Miracle-dz/UniFAR-FTUs huggingface dataset:
User Story Driven Adaptive Planning Framework in Personal Daily Context

This paper proposes a just-in-time approach to introduce adaptive planning in personal daily context. Adaptive planning is known to be effective in agile development, which is expected to be useful for our daily life as well. The chatbot-based approach has been proposed for modeling personal daily context as user story graph. This paper extends this approach by adopting the design of sprint planning. To evaluate the effectiveness of the proposed method, a short-term real-world simulation using a storytelling interface is conducted. The result shows that the proposed adaptive planning framework could lead to better decision-making during individual’s daily life.
Bit Error Rate Evaluation of Digital Terrestrial TV Broadcast Based on Field Measurement in Urban Area

Considering the advantages of digital TV broadcasting, Analog Switch Off (ASO) has been carried out in total in many countries. However, until the beginning of 2019, ASO had not been fully implemented in Indonesia. This is certainly influenced by various parties ranging from the government as a regulator, TV stations as service providers, and the public as consumers. Research related to performance evaluation of digital TV broadcast especially DVB-T2 standards has been conducted in urban areas in several countries. But there is no research related to exploring the performance of terrestrial digital TV in urban, especially discuss the bit error rate of digital TV broadcasts using the DVB-T2 standard. To oversee the process of migrating analog TV broadcasts to digital TV broadcasts in Indonesia, we conducted a study of digital terrestrial TV broadcasts, in particular, the evaluation of BER and CNR in urban areas through the results of field measurements in Surabaya. We have measured a bit error rate of two TV stations that have carried out digital broadcasts using DVB-T2 standard in Surabaya, namely TV X and TV Y. Each TV station has a transmitter in Sambikerep sub-district for TV X and Sawahan sub-district for TV Y. Measurements were taken at 22 points in Surabaya as one of the representatives of urban areas for the fixed condition. The results of field measurements show that TV X and TV Y have not fully provided adequate broadcasts in the city of Surabaya. There are two blank spots in Kusuma Bangsa Street and Dr. Ir Soekarno Street. Unfortunately, the results of field measurements at Oso Wilangun recorded a bit error rate up to touch 9.9 x 10-2. It means that of the 100 bits sent to have an error at the receiver of almost 10%. Oso Wilangon is the farthest measurement point of TV Y where the distance is around 10.28 km and this area is bordered by Gresik. But officially this area is still included in the city of Surabaya which should be within the coverage area of the TV Y. With this case, TV stations should improve their broadcasts, such as re-adjust the signal transmit power to cover the blank spot area.
UAV Relaying Enabled NOMA Network With Hybrid Duplexing and Multiple Antennas

This article studies the wireless systems by implementing the full-duplex (FD) unmanned aerial vehicle (UAV) relay to allow two nearby base stations joint communicate to distant users. The non-orthogonal multiple access (NOMA) assisted networks and design of multiple-antenna users are considered in order to improve the users’ performance. To overcome obstacles in transmission environment, such a model of the uplink (UL) and the downlink (DL) relying on UAV relay is suitable to forward signals to far users. Moreover, practical scenario of imperfect successive interference cancellation (SIC) at each receiver is considered as main reason of degraded performance. To evaluate specific performance metric, we derive the closed-form expressions of outage probability. In addition, the throughput in delay-limited transmission mode of UAV relay assisted UL/DL NOMA system is also considered thoroughly. The derivations and results showed that the higher number of antennas at users could effectively improve the system throughput and reduce the outage probability. The numerical simulation results further indicate the effectiveness of the proposed system and the correctness of theoretical analysis.
Benthic organisms as ecological indicators for the status assessment of coastal ecosystems

The presence of potentially toxic elements (PTE) was determined in different tissues of five selected marine organisms. The As, Cd, Cu, Mn, Mo, Ni, Pb and Zn concentrations were measured in the seagrass Cymodocea nodosa, the green alga Cladophora echinus, the red alga Gelidiella lubrica, the marine topshell Phorcus turbinatus and the littoral crab Carcinus aestuarii, as well as in seawater from Makirina Bay. The levels of As, Cd, Cu, Mn and Zn in the biota were found to exceed those in previously analysed sediments, indicating the bioaccumulation of these PTE. The biota-sediment accumulation factor (BSAF) and concentration factor (CF) varied among different organisms. As regards the five selected species, C. nodosa, C. echinus and G. lubrica proved to be the strongest accumulators of Mn, while P. turbinatus and C. aestuarii showed a high capacity to accumulate As, Cd, Cu and Zn. These species can be considered as good ecological indicators in the assessment of PTE pollution in marine littoral environments.
Graphene Induced Using 405 nm Laser as Electrode Material for the Electrochemical Sensing Application

