Miracle-dz/UniFAR-FTUs huggingface dataset:
A CONCEPTUAL MODEL ON HOW TECHNOLOGY ASSISTED SUPPLEMENTAL WORK LEADS TO WORK-LIFE CONFLICT: ENTANGLEMENTS OF ICTS WITH WORK-LIFE EXPERIENCES

Empirical evidence suggests that technology-assisted supplemental work is becoming more prevalent in modern workplaces. Employees are often expected to be available to work even after working hours via Information Communication Technologies (ICTs) devices, which leads to work-life conflict. Using three recent empirical studies and relevant literature, this conceptual paper argues that ICTs alone cannot contribute to the employees’ work-life conflict because the work-life conflict in today’s hyper-connected world results from the constitutive intertwining between ICTs and people’s choices, beliefs, norms, and behaviors. Hence, to understand the work-life experiences in modern-day workplaces, it is vital to study the mutual interdependencies between ICT and social elements. Therefore, it is proposed to view contemporary employees’ work-life experiences as socio-material assemblages. Depending on the nature of the intertwining between ICTs and social elements, these socio-material assemblages create different experiences for different people. Using this socio-material perspective, the proposed conceptual model calls for more research on technology-assisted supplemental work and work-life conflict. Future research in this area could focus on the role of organizational norms, organizational and national culture, gender norms, segmentation preferences, boundary management tactics, and policies to manage after-hours work via ICTs. The model is presented with possible research suggestions and recommendations for theory and practice.
Dependence of Northern Hemisphere Tropospheric Transport on the Midlatitude Jet Under Abrupt CO2 Increase

Understanding how the transport of gases and aerosols responds to climate change is necessary for policy making and emission controls. There is considerable spread in model projections of tracer transport in climate change simulations, largely because of the substantial uncertainty in projected changes in the large‐scale atmospheric circulation. In particular, a relationship between the response of tropospheric transport into the high latitudes and a shift of the midlatitude jet has been previously established in an idealized modeling study. To test the robustness of this relationship, we analyze the response of a passive tracer of northern midlatitude surface origin to abrupt 2xCO2 and 4xCO2 in a comprehensive climate model (Goddard Institute for Space Studies E2.2‐G). We show that a poleward shift of the northern midlatitude jet and enhanced eddy mixing along isentropes on the poleward flank of the jet result in decreased tracer concentrations over the midlatitudes and increased concentrations over the Arctic. This mechanism is robust in abrupt 2xCO2 and 4xCO2 simulations, the nonlinearity to CO2 forcing, and two versions of the model with different atmospheric chemistry. Preliminary analysis of realistic chemical tracers suggests that the same mechanism can be used to provide insights into the climate change response of anthropogenic pollutants.
Labeled Interactive Topic Models

Topic models are valuable for understanding extensive document collections, but they don't always identify the most relevant topics. Classical probabilistic and anchor-based topic models offer interactive versions that allow users to guide the models towards more pertinent topics. However, such interactive features have been lacking in neural topic models. To correct this lacuna, we introduce a user-friendly interaction for neural topic models. This interaction permits users to assign a word label to a topic, leading to an update in the topic model where the words in the topic become closely aligned with the given label. Our approach encompasses two distinct kinds of neural topic models. The first includes models where topic embeddings are trainable and evolve during the training process. The second kind involves models where topic embeddings are integrated post-training, offering a different approach to topic refinement. To facilitate user interaction with these neural topic models, we have developed an interactive interface. This interface enables users to engage with and re-label topics as desired. We evaluate our method through a human study, where users can relabel topics to find relevant documents. Using our method, user labeling improves document rank scores, helping to find more relevant documents to a given query when compared to no user labeling.
Lifelong Formal Modeling Agents – Definitions and Implementation Strategies

This paper summarizes the current vision and results of our research since 2016, the beginning of our Jazz platform project. We explain the design of Lifelong Formal Modeling Agents by starting with describing and defining basic ideas and discussing their importance. We present a complete architecture built upon these ideas, summarize our experimental results where available and discuss how they represent minimum requirements towards building reliable human understandable AI agency. Finally, we briefly touch on how these agents could produce more natural human-computer interfaces. It should be noted that the definitions presented in this paper of intelligence, understanding, concept, object, symbol and other terms are intended for practical implementation and will not completely align with other academic definitions of the same terms.
Battery Modelling and Performance Analysis of Time-Varying Load Using Simulink

