function aimed at modeling the concurrent behavior of methods in both Android apps and libraries. In addition, it leverages a novel, statically constructed Vector Clock for rapid analysis of happens-before relations. Altogether, these design choices enable ER Catcher to not only detect event-race defects with a substantially higher degree of accuracy, but also in a fraction of time compared to the existing state-of-the-art technique.
An LSTM-Based Speed Predictor Based on Traffic Simulation Data for Improving the Performance of Energy-Optimal Adaptive Cruise Control

When the host car is equipped with predictive adaptive cruise control (ACC), the prediction of the preceding car’s future speed is useful for improving the host car’s performance, while getting the accurate prediction result is challenging as it requires a complex model with multiple information inputs. This paper presents a speed predictor based on a long short-term memory (LSTM) deep recurrent neural network (RNN), which utilizes various driving data (e.g., historical speed trajectories, the traffic light’s statuses and road conditions) created by the traffic simulator VISSIM. The investigation results explain how the different information inputs affect the prediction’s accuracy. Furthermore, the function of energy-optimal adaptive cruise control (EACC) based on model predictive control (MPC), which optimizes the host car’s speed through taking the predicted speed of its preceding car into account, is presented in this work. The influence of predicted information on the performance of MPC-based EACC is also demonstrated in this work.
Room Acoustical Parameter Estimation From Room Impulse Responses Using Deep Neural Networks

We describe a new method to estimate the geometry of a room and reflection coefficients given room impulse responses. The method utilizes convolutional neural networks to estimate the room geometry and multilayer perceptrons to estimate the reflection coefficients. The mean square error is used as the loss function. In contrast to existing methods, we do not require the knowledge of the relative positions of sources and receivers in the room. The method can be used with only a single RIR between one source and one receiver. For simulated environments, the proposed estimation method can achieve an average of 0.04 m accuracy for each dimension in room geometry estimation and 0.09 accuracy in reflection coefficients. For real-world environments, the room geometry estimation method achieves an accuracy of an average of 0.065 m for each dimension.
Performance Evaluation of Vertically Stacked Nanosheet InGaAs/InAlAs/InP Double Quantum Well FinFET on Si Substrate

We study the performance of vertically stacked InGaAs/InAlAs/InP Double Quantum Well (DQW) Fin Field Effect Transistor (FinFET) on Si substrate using threedimensional Technology Computer-Aided Design (TCAD) simulation. In0.53Ga0. 47As and In0.52Al0. 48As are used as the quantum well channel and the barrier material, respectively. Both materials are lattice matched to the InP buffer layer material on Si substrate. The device is simulated with gate lengths (LG) of 15 nm and gate stack (Al2O3/HfO2) of 1nm/2nm (EOT~0.75nm). The simulation results reveal that by using double channel quantum well (super lattice structure) can enhance the saturation current up to 24% compared to Single Quantum Well (SQW) device. The maximum capacitance of the DQW also outperforms the SQW FinFET approximately 34%. The higher capacitance indicates the higher carrier density. The electron density of DQW prefers to localize in the InGaAs layer as the quantum well channel, thus allow greater control over the electron behavior. The device characteristics indicate that the DQW FinFET on Si substrate can be alternated to enhance the device performance for prospective high performance logic device applications.