Miracle-dz/UniFAR-FTUs huggingface dataset:
Technology Entrepreneurship Intention among Higher Education Institutions Students: A Literature Review

Technology entrepreneurship is a new breed of entrepreneurship in a technology context, where it merges technology prowess and entrepreneurial skills. The intention to become technopreneurs among students from higher education institutions has become more and more prominent since it can be seen as a valuable way to contribute to the labor market to solve unemployment issues. However, despite data showing that the unemployment rate in Malaysia post-COVID is at a worrying state, studies on technology entrepreneurship intention are still lacking, with few areas being under-researched. This paper attempts to provide a review of existing academic literature on technology entrepreneurship intention among university students, revealing the current state and future direction of technology entrepreneurship intention research. Using a systematic literature review (SLR) method, the research papers were analyzed between the years 2017 and 2023 (7 years) to see the trend in the current years. The result shows that previous studies do not cover the subject comprehensively. Subjective norms were the least studied factor, despite their importance in shaping an individual’s behavior and intention. It was also found that none of the papers studied the role of government support and social media towards technology entrepreneurship intention. The identification of these under-researched areas will provide future directions for researchers to empirically study the factors. This paper may also provide future directions for the government in ensuring that more graduates have the intention to pursue technology entrepreneurship which will in turn contribute to the country’s economic growth.
Directional Source Separation for Robust Speech Recognition on Smart Glasses

Modern smart glasses leverage advanced audio sensing and machine learning technologies to offer real-time transcribing and captioning services, considerably enriching human experiences in daily communications. However, such systems frequently encounter challenges related to environmental noises, resulting in degradation to speech recognition and speaker change detection. To improve voice quality, this work investigates directional source separation using the multi-microphone array. We first explore multiple beamformers to assist source separation modeling by strengthening the directional properties of speech signals. In addition to relying on predetermined beamformers, we investigate neural beamforming in multi-channel source separation, demonstrating that automatic learning directional characteristics effectively improves separation quality. We further compare the ASR performance leveraging separated outputs to noisy inputs. Our results show that directional source separation benefits ASR for the wearer but not for the conversation partner. Lastly, we perform the joint training of the directional source separation and ASR model, achieving the best overall ASR performance.
Keeping track of reality: embedding visual memory in natural behaviour

ABSTRACT Since immersive virtual reality (IVR) emerged as a research method in the 1980s, the focus has been on the similarities between IVR and actual reality. In this vein, it has been suggested that IVR methodology might fill the gap between laboratory studies and real life. IVR allows for high internal validity (i.e., a high degree of experimental control and experimental replicability), as well as high external validity by letting participants engage with the environment in an almost natural manner. Despite internal validity being crucial to experimental designs, external validity also matters in terms of the generalizability of results. In this paper, we first highlight and summarise the similarities and differences between IVR, desktop situations (both non-immersive VR and computer experiments), and reality. In the second step, we propose that IVR is a promising tool for visual memory research in terms of investigating the representation of visual information embedded in natural behaviour. We encourage researchers to carry out experiments on both two-dimensional computer screens and in immersive virtual environments to investigate visual memory and validate and replicate the findings. IVR is valuable because of its potential to improve theoretical understanding and increase the psychological relevance of the findings.
A shadow preservation framework for effective content-aware image retargeting process

In the discipline of image retargeting, the structure of shadows in the image is required to be preserved since it gives structural information about the objects. Many traditional image retargeting techniques do not pay attention to preserve the shadow objects present in the image. In this paper a shadow-preservation framework is proposed in which the saliency map is derived using segmentation-based gPb-owt-ucm approach to emphasize the salient regions. To assess the quality of saliency map a fixation prediction analysis is conducted on each section of the image using the eye tracker technology. Second, to pay more attention on the shadow objects a shadow map is developed after executing image pre-processing, shadow detection, region growing, and region filling operations. To rectify the issue of misclassification of shadow pixels erosion and dilation operations are performed. To obtain an efficient importance map, a gradient map is prepared using canny operator which is further combined with the saliency map and shadow map. The obtained importance map is supplied to the seam diversion based image retargeting (SDIR) technique to resize the image in horizontal and vertical directions. To justify the efficiency of the proposed framework’s the obtained results are compared with the existing state-of-the-art. By preserving shadows, the proposed framework enhances the overall visual quality and maintains the integrity of objects in the retargeted images. The framework’s effectiveness is validated through quantitative evaluations and visual comparisons with existing methods. The results demonstrate its potential for improving content-aware image retargeting applications, paving the way for more realistic and visually appealing image resizing techniques in various domains.
Process Optimization for the Bioinspired Synthesis of Gold Nanoparticles Using Cordyceps militaris, Its Characterization, and Assessment of Enhanced Therapeutic Efficacy

