Miracle-dz/UniFAR-FTUs huggingface dataset:
User Acceptance of Predictive Analytics on Violence Against Women and Children at Risk: A Field Survey in the Philippines

Government agencies always need innovative technology solutions to cope with changes and manage critical processes, especially in creating policies and decision-making to benefit the vulnerable members of society. To address these needs, information technology solutions with analytics are introduced to government institutions. This study explores the user acceptance of information technology solution with analytics to aid in decision-making in the areas of children at risk, children in conflict with the law and violence against women. This study used the Technology Acceptance Model in assessing the users’ acceptance through a survey conducted among professionals working in a social welfare and development agency in the Philippines. Results show that several variables have significant relationships. Perceived usefulness and perceived ease of used plays an essential role in users’ intentions to use predictive analytics. Further, self-efficacy and facilitating condition positively influences perceived of usefulness and perceived ease of use, while technological complexity is not a significant factor to perceived usefulness. However, it positively influences the perceived ease of use. Overall, the respondents positively accept the predictive analytics on violence against women and children at risk. This work contributes by providing an understanding of user acceptance of innovation used in social welfare and development agency in the Philippines. Practical and research implications are presented.
Generalized Time-Domain Index Modulation with A Low Complexity Detection Method

SC-based index modulation (SC-IM) is a recently developed communication scheme that uses the optimal high complexity maximum likelihood (ML) detection algorithm at receiver. In this paper, we propose a single-carrier-based generalize time-domain index modulation (GTD-IM) scheme whose receiver employs a square-law envelope detector (SLED) to detect the active indices. Through analyses and simulations, we find that this system can achieve approximate energy efficiency and error performance of SC-IM with a lower implementation complexity.
Improved Regularized Reconstruction for Simultaneous Multi-Slice Cardiac MRI T1 Mapping

Myocardial $T_{1}$ mapping is a quantitative MRI technique that has found great clinical utility in the detection of various heart disease. These acquisitions typically require three breath-holds, leading to long scan durations and patient discomfort. Simultaneous multi-slice (SMS) imaging has been shown to reduce the scan time of myocardial $T_{1}$ mapping to a single breath-hold without sacrificing coverage, albeit at reduced precision. In this work, we propose a new reconstruction strategy for SMS imaging that combines the advantages of two different k-space interpolation strategies, while allowing for regularization, in order to improve the precision of accelerated myocardial $T_{1}$ mapping.
V2G for Power Distribution Line Stabilization

The ‘Vehicle to Grid (V2G)’ has been known as the key technology combining both electric power and transportation industries. V2G concept in this paper contains both ‘Smart Charging (SC)’ and ‘Reverse Power Transfer (RPT)’ initiated by Demand Response (DR) signal from power utility, to stabilize Power Grid. Since the national power demand-supply balance is very stable in Korea, our research has been focused on EV charging/discharging technology for stabilizing ‘local’ power load on each Distribution Lines (DL). The SC and RPT with real V2G EV, DL power load prediction, and power control simulation will be presented in the paper.
Bias Effects on Power MOSFET in Total Dose Irradiation

In this paper, relationships between bias and total ionizing dose (TID) effects are studied. 40V NLDMOS and 5V NMOS are designed and fabricated in 0. 35μm commercial BCD process. Co-60 gamma irradiation and test are carried out under different configurations to compare the degradation, and find out the worst-case bias.
Instantaneous Target Velocity Estimation Using a Network of a Radar and Repeater Elements

This paper presents an approach to estimate the true direction of motion and the velocity of a target within a single measurement snapshot. This is achieved by using a single radar sensor in combination with an active repeater element that is operated as a displaced virtual sensor. Coherency between sensor and repeater tag is only obtained by signal processing; on a hardware level both operate incoherently. This setup provides the necessary multi-perspective view on the target at low effort and large freedom of placement. The results of simulations and measurements in the 77 GHz band with one radar sensor and two repeater elements are presented to validate the theory.
Filtering for Uplink Non-Orthogonal Multiple Access with Imperfect Received Power Control

In this work, we propose a low-complexity enhancement for uplink non-orthogonal multiple access (NOMA). The proposed scheme, called Filtered NOMA (F-NOMA) aims to improve the error performance for scenarios in which the received user powers cannot be controlled perfectly at the receiver. The proposed scheme benefits from the uniqueness of the frequency spectrums of the superimposed signals to detect the signals of multiplexed users at the receiver. We demonstrate that the proposed scheme improves the error performance of uplink power-domain NOMA significantly and increases the number of supported users. We also show that the scheme also brings an improvement in the error performance of other uplink NOMA methods, which already benefit from different types of user specific signatures.
Uplink Boost Eliminate User in Massive MIMO System Using Reinsch Algorithm

