Miracle-dz/UniFAR-FTUs huggingface dataset:
HEROES: A Video-Based Human Emotion Recognition Database

Recognizing emotions from body movements represents a challenge in affective computing. Most methods in the literature focus on analyzing speech features and facial expressions; yet, even considering body postures and motions can help in identifying emotions. To this end, datasets have been designed to assess upper limb movement and hand gestures. However, even the lower body (legs and feet) can be used to reveal information about the user’s attitude. In this paper a new video database for emotion recognition is presented. 16 non-professional actors express four emotions (happiness, interest, disgust, and boredom). The videos have been acquired by using four GoPro cameras to record whole body movements in two different scenarios: observational and interaction with another person. 14 body joints are extracted from each frame of each video and they are used to derive features to be used for emotion identification and recognition.
Enhanced Personality Prediction: Fusing Structural and BERT-Based Lexical Features on Augmented Twitter Data

Conventionally, assessing people's personality done by psychology experts requires more time and effort. Hence automatic personality prediction is starting to get noticed by researchers. Nowadays, many people engage themselves in social media platforms, one of which is Twitter. This platform stores actions done in this media which reflect one's personality. This research focuses on improving the performance of personality prediction models based on machine learning algorithms, by augmenting the dataset using Easy Data Augmentation to overcome imbalance dataset, thereafter combining n-grams with POS-tag weighted by TF-IDF. Results show that this method outperforms other machine learning approaches and decreases bias, with a maximum of 72.62% of the best average accuracy and 72.72% best average Fl score using Support Vector Machine classifier. The second-best model is obtained by XGBoost and, lastly, Naive Bayes classifier. Overall, this study shows that data augmentation with the addition of POS-tag feature is evident in improving the performance of personality prediction models. However, larger data is needed.
Vertical Take-Off and Landing (VTOL) System Based On Noise Parameter for Exploring of Continuous Reinforcement Learning Approach

Research about air-craft autonomous vehicles has become a trend issue among researchers. Vertical Take-Off and Landing (VTOL) controlling for a hover-craft vehicle is one of benchmarking control problems. VTOL ability of hover-craft is significantly crucial for the vehicle to do surveillance and rescue, military, farming, etc. There are many papers concerned with this VTOL benchmarking control. The purpose of this paper is applying Deep Deterministic Policy Gradient (DDPG) as the brand new continuous deep reinforcement learning approach to solve a complex nonlinear system, such as air-craft VTOL problem. Furthermore, this paper analyses the effect of Ornstein-Uhlenbeck (OU) Noise injection to action as the exploration key issue of DDPG algorithm, in addition of the analysis, this paper also applies the gaussian noise to the plant as the disturbance of system. The test was performed in the VTOL simulation. The evaluation used the episode reward, average reward, total reward from the training process. Further analysis also uses IAE (Integral of Absolute Magnitude of Error), ISE (Integral of the Square Error) and MSE (Mean of the Square Error) as the parameter error evaluation from the system. The comparison results of all conditions this paper show that applying DDPG with the injection of OU noise to VTOL system with the disturbance of Gaussian noise achieves the best total reward of −14,355.37 with the shortest episode training of 243, thus it works best. The VTOL system without the disturbance has higher IAE and VTOL with Gaussian noise as the disturbance has higher ISE and MSE.
Spatio-Temporal Analysis Coastal Areas for Detection Mangrove Greenery Using Combined Mangrove Recognition Index

Mangroves provide various purposes, including avoiding coastal erosion, acting as a water filter, and breeding grounds for marine animals. Mangrove forest areas have experienced a significant decline in crucial functions due to changes, such as the transition of forest areas into ponds, environmental pollution, and illegal logging. This research obtained data using Landsat 8 OLI imagery to detect Tanara Coast, Serang, Banten changes. Because of this problem, research was conducted using the extraction of the Combined Mangrove Recognition Index (CMRI) feature and the Support Vector Regressor (SVR) algorithm to measure what percentage of land changes occur in the mangrove forest area. The research took data from May 01 to August 01, 2019, with clipping as a preprocessing process. This research obtained the highest vegetation value of 0.974966, which means that from May 01 to August 01, mangrove forest vegetation is in good condition.
Local flexibility need estimation based on distribution grid segmentation