In this work, 405 nm wavelength laser is used to transform orange plastic Kpton to carbonaceous material graphene induced by laser. This thermal process enable the design of three like screen-printed electrodes for the electrochemical application. The effect of the pulse width variation of the laser during the patterning on the electrochemical behavior of the induced material was studied. Then the selected electrode 20 ms as pulse width was used to test the aptitude to detect 4-Aminophenol.
Multistep Deep System for Multimodal Emotion Detection With Invalid Data in the Internet of Things

The Internet of Things (IoT) technologies such as interconnection and edge computing help emotion recognition to be applied in healthcare, smart education, etc. However, the acquisition and transmission processes may have some situations, such as lost signals and serious interference noise caused by motion, which affect the quality of the received data and limit the performance of IoT emotion detection. We collectively refer to these as invalid data. A multi-step deep (MSD) system is proposed to reliably detect multimodal emotion by the collected records containing invalid data. Semantic compatibility and continuity are utilized to filter out the invalid data. The feature from invalid modal data is replaced through the imputation method to compensate for the impact of invalid data on emotion detection. In this way, the proposed system can automatically process invalid data and improve the recognition performance. Furthermore, considering the spatiotemporal information, the features of video and physiological signals are extracted by specific deep neural networks in the MSD system. The simulation experiments are conducted on a public multimodal database, and the performance of the MSD system measured by the unweighted average recall is better than that of the traditional system. The promising results observed in the experiments verify the potential influence of the proposed system in practical IoT applications.
Evolving Initial Heuristic Functions for Agent-Centered Heuristic Search

Heuristic functions guide search algorithms and have a profound impact on their performance. In the context of agent-centered real-time heuristic search (RTHS), a heuristic represents the agent’s initial domain knowledge which the agent then updates as it explores the search graph. An ideal initial heuristic should capture some specific domain knowledge to guide the agent effectively yet be general enough for a broad class of problems. It should also be computationally efficient, compact in its representation and human-interpretable. Traditionally initial heuristics in RTHS have been designed by humans (e.g., Manhattan distance). In this paper we explore the alternative of building initial heuristics by machines. To keep them portable and human-interpretable we represent each heuristic as a closed-form algebraic formula. Yet to make the heuristics capture problem specifics and thus be more effective in guiding the search, we automatically build a heuristic tailored to a class of problems. To achieve both objectives, we propose and evaluate automatically searching the space of heuristic functions. As a preliminary demonstration, we find closed-form heuristics that outperform Manhattan distance in grid-based pathfinding. We then develop an insight on how such formula-based heuristics are able to exploit characteristics of certain pathfinding maps.
Finite Time Adaptive Neural Dynamic Surface Control of Nonstrict Feedback Nonlinear Systems Including Dead-Zone and Full State Restrictions

In this paper, finite time adaptive neural network (NN) tracking control is investigated for a class of uncertain nonstrict feedback systems with unknown dead-zone and unmodeled dynamics as well as full state restrictions. Using the linearized representation of dead-zone, finite-time adaptive control is presented by the aid of dynamic surface control (DSC) technique and the property of Gaussian function. By introducing logarithmic function as invertible nonlinear mapping, the full state restriction problem is solved. The dynamic signal generated by the low-pass filter is used to handle unmodeled dynamics. Through finite-time stability theory and introducing the bounded closed set in the proof, all the signals in the closed-loop system are proved to be semi-globally practical finite time stable (SGPFS). The restriction conditions are not triggered for all the states. Finally, two numerical examples are used to illustrate the feasiability of the finite time adaptive control strategy.
Reference-Free Material Characterisation of Objects Based on Terahertz Ellipsometry

Material characterization in the 0.1 – 10 THz range has been a major topic of research since its first demonstration 30 years ago. Advances in terahertz generation, detection, and data acquisition have contributed to improved bandwidth, signal power and signal-to-noise ratio. However, material characterization is still performed using conventional spectroscopic measurement schemes which require detailed information about the test object’s shape, location, orientation relative to the measurement system, and a reference measurement. Here, we present a method for reference-free material characterization of dielectric objects using a terahertz time-domain spectroscopy system without a priori knowledge about the object and its position. The proposed method is based on ellipsometry combined with lensless imaging for the estimation of the refractive index. The method is a multistage procedure designed for small test objects in reflection mode. The diffracted terahertz radiation is separated from the specular reflected radiation in a post-processing step to enable a valid material parameter estimation. In this way, small dielectric objects can be located, imaged with a sub-mm resolution, and their material parameters extracted.
A New Real Time Clinical Decision Support System Using Machine Learning for Critical Care Units