In the multiuser uplink system, Multiple Input Multiple Output (MIMO) linear detectors as Zero forcing or Minimum Mean Square Error (MMSE) is widely adopted. However, the computational complexity of the linear detector remains high due to the significant vast matrix, especially with the growing number of antennas and the user. Therefore, some strategies have been adopted to approach linear detectors to reduce complexity, but among previous research just focused on one linear detector. We utilize the singular value decomposition based on MMSE or ZF detector (SVD) and propose two algorithms to update and update the channel matrix SVD. The proposed update algorithm for the case when one user is attached to the base station. Golub Kahan and Reinsch, Gram Schmidt and Householder transform recursively to approach the diagonal matrix of singular values. This shows through simulation results that the proposed scheme exceeds the conventional scheme with reduced complexity and error 1-5%.
Towards Model-Based Generation and Optimization of AUTOSAR Runnable-to-Task Mapping

In this paper we present work towards automating two process steps supporting the optimization of the runnable-to-task mapping in automotive multi-core control units. We describe these steps in close relation to the AUTOSAR methodology to facilitate the integration with existing design processes. The first step is the automated generation of an initial configuration that balances the core utilization using constraint programming. The second step is the optimization of an existing configuration based on dynamic system behavior using an evolutionary algorithm. An abstract intermediate representation provides interoperability with existing AUTOSAR tools. We use a small case study to evaluate the feasibility of our approach.
Semantic Segmentation and Scene Reconstruction for Traffic Simulation Using CNN

In this paper, we propose a framework for 3D traffic scenes construction based on semantic segmentation using convolutional neural networks (CNNs). Firstly, the segmentation network, whose architecture is constructed with encoder-decoder model and a hall module, divides traffic scenes into different parts: Road, sky, vehicle and other regions. Furthermore, we generate spatio-temporal graph models and construct 3D traffic scenes according to semantic segmentation results. The applications for scene simulation are then developed. The experimental results on the KITTI dataset demonstrate the effectiveness of the proposed framework.
Segmentation of Surface Cracks Based on a Fully Convolutional Neural Network and Gated Scale Pooling

Continual use, as well as aging, allows cracks to develop on concrete surfaces. These cracks are early indications of surface degradation. Therefore, regular inspection of surfaces is an important step in preventive maintenance, allowing reactive measures in a timely manner when cracks may impair the integrity of a structure. Automating parts of this inspection process provides the potential for improved performance and more efficient resource usage, as these inspections are usually carried out manually by trained inspectors. In this work we propose a Fully Convolutional, U-Net based, Neural Network architecture to automatically segment cracks. Conventional pooling operations in Convolutional Neural Networks are static operations that reduce the spatial size of an input, which may lead to loss of information as features are discarded. In this work we introduce and incorporate a novel pooling function into our architecture, Gated Scale Pooling. This operation aims to retain features from multiple scales as well as adapt proactively to the feature map being pooled. Training and testing of our network architecture is conducted on three different public surface crack datasets. It is shown that employing Gated Scale Pooling instead of Max Pooling achieves superior results. Furthermore, our experiments also indicate strongly competitive results when compared with other crack segmentation techniques.
The place of IDegLira in the management of patients with Type 2 diabetes

Type 2 diabetes mellitus is a progressive disease and this means that treatment intensification is needed to achieve glycemic goals and, for many patients, insulin will ultimately be needed. There is the potential for insulin to achieve any glycemic target, however, its initiation and up-titration is frequently delayed, partly due to the attendant weight gain and risk of hypoglycemia. Several clinical trials have demonstrated that combining a GLP-1 receptor agonist with basal insulin produces significant reductions in HbA1c without substantially increasing hypoglycemia or promoting weight gain. A once-daily coformulation of ultra-long-acting basal insulin degludec and the GLP-1 analog liraglutide, termed ‘IDegLira’, has now been developed for patients with Type 2 diabetes. Data from two Phase III clinical trials are encouraging, demonstrating marked HbA1c reductions with weight loss, low levels of hypoglycemia and limited gastrointestinal side effects. The types of patients that may benefit from a fixed-r.
Front-End Feature Compensation for Noise Robust Speech Emotion Recognition

Robust feature compensation and selection are important aspects of noisy speech emotion recognition (SER) task, especially in mismatched condition, when the models are trained on clean speech and tested in the noisy scenarios. Here we propose the use of front-end feature compensation techniques based on Vector Taylor Series (VTS) expansion and VTS with auditory masking (VTS-AM) to improve the performance of SER systems. On top of VTS and VTS-AM, we compare the performances of log-compression and root-compression to the mel-filter-bank energies. Further, we demonstrate the benefit of feature selection applied to the non-MFCC high-level descriptors in conjunction with VTS, VTS-AM and root compression. The system performance is compared with popular Non-negative Matrix Factorization (NMF) based enhancement and energy based voice activity detector (VAD) technique, which discards silence or noisy frames in the spoken utterances. To demonstrate the efficacy of our proposed techniques, extensive experiments are conducted on 2 standard datasets (EmoDB and IEMOCAP), contaminated with 5 types of noise (Babble, F-16, Factory, Volvo, and HF-channel) from the Noisex -92 noise database at 5 SNR levels (0dB, 5dB, 10dB, 15dB and 20dB).
Micro - Strip Patch Antenna Using Various Types of Feeding Techniques: An Implementation