This work presents a methodology to segment the MV electric grid into grid zones for which the active power flexibility needs that solve the forecasted voltage and current issues are computed. This methodology enables the Distribution System Operator (DSO) to publish flexibility needs per zones, allowing aggregators to offer flexibility by optimizing their portfolio of resources in each grid zone. A case study is used to support the methodology results and its performance, showing the feasibility of solving grid issues by activating flexibility per grid zones according to the proposed methodology.
Extracting Attractive App Aspects from App Reviews using Clustering Techniques based on Kano Model

Kano model is a technique to evaluate product features based on users’ satisfaction. The problem with the traditional Kano approach is that it is limited to the small amount of data collected manually from users and the sample of participants might not be representative. Many users are interested in evaluating product features via social media platforms. The limitation of the Kano model can be mitigated by using users’ feedback from social media as a source to understand their satisfaction. Nowadays, several mobile applications are developed to solve the same problem and serve the same domain. Hence, it has become difficult to compete with similar products and increase users’ satisfaction to win the market advantage. In this research, app reviews were analyzed using natural language processing and clustering techniques to extract app aspects that increase user satisfaction by labeling them according to the Kano model categories. The clustering was based on aspects dissatisfaction and satisfaction values. We evaluated the results of the clustering technique based on a ground truth that was built from a user survey. Experiments showed that our clustering and labeling approach was able to identify the attractive app aspects better than must-be and one-dimensional aspects.
Modelling of Wind Power Plant for Voltage Regulation Simulations

The increasing penetration of renewable energy sources (RES) in networks causes the interest of distribution system operator (DSO) and transmission system operator (TSO) not only in electrical energy generation, but also in a view of voltage regulation possibilities. This article investigates voltage regulation with wind farm or wind power plant (WPP) using two approaches - modelling of each wind turbine (WT) individually and aggregation of all WTs into one. The first approach uses a detailed WPP electrical network with all WTs, cables and transformers modelled. The second approach uses a single WT connected to the WPP site and aggregates all WTs located in the actual WPP. The voltage regulation with both approaches of WPP connected to the real-life Croatian transmission system is simulated in PowerWorld Simulator. Different scenarios of consumption and voltage regulation are used in simulation. It is observed that voltage regulation with WPP is possible and from TSO point of view is negligable which model is used for voltage regulation due to the difference in voltage on the point of common coupling (PCC) being about 0.7%. From owner of WPP point of view there is question about profitability of WPP due to power losses accuring at voltage regulation.
Using fuzzy sets Qualitative Comparative Configuration to Predict Consumer Travel Behavior from Instagram: The Role of Social Media Influencers

During the last few years, social media influencers (SMIs) have increasingly evolved into independently operating entities with the capability of influencing their audiences. SMI on Instagram is increasingly being used as a marketing communication tool by businesses due to its popularity and effectiveness. Only a few studies have investigated how social media influence travel behavior in tourism. This study examines whether SMI can affect the travel behavior of Instagram followers. SMI is determined by taking into account attractiveness, similarity, and expertise. SMI generates Para social interactions with followers and establishes trust by promoting destinations based on these three dimensions. In this study, the sample consists of Instagram users who watch travel-related content. A purposive sampling technique is used in which there is a set of criteria for selecting a participant. We obtained 364 responses from an online survey. The data were analyzed using fsQCA 3.0. The fsQCA results confirm the presence of two configurations with high travel intentions. According to the causal conditions configuration presented in this study, one exists an interdimensional relationship between SMIs (attractiveness, similarity, and expertise), trust, and parasocial interaction in terms of travel intention. This study also contributes a great deal of theoretical insight and managerial implications regarding how scholars and tourism managers can leverage SMIs to create high travel intentions.
Studying response to external driving in a model of thalamocortical system with specialized neuron equations