Mean arterial pressure (MAP) is an important clinical parameter to evaluate the health of critically ill patients in intensive care units. Thus, the real time clinical decision support systems detecting anomalies and deviations in MAP enable early interventions and prevent serious complications. The state-of-the-art decision support systems are based on a three-phase method that applies offline training, transfer learning, and retraining at the bedside. Their applicability in critical care units is challenging with delay and inaccuracy. In this article, we propose a real time clinical decision support system forecasting the MAP status at the bedside using a new machine learning structure. The proposed system works in real time at the bedside without requiring the offline phase for training using large datasets. It thereby enables timely interventions and improved healthcare services. The proposed machine learning structure includes two stages. Stage I applies online learning using hierarchical temporal memory (HTM) to enable real time stream processing and provides unsupervised predictions. To the best of our knowledge, this is the first time it is applied to medical signals. Stage II is a long short-term memory (LSTM) classifier that forecasts the status of the patient’s MAP ahead of time based on Stage I stream predictions. We perform a thorough performance evaluation of the proposed system and compare it with the state-of-the-art systems employing logistic regression (LR). The comparison shows the proposed system outperforms LR in terms of the classification accuracy, recall, precision, and area under the receiver operation curve (AUROC).
An Improved Phase-Shifted DPWM Method for Reducing Switching Loss and Thermal Balancing in Cascaded H-Bridge Multilevel Inverter

This paper proposes an improved discontinuous pulse-width modulation (DPWM) method based on phase-shifted PWM (PS-PWM) for three-phase cascaded H-bridge (CHB) multilevel inverters. Due to the CHB multilevel inverters employ a large number of power semiconductor switches, they suffer from device failures because the switches are one of the major failure factors in a power electronic system. Therefore, the reliability and life-time of CHB multilevel inverters are mainly determined by the reliability of switches. The most failure of switches is occurred by its accumulated thermal stress which is generated by power losses including the conduction and switching loss. By applying the conventional DPWM, the life-time of switches can be extended because it reduces the switching loss. The conventional DPWM, however, still operates with high speed carrier frequency and it is not enough to reduce the switching loss of the CHB multilevel inverter with a large number of inverter cells. The proposed novel DPWM method provides the improved switching loss reduction through the different switching behaviors for each leg. Additionally, the rotation scheme promises even power distribution and life-time management among inverter cells in a CHB multilevel inverter. The simulation results and experimental results confirm the switching loss reduction and even power distribution abilities of the proposed modulation method.
Development of e-Collab Classroom for CNN Practice on Electrical Engineering, Universitas Negeri Malang

Intelligent Systems is one of the courses taken by Electrical Engineering Education students to improve their competence in the engineering field. One of the competencies is that students can apply learning problem-solving methods in their daily needs. On the topic of learning, there are materials such as Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) that require computer specifications that have a Graphics Processing Unit (GPU) so that the computing process can run faster. During the implementation of learning, some students experienced learning constraints due to inadequate infrastructure such as inadequate computer specifications. One popular effort to overcome the high cost of these devices, learning activities can continue to run using cloud computing. Google Collaboratory is a free GPU service provider that can be used as a CNN computing tool. The e-Collab classroom is a collaboration carried out by utilizing online features into an effective and systematic learning system. In this research, practicum media and online class systems are designed so that lectures continue optimally.
Prediction of Drug-Target Interactions by Ensemble Learning Method From Protein Sequence and Drug Fingerprint

Predicting the target-drug interactions (DITs) is of great important for screening new drug candidate and understanding biological processes. However, identifying the drug-target interactions through traditional experiments is still costly, laborious and complicated. Thus, there is a great need for developing reliable computational methods to effectively predict DTIs. In this study, we report a novel computational method combining local optimal oriented pattern (LOOP), Position Specific Scoring Matrix (PSSM) and Rotation Forest (RF) for predicting DTI. Specifically, the target protein sequence is firstly transformed as the PSSM, in which the evolutionary information of protein is retained. Then, the LOOP is used to extract the feature vectors from PSSM, and the sub-structure information of drug molecule is represented as fingerprint features. Finally, RF classifier is adopted to infer the potential drug-target interactions. When the experiment is carried out on four benchmark datasets including enzyme, ion channel, $G$ protein-coupled receptors (GPCRs), and nuclear receptor, we achieved the high average prediction accuracies of 89.09%, 87.53%, 82.05%, and 73.33% respectively. For further evaluating the proposed method, we compare the prediction performance of the proposed method with the state-of-the-art support vector machine (SVM) and K-Nearest Neighbor (KNN). The comprehensive experimental results illustrate that the proposed method is reliable and efficiency for predicting DTIs. It is anticipated that the proposed method can become a useful tool for predicting a large-scale potential DTIs.
Exploiting Category Similarity-Based Distributed Labeling for Fine-Grained Visual Classification