The promising therapeutic implications of nanoparticles have spurred their development for biomedical applications. An eco-friendly methodology synthesizes gold nanoparticles using Cordyceps militaris, an edible mushroom (Cord-Au-NPs), using a quality-by-design approach (central composite design). UV-visible spectroscopy analysis revealed an absorption peak at 540–550 nm, thus confirming the synthesis of gold nanoparticles. Cord-Au-NPs have a crystalline structure, as evidenced by the diffraction peaks. The zeta potential value of −19.42 mV signifies the stability of Cord-Au-NPs. XRD study shows gold facets and EDX analysis revealed a strong peak of spherical nanoparticles in the gold region with a mean particle size of 7.18 nm and a polydispersity index of 0.096. The obtained peaks are closely associated with phenolic groups, lipids, and proteins, as examined by FTIR, suggesting that they function as the reducing agent. Cord-Au-NPs exhibited dose-dependent antioxidant, antidiabetic, and antibacterial activity. The method is eco-friendly, nontoxic, less time-consuming, and does not use synthetic materials, leading to higher capabilities in biomedical applications.
GECTurk: Grammatical Error Correction and Detection Dataset for Turkish

Grammatical Error Detection and Correction (GEC) tools have proven useful for native speakers and second language learners. Developing such tools requires a large amount of parallel, annotated data, which is unavailable for most languages. Synthetic data generation is a common practice to overcome the scarcity of such data. However, it is not straightforward for morphologically rich languages like Turkish due to complex writing rules that require phonological, morphological, and syntactic information. In this work, we present a flexible and extensible synthetic data generation pipeline for Turkish covering more than 20 expert-curated grammar and spelling rules (a.k.a., writing rules) implemented through complex transformation functions. Using this pipeline, we derive 130,000 high-quality parallel sentences from professionally edited articles. Additionally, we create a more realistic test set by manually annotating a set of movie reviews. We implement three baselines formulating the task as i) neural machine translation, ii) sequence tagging, and iii) prefix tuning with a pretrained decoder-only model, achieving strong results. Furthermore, we perform exhaustive experiments on out-of-domain datasets to gain insights on the transferability and robustness of the proposed approaches. Our results suggest that our corpus, GECTurk, is high-quality and allows knowledge transfer for the out-of-domain setting. To encourage further research on Turkish GEC, we release our datasets, baseline models, and the synthetic data generation pipeline at https://github.com/GGLAB-KU/gecturk.
Evaluating HPC Kernels for Processing in Memory

Memory subsystems contribute significantly to the performance and energy efficiency of high-performance computing (HPC) applications. Traditional memory technologies with conventional organization (e.g., DRAM) are struggling to keep up with the increasing memory requirements of modern applications. Techniques such as multilayer cache hierarchy and out-of-order execution are still falling short of mitigating the penalty incurred by memory accesses. Processing-in-memory (PIM), which involves moving memory-intensive kernels to memory for execution instead of bringing the data to the processing unit, is emerging as a promising technique. PIM has recently received traction among computer architecture researchers, and the increasing research activity surrounding this technique indicates its potential to alleviate main memory performance bottlenecks. In this paper, we characterize and identify memory-intensive HPC kernels, perform a first-order evaluation of the PIM technique for selected HPC kernels, quantify performance deviation, and analyze the key factors that affect PIM efficiency.
Quantized Feedback Stabilization of Switched Linear Systems under Denial-of-Service Attacks Based on Event-Triggering