This paper tries to implement Micro-Strip rectangular patch antenna and tries to analyze its performance for various types of feeding techniques. FR4 epoxy is utilized as a substrate. The 2D and 3D radiation pattern are also discussed. The parameter scattering matrix is analyzed. This design is intended for its operation on the L-band. The L-band has a bandwidth of 17% over the band range of 1.6GHz – 1.8GHz. This system generates the model of the Micro - Strip Antenna for various feeding techniques using HFSS Ansoft 13.0 tool.
Rich Interactions in Virtual Reality Exposure Therapy: A Pilot-Study evaluating a System for Presentation Training

Public Speaking Anxiety (PSA) or discomfort while speaking in public is a wide spread cognitive disorder. Exposure therapy offers the opportunity to treat patients suffering PSA by exposing them to the phobic stimulus. To plan and organize an in-vivo exposure takes a lot of effort in recruiting people for an audience and orchestrating their behavior. Virtual Reality (VR) offers the possibility to generate audiences that can be controlled by an orchestrator according the patient’s individual needs. This paper explores a system that enables the therapists to richly interact verbally and non-verbally with immersed presenters. For evaluation, we conducted a study with 24 healthy participants in two groups (12 participants each). Our results indicate that the direct verbal interaction between an orchestrator outside the VR and an immersed presenter enhances the presenter’s experience and increases the efficiency of therapy process. The non-verbal dimension enables an orchestrator to take-over an avatar using a motion-tracking camera that controls the avatar’s movements. Our results indicate that the transition of animations and movements do not impacting the experience negatively.
Research on Detection Method of Small Size Weld Bead Defects Based on Reluctance Measurement

To achieve non-destructive testing for small size weld beads of metal workpieces, we developed a new testing method of closed magnetic circuit reluctance measurement. According to the shape and volume of the small size weld defects of the metal workpiece, a reluctance detection model was established in the detection environment of alternating magnetic field. The relationship between different weld defects and closed magnetic circuit reluctance was quantitatively analyzed to achieve the identification of weld defects. A particle swarm optimization algorithm (PSO) was applied to optimize the cost-sensitive support vector machine (CS-SVM), which effectively reduced the coupling errors caused by the limitation of the workpiece coupling size. This new method was used to verify the weld bead detection of representative carbide saw blades. Compared with the basic support vector machine, the improved cost-sensitive support vector machine has better performance in the classification of unbalanced samples. The experimental results showed this new method can detect the weld bead of carbide saw blade with the correct rate to 98.2%. It reduced the interference of coupling error effectively. The improved cost-sensitive support vector machine not only improved the detection accuracy, but also avoided the possibility that the defective weld workpiece samples are misclassified into qualified workpieces. This study provides a guarantee for safe production and has great significance in engineering applications. The new method provides an effective solution for the application of reluctance testing technology in small size weld bead detection.
Empirical Predictability Study of SDN Switches

To meet their increasingly stringent dependability requirements, communication networks need to be predictable, both in terms of correctness and performance. In principle, Software-Defined Networks (SDN) enable such more predictable networks, however, these networks still depend the underlying switches. This paper presents an empirical study of the predictability of SDN switches. Our extensive benchmarking of seven hardware OpenFlow switches from four different manufacturers raises several concerns regarding the dependability of these switches. We uncover several incorrect and unpredictable behaviors and performance issues. In particular, we identify unpredictable behaviors related to the management of flows and buffers, and observe that existing quality-of-service mechanisms, such as priority queuing, introduce unexpected overheads. The latter, in turn, can lead to violations of latency guarantees. Based on our insights, we discuss first solutions toward more predictable architectures.
Optimal Allocation of Electric Vehicle Parking lots and Renewable Energy Sources Simultaneously for Improving the Performance of Distribution System

