Miracle-dz/UniFAR-FTUs huggingface dataset:
Ring cavity widely tunable continuous wave Ytterbium-doped double-clad fiber laser

We present design development and successful demonstration of highly stable, broadly tunable ytterbium-doped double clad fiber ring laser. A tunable Fabry-Perot filter is placed in a unidirectional ring cavity for the purpose of tuning the wavelength of the laser over a range of 60nm (16 THz) from 1040nm to 1100nm with 0.02nm tuning resolution. The linewidth of the laser is smaller than 0.8nm for maximum power of 30W is achieved and the laser exhibits nearly constant output power for the entire wavelength tuning range.
Multichannel Separation and Classification of Sound Events

We investigate the use of determined blind source separation for sound event detection (SED) and classification using multichannel recordings. Our proposed system appends a single channel SED model to each of the output channels of the separation algorithm. We expect the number of events per channel to be reduced and overall performance increased. Such a system allows different number of channels at training and test time. We demonstrate the performance on the DCASE 2020 Sound Event Localization and Detection dataset. We compare baseline training on single channel recordings to using different combinations of 1, 2, 3, or 4 channel recordings. For the separation, we compare both a traditional source prior and a neural prior, trained without groundtruth signals available. First, we show that with the former, performance increases when using more channels. In addition, mixing different number of channels during training yields a system that is robust across varying number of channels. Second, while the training scheme proposed for the tailored source prior is not found effective for separation, it seems to be effective for data augmentation. This indicates that multichannel training data is beneficial, even when the target systems are single channel.
A Penalized Subspace Strategy for Solving Large-Scale Constrained Optimization Problems

Many data science problems can be efficiently addressed by minimizing a cost function subject to various constraints. In this paper a new method for solving large-scale constrained differentiable optimization problems is proposed. To account efficiently for a wide range of constraints, our approach embeds a subspace algorithm into an exterior penalty framework. The subspace strategy, combined with a Majoration-Minimization step search, takes great advantage of the smoothness of the penalized cost function. Assuming that the latter is convex, the convergence of our algorithm to a solution of the constrained optimization problem is proved. Numerical experiments carried out on a large-scale image restoration application show that the proposed method outperforms state-of-the-art algorithms in terms of computational time.
A Deep Learning Model for Detecting PPE to Minimize Risk at Construction Sites

The prominent reasons for accidents occurring on construction sites are traumatic brain injuries, collisions, being stuck by objects and sometimes these lead to death. Personal Protective Equipment (PPE) is one of the safety measures used to mollify those. In this paper, a deep learning model is developed for detection of PPE ignorance and the presence of unauthorized persons which will help security officers to take the right action to reduce the risk at the construction site. Also, this automated deep learning model alerts the security officers when workers or anyone entering the construction site without PPE with the help of face recognition technology. The experimental results showed 0.95 Mean Average Precision.
Feature Analysis to League of Legends Victory Prediction on the Picks and Bans Phase

Victory prediction in online video games has become an important application for machine learning due to the large amount of data generated by these games and their growing popularity. The creation of professional leagues also drives these applications, as teams want to know their chances of victory and know what are the determining factors to achieve it. Thus, in this research, we analyze whether pre-game information can be explored for victory prediction of professional matches of League of Legends (LoL), one of the main MOBA games. In experiments, we benchmarked different feature sets and algorithms to assess the victory predictions in LoL. The results show that historical performance information is the most accurate features for performing this task. The induced models, especially Random Forest and Logistic Regression, achieved AUC values of 0.97.
A Systematic Comparison of Traditional and Hybrid AI Models for NHANES Big Data Analytics

The National Health and Nutrition Examination Survey (NHANES) is a big health dataset, which has recently raised much research interest to use data mining and analytics techniques to examine the prevalence and risks of chronic diseases related to sedentary lifestyle, inadequate dietary, nutritional and behavioral habits, household environment, whole body measurement, bone measurements, and so forth. In this paper, we carry out a systematic investigation of comparative analytics based on several traditional machine learning techniques and hybrid AI models to estimate the association among all the features of the 2017-2018 NHANES dataset and classify hypertension diseased participants as a use-case. Based on the use-case, our research work utilizes a highly imbalanced dataset of 8,366 people with 81.20% participants with no hypertension and 18.80% participants with hypertension. Empirical results demonstrate that our proposed AI model, with an accuracy of 94%, significantly outperforms the other machine learning techniques and two hybrid model variants in identifying patients with a risk of having hypertension by considering all the health conditions.
An Exploration into “Perceived Sense of Challenge” in Level Design for fast paced Casual Mobile Games

Level Design is one of the core pillars of Game design. As a level designer, one creates experiences along the fine line of flow between frustration and fun. This paper takes a heuristic approach in understanding a player's perception of a level's challenge through case studies and by conducting an experiment (focusing primarily on single player fast paced casual mobile platformers and racers) with a sample size of 50 participants. Figuring out what constitutes as a challenge for the player, how it can be increased without drastically affecting the difficulty and how analysing this ‘perceived sense of challenge’ can help designers influence a player's expected sense of challenge and create less frustration.
What's Next For Wearable Sensing?

