Miracle-dz/UniFAR-FTUs huggingface dataset:
Smart E-Waste Bin Development Based on YOLOv4 Model

As smart cities are being developed, innovations to the waste management are one of the focus areas of research. Various efforts are done to make the process seamless on the side of the consumer. In this research, the proponents focus on the development of a smart e-waste bin that can identify the type of e-waste deposited and reward users for their deposit. The system makes used of You Only Look Once v4 (YOLOv4) in identifying types of e-waste. In testing the device, various tests are performed and the precision, recall, and accuracy are all evaluated. In this research, the developed system can successfully identify the type of e-waste deposited with an average accuracy of 93.33%.
Sentinel 1 SAR for Rapid Identification of Flooded Inquiries in South Kalimantan Province

An 2021-flood occurred in South Kalimantan Province caused severe moral and material losses, the destruction of nature, and weakened the wheel of the economy. Rapid identification of affected flood inundation areas spatially is essential for monitoring and assisting in the preparation of emergency responses. Spatial analysis of flood areas utilizes SAR (Synthetic Aperture Radar) images because its wavelength is able to penetrate well through clouds or hazy atmosphere. This study utilizes Sentinel-1A level GRD (Ground Range Detect) - IW (Interferometric Wide Swath Mode) images with VV and VH polarization. We used Remote Sensing Change Detection (RDCD) method and then implemented a flood threshold. Flood event verification is obtained from the crowdsourcing report of flood events through social media. Results shows that 11 districts of South Kalimantan were affected by flooding, namely Balangan Regency (3,498 ha), Banjar (47,548 ha), Barito Kuala (37,530 ha), Hulu Sungai Selatan (9,939 ha), Hulu Sungai Tengah (9,789 ha), Hulu Sungai Utara (13,002.24 ha), Tabalong (4,876 ha), Tanah Laut (25,931 ha), Tapin (15,597 ha), Banjarbaru (2,812 ha) and Banjarmasin (6,570 ha).
From Photogrammetric Reconstruction to Immersive VR Environment

There are several steps to generate a VR environment from images: choose experimental conditions (scene, camera, trajectory, weather), take the images, reconstruct a textured 3D model thanks to a photogrammetry software, and import the 3D model into a game engine. This paper focuses on a post-processing of the photogrammetry step, mostly for outdoor environments that cannot be reconstructed by UAV. Since VR needs a 3D model in a good coordinate system (with a right scale and an axis that is vertical), a simple method is proposed to compute this. In the experiments, we first reconstruct both urban and natural immersive environments by using a helmet-held Gopro Max 360 camera, then import into Unity the 3D models in good coordinate systems, last explore the scenes like a pedestrian thanks to an Oculus Quest.
Continuous Angle Prediction of Lower Limb Knee Joint Based on sEMG

With the development of rehabilitation robots in the field of medical rehabilitation, the human action classification based on surface electromyography (sEMG) has been widely used in robot human-computer interaction. Since the estimation of continuous joint angle can be employed to improve the performance of human-machine coordination, the accurate extraction of continuous joint angle from sEMG is one of the research difficulties in the field. This paper presents an Attention-LSTM model based on the electromyographic fusion features. The sEMG of the lower limb in four channels and the corresponding knee angles of the lower limb during normal walking are collected by the sEMG acquisition system and the motion capture system. The Butterworth band-pass filter is used to reduce the out-of-band high-frequency and low-frequency noise of the data, and then the data is normalized. The sliding window is utilized to extract the time advance features and time delay features of sEMG. After the smoothing, the data is input into the Attention-LSTM model. The mean square error of prediction accuracy is used as the evaluation index. Finally, the data are analyzed by one-way analysis of variance (ANOVA) for significant differences $(\mathrm{p}\lt0.05)$. The experimental results show that the RMSE difference of the proposed fusion feature for predicting the knee angle is significantly smaller than that of the control group, and can accurately predict the knee angle of the lower extremity in the future from 75ms to 200ms (RMSE=$1.7444 \pm 0.2031)$, thus achieving the better human-computer interaction.
Cost Effective Accident Prevention System for Vehicles

This research is based on providing an automatic pre-crash danger indication strategy in order to avoid a collision. As the name indicates that this paper focuses on providing an attractive, low in price and intelligent environment with precise measurements to avoid accidents on roads. Inputs and outputs are being controlled by the combination of ultrasonic sensor, laser sensors, GPS sensor and rain sensor to achieve the best response time. A perfect combination of collision detection and blind-spot assist with an external output display makes it an interactive project with numerous options of upgrading and flexibility for future extensions. As a result, this system is capable of assuring the driver's and co-passengers' safety, and it can simply be linked with the car's existing safety systems.
A Blockchain-enabled Anonymous-yet- Traceable Distributed Key Generation