The fine-grained visual classification (FGVC) which aims to distinguish subtle differences among subcategories is an important computer vision task. However, one issue that limits model performance is the problem of diversity within subcategories. To this end, we propose a simple yet effective approach named category similarity-based distributed labeling (CSDL) to tackle this problem. Specifically, we first obtain the feature centers for various subcategories and utilize them to initialize the label distributions. Then we replace the ground-truth labels in a Deep Neural Network (DNN) with the distributed labels to calculate the loss and perform the optimization. Finally, the joint supervision of a softmax loss and a center loss is adopted to update the parameters of the DNN, the deep feature centers, and the distributed labels for learning discriminative deep features. Comprehensive experiments on three publicly available FGVC datasets demonstrate the superiority of our proposed approach.
Why To Model Remote Sensing Measurements In 3d? Recent Advances In Dart: Atmosphere, Topography, Large Landscape, Chlorophyll Fluorescence And Satellite Image Inversion

Remote sensing (RS) dedicated to the study of land surfaces benefits from more and more advanced sensors. However, the interpretation of RS data is often is often inaccurate due to the complexity of the observed land surfaces. Therefore, RS models, in particular physical models, that simulate RS observations of the three-dimensional (3D) landscapes are critical to correctly interpret RS data. DART is one of the most comprehensive 3D models of Earthatmosphere optical radiative transfer (RT), from ultraviolet (UV) to thermal infrared (TIR). It simulates the optical signal of proximal, aerial and satellite imaging spectrometers and laser scanners, the 3D RB and solar-induced chlorophyll fluorescence (SIF) signal, for any urban or natural landscape and any experimental or instrument configuration. It is freely available for research and teaching activities (dart.omp.eu). After illustrating three significant sources of inaccuracy in RS interpretation, five recent DART advances are presented: RT in the atmosphere and topography, fast RS image simulation of large landscapes, SIF modelling, and satellite image inversion.
Unsupervised Fuzzy Inference System for Speech Emotion Recognition using audio and text cues (Workshop Paper)

Speech Emotion Recognition (SER) is the technique for determining underlying emotions from speech samples. Usually text transcripts supplement vocal cues and contains additional information that boosts the SER process. An unsupervised Fuzzy Inference System (FIS) is developed for SER, in this paper, that incorporates audio and text features. The extracted features are: pitch, energy and textual sentiment score. The proposed system is based on Mamdani Fuzzy Inference model and is capable of determining four emotions: happy, sad, angry and neutral. Our FIS has three variants based on the sentiment lexicon- AFINN, SentiWordNet and VADER selected for computing textual sentiment score as the text feature. The main highlights of this work are: i) formulation of eleven novel fuzzy rules based on audio and text cues for SER ii) comparative analysis of all variants of our proposed unsupervised FIS with six state-of-the-art supervised machine learning approaches for SER iii) both speaker independent SER and speaker dependent SER are executed iv) investigations reveal that for few speakers in speaker-dependent SER have received higher accuracies than the others and v) the proposed unsupervised FIS can handle multiple datasets without any training while the supervised machine learning algorithms fail for cross-dataset evaluation. The experiments conducted on speech datasets: SAVEE and RAVDESS, indicate that our FIS has achieved higher accuracy and f1-scores as compared to the other state-of-the-art-methods.
Dietary Short-Term Fiber Interventions in Arthritis Patients Increase Systemic SCFA Levels and Regulate Inflammation

Chronic inflammatory diseases are often initiated and guided by the release of proinflammatory mediators. Rheumatoid arthritis (RA) is caused by an imbalance between the pro- and anti-inflammatory mediators in the joints, thereby favoring chronic inflammation and joint damage. Here, we investigate if short-term high-fiber dietary intervention shifts this towards anti-inflammatory mediators. Healthy controls (n = 10) and RA patients (n = 29) under routine care received daily high-fiber bars for 15 or 30 days, respectively. Stool and sera were analyzed for pro- and anti-inflammatory mediators. A high-fiber dietary intervention resulted in increased anti-inflammatory short-chain fatty acids (SCFA), decreased proarthritic cytokine concentrations, along with a durable shift in the Firmicutes-to-Bacteroidetes ratio. Together, these results further strengthen high-fiber dietary interventions as a practical approach complementing existing pharmacological therapies.