In recent years, each area has emerged with autonomous capabilities such as vehicular navigation, aquaculture, and industrial appliances. Especially, in the field of vehicular transportation, power is the main constraint for autonomous operations. The battery is the main source of energy or power storage. Especially, rechargeable batteries are potentially utilized as energy storage systems due to their high energy density. However, battery modelling is an indispensable tool for designing a real-time battery management system (BMS) that estimates the run-life time of autonomous battery power systems. In this paper, an easy-to-use battery Simulink model with time-varying dynamic load has been designed as a tool for all autonomous vehicles to estimate the State of Charge (SOC). This proposed model comprises a Controlled-Voltage source in series with internal resistance and time-varying resistive load. The proposed model differs from the ideal model by 0.01 (1%) and mirrors the general behaviour of the ideal model. The performance analysis of both the ideal and proposed model is evaluated by Root Mean Square Error (RMSE) which must be less than 0.4 to accept the battery model. However, the proposed model achieved the RMSE value of 0.1 and estimates SOC which is widely acceptable.
Robust Integral Sliding Mode Control for Transient Voltage Support From Bidirectional Converter-Based Active Filters in Microgrids

The dc–ac conversion in microgrids leads to second-order oscillations in the dc side voltages and source currents. These oscillations are mitigated using either active or passive filters. Active filters are preferred, as they mitigate oscillations with lesser component size than passive filters. The active filters are regulated to absorb the harmonic ripples at the ripple frequency, and do not provide any voltage support during dc bus voltage fluctuations due to load changes. In this article, a robust integral sliding mode control (RISMC) is proposed to regulate bidirectional converter (BDC)-based dc active filters to facilitate voltage conditioning along with reduction of second-order harmonics which are caused due to dc–ac conversions. The proposed RISMC enhances capability of the BDC active filters to provide dc bus voltage conditioning in case of transience during load variations and uncertain voltage fluctuations. The proposed control consists of a nominal dual-loop PI control which regulates the voltage of the storage capacitor of BDC and a nonlinear integral sliding mode control which regulates dc bus voltage. The proposed control mitigates voltage and current ripples hence preventing them to propagate toward the sources while providing additional voltage support functionality. The proposed control is verified through simulations and experimentation.
Implementation of Voltage Regulation in a Spread-Spectrum-Clocked Buck Converter

Buck converters use spread-spectrum technique to reduce the electromagnetic emissions by distributing the electromagnetic interference in a frequency band around the switching frequency which consequently reduces the amplitude of the interference. Changing the switching frequency also changes the output voltage and causes an unwanted output voltage ripple. The output voltage ripple is then reduced by properly controlling the time delay used in the duty cycle controller of the buck converter. This paper presents a digital implementation of the duty cycle controller used in the buck converter which uses a phase-locked loop to generate the switching frequency. Changing the divider value of the phase-locked loop changes its output frequency, but this frequency change is not instantaneous and it is modelled as a second-order system response. The implementation of the duty cycle controller presented and analysed in this paper aims to compensate for this transient change of the output frequency, i.e. the switching frequency and keep the duty cycle of the buck converter constant. The paper focuses on the compensation algorithm used to generate a constant duty cycle, as well as on the challenges of its digital implementation.
A novel Optimization technique in 5G based IoVs using hybrid Fuzzy Weight-NSGA Scheme

With the advancement of 5G based Internet-of-Vehicles (IoVs) networks, to inculcate the current demand in resource sharing among connected devices require Quality of Services (QoS) in data traffics, link capacity and coverage. In this paper we address the multi-objective resource optimization problem in Software-Defined-Network of 5G enabled IoV. A novel technique is proposed as hybrid Fuzzy Weight-NSGA to incorporate optimization of three diversified cost objective functions as connections and the end-to-end delays. The empirical simulation proposed FW-NSGA outperforms with respect to optimization of connections among existing literatures.
Visualization of Three-Dimensional Ultrasound Data

This paper presents visualization of an ultrasonic probe beam from a series of cross-section images. The goal was to simulate scanning with an ultrasonic probe as it would be done in real-life. Cross-section slices that represent the detection sensitivity of the probe were obtained by simulating a sound pressure field in the metal sample and were provided by INETEC. Different techniques for rendering the three-dimensional model of a sequence of images used for beam visualization in the making of the software are presented. The beam and its inner structure were rendered with the Volumetric Ray Marching algorithm while the polygonal mesh approximation was constructed with the Marching cubes algorithm. The final software was developed using the Unity engine. The results and visualization possibilities were presented and analyzed along with the possible improvements for the future work. The developed simulation software can be used as a helping tool in minimizing the number of redundant scans in search of defects inside an object structure during the quality control phase.
Improving Performance of InGaP/GaAs HBT Arrays by means of Temperature-Dependent Base Ballasting Resistors