Many threshold systems require some kind of secret sharing during their set-up phase. There has been several recent work in doing secret sharing in a distributed manner through the use of Blockchain so that one does not have to rely on trusted third parties nor strong assumptions about the communication medium. This paper continues this line of work and presents a new protocol for distributed key generation (DKG). We develop a new technique that leverages the mainchain of a popular blockchain platform like Bitcoin, together with a mixing service and a group signature scheme, to provide anonymity and traceability; we connect this to a sidechain, pegged to the mainchain, that brings a better scalability. We implemented the technique on the joint platform Bitcoin/RootStock and use CoinJoin/CoinShuffle as the mixing service. The evaluation section shows a decrease by 99 % in the execution cost of our protocol compared to the same implementation on a smart-contract-friendly blockchain such as Ethereum.
Voltage Control Strategy Considering Security Constraints of Predictive Fault Set

With the development of AC/DC hybrid power grids and a high proportion of new energy sources, grid operation risks have increased. The voltage constraints in traditional automatic voltage control systems are generally given offline, which may not be able to meet the requirements of security and stable operation of the actual voltage of the power grid. It is necessary to comprehensively consider the voltage security region under grid fault to support the security and stable operation of the grid. This paper proposes a voltage control strategy that considers the voltage security region of the predictive fault set. Through the defined predictive fault set, analyze and evaluate the voltage operation risk under the fault, add voltage security region to the existing voltage control model to ensure that the voltage is qualified and security, reduce the voltage risk at the time of fault, and realize the active control of the voltage. Finally, through simulation analysis, the effectiveness of the proposed method is verified and it has a certain promotion value.
Toward AI-Assisted UAV for Human Detection in Search and Rescue Missions

Search and rescue missions during and after disasters require all efforts and high financial expenses. Rapid locating of wounded and lost individuals contributes in directing the rescuers and medical teams. This may increase the probability of saving human lives and plays a significant role in reducing expenses. Currently, the use of unmanned aerial vehicles (UAVs) or drones for remote surveillance and reconnaissance is becoming increasingly popular. On the other hand, emergent artificial intelligence (AI) algorithms based on convolution neural networks (CNN) reveal the ability of real-time detection. Combining the high-performance detection and classification capabilities provided by emergent AI techniques with the exploratory abilities of UAVs allows the UAVs to process the captured sequence of images and report back results in real-time. The evolution of AI-assisted UAVs enables the detection of wounded and trapped persons while flying and allows proper and fast transfer of information to ground stations to lead the rescuers and medical teams to victims' locations. In this paper, we explore augmenting UAVs with processing units executing emergent AI-based detectors. The proposed system can detect humans in real time and send the corresponding coordinates to the ground station.
Power Losses Evalutation of Hybrid Semiconductor Modules for Photovoltaic Inverters

String inverters are usually based on Silicon (Si) IGBT, which leads to limited switching frequency and power density. In this scenario, silicon carbide (SiC) MOSFET could be an interesting solution. Nevertheless, the SiC-MOSFET is quite expensive for devices with a high current rating. In this context, this work investigates the potential of power losses reduction provided by hybrid power modules in PV inverters. The hybrid power module is based on the parallel connection of a Si-IGBT and a SiC-MOSFET. This approach reduces the switching losses of Si-IGBT systems since the IGBT reaches soft switching. Also, this approach employs a lower SiC area than the full-SiC solution. The losses for the Si-IGBT and hybrid switch are obtained from double-pulse test LTSPICE simulations and compared.
Finite Control Set Model Predictive Control with Kalman Filter Estimation for LCL-type Grid-Tied Inverter

As one of the most effective control strategies for LCL-type grid-tied inverters (GTIs), the finite control set model predictive control (FCS-MPC) strategy can achieve accurate tracking of the reference value and handle a variety of constraints at the same time. Therefore, it has been employed in industrial applications. However, in order to achieve a better control effect, the traditional FCS-MPC requires multiple voltage and current sensors, which greatly increases the system cost. In addition, sensor failures occur frequently in practical applications, which will significantly reduce the overall performance of the system. Therefore, this paper proposes an improved FCS-MPC strategy (KFE-MPC) based on Kalman filter (KF). The proposed KFE-MPC strategy can reduce the number of sensors and improve the system’s ability to resist sensor failures effectively by utilizing a Kalman filter to estimate voltage and current. A three-phase / 110V LCL-type grid-tied inverter model is established to verify the feasibility and effectiveness of the proposed control strategy.
Research on Three-phase Voltage Source Type PWM Rectifier Based on Model Predictive Control

