Miracle-dz/UniFAR-FTUs huggingface dataset:
Two-Timescale Resource Allocation for Wireless Powered D2D Communications With Self-Interested Nodes

Wireless powered device-to-device (D2D) communications can empower D2D communications via a power station (PS) in a self-sustainable manner. In this paper, aided by an incentive mechanism, we investigate the dynamic resource allocation for content transmission in wireless powered D2D communications with self-interested nodes including D2D transmitters (D2D-Txs) and PS. Stochastic optimization is developed to maximize the average network utility under the constraints of the limited data buffer, energy capacity, and incentives. Then, to coordinate the different timescales for the network state variation, the problem of stochastic optimization is converted into two subproblems via two-timescale Lyapunov optimization technique. Specifically, at the large timescale where the D2D-Tx’s resource states (data queue, energy resource, and incentive) change slowly, we obtain the solution of a joint rate adaption and energy trading problem. While, at the small timescale where the channel state experiences rapid variation, we develop a closed-form expression of power factor by solving a transmit power control problem, which is non-convex owing to the interference among D2D-Txs. Additionally, an online two-timescale resource allocation (OTTRA) algorithm is proposed, and the performance bounds of the algorithm are characterized theoretically in terms of the utility-delay tradeoff. The numerical results exhibit that the OTTRA algorithm not only encourages the cooperative content transmission and ET among self-interested nodes, but also ensures a satisfied network performance in the long term.
A Comparison Between Decentralized Local and Global Methods for Connectivity Maintenance of Multi-Robot Networks

Range limitation of communication links among robots in a multi-robot network, mixed with the mobility of the robots, creates a potential threat of disconnection of the communication network due to the robots movements. Recently, local methods have been proposed for maintaining the initial graph of connections, as well as global methods for maintaining a path between each pair of robots. This letter compares these methods in different aspects: the freedom of movement and the workspace provided by global methods is challenged by the amount of data needed to be exchanged with the local methods. Also, in the delayed networks, it is shown that local methods are leading to less restriction on the speed of the robots involved in the network. Simulation and numerical results are provided to further illustrate this comparison.
Model Decomposition for Forward Model Approximation

In this paper we propose a model decomposition architecture, which advances on our previous attempts of learning an approximated forward model for unknown games [1]. The developed model architecture is based on design constraints of the General Video Game Artificial Intelligence Competition and the Video Game Definition Language. Our agent first builds up a database of interactions with the game environment for each distinct component of a game. We further train a decision tree model for each of those independent components. For predicting a future state we query each model individually and aggregate the result. The developed model ensemble does not just predict known states with a high accuracy, but also adapts very well to previously unseen levels or situations. Future work will show how well the increased accuracy helps in playing an unknown game using simulation-based search algorithms such as Monte Carlo Tree Search.
Reinforcement Learning Scheduler for Vehicle-to-Vehicle Communications Outside Coverage

Radio resources in vehicle-to-vehicle (V2V) communication can be scheduled either by a centralized scheduler residing in the network (e.g., a base station in case of cellular systems) or a distributed scheduler, where the resources are autonomously selected by the vehicles. The former approach yields a considerably higher resource utilization in case the network coverage is uninterrupted. However, in case of intermittent or-of-coverage, due to not having input from centralized scheduler, vehicles need to revert to distributed scheduling.Motivated by recent advances in reinforcement learning (RL), we investigate whether a centralized learning scheduler can be taught to efficiently pre-assign the resources to vehicles for-of-coverage V2V communication. Specifically, we use the actor-critic RL algorithm to train the centralized scheduler to provide non-interfering resources to vehicles before they enter the-of-coverage area.Our initial results show that a RL-based scheduler can achieve performance as good as or better than the state-of-art distributed scheduler, often outperforming it. Furthermore, the learning process completes within a reasonable time (ranging from a few hundred to a few thousand epochs), thus making the RL-based scheduler a promising solution for V2V communications with intermittent network coverage.
Visualization of Gathering Sources of News by Journalist

For the rapid process of gathering of information, journalists tend to write news without seeing the real fact entirely, generate the news which does not fulfil the established standards. In a professional study, the gathering sources of news ideally use two methods; field study and literature study methods. Therefore, it is necessary to emphasize the principle of good news. This paper presents a new learning method in Journalism class using 2D animation. The use of graphics animation for educational purposes could make fun and better learning quality because the animation provides visualization as desired. This method can support people to learn the principles of journalism, where many theories cannot be afforded in the classroom. A 2D animated video with investigation concept that delivered by a journalist character was considerably chosen.
A novel self-adaptive differential evolution for feature selection using threshold mechanism

