Miracle-dz/UniFAR-FTUs huggingface dataset:
Data Factors as a Source of Innovation: Evidence from Chinese Cloud Computing Listed Firms

With the rise of digital economy, data has become a new factor of production. Based on data from A-share Chinese Cloud Computing listed firms, this study examined the nexus between using data factors and corporate innovation. Empirical evidence indicates that Cloud Computing listed firms have significantly increased their innovation performance in the process of using data factors, and this conclusion is still valid after a series of robust analyses. The rational use of data factors in daily business activities can help firms learn and apply external knowledge and technology. These findings provide useful policy implications for promoting the integration of data factors and corporate development to spur vitality of market entities at a micro level.
Technology acceptance model (TAM) for mobile durian plantation apps(Pro Durian); the analysis for Malaysian farmers

During the application development process, it is important for a developer to analyze user requirements. This is crucial to the success of interactive systems and is an essential component of the design of information systems. User requirement analysis aids developers in understanding and analyzing users' needs and technology requirements. Throughout this case study, it is able to forecast the understanding, and technology acceptance, and at the same time improve the quality of work, reducing training and support costing and perennially improve customer or user satisfaction. The method of research selected is questionnaires that focused on collecting the data directly from Malaysian farmers through Google Forms as an online medium. The method was chosen because it is easy to implement and inexpensive due to the current situation of Covid-19. The questionnaire is designed with two sections where; a) includes the participant's demography to analyze information of the intended audience while b)focused on the knowledge of participants about recognition images. Results demonstrate the farmer's opinion and knowledge of the technology of image recognition in the agriculture sector. The quantitative research was undertaken using an online platform due to the Covid-19 pandemic. From the analysis, it is justified that the Technology Acceptance Model (TAM) together with the mobile farming image recognition app is useful in collecting farmers' feedback and positive results have an outcome on the challenges faced by the durian farmers.
Practice-oriented intuitive approach for engineering undergraduates: a case study

Although mathematical illustrations are widely used to explain concepts in control engineering education, however it appearssomewhat difficult for some students to understand in the initial stage and then results in reducing of their learning enthusiasm. Wenoticed that students have huge interesting and active attitudes about experiment. In this paper, we introduced the experiment ofpoint-to-point movement, typically in manufacture industry, for the students with no any control knowledge. Based on theprinciples of traditional rotary motors, students are guided to design a linear motor platform. The linear motor moves from zeroposition to a desired position. Students are requested to do their best to design a controller based on their intuitions andexperiment results. The results and problems occurred will encourage students to study control knowledge and back to solve theproblems. This practice-oriented intuitive approach (POIA) offers an alternative to learning control theory from concreteexperience, so students can grasp knowledge and relate control concepts to simple events. A case study is provided, which involvesa team of five undergraduates with a project named linear motor driven inverted pendulum and controller design sponsoredthrough the Undergraduate Research Program (URP) by China Agricultural University. The results confirm the effectiveness of theproposed method.
Optimal Control of State-Space Systems with Hard Bounds on Control Inputs and State Variables

This paper presents a new numerical method for treating the problem of optimal control when there are hard bounds on the control variables (e.g., limit switches on a linear drive, current limits to motor input, etc.) and/or on the state/output variables (e.g., obstacle avoidance). This is accomplished by means of a new approach for discretizing the optimal control problem, while introducing regularization terms to reduce the solution space to smooth functions. Further, by introducing a consistent discretization of the state-space equations with arbitrary boundary conditions, the problem is cast as a problem of quadratic programming, whereby (hard) bounds can be put on any of the state-space variables (i.e., input or output). The method is demonstrated on the example of a pendulum on a cart. Bounded optimal control solutions are computed for two examples: Velocity bounds are placed on the cart in the classic optimal control problem; a variation of trajectory tracking where instead of specifying a single valued path, the bounds of the trajectory of the pendulum bob are specified, and the required input to keep the bob within these bounds during its motion is computed.
Wideband Matching Circuit for mmWave Series Fed Patch Array Antenna

A novel wide band matching circuit for the series fed patch array antenna at 80 GHz is proposed in this paper. The series fed patch array is designed with amplitude tapered configuration. First, the series fed patch array is matched using stub loaded transmission line (TL) with narrow band. Later, it is converted to wide band matching circuit using three quarter wave transformers and two half wavelength stubs. The sizes of these half wavelength stubs are reduced using the stub loaded and step impedance TLs. The simulated results of the series fed patch array with and without the connector are presented. The simulated −10 dB reflection coefficient bandwidth with connector is from 77.125 GHz to 83.95 GHz and the simulated co-polarized gain with connector is 13.73 dBi. The designed antenna array is fabricated along with the matching circuit and tested experimentally. The measured −10 dB reflection coefficient bandwidth is from 76.73 GHz to 86.08 GHz with the measured gain of the antenna is 13.3 dBi at 81.44 GHz.
Study of a 0.34THz Confocal Gyrotron Traveling Wave Tube