Base ballasting resistors (BBRs) are commonly adopted to minimize electrothermal(ET) effects in arrays of bipolar transistors for power amplifier (PA) applications. In this paper, innovative BBRs with temperature-dependent resistivity are proposed in order to improve the trade-off between safe-operating area (SOA) and radiofrequency (RF) performances. As a simple case-study, a three-cell heterojunction bipolar transistor (HBT) fabricated by Qorvo is considered and circuital ET simulations are performed in ADS. Results of the comparison between the proposed strategy and the traditional one are discussed, thus providing useful insights into the advantages of temperature-dependent BBRs.
Robust Admittance Control With Complementary Passivity

This letter studies a robust admittance control problem with a passivity requirement for stable and unstable linear time-invariant systems, motivated by control issues originated from physical human-robot interaction. A complementary admittance control structure is proposed and analyzed, revealing that the nominal performance (admittance tracking and passivity) is decoupled from robustness. Simulations on the admittance control for human arm strength augmentation with a passivity requirement validate the proposed controller design.
Performance Limits of Fractional-N Digital PLLs with Mid-Rise TDCs

Fractional-N digital phase-locked loops (DPLL’s) typically use a multi-stage noise-shaping (MASH) digital $\triangle-\Sigma$ modulator (DDSM) divider controller to synthesize an output frequency that is a non-integer multiple of the reference. The quantization error (QE) arising from the divider controller, if not being canceled prior to reaching the time-to-digital converter (TDC), can overwhelm the TDC input jitter level and in turn deteriorate the phase noise (PN) of the TDC, and hence that of the DPLL at the system level. This sets a performance limit for such DPLLs, for which case it is crucial to predict explicitly the worst-case output PN in order to analyze fractional-N PN mitigation techniques critically. This paper investigates this scenario, for which the input jitter analysis and output PN prediction are performed. Multi-rate discrete-time behavioral simulation at the system level confirms the accuracy of our analytical predictions.
A Power-Efficient Successive Approximation Algorithm for Low-Activity Signals

This paper presents a new successive approximation algorithm that can digitize the second-order difference of signal samples rather than each sample point individually or plain difference of sample points. This method is able to drastically reduce the number of comparisons required to convert a new signal sample to digital numbers, from a fixed N comparisons commonly used in a conventional successive-approximation-register (SAR) analog-to-digital converter (ADC), to a number in between 2 and N for almost all the signal samples in an N-bit ADC. This proposed algorithm is implemented in MATLAB and tested on electrocardiogram (ECG) signals in this work. The experimental results show that our algorithm can reduce the number of comparisons by 58.75% compared to the conventional SAR ADC, and by 17.78% or more compared to several other state-of-the-art methods. In addition, it is able to reduce the DAC updating time by the same percentages, leading to lower power consumption in both DAC and digital parts of SAR ADC.
Consensus Control Based on Privacy-Preserving Two-Party Relationship Test Protocol

Preservation of privacy is a challenging and significant constraint in multi-agent systems. This letter aims to introduce a framework that enables the states of a multi-agent system to reach a consensus while preserving the confidentiality of each agent’s initial states from others. First, a protocol for a privacy-preserving two-party relationship test is proposed. Subsequently, the protocol is employed to devise the average consensus controller for the first-order system, and the rendezvous controller for the second-order system. In contrast to prior research that relies on stochastic coupling weights, our approach circumvents the random chattering problem of the control input, resulting in improved convergence performance. Finally, numerical verification is conducted to demonstrate the effectiveness of the proposed controllers in both first- and second-order systems.
Electrical Scheme Digitization Using Deep Learning Methods

The use of software tools and applications progressively became a standard in both education and industry. A solution for hand-drawn electrical scheme digitization has been proposed to match the fast-paced dynamic of the modern world in the field of electrical engineering. The aim is to notably reduce time-consuming and error-prone electrical scheme tracing from hand-drawn to simulating software. The means have been achieved through the usage of state-of-the-art deep learning model YOLOv5 for electrical elements detection along with Python and OpenCV library for data processing. The user’s input is an image of a hand-drawn circuit, and the end result is an LTspice digitized electrical scheme ready for simulation.
OSMPS: Many-body entangled open quantum systems