In this work, an ensemble of 28 connected neurooscillators for modeling epilepsy was considered. There were five types of neurons in this ensemble, with specific physiologically proved neuron models with physiologically reasonable parameters selected for each type of models. In addition, due to the presence of excitatory and inhibitory connections in the network, it was decided to use physiological synapse models for AMPA and GABA receptors, respectively. As a result, it was shown that such a system is capable of demonstrating long transients simulating epileptiform activity similarly to the previously considered models. This confirms the hypothesis of the network structure, key role in the occurrence of epileptic seizures, with validating this idea based on physiologically proved models and parameters and increasing the scientific significance of the underlying idea.
Finite-size scaling tests for SU(3) lattice gauge theory with color sextet fermions

The observed slow running of the gauge coupling in SU(3) lattice gauge theory with two flavors of color sextet fermions naturally suggests it is a theory with one relevant coupling, the fermion mass, and that at zero mass correlation functions decay algebraically. I perform a finite-size scaling study on simulation data at two values of the bare gauge coupling with this assumption and observe a common exponent for the scaling of the correlation length with the fermion mass, y{sub m}{approx}1.5. An analysis of the scaling of valence Dirac eigenvalues at one of these bare couplings produces a similar number.
Honeypot Generator using Software Defined Networks and Recursively Defined Topologies

The domain of cyber security represents a constant struggle between researchers and hackers, a continuous competition where the global digital infrastructure is at stake. Throughout the years, various protective measures have been developed to prevent against an increasing number of exploits that are becoming more and more complex and refined. Instead of concentrating on general mitigating techniques, our proposal orients towards creating performant honeypots which are safe and isolated environments that attract malicious users for the purpose of studying their invasive techniques. Firstly, this paper introduces an original method of describing, from a mathematically point of view, recursively defined topologies and presents a proposed algorithm used in constructing them. Secondly, we suggest a novel architecture which combines recursively defined topologies (RDT), software-defined networks (SDN) and an orchestrator engine for containerized infrastructure in order to develop a high-interaction honeypot which simulates an entire data center using a single physical host. The proposed implementation may represent a promising framework used as a developing platform for more complex honeypots used in either researching malicious human behavior or in the IT industry as a defensive measure. Regarding achieved results, the proposed implementation accomplishes notable results in deceptive techniques, isolation and effortless configurability.
Socio-Technical Modelling for GDPR Principles: an Extension for the STS-ml

Compliance with data protection regulations is vital for organizations and starts at the requirements level. The General Data Protection Regulation (GDPR) has been the European Union (EU) regulation on the topic since 2018. Organizations that operate within the territorial scope of the GDPR are expected to be compliant; otherwise, they can get high fines, and their reputation can be damaged. Thus, GDPR compliance sets challenges for the design of information systems that must be tackled starting from the requirements level.Given the difficulties of translating regulations and the drawbacks of natural language requirements, modeling languages can help requirements engineers analyze data protection. Socio-Technical Security modeling language (STS-ml) is a security modeling method that has been already extended for modeling privacy issues such as personal data, data controllers and processors, and specifying the legal basis for data processing. However, information critical for complying with GDPR principles still lacks modeling support. This article presents a proposal for extending the STS-ml to address GDPR principles. We show the need for modeling data protection requirements for each GDPR principle through a working privacy case and propose a set of five lightweight but meaningful extensions for the method. The extended language is intended to help requirements engineering practitioners with privacy requirements with little additional effort while preventing significant fines for EU organizations.
An Ultra-Low-Voltage Bit-Interleaved Synthesizable 13T SRAM Circuit