Gyrotron traveling wave tube (gyro-TWT) with Confocal waveguide structure can generate high power due to operating in higher order modes. In this paper, the linear and nonlinear theory of a 0.34THz confocal waveguide gyro-TWT is studied. a numerical code based on the self-consistent nonlinear theory is written to simulate the beam wave interaction. The predicted output power of 35kW can be achieved at an electron beam of 60kV, an efficiency of 12% with a saturated gain of at least 60dB.
Realization of DTMOS based 6T SRAM cells and their performance analysis at 32nm technology node

The SRAM cells are extensively used in on-chip cache memory in devices such as microprocessors, computers, games controller, and mobile phones etc. This paper examines the performance of six transistor SRAM cell with DTMOS as access/pull-up/pull-down transistors and in combination totaling to seven topologies. Copious simulations in LT spice environment have been performed to compute static and dynamic performance of all the seven topologies using 32 nm PTM model parameters. It is found that pull-up and pull-down based DTMOS structure gives best matrices for stability while access only DTMOS structure gives best delay performance.
Training Data Selection and Optimization of English Translation System Based on Data-Driven Learning

Systematic programming of translation and translation optimization is a translation method of system structure and function, and a translation criterion with general meaning. Relying on modern system theory, it studies the organic connection of elements, structures and functions of the semantic system from the aspects of structural form, functional process and optimization method, and constructs the translation route, translation scope, translation and optimization of the structure and function of the semantic system as a whole. Data-driven learning has opened up a new path for college English translation teaching, which is a new trend in college English teaching reform. This paper discusses the selection and optimization of training data for English translation systems based on data-driven learning, and analyzes the optimization principles of current college English translation systems. On this basis, this paper further illustrates the application case of data-driven learning in college English translation teaching, aiming at providing constructive suggestions for corpus-based data-driven college English translation teaching.
Prediction and Detection of Insider Threat Detection using Emails: A Comparision

Recent breaches have proved that the insider threats are the most challenging type of threat and have shown the importance of research of insider threat in cybersecurity. As this problem is being researched by security communities using the traditional machine learning techniques. These techniques use feature engineering and some use anomaly-based detection which are based on high false positives. As these techniques are not able to identify the difference in the behavior of normal and malicious user because such characteristics like complexity, high dimensionality, lack of labelled threats, the nature (subtle and adaptive) of insiders, heterogeneity and the data related to personality and psychological traits is hard to collect as these do not capture motivations behind attacks. As compared to the traditional techniques, advanced machine learning techniques provide better detection for insiders, but the detection still has some limitations like lack of labelled data and adaptive attacks. In this paper, a new methodology is proposed for psychological sentiment analysis based on the email and the network browsing done by the insiders using LDA and SMO. After demonstration, the technique is then being compared with traditional methods and the malicious insiders with negative emotions are detected. This research is built to identify the emotions of the insiders based on the text and sentiment analysis of the emails and the webpages browsed.
Mitigating Aging Effects in Fault-Tolerant FPGA-Based Controllers for Flexible Manufacturing

This paper focuses on the issue of FPGA-based controller reliability in the context of Flexible Manufacturing Systems (FMS). Due to the industrial harsh environment, FPGA-based controllers are susceptible to both failures and aging effects. A framework that integrates Fault-Tolerant (FT) FPGAs with aging mitigation capabilities is proposed to improve FPGA-based industrial controllers' reliability and extend their lifetime. The fault set includes soft faults represented by both Single Event Upsets (SEUs) and Multiple Event Upsets (MEUs), hard faults, and aging effects. The design relies on a customized Triple Modular Redundancy (TMR) technique for fault detection and masking with Dynamic Function eXchange (DFX) capability for partial reconfiguration. In most cases, the reconfiguration does not require any system interruption. The design is also able to differentiate between hard, soft and aging failures. Finally, a simple case study is described to illustrate the system's behavior upon infection by hard fault or aging. The Zybo Z7-20 board is used in this case study.
Age And Gender Detection By Face Segmentation And Modefied CNN Algorithm