Nowadays, most of databases for classification or regression consists of numerous features that describe the domain of interest. Therefore, they may have a huge influence on the results of classification/regression. A lot of research has shown that some features can be eliminated before the classification/ regression in order to obtain better results. In this paper, we propose a novel solution that is based on self-adaptive differential evolution for feature selection on a econometric database. A new solution is systematically presented in this paper. Results of the proposed feature selection method, according to the ROC-AUC score, overcome results, obtained without using it.
Guide Gesture Application of Hand Exercises for Post-Stroke Rehabilitation Using Myo Armband

Traditional Rehabilitation cost more money and time. One part of the body that often experience paralysis due to impaired neural function. As for efforts to increase motor function in patients with post-stroke by doing the training given in therapy. Now has begun to develop application of stroke rehabilitation using modern technology such as Kinect and AR. Will be in the existing rehabilitation applications there is none focus on the hands and fingers for patients post-stroke. And also traditional therapy now requires more time and money to go to hospital and rehabilitation clinic.
Robust Manifold Learning via Conformity Pursuit

This letter presents an effective and simple method, termed Global Conformity Pursuit (GCP), for robust manifold learning. Data points lying on a union of low-dimensional nonlinear manifolds are expected to be highly conforming. On the other hand, outliers do not typically adhere to low-dimensional structures or otherwise do not exist in large numbers. Hence, they can be identified by their low overall resemblance to the rest of the data. Our kernel-based setting allows us to capture the underlying nonlinear structure of the manifold data, while avoiding the construction of local neighborhood regions which typically causes extreme sensitivity to noise and outliers. Aside from its significantly simple structure—involving only a matrix evaluation and multiplication—GCP is the first manifold learning approach that is simultaneously noniterative and capable of tolerating a large number of outliers, dependent outliers, and noise components. Theoretical analysis guarantees a large gap between conformity values of inliers and outliers. Experimental results showcase potential uses of the proposed framework on benchmark datasets.
Studying the Effect of Robustness Measures in Offline Parameter Tuning for Estimating the Performance of MOEA/D

Offline parameter tuning (OPT) of multi-objective evolutionary algorithms (MOEAs) has the goal of finding an appropriate set of parameters for solving a large number of problems. According to the no free lunch theorem (NFL), no algorithm can have the best performance in all classes of optimization problems. However, it is possible to find an appropriate set of parameters of an algorithm for solving a particular class of problems. For that sake, we need to study how to estimate the aggregation quality function for an algorithmic configuration assessed on a set of optimization problems. In this paper, we study robustness measures for dealing with the parameter settings of stochastic algorithms. We focus on decomposition-based MOEAs and we propose to tune scalarizing functions for solving some classes of problems based on the Pareto front shapes using up to 7 objective functions. Based on our experimental results, we were able to derive interesting guidelines to evaluate the quality of algorithmic configurations using a combination of descriptive statistics.
Speech to Text of Patient Complaints for Bahasa Indonesia

Speech to text is a challenging task in the natural language processing research area. In this paper, we proposed a preliminary study in building speech to text application of patient complaints for Bahasa Indonesia during anamnesis. This study aims to apply speech to text using TensorFlow for Bahasa Indonesia in the medical domain. The voice data used in this research were not only the words in Bahasa Indonesia but also some complaints in Javanese language. We have collected 500 voices recorded data from 10 persons. We have tested 50 data and obtained the accuracy of 64°/0. In addition, we have also performed the similarity calculation to compare between the spoken word and the generated text. From this similarity calculation, we classify our generation text data into three categories, good, fair, and bad. For future work, it is important to add more voice data, both in Bahasa Indonesia and Javanese Language.
Text Classification for Sentiment Prediction of Social Media Dataset using Multichannel Convolution Neural Network

Text classification of social media documents requires a specified model for a particular dataset. We investigated the public opinion and sentiment from Indonesian twitter toward the employment problem. To predict the sentiment about this topic, we collected a total of 75,126 tweets which correlate with employment conversation for the training and testing process. We analyzed the collected tweets by applying convolution neural networks (CNN) model which expanded from the standard one-dimensional deep learning model to a multichannel model. Applying multiple parallel CNN processes, the documents were processed using different CNN topologies by varying word embedding dimension, kernel size, number of hidden layers or nodes, and also different n-grams. Experiment results with three-dimension channel have 0.0172 loss error and 0.9917 accuracy. CNN model with multichannel configuration provides maximum accuracy and small loss error in training and testing process of model development.
Improving shallow neural network by compressing deep neural network