We present an open source code to simulate entangled dynamics of open quantum systems governed by the Lindblad master equation with tensor network methods. Tensor network methods using matrix product states have been proven very useful to simulate many-body quantum systems and have driven many innovations in research. Since the matrix product state design is tailored for closed one-dimensional systems governed by the Schr\"odinger equation, the next step for many-body quantum dynamics is the simulation of one-dimensional open quantum systems. We review the three dominant approaches to the simulation of open quantum systems via the Lindblad master equation: quantum trajectories, matrix product density operators, and locally purified tensor networks. The package Open Source Matrix Product States combines these three major methods to simulate one-dimensional open quantum systems with the Lindblad master equation. We access the same underlying tensor network algorithms for all techniques, e.g., the tensor contractions optimized in the same way, allowing us to have a meaningful comparison between the different approaches based on selected examples. These examples include the finite temperature states of the transverse quantum Ising model, the dynamics of an exciton traveling under the influence of spontaneous emission and dephasing, and a double-well potential simulated with the Bose-Hubbard model including dephasing. We analyze which approach is favorable leading to the conclusion that a complete set of all three methods is most beneficial, pushing the limits of different scenarios. The convergence studies using analytical results for macroscopic variables and exact diagonalization methods as comparison, show, for example, that matrix product density operators are favorable for the exciton problem in our study.
Automatic Evaluation of Student Software Solutions in a Virtualized Environment

Universities are using Automated Programming Assessment Systems (APAS) to minimize problems that emerge by manually managing student software solutions: subjectivity, inefficiency in case of many solutions, and lack of fast and rich feedback for the student. Lately, handling code executions in a secure way has become a requirement. Researchers addressed this topic and proposed generic security models: user-level restrictions, process-level restrictions, and virtualization. In the case of virtualization, commonly suggested solutions are virtual machines and containers. With all the benefits of virtual machines, but less resource-demanding, containers are becoming widely used last years. This paper proposes a newly developed assessment model and an information system and proposes a solution for the automatic evaluation of student software solutions in a virtualized environment. It analyzes the key parts of the student solution source codes and commands against a combination of the test case input data, using the pattern-finding method. This method was used in the context of finding the required lexical structures. The proposed system is used during the exam evaluation of the Programming course at the College of Algebra, and the relational database courses, for evaluating SQL solutions in combination with the previously developed system at the College of Algebra.
Determining Graphene and Substrate Quality from the Coupled Hall Mobility Measurements and Theoretical Modeling

An increase of mobility up to ~2600 cm2/Vs is observed in graphene by Hall bar characterization within the temperature range from 40 K to 300 K. The increasing trend is attributed to Coulomb scattering by employing theoretical modeling based on the momentum relaxation time approximation of the Boltzmann transport equation. We also find that at room temperature and for higher charge densities additional mechanisms such as lattice defect and/or substrate corrugation scattering become important and restrict the mobility down to only ~200 cm2/Vs and carrier mean free paths well under ~35nm.
Cascaded Deep Video Deblurring Using Temporal Sharpness Prior and Non-Local Spatial-Temporal Similarity

We present compact and effective deep convolutional neural networks (CNNs) by exploring properties of videos for video deblurring. Motivated by the non-uniform blur property that not all the pixels of the frames are blurry, we develop a CNN to integrate a temporal sharpness prior (TSP) for removing blur in videos. The TSP exploits sharp pixels from adjacent frames to facilitate the CNN for better frame restoration. Observing that the motion field is related to latent frames instead of blurry ones in the image formation model, we develop an effective cascaded training approach to solve the proposed CNN in an end-to-end manner. As videos usually contain similar contents within and across frames, we propose a non-local similarity mining approach based on a self-attention method with the propagation of global features to constrain CNNs for frame restoration. We show that exploring the domain knowledge of videos can make CNNs more compact and efficient, where the CNN with the non-local spatial-temporal similarity is <inline-formula><tex-math notation="LaTeX">$3\times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>3</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="yang-ieq1-3243059.gif"/></alternatives></inline-formula> smaller than the state-of-the-art methods in terms of model parameters while its performance gains are at least 1 dB higher in terms of PSNRs. Extensive experimental results show that our method performs favorably against state-of-the-art approaches on benchmarks and real-world videos.
Design Space Exploration of TRNG Latches for Improved Entropy and Efficiency

True Random Number Generators (TRNGs) are a key building block in cryptography. In order to obtain random outputs, the TRNG must produce sufficient noise such that the probability of overcoming any offsets caused by on-die variations between devices, which are inescapable in CMOS processes, is high. In this paper, we analyze the metastable latch based TRNG design with regards to relative device strength, type, and size to determine the tradeoffs in robustness to offsets, bit-rate, and power.
Fake News Detection: A Comprehensive Survey

The growth and increasing use of digital information platforms have dramatically changed the way news is produced, disseminated, and consumed in our society. Fake news can be found everywhere through popular platforms like social media and the internet. Efforts to develop an effective system for identifying fake news are numerous. Artificial intelligent tools are included to address this difficult issue. Fake news appears in different forms based on the features of their content. The aim of this research is to provide a comprehensive understanding of the various techniques within the domain of fake news detection through a systematic literature review of the existing work. This literature will demonstrate the most significant and relevant models to provide orientations in future research.