The fundamental purpose of the field of research known as “biometrics” is to explore the development of reliable approaches for identifying individuals using their observable traits. Examples of biometric identification include both physical and mental traits of an individual. As a kind of physical identification, fingerprints and facial features may be compared and analyzed. Human gait has been researched because it has the potential to be used as a behavioral identifier in computer visionUsing a Convolutional Neural Network (CNN) and a Support Vector Machine, the capacity to estimate a person's gender based on their stride was explored and investigated (SVM). Throughout our examination of CNN's potential uses for gait-based gender identification, we will strive for both a high degree of accuracy and a cheap computational cost. We analyzed and experimented with a variety of CNN architectures and hyperparameters in this setting.
An improved computation method of word's semantic similarity based on HowNet

Semantic similarity computation of Chinese words is a key issue in Chinese information processing.Under HowNet,by analyzing the impact factor of vocabulary similarity calculations in the level of concept tree structure,a similarity calculation method was proposed.This method takes the level of depth,density and semantic factors into account,and it was used in similarity calculation.Experimental results show that the calculating results of word similarity by this method have become more reasonable,and the majority of the results are more in line with people's daily experience,which effectively improves the precision and exactness of word similarity calculations.
How Indoor Positioning Technology Can Enhance Our Understanding of Communication Patterns in Office Environments

This paper presents an early-stage process to track communication patterns in office spaces using a real-time location system with sub-meter accuracy. A custom frontend application is used to assess and map the positioning data, with the goal of gathering empirical evidence on how spatial intervention can improve workplace communication and productivity. The case-study involved equipping an architectural firm’s office and employees with tracking devices to gather data on their communicative behavior. Unsupervised machine learning algorithms are compared to identify location, quantity, and duration of tracked meeting events, providing insights into participants’ communication behavior. While initial results are promising, this is a work in progress. The effect of tracking on individuals is being investigated through self-assessment questionnaires. Further research is needed to understand the potential impact of spatial intervention on workplace communication and productivity. The paper aims to contribute to the development of evidence-based design strategies in architecture and related fields.
Uncertainty Analysis in Cryptographic Key Recovery for Machine Learning-Based Power Measurements Attacks

The present work concerns side-channel attacks on cryptographic devices protected with the advanced encryption standard (AES). In this regard, the assessment of guessing entropy (GE) and the related uncertainty is proposed for machine-learning-based attacks based on power measurements. For the first time, the GE was assessed on the entire key while uncertainty was introduced in the field of side-channel attacks, thus allowing a more rigorous vulnerability test for a device. Notably, a state-of-the-art attack relying on a multilayer perceptron is exploited for classifying power traces leaked from physically accessible devices. A public database was exploited for the sake of results’ reproducibility. Thanks to cross-validation, the uncertainty associated with retrieving a single key byte can be quantified and then propagated to the entire key by means of the Monte Carlo method. It is thus shown that when exploiting about 4000 attack records (traces), there is a 10% probability to retrieve the secret key as a whole with less than ten attempts. This implies that a full cryptographic key can be discovered on average ten times for every 100 similar devices by a side-channel attack. This poses security threats particularly relevant in an Internet-of-Things scenario and addresses the need for improved vulnerability testing and proper countermeasures.
Fully-actuated, corner contact aerial robot for inspection of hard-to-reach bridge areas

This paper presents the design and development of a fully-actuated platform for the visual inspection of difficult-to-reach areas of bridges, such as bridge beams and bearings. The aerial platform incorporates a carbon fiber structure with spherical wheels that facilitates safe contact with the bridge, while the fully-actuated configuration allows the movement of the platform while keeping the contact with the surface. In addition, the system mounts a camera that allows an operator to supervise the inspection from the ground. Compared to other solutions developed for bridge inspection, our solution is able to maintain contact while moving across the inspection surface and is even capable of maintaining contact with two surfaces in corners, allowing the inspection of difficult-to-reach zones like bridge bearings. We describe the design and build process, the dynamic model and the control of the system proposed. We show the capabilities of the fully-actuated platform by indoor flights, while the proposed aerial platform is tested in a real scenario, for bridge beams and bearings inspection. The results of field tests demonstrate the feasibility and effectiveness of the proposed platform for bridge inspections.
The Effect of Empathy on Happiness in Social Networks: An Agent-Based Simulation Study

The ethics of reciprocity, also known as the Golden Rule, can be found in the moral frameworks of most major religions. The concept pertains to treating others as you wish to be treated; furthermore, that belief is often taken for granted and is rarely investigated beyond qualitative studies. This paper aims to act as a steppingstone to computational rigor application as a means of testing the moral expertise of common religious beliefs. If an agent-based model can model such concepts, that may open the door to obtaining objective measures of correctness for certain philosophical beliefs. This paper presents an agent-based model to test the concept of the Golden Rule and whether it might be correct in its intention to promote happiness and wellness. Generally, the results of our model support that showing more empathy via application of the Golden Rule leads to greater happiness for individuals in a social network.
Energy Efficient SDN-assisted Routing Scheme in Cloud Data Center