This paper focuses on a simple technique to extract the dark knowledge of a Deep Multi-Column Deep Learning Network, and its compression into a shallow neural network, causing not only the improvement of the train and test performance of the latter but a cheap way to approximate the former results but with fewer parameters. First, we built a Multi-Column Deep Learning Network, i.e., a Committee Machine, using simple techniques to improve its training accuracy. Finally, we transfer its knowledge to a shallow neural network, compressing its learned information and demonstrating that dark knowledge techniques still have a huge impact on Deep Multi-Layer Perceptron’s studies. This paper validates the performance of the proposed model in the MNIST database, comparing it with popular neural nets used before, where we were able to achieve better scores.
Precise Performance Analysis of Dual-Hop Mixed RF/Unified-FSO DF Relaying With Heterodyne Detection and Two IM-DD Channel Models

This paper provides precise performance analysis of the dual-hop mixed radio frequency (RF)/unified free space optical (FSO) decode-and-forward (DF) relaying system, in which the heterodyne detection and the intensity modulation-direct detection (IM-DD) are taken into account for FSO detection. To this end, we derive closed-form expressions for the outage probability, average bit error rate (BER), and the ergodic channel capacity of this system. In this analysis, we utilize, for the first time as per our knowledge, a precise channel capacity result for the IM-DD channel. Moreover, this is the first time that not only the (IM-DD input-independent) but also the (IM-DD cost-dependent) additive white Gaussian noise (AWGN) channel is considered in such system analyses. Additionally in this study, we assume that the first hop (RF link) follows Nakagami-$m$ fading, and the second hop (FSO link) follows Málaga ($\mathcal {M}$) turbulence with pointing errors. These fading and turbulence models contain other models (such as Rayleigh and Gamma–Gamma) as special cases, thus, our analyses can be seen as a generalized one from the RF and FSO fading models point of view. Also, in BER derivation, we assume that the modulation schemes in the two hops can be different, since not all modulation schemes are suitable for IM-DD FSO links. In addition, the system performance is investigated asymptotically at high signal to noise ratios. This investigation leads to new nonreported coding gain and diversity order analyses of such system. Interestingly, we find that in the FSO hop, at high transmitted powers, all the considered FSO detectors result in the same diversity order. Furthermore, we provide simulation results that verify the accuracy of the obtained analytical and asymptotic expressions.
Domain Specific Intent Classification of Sinhala Speech Data

Building an open domain automatic speech recognition(ASR) system can be accomplished by converting voice into text and performing a text classification on top of the converted text. However, with the inherent challenges in the approach mentioned above, it is not the most feasible way of the deriving intent of speech queries in a specific domain. This paper proposes a domain-specific intent classification for Sinhala language utilizing a feed-forward neural network with backpropagation. For the purposes of this research, a Neural network is trained from Mel Frequency Cepstral Coefficients (MFCC) which are extracted from a Sinhala speech corpus of 10 hours and the performance of the system is evaluated using the recognition accuracy of the speech queries. Further, the proposed solution in the paper introduces the first-of-its-kind for domain-specific intent classification for Sinhala language.
Course: Energy Efficiency in Embedded Systems – A System-Level Perspective for Computer Scientists

The improvement of the energy efficiency of embedded devices has always been an important research topic. Nowadays with more and more autonomous devices, e.g. IoT, this topic is yet popular as questions arise "how to power all these devices? " or "how to enable lifetimes of years or decades? ". As theses challenges are not solely an issue of computer architects, this course specifically addresses computer scientists. While computer scientists would not be able to influence the basic architecture of an e.g. MCU, the knowledge on the basics, the architectures, mechanisms and software paradigms should sensitize students how to implement energy efficiency on system level.
Developing a voice controlled wheelchair with enhanced safety through multimodal approach

The rising population of disabled and elderly community and lack of caretakers to look after them have become a crisis in most countries. Voice controlled wheelchairs give its user a chance to interact with the wheelchair in a humane manne. Due to the uncertaities of the voice commands and unreliability of using one interaction modality, user safety is compromised. In natural wheelchair user and caretaker conversation a lot of distance related uncertain instructions like ‘little’ and ‘far’ will be used. With incorporating these uncertain terms, the interpretation of voice commands will be enhanced. In most existing systems, Joystick support is used as backup modality. Even though this is intended as a safety feature, accidental joystick operation can lead to unfortunate situations. Therfore a reliable safety system considering multimodal aspect of two modalities is needed. Therefore this paper proposes a voice controlled intelligent wheelchair system incorporating uncertain voice information with safe navigation system using multmodal approach. Uncertain Information Module(UIM) have been introduced in order to interpret uncertain voice information and Multimodal Safety System is introduced in order to ensure user safety. Usability experiments have been carried out to evaluate the reliability of voice control system and Multimodal safety system.
An Enabling Multi-Operation Branch-Line Coupler