In this paper, the sphere decoding algorithm for three-phase two-level voltage source type rectifier is proposed, which transforms the traversal optimization problem of model predictive control into the least squares problem. It reduces the number of rolling optimizations significantly, and achieves the goal of decreasing the amount of calculation. Experimental results verify the effectiveness of the control strategy proposed in this paper.
Price-Based DC Bus Signaling for Nanogrids Power Management

Nanogrids are small electrical installations used to manage loads and distributed energy resources (DERs), locally. Commonly, hierarchical control strategies and conventional state machines are used for power-sharing and nanogrid management. However, these strategies are complex and no very flexible. In this paper, a dc bus signaling (DBS) strategy is used to relate an internal price concept to power management. This allows managing the nanogrid according to an economic market idea. This simplifies the state machine's design because is defined for the power elements, operation modes to sell or buy energy - in response to the price. This control strategy is called Price-Based Power Management (PBPM) and uses the DBS to define when energy lacks, or surplus occurs in the nanogrid. To corroborate this proposal, the PBPM algorithm is implemented in a Texas Instruments DPS F28379S, and Typhoon-HIL 602+ is used to carry out the real-time domain emulation results.
Pre-Trained Neural Language Models for Automatic Mobile App User Feedback Answer Generation

Studies show that developers’ answers to the mobile app users’ feedbacks on app stores can increase the apps’ star rating. To help app developers generate answers that are related to the users’ issues, recent studies develop models to generate the answers automatically. Aims: The app response generation models use deep neural networks and require training data. Pre-Trained neural language Models (PTM) used in Natural Language Processing (NLP) take advantage of the information they learned from a large corpora in an unsupervised manner, and can reduce the amount of required training data. In this paper, we evaluate PTMs to generate replies to the mobile app user feedbacks. Method: We train a Transformer model from scratch and fine tune two PTMs to evaluate the generated responses, which are compared to RRGEN, a current app response model. We also evaluate the models with different portions of the training data. Results: The results on a large dataset evaluated by automatic metrics show that PTMs obtain lower scores than the baselines. However, our human evaluation confirm that PTMs can generate more relevant and meaningful responses to the posted feedbacks. Moreover, the performance of PTMs has less drop compared to other model when the amount of training data is reduced to 1/3. Conclusion: PTMs are useful in generating responses to app reviews and are more robust models to the amount of training data provided. However, the prediction time is 19X than RRGEN. This study can provide new avenues for research in adapting the PTMs for analyzing mobile app user feedbacks.
Miniature loudspeaker for acoustical scale model test

A miniature loudspeaker assembly has been manufactured for acoustical scale model testing using parts for a commercially available studio monitor headphone. The loudspeaker assembly consists of a dodecahedron loudspeaker for middle and high frequency sounds and woofers for low frequency sounds, respective to the scaling required for a scale model test. The loudspeaker can reproduce sound with a frequency range from 1kHz to 80kHz (with an nearly flat response from 2kHz-40kHz), equivalent from 100Hz to 8kHz for a 1/10 scale model test. The frequency and directional characteristics of the miniature loudspeaker assembly are discussed in this paper, and some scale model test examples are introduced.
APPLICATION OF EXTENICS IN EVALUATING OF ENGINEERING QUALITY OF ROCK MASSES

There are various methods for evaluating engineering quality of rock masses,and indexes for different methods are different. Those different factors or indexes in different methods cannot be employed together due to the difference in the evaluation standard of different methods. This leads to the limitation of the traditional evaluation method. Based on extenics,a quantitative evaluation model of rock mass quality of multiple parameters is developed. The advantage of this model is that different factors from different traditional evaluation methods can be considered simultaneously. For example,the factor RQD in RMR can not be used together with the factor Jr in Q system previously because of differences in evaluation standard. But through linear dimensionless method, factors are converted to dimensionless numbers,so those two factors can be used together. Based on the concept of degree of membership and extenics,different factors from different traditional methods are selected and converted to dimensionless numbers. A quantitative method for define the weight of those factors are put forward according to importance of factors. The results of a case show that this evaluation method is feasible.
Trace Minerals for Poultry

Requirements for TM are not well defined and more research is needed to determine the most adequate levels in practical diets. Factors such as eggshell strength, internal egg quality, deposition of TM in yolk, bone strength, reductions in leg problems and footpad dermatitis, improvements on bone and skin biomechanical properties and collagen formation in several tissues, and immunity parameters should be considered as better parameters to estimate requirements and bioavailability values than just mineral accumulation in bones or other tissues. There is a considerable amount of data that demonstrates that TM affect all parameters previously mentioned. Requirements should be evaluated using practical diets and normal levels of all other TM. It will become more important to obtain absolute bioavailability values at least for those sources considered standards. If relative bioavailability values will continue to be used for TM, these evaluations should additionally use molecular biomarkers such as protein gene expression in several tissues to test relative absorption and utilization simultaneously. Organic TM sources definitely have better bioavailability and cause more positive responses in poultry than ITM sources based on the meaningful biological parameters previously listed. The methodologies to evaluate OTM product quality and relative bioavailability may not be adequate at this moment. The optimum combination ratios of ITM with OTM remain to be established.
Progressive Scrutiny: Incremental Detection of UBI bugs in the Linux Kernel