Electric vehicles (EVs) and renewable energy sources (RES) are introduced to achieve three of the most pivotal objectives of this century: using environmentally-friendly energy sources, reliable supply of the load demand, and sustainable development of power systems. The spread use of distributed renewable sources in the power grid and the ever-increasing use of electric vehicles have faced the distribution network operators with new challenges. These challenges must be minimized by careful planning based on the output changes of these sources to supply the additional demand resulting from charging of vehicles. To achieve the aforementioned goals, simultaneous utilization of RES and EVs should be implemented in a scheduled manner. In this paper the optimal allocation of parking lots (PLs) and renewable energy sources simultaneously is carried out with consideration of the uncertainty associated with the output power of these resources, which PLs provide vehicle-to-grid (V2G) power in distribution system as new type of distributed generations (DGs). The optimization problem is solved using the salp swarm algorithm (SSA) taking into consideration the operational constraints of the power system, RES and EVs, in order to decrease power losses and improvement of the voltage profile and system reliability. The performance of the proposed method is evaluated on the IEEE 33-bus distribution system. The results of simulation show the planned use of renewable energy sources, because of its proximity to the load site, has decreased the impacts of the overload caused by the charge of electric vehicles.
On the Accuracy of SonarQube Technical Debt Remediation Time

[Context] The popularity of tools for software quality analysis has increased over the years, with special attention to tools that calculate technical debt based on violations of a set of rules. SonarQube is one of the most used tools and provides an estimation of the time needed to remediate technical debt. However, practitioners are still skeptical about the accuracy of its remediation time estimation. [Objective] In this paper, we analyze the accuracy of SonarQube remediation time on a set of 15 open source Java projects. [Method] We designed and conducted a case study where we asked 65 novice developers to remove rule violations and reduce the technical debt of 15 projects. [Results] The results point out that SonarQube remediation time, compared to the actual time for reducing technical debt, is generally overestimated, and that the most accurate estimation relates to code smells, while the least accurate concerns bugs. [Conclusions] Practitioners and researchers could benefit from the results of this work to understand up to which extent technical debt is overestimated and have a more accurate estimation of the remediation time.
Simplified Minimum Fourth-Order Moment Haze Channel Equalization Algorithm

Aiming at the problem of high cost in channel equalization in the traditional haze channel, the paper presents a channel equalization algorithm based on simplified minimum fourth moment. The algorithm can be combined with a second power quantizer to provide superior performance over time. The linearized description of the second power quantizer (PTQ) is obtained by the simplified-linearization method in the algorithm, and then used to stabilize the steady-state mean square analysis of the LMF-PTQ algorithm. Finally, using the accurate model of LMF-PTQ, the experimental results show that the proposed LMF-PTQ algorithm can have better performance in non-Gaussian channels and has improved ability to track time-varying channels.
Intelligent Vehicles Localization Approaches between Estimation and Information: A Review

Localization and Simultaneous Localization and Mapping (SLAM) is a fundamental module in any Autonomous vehicle or Autonomous Mobile Robot. In order for an autonomous system to be able to plan, navigate and perform tasks in its environment, it has to be able to localize itself in this environment. This led to a huge amount of research during the past few years in the field of localization and SLAM algorithms. These algorithms are composed of two main parts, a front-end responsible for extracting important features from the sensors data as well as data association. The second part, namely, the back-end is responsible for the probabilistic estimation step. The back-end of these algorithms is heavily dependent on the estimation theory and almost all the related work utilizes one estimation algorithms or another. Throughout this paper we review the most popular estimation algorithms used in localization and SLAM, we show the relations between different estimation methodologies and state some of the advantages and disadvantages for each of them.
Service Function Chain Embedding for NFV-Enabled IoT Based on Deep Reinforcement Learning

It is challenging to efficiently manage different resources in the IoT. Recently, Network function virtualization has attracted attention because of its prospect to achieve efficient resource management for IoT. In NFV-enabled IoT infrastructure, a service function chain (SFC) is composed of an ordered set of virtual network functions (VNFs) that are connected based on the business logic of service providers. However, the inefficiency of the SFC embedding process is one major problem due to the dynamic nature of IoT networks and the abundance of IoT terminals. In this article, we decompose the complex VNFs into smaller VNF components (VNFCs) to make more effective decisions since VNF nodes and physical network devices are usually heterogeneous. In addition, a deep reinforcement learning (DRL)-based scheme with experience replay and target network is proposed as a solution that can efficiently handle complex and dynamic SFC embedding scenarios. Simulation results present the efficient performance of the proposed DRL-based dynamic SFC embedding scheme.
Impact of Non-idealities on the Performance of Delta-Sigma/Manchester-modulated Microwave signals

The effects of non-idealities including clock jitter, rise/fall-time mismatch of a pulse, and pulse-amplitude-level non-linearity on conventional delta-sigma-only and Manchester/delta-sigma-modulated microwave signals are studied. According to the experimental and simulation results, the Manchester/delta-sigma scheme is more vulnerable to jitter than the conventional delta-sigma scheme due to the higher number of pulse transitions by Manchester codes. It is found that, however, because of the repetitive (or multi-pulse) nature, the Manchester/delta-sigma system shows better signal quality performance against a rise and fall time asymmetry. This is mainly because the Manchester pulse waveform is more similar to a return-to-zero format where every encoded bit has both rising and falling edges and is therefore immune to the waveform asymmetry.