For the past few years, cloud data centres have used more electricity. Many servers running at low utilisation rates waste a large amount of energy. In cloud data center to conserve more electricity, an energy-efficient traffic management approach with server sleep/switching on mode is discussed. The fundamental concept is that, using the software-defined network (SDN) technologies, the virtual machines (VM) in the cloud system are either running inside the physical machines (PM) and the PM goes into sleep mode. The objective of the energy-saving optimisation challenge is to minimize the number of active VMs while preserving the QoS standards. After moving all active jobs to other servers, the PM having maximum idle VMs will undergo to switched off or sleep mode as the no. of sleep/idle VMs hits a predetermined threshold. A 2D markov chain is considered for analysing the suggested energy-efficient technique. Our suggested energy-efficient technique successfully optimizes the energy consumption and finds the optimal number of VMs inside each PM. The suggested model is demonstrated by both numerically and simulated trials.
Sexually Antagonistic Variation and the Evolution of Dimorphic Sexual Systems

Multicellular Eukaryotes use a broad spectrum of sexual reproduction strategies, ranging from simultaneous hermaphroditism to complete dioecy (separate sexes). The evolutionary pathway from hermaphroditism to dioecy involves the spread of sterility alleles that eliminate female or male reproductive functions, producing unisexual individuals. Classical theory predicts that evolutionary transitions to dioecy are feasible when female and male sex functions genetically trade off with one another (allocation to sex functions is sexually antagonistic) and rates of self-fertilization and inbreeding depression are high within the ancestral hermaphrodite population. We show that genetic linkage between sterility alleles and loci under sexually antagonistic selection significantly alters these classical predictions. We identify three specific consequences of linkage for the evolution of dimorphic sexual systems. First, linkage broadens conditions for the invasion of unisexual sterility alleles, facilitating transitions to sexual systems that are intermediate between hermaphroditism and dioecy (androdioecy and gynodioecy). Second, linkage elevates the equilibrium frequencies of unisexual individuals within androdioecious and gynodioecious populations, which promotes subsequent transitions to full dioecy. Third, linkage dampens the role of inbreeding during transitions to androdioecy and gynodioecy, making these transitions feasible in outbred populations. We discuss implications of these results for the evolution of dimorphic reproductive systems and sex chromosomes.
Towards A Conceptual Model for Citizen's Adoption of E-Government Services in Developing Countries

E-governments in developing countries should seek to keep pace with the rapid technology development in order to improve their systems and provide better services to citizens through the governments' portals with minimal effort and time. However, many developing countries, particularly most Arabic countries, face many e-government implementation and adoption challenges. The citizen's adoption is still beyond the ambition despite utilizing several models to understand the factors affecting the user's intention to use the e-government services. All the models did not consider the external factors that may hinder the citizen adoption process. This study proposes a new conceptual model based on integrating the unified theory of technology acceptance and use (UTAUT) with the external factors represented by the PEST framework. This model helps to understand the effect of the specific factors related to user's perception in addition to the external factors that may play a significant importance in shaping the intentions and behavior of e-government users.
ARWGAN: Attention-Guided Robust Image Watermarking Model Based on GAN

In the existing deep learning-based watermarking models, extracted image features for fusing with watermark are not abundant enough and more critically, and essential features are not highlighted to be learned with the purpose of robust watermarking, both of which limit the watermarking performance. To solve those two drawbacks, this article proposes an attention-guided robust image watermarking model based on a generative adversarial network (ARWGAN). To acquire a great deal of representational image features, a feature fusion module (FFM) is devised to learn shallow and deep features effectively for multilayer fusion with watermark, and meanwhile, reuse of those features by the dense connection enhances robustness. To alleviate image distortion caused by embedding watermark, an attention module (AM) is deployed to compute the attention mask by mining the global features of the original image. Specifically, with the guidance of the attention mask, image features representing inconspicuous regions and texture regions are enhanced for embedding the high strength of watermark, and simultaneously, other features are suppressed to improve the watermarking performance. Furthermore, the noise subnetwork is adopted for robustness enhancement by simulating various image attacks in iterative training. The discriminator is used to distinguish the encoded image from the original image for improving watermarking invisibility continuously. Experimental results demonstrate that the ARWGAN is superior to the existing state-of-the-art (SOTA) watermarking models, and ablation experiments prove the effectiveness of the FFM and the AM. The code is available at https://github.com/river-huang/ARWGAN.