—The Linux kernel has a rapid development cycle, with 10 commits every hour, on average. While these updates provide new features and bug ﬁxes, they can also introduce new bugs and security vulnerabilities. Recent techniques showed how to detect some types of vulnerabilities using static analysis, but these tools cannot run quickly enough to keep up with the pace of kernel development. Ideally, an incremental analysis technique could address this problem, by doing a complete analysis once and then only analyzing changed portions of the code subsequently. However, incremental analysis of the Linux kernel poses unique challenges, due to its enormous scale and the high precision required to reduce false positives. In this paper, we design and implement I NCRE L UX , a novel Linux kernel incremental analysis tool. It allows rapid vulnerability detection after each update, via targeted analysis of the new code and affected prior code, and also speeds the tracking of pre-existing bugs to understand how long they have been present, thereby increasing awareness of such bugs. Our approach hinges on a bottom-up, function-summary-based approach, which leverages the beneﬁts of a one-time clean-slate, but expensive analysis of a prior Linux baseline. I NCRE L UX also uses an effective heuristic to apply symbolic execution to incremental results to improve precision. Via extensive experiments on the challenging problem of ﬁnding use-before-initialization (UBI) bugs, we showcase a
Conﬁgured-Grants Optimization in Grant-Free NOMA for Massive URLLC

Massive Ultra-Reliable and Low-Latency Communications (mURLLC), which integrates URLLC with massive access, is emerging as a new and important service class in the next generation (6G) for time-sensitive trafﬁcs and has recently received tremendous research attention. However, realizing efﬁcient, delay-bounded, and reliable communications for a massive number of user equipments (UEs) in mURLLC, is extremely challenging as it needs to simultaneously take into account the latency, reliability, and massive access requirements. To support these requirements, the third generation partnership project (3GPP) has introduced enhanced grant-free (GF) transmission in the uplink (UL), with multiple active conﬁgured-grants (CGs) for URLLC UEs. With multiple CGs (MCG) for UL, UE can choose any of these grants as soon as the data arrives. In addition, non-orthogonal multiple access (NOMA) has been proposed to synergize with GF transmission to mitigate the serious transmission delay and network congestion problems. In this paper, we develop a novel learning framework for MCG-GF-NOMA systems with bursty trafﬁc. We ﬁrst design the MCG-GF-NOMA model by characterizing each CG using the parameters: the number of contention-transmission units (CTUs), the starting slot of each CG within a subframe, and the number of repetitions of each CG. Based on the model, the latency and reliability performances are characterized. We then formulate the MCG-GF-NOMA resources conﬁguration problem taking into account three constraints. Finally, we propose a Cooperative Multi-Agent based Double Deep Q-Network (CMA-DDQN) algorithm to balance the allocations of the channel
COUNTERPRODUCTIVE BEHAVIOR AND ITS MANIFESTATION IN MANAGEMENT

Counterproductive behaviors (CB) have two organizational and interpersonal dimensions that can generate problems with negative effects for both the company and the employees. Consequently, they are a combination of personal and organizational factors.This article presents an analysis of the literature on organizational behavior regarding CB and its role in achieving performance and certain conclusions and perspectives for organizational managers.
Development of information and communication technology in an educational space: Students’ view

In educational environments, students are considered to be one of most crucial subjects. Identifying and supporting students’ voices concerning changes in the educational process are important for adaptation and developing Information and Communication Technology (ICT) in an educational space. This study identified the level of student readiness for changes in an educational space concerning ICT development and their views on utilizing ICTs in the educational process, thereby developing perspectives. Further, the association between student age, manner in which the study is adopted, and their level of proficiency in use of computers and the Internet were verified. A sample of 379 students at the Industrial University of Tyumen completed the survey. We used a sociological survey with a multiple-choice questionnaire about educational preferences and the weaknesses of higher learning educational space, where ICTs are used. Results have shown that there is an inverse association between age and level of proficiency in use of computers and the Internet. However, no association was observed between the manner in which the students conducted their studies and their perspective on the level of proficiency in personal computers and the Internet. Students were able to identify weaknesses in the organization of the virtual educational space, professional preparation of teachers, and problems, such as lack of self-organization skills, low level of proficiency of ICTs, etc. Students showed interest in e-learning and believed that virtual educational space makes professional education better.