The commercial explosion of wearable sensing devices in the early 2010s forever changed the landscape of wearable computing. In a few short years, wrist-mounted devices such as wristbands and smart watches dominated the market.1 In 2017, this department featured an article titled “What will we wear after smartphones? ” highlighting potential pathways for wearable computing as the early enthusiasm for commercial wearable sensors began to wane, and new form factors like on-skin devices gained traction in the research community.2 In the past few years, we have witnessed substantial changes in many of the domains discussed in that article. Sensor validation and comparison with other state of the art or reference systems has become of paramount importance in a saturated wearables market. Similarly, FDA approval or CE marking of smartphone or sensor-based medical applications is now a priority of many of the players targeting healthcare applications. For traditional form factors such as wristbands and other accessories, large improvements have also been made in hardware, thanks to further miniaturization and improved design (see Figure 1). Figure 1. Phone cameras, watches, and rings have become widespread sensing modalities for accurate monitoring of biometric data. Figure 2. Graphs show mean deviation from baseline (lines) with 95% CIs (shaded areas) for daily resting heart rate (RHR), sleep quantity, and step count during −7 to 133 days after symptom onset for COVID-19–positive versus COVID-19–negative participants (panels (a), (c), and (e)) and for COVID-19–positive participants grouped by mean change in RHR during days 28 to 56 after symptom onset (panels (b), (d), and (f)). Acquired with permission from Radin et al.8
Visual Brain Decoding for Short Duration EEG Signals

In this work, we propose a CNN-based approach for classification of short duration EEG signals for visual brain decoding. These signals are captured for a visual perception task by showing digit images on a computer screen, and the task involves classification of the EEG signals into 10 classes, corresponding to the digits shown. The captured EEG signals are of very short duration (approx. 2sec), which are typically very noisy. We use a correlation based technique for the removal of highly noisy samples. Further, a sample refinement approach for the selection of relevant channels is also proposed. Both these steps constitute the data refinement process, which we demonstrate has a significant effect on the CNN classification performance. We validate the proposed approach on a publicly available MindBigData (The “MNIST” of Brain Digits) dataset.
Cost Reduction in Fingerprint-Based Indoor Localization using Generative Adversarial Network

Among the various methods of Wi-Fi localization, fingerprint-based indoor localization is a method with relatively high accuracy. However, it takes a lot of time and labor to initially build an offline database. In particular, as the size of the space increases, the number of reference points to be investigated also increases, so the cost increases proportionally. To solve this problem, a few recent studies have used machine learning to reduce the number of samples collected at each reference point. In this paper, we propose a method to reduce the number of reference points that need to be investigated. We collect channel state information (CSI) at a few reference points sparsely and convert it into a CSI amplitude image. Then, the generative adversarial network (GAN) interpolates the values between each reference point by learning these CSI amplitude images and walking in the latent space to synthesize a dense fingerprint map for improved localization accuracy. Our preliminary results show that GAN can create artificial CSI amplitude data that are similar to the actually measured samples for locations in between trained reference points.
Device Independent YANG Auto-generation Mechanism

With the increasing demand for network capacity, the scale of network infrastructure has been extending, and it has consisted of a number of network devices that are heterogeneous (i.e., manufactured by different vendors or different network operating systems or different generations of management software). In particular, even if the network devices support the same protocol such as CLI, SNMP, etc., each device has its own management tools. Different method of configuration causes that quick service deploy was difficult. Therefore, NETCONF is released as a new solution in IETF. To deploy service with NETCONF, network modeling is required. With the YANG modeling language, NETCONF has capability to configure with network devices. Nevertheless, lack of automatic generation YANG is another issue with multiple vendors and network domains. This paper proposes NDYA (Network Device independent YANG Auto-generator) which generates YANG to control network device with vendor, service and domain independently. Experiment result shows that our proposal NDYA mechanism achieves high performance and accuracy.
Power Factor Correction Control Based on Internet of Things Using Lumped Compensation Capacitor Bank