Standard-cell-based memory (SCM) circuits with fully digital signals are attractive for power-/energy-constrained edge devices due to the strong voltage scaling capability, fast design iteration, and flexibility in integration. In this article, a 13-transistor (13T) static-random access memory (SRAM) circuit with ultra-wide range voltage scaling capability is proposed for ultra-low-power applications. By adopting the concept of SCM, the 13T SRAM cell is custom-designed, while providing fully digital inputs and outputs. Without any analog circuitry, the 13T memory array is fully synthesizable and compatible with the commercial semi-custom design flow. A specialized circuitry is employed in the 13T SRAM cell to enable cell-level bit-interleaving. An 8-kb 13T SRAM bank is fabricated in the UMC 55-nm low power CMOS technology, achieving an area density of $5 \mu \text{m}$ 2/bit. The minimum operational voltage for the 13T SRAM circuit is 324 mV, while the data retention voltage is down to 279 mV. The 13T SRAM circuit achieves the minimum energy point at 0.4 V for both the read (32.8 fJ/bit) and write (54.1 fJ/bit) operations, providing a good opportunity to perform voltage scaling together with logic blocks when embedded in the same power domain.
Social Media Analysis in Building Customer Trust - Systematic Literature Review

Social media usage growth is so rapid that many companies are competing using social media as their marketing vehicle to improve customer engagement and improve their services to their customers. This research aims to analyze the use of social media in e-commerce in various types of industries and countries. The method used in this study is SLR - Systematic Literature Review. The results of this study identify the types of social media platforms used according to the type of industry and country, analysis of the main focus of journal studies regarding the use of social media in building the customer trust and methods used related to the case study. Related to social media big data analytic the most method use is Lexicon-based approach with Twitter is the context that is most often used. The general framework of Lexicon-based approach social media analysis is also depicted. This research can ultimately provide information to researchers and interested parties regarding the methods used by companies in analyzing social media to increase customer trust.
A 24-to-44 GHz Compact Linear 5G Power Amplifier with Open-Terminated Balun in 65nm Bulk CMOS

This paper presents a broadband linear CMOS power amplifier (PA) for mm-wave 5G applications. A broadband low-loss open-terminated balun is proposed for a PA output network with a smaller size. The PA fabricated using a 65-nm bulk CMOS process shows flat saturated output power, Psat, ranging from 17.5 to 19.5 dBm, within 2-dB difference, while achieving over 24.5% peak PAE for CW signals ranging from 24 to 44 GHz. It achieves a flat linear output power, Plinear, of 10.4/11.6/10.9/8.8 dBm and a PlinearPAE, PAElinear, of 6.24/8.94/7.9/5.1% with -25-dB rms EVM, EVMrms, and -23.3/-25.7/-25.9/-22.8-dBc ACPR at 24/28/39/44 GHz. Using the 5G NR FR2 64-QAM 8-CC×100-MHz OFDM signal (PAPR = 9.7 dB), broadband operation was demonstrated over 24–44 GHz covering n257-261 5G bands. It has a core size of 0.15 mm2.
Design and Implementation of the Irrigation Robot Movement System on Grid Land using 2D Array Algorithm

Currently, the use of robots in agriculture is one of the studies carried out by many researchers. The ability of a robot that can consistently work will make it easier to monitor and water agricultural crops, in addition to the nutrient requirements of plants. There are several parameters that must be considered consistently every day so that plants can grow optimally, namely soil moisture, the amount of water needed by plants, and spacing, so as to produce a maximum broccoli harvest with good quality. This study, study was conducted on the design and implementation of an irrigation robot movement system to monitor the growth of broccoli plants. This irrigation robot has the ability to do watering, and plant monitoring and can move directly to the work point automatically. This irrigation robot is designed using a 2D array algorithm so that the irrigation robot is able to move to a predetermined coordinate point, to enable the robot to move according to the coordinates, the land planted with broccoli will be divided into grids. Testing the functionality of the robot is done by giving a step to the stepper motor and dividing the land with a box width of 7.5 cm on the land prototype. Based on the implementation results obtained 97% accuracy for movement on the x-axis, 99% for movement on the y-axis, and 100% for the z-axis. The robot has worked well and fulfilled the expected functionality to carry out the task of moving the robot to coordinates for monitoring and watering plants.
Proposing Tourism Chatbot by Employing The Wisdom of Crowds in Building Its Knowledge Base