In this paper, an enabling multi-operation branch-line coupler is proposed initially. The proposed design is configured by ±90° and 180° transmission lines, and the main advantage is that the proposed design can be applied in three different system connection schemes as shown in <xref rid="fig1" ref-type="fig">Fig. 1</xref>. Corresponding to the three schemes, the coupler can be with three operations (e.g. the operations of balanced input with single-ended outputs, single-ended input with single-ended outputs, and single-ended input with balanced and single-ended outputs). This can increase the flexibility and freedom of the coupler for systems with different connection types. Furthermore, the common-mode suppression can be achieved under the operation. The standard and mixed-mode S-parameters are utilized to deduce the circuit parameters. Two prototypes using 3/<inline-formula> <tex-math notation="LaTeX">$4~\lambda _{\mathrm {g}}$ </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">$\lambda _{\mathrm {g}}$ </tex-math></inline-formula> is the guided wavelength at the center frequency) transmission line (design I) and coupled line with two shorts (design II) to realize the −90° transmission line were designed, fabricated, and measured to verify the theoretical prediction. The sizes of the two prototypes are <inline-formula> <tex-math notation="LaTeX">$0.98\,\,\lambda _{\mathrm {g}} \times 0.2 \lambda _{\mathrm {g}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$0.65 \lambda _{\mathrm {g}} \times 0.2 \lambda _{\mathrm {g}}$ </tex-math></inline-formula>, respectively. Design I (design II) exhibits the 15-dB operating bandwidths for the three operations of 8% (9%), 7.3% (9%), and 6.7% (7.8%), respectively.
Detecting Adverse Drug Reaction with Data Mining And Predicting its Severity With Machine Learning

Adverse Drug Reaction (ADR) is one of the many uncertainties that are considered a fatal threat to the pharmacy industry and the field of medical diagnosis. Utmost care is taken to test a new drug thoroughly before it is introduced and made available to the public. However, these pre-clinical trials are not enough on their own to ensure safety. The increasing concern to the ADRs has motivated the development of statistical, data mining and machine learning methods to detect the Adverse Drug Reactions. With the availability of Electronic Health Records (EHRs), it has become possible to detect ADRs with the mentioned technologies. In this work, we have proposed a hybrid model of data mining and machine learning to identify different Adverse Reactions and predict the intensity of the outcome. We have used the Proportionality Reporting Ratio (PRR) along with the precision point estimator test called the Chi-Square test to find out the different relationships between drug and symptoms called the drug-ADR association. This output from the data mining technique is used as an input to the machine learning algorithms such as Random Forest and Support Vector Machine (SVM) to predict the intensity of the outcome of ADR, depending on a patient’s demographic data such as gender, weight, age, etc. In this work, we have achieved an accuracy of 91% to predict 'death' as the outcome from an ADR.
A Novel Approach of Neural Topic Modelling for Document Clustering

Topic modelling is a text mining technique to discover common topics in a collection of documents. The proposed methodology of topic modelling used artificial neural networks to improve the clustering mechanism of similar documents by modelling probabilistic relations between the topics, documents and vocabulary. Currently, while topic modelling and clustering are considered to be manifestations of unsupervised learning, and neural networks on the other hand are used for supervised learning problems, Neural Topic Modelling reformulated topic modelling into a supervised learning task by defining an objective function whose loss function had to be minimized. Custom input embedding layers were designed in order to extract the semantic relationships between the words in the corpus, and the output of the model presented a topic probability distribution for each document. The documents with similar distributions were then bucketed together based on the criteria of meeting the threshold value of a simple distance based similarity metric, such as cosine similarity. The model was implemented using Keras with TensorFlow backend and the effectiveness of the clustering was validated on the IMDB Movie dataset and the News Aggregator dataset from UCI. On comparison with other commonly used clustering mechanisms in combination with traditional topic models, the proposed model delivered an improved Silhouette Co-efficient Score and Davies-Bouldin Index, along with an increased data handling capacity, thereby making the solution scalable.