Power factor is often used as an indicator of a good or bad power supply in a system Electrical power in household and industrial needs for inductively reactive loads can cause a decrease in power factor The decrease in power factor is a problem that must be minimized because it can make the quality of electrical power not optimal which is detrimental to consumers and a decrease in the ability to supply electrical power to electrical energy suppliers. Capacitive reactive power can reduce this loss by installing capacitors. Therefore, we need a tool that can improve power factor value and increase efficiency for distribution systems under inductive loads. The tool that will be made using a lumped compensation capacitor bank uses the reactive power compensator calculation method to get the required capacitor capacitance. In this research, a power factor repair tool has been made using the NodeMCU ESP8266. Microcontroller and also an android application as a connection tool with the Internet of Things server. MQTT protocol was chosen as IoT protocol after comparing speed and energy consumption with HTTP protocol. Also in this research, added recommendation features to calculate the value of capacitance capacitors that are installed in the devices to make them easier to use during installation. The system is implemented in a house with a power rating of 900 Watt and uses 4 capacitors with each size in micro Farad $(\mu F)$ units of 2.5, 3, 5, and 5. The result takes 1 minute 45 seconds to reach a set point of 0.95 Power Factor with an average error of 0.8%.
BCIManager: A library for development of brain-computer interfacing applications in Unity

BCIManager is a customizable library helping with the development of brain-computer interfaces (BCIs) with 3D graphics scenes, suitable for virtual reality (VR) BCI feedback, gamified BCI training, using BCIs as game inputs, and similar use cases. The library forms a layer between Unity game engine and Openvibe (software for development of BCIs) that provides control of the EEG recording process from applications made with Unity. The main feature of the BCIManager is the interface for bi-directional data exchange between Unity and Openvibe; for sending markers (stimulations) from the experimental 3D applications to the EEG recordings, and for sending classification results, features, or other data from Openvibe to the 3D applications. BCIManager also wraps around the process of starting Openvibe and can be used for arbitrary scenarios that include EEG recording with 3D/VR applications. This project is an open source, freely available at https://github.com/xskola/BCIManager.
Análisis e implicaciones de la detección del humor en textos

The paper aims to analyze a part of the phenomena of humor detection in texts and its implications, such as preparing the data to have a correct analysis of the set taken, as well as current methodologies. The analysis is based on a selection of works from the period 2016-2019, which talk about humor detection, creation of data sets and humor detection workshops. The works were selected given the interest and the results obtained by the authors, since among them there are some with a more avant-garde approach. As a result, an analysis of the chosen works was obtained. It can be seen that the methods for detecting different types of humor, even having the same task to be solved, can be varied and yield competitive results against each other.
Procedural Terrain Generation Using Generative Adversarial Networks

Synthetic terrain realism is critical in VR applications based on computer graphics (e.g., games, simulations). Although fast procedural algorithms for automated terrain generation do exist, they still require human effort. This paper proposes a novel approach to procedural terrain generation, relying on Generative Adversarial Networks (GANs). The neural model is trained using terrestrial Points-of-Interest (PoIs, described by their geodesic coordinates/altitude) and publicly available corresponding satellite images. After training is complete, the GAN can be employed for deriving realistic terrain images on-the-fly, by merely forwarding through it a rough 2D scatter plot of desired PoIs in image form (so-called “altitude image”). We demonstrate that such a GAN is able to translate this rough, quickly produced sketch into an actual photorealistic terrain image. Additionally, we describe a strategy for enhancing the visual diversity of trained model synthetic output images, by tweaking input altitude image orientation during GAN training. Finally, we perform an objective and a subjective evaluation of the proposed method. Results validate the latter's ability to rapidly create life-like terrain images from minimal input data.
GLUE based marine X-band weather radar data calibration and uncertainty estimation

The Generalized Likelihood Uncertainty Estimation methodology (GLUE) is investigated for radar rainfall calibration and uncertainty assessment. The method is used to calibrate radar data collected by a Local Area Weather Radar (LAWR). In contrast to other LAWR data calibrations, the method combines calibration with uncertainty estimation. Instead of searching for a single set of calibration parameters, the method uses the observations to construct distributions of the calibration parameters. These parameter sets provide valuable knowledge of parameter sensitivity and the uncertainty. Two approaches are analyzed; the static calibration approach, where the LAWR is calibrated once for a long period and the dynamic approach, where the estimate is continuously adjusted based on ground observations. The analysis illustrates that the static calibration performs insufficiently, whereas the dynamic adjustment improves the performance significantly. It is found that even if the dynamic adjustment method is used the uncertainty of rainfall estimates can still be significant.
Exploring Q-Learning for Adaptive Difficulty in a Tile-based Image Labeling Game