This study aims at proposing to develop a tourism chatbot by employing the wisdom of crowds in building its knowledge base. The importance of this chatbot is that it provides an information system perfectly matching with tourists' characteristics that are most likely accessing information while travelling or mobile. In other words, tourists need an information system with a good capability and flexibility in dialogical communication instead of querying information many times in any search engines. The methodology used is quantitative by conducting initial questionnaires with closed-ended questions and using non-probabilistic sampling (n=40). Later, we do several technical steps to build a knowledge base. The uniqueness of this proposal is that this chatbot will have a capability to display aggregate data about the comparative judgement of tourism sites made by the crowds. It is highly recommended to implement this proposed chatbot as it will give benefit to the visiting tourists as well as tourism sites' managements. For tourists, they can access information about the comparative judgement about tourism sites by the crowds. It can help tourists choose which sites to visit. And for tourism sites' managements, they can take benefit of the comparative judgment by the crowds as part of their decision-making processes to improve their business.
Transfer Learning Based Retinal Layer and Fluid Segmentation using Fully Convolutional Networks

Automatic segmentation of retinal structures in optical coherence tomography (OCT) b-scan images is still an open problem, especially when there is a variation in the normal structure of the retina due to the presence of diseases. This line of research has shown enormous achievements with the introduction of deep learning algorithms. This paper presents a comparative study of pre-trained fully convolutional networks for end-to-end segmentation of retinal boundaries and fluid pockets. The models are trained using a novel publicly available dataset of 1136 annotated OCT b-scans with segmentation masks for eight retinal regions. The dataset was assembled using eye images from patients with neovascular age-related macular degeneration. Empirical results show an improvement in the state-of-the-art segmentation, and near ophthalmologist level performance, with a mean dice score of 84.6% for the best model.
Dynamical Causal Effects in Bidirectionally Coupled Stochastic Damped Oscillators

Diverse causality quantifiers used in time series analysis are promising tools for solving many research and diagnostic problems. The recently proposed framework of dynamical causal effects (DCEs) provides a unifying formalism that allows one to express various causality quantifiers as specific DCEs and relate them to each other. Within the framework of DCEs, this work studies relationships between several well-known quantifiers (transfer entropy, Granger-Geweke causality spectrum, coupling spectral effects) and oscillation-theoretic phenomena (arousal of spectral peaks, amplitude modulation) in the classical system of two stochastic linear damped oscillators. Thereby, a qualitative oscillation-theoretic interpretation of the numerical values of the causality quantifiers is established that promises to make their future applications more efficient.
Improving Employee Performance Through Digitalization: Designing a Web Based Human Resource Management

The purpose of this research is to develop a web based human resource management application to help in monitoring and improving overall employees' performance from small to medium sized companies with online and remote direct access. The research methods consist of data collecting using literature study and comparison of similar applications, and design using UML Diagrams, the application is expected to assist small to medium sized companies in monitoring and improving overall employee performance with the evaluation results that can be applied to improve application effectiveness.
Adaptive IIR Filtering in Signal Processing and Control

Recursive filter structures the Beurling-Lax theorem, Hankel forms and classical identification adaptive IIR filtering in signal processing and control stability of time-varying recursive filters gradient descent algorithms the Steiglitz-McBride family of algorithms hyperstable algorithms adaptive notch filters perspectives and open problems computations with lattice filters.