Participant disengagement in citizen science tasks remains a significant challenge for crowdsourcing platform creators, in their efforts to generate meaningful data and connect with their membership. Reinforcement learning is increasingly used to take advantage of the plethora of available data to learn to sequence tasks for participants. To this end, we extend the reinforcement learning techniques used in Tile-o-Scope Grid, an image matching web game, by introducing an adaptive Q-learning based approach that incorporates participant performance in sequencing the difficulty of levels. We compared our adaptive version against both a previous non-adaptive algorithm, as well as a greedy approach. We found that the adaptive extension outperformed both, in terms of total reward. This work contributes to the growing literature on reinforcement learning approaches applied to citizen science.
Constrained Block-Term Tensor Decomposition-Based Hyperspectral Unmixing via Alternating Gradient Projection

Block-term tensor decomposition (BTD)-based hyperspectral unmixing (HU) is well-motivated because of its identifiability of the endmembers and abundance maps under relatively mild conditions. However, algorithm design of BTD-based HU faces challenges in enforcing hyperspectral image-related structural constraints, e.g., the probability simplex constraints on the abundance vectors-while incorporating structural information is critical for combating noise and enhancing interpretability. Existing work uses a three-block alternating least square (ALS) framework, and employs the multiplicative update (MU) method to handle constraints, but this ALS-MU approach has high per-iteration complexity and often converges slowly. This work puts forth an alternating gradient projection (GP) algorithm for the problem of interest. Our method leverages a two-block parameterization of the BTD model to avoid encountering heavy updates, thereby exhibiting high efficiency. Another core contribution lies in a fast solver for computing a key step in the GP algorithm, namely, the orthogonal projection onto the set of matrices with low-rank and probability simplex structures. Simulations show that the GP framework attains order-of-magnitude speedup and accuracy improvement relative to the state-of-the-art.
Root Cause Analysis of Data Integrity Errors in Networked Systems with Incomplete Information

There are renewed interests recently in data integrity error detection and localization driven by exponentially growing data volumes over large-scale networked systems. Most existing RCA (Root Cause Analysis) systems take an infrastructure operator's view and rely on dedicated and expensive monitoring capabilities to instrument and facilitate the analysis. Unfortunately, in our targeted wide area network environment, complete network information and monitoring capability are normally lacking. In this paper, we present a RCA system that leverages the end-to-end flow monitoring information from the application layer, augmented by limited network information. We demonstrated that root cause localization with high accuracy can be obtained using multi-class classification models. We specifically studied the impacts of different realistic combinations of features based on the available yet incomplete information from both application and network layers.
The Experience, Dynamics and Artifacts Framework: Towards a Holistic Model for Designing Serious and Entertainment Games

Games have been used by educators as a mean for increasing learning retention and providing students with entertainment experiences that go beyond the classroom. However, the process of creating a game for educational purposes (serious game) is segmented into its entertainment structures, which usually are underemployed in a pedagogical application, and in purely educational components, which have been extensively detailed in the literature. This division can hinder the player/learner experience as too much focus in one of these areas can either compromise knowledge acquisition or engagement. To overcome this issue, this work proposes a holistic view of games that includes both learning and entertainment elements, making them indistinguishable in creating either an entertainment or serious game. This notion is further reinforced by reviewing common game design frameworks found in the literature that have an empirical background, and by proposing a unified game design framework that uses elements from the analyzed frameworks and enhances them by including the latest research into game-based learning and serious games guidelines. By proposing a holistic view of games and an accompanying framework, a new way of facing game design is proposed that encourages designers to consider the postgame learning experiences and stimulates educators to take the entertainment aspects of their game more deeply.
Modal analysis and dynamic responses of board level drop test

Board level solder joint reliability during drop test is a great concern to semiconductor and electronic product manufacturers. In this paper, comprehensive dynamic responses of printed circuit boards (PCBs) and solder joints, e.g., acceleration, strains, and resistance, are measured and analyzed in detail with a multi-channel real-time electrical monitoring system. Control and monitoring of dynamic responses are very important to ensure consistent test results and understand the mechanical behaviors, as they are closely related to solder joint failure mechanisms. The effects of test variables, such as drop height, number of PCB mounting screws, tightness of screws, and number of felt layers, are studied by comparing and analyzing the dynamic responses.