This paper investigates the stabilization of switched linear systems under denial-of-service (DoS) attacks with event-triggered strategies and finite bit rate quantization. Unlike previous research that only considers switching when DoS attacks are inactive, we investigate a general case where unknown switches are allowed during DoS activation, as well as asynchronous communication between controller modes and subsystem modes. We assume that switching instants follow the average dwell time and that DoS attacks with limited energy are described by restricted frequency and duration. Firstly, event-triggered strategies and finite bit rate quantized policy are designed to estimate the bounds of the state estimation error under unknown switches and DoS attacks, and achieve the quantized state feedback under finite bandwidth. Additionally, by using multiple Lyapunov functions, we establish a joint constraint of switching signals and DoS attacks energy, which implicates the influence of multiple switches in DoS attacks and quantization error on system stability. Based on the above discussions, the global asymptotic stability of the closed-loop system is established. Finally, simulations are conducted to confirm the obtained results.
Multigrade Artificial Neural Network for the Design of Finite Periodic Arrays

To solve the restriction of prior knowledge in artificial neural networks (ANNs) for the modeling of finite periodic arrays, a new multigrade ANN model is proposed in this paper. Considering mutual coupling and array environment, the proposed model is designed with two sub-ANNs, element-ANN and array-ANN. Based on the relationship between the geometrical variables and the electromagnetic (EM) behavior of elements in an array, element-ANN is built to provide prior knowledge for the modeling of array-ANN. Then, in a review of mutual coupling and array environment, array-ANN is modeled to obtain the EM response of the whole array from the nonlinear superposition of the element responses. Three numerical examples of a linear phased array, a six-element printed dipole array, and a U-slot microstrip array are employed to verify the effectiveness of the proposed model.
Symbolic Regression on Sparse and Noisy Data with Gaussian Processes

In this paper, we address the challenge of deriving dynamical models from sparse and noisy data. High-quality data is crucial for symbolic regression algorithms; limited and noisy data can present modeling challenges. To overcome this, we combine Gaussian process regression with a sparse identification of nonlinear dynamics (SINDy) method to denoise the data and identify nonlinear dynamical equations. Our simple approach offers improved robustness with sparse, noisy data compared to SINDy alone. We demonstrate its effectiveness on a Lotka-Volterra model, a unicycle dynamic model in simulation, and hardware data from an NVIDIA JetRacer system. We show superior performance over baselines including 20.78% improvement over SINDy and 61.92% improvement over SSR in predicting future trajectories from discovered dynamics.
Continuous wave quantum light control via engineered Rydberg induced dephasing

We analyze several variations of a single-photon optical switch operating in the continuous wave regime, as presented in the accompanying paper [Tsiamis et al., Continuous wave single photon switch based on a Rydberg atom ensemble]. The devices are based on ensembles of Rydberg atoms that interact through van der Waals interaction. Continuously probing the atomic cloud with a weak coherent probe field, under the conditions of electromagnetically induced transparency (EIT) leads to total reflection/transmission of the probe in the absence of control photons. Exciting a Rydberg state with a single control photon breaks the EIT conditions, drastically altering the probe's reflectance/transmittance. We examine how the collective Rydberg interaction in an atomic ensemble enclosed in an optical cavity or in free space induces two probe-induced dephasing processes. These processes localize the control photons and modify the probe's reflectance/transmittance, enhancing the lifetime of control excitations and increasing the devices' efficiency. The devices are characterized by the probability to absorb a control photon and the associated gain as described by the change in the probe's reflectance/transmittance. The results are confirmed through numerical calculations of realistic one- and three-dimensional atomic ensembles in a cavity and an one-dimensional atomic ensemble in free space. The proposed continuous wave devices complement previously realized single photon transistors and expand the possible quantum light manipulation circuitry.
Infrared Spectral Fingerprint of Neutral and Charged Endo- and Exohedral Metallofullerenes

Small metal-containing molecules have been detected and recognized as one of the hybrid species that are efficiently formed in space, especially in the circumstellar envelopes of evolved stars. It has also been predicted that more complex hybrid species such as those formed by metals and fullerenes (metallofullerenes) could be present in these circumstellar environments. Recently, quantum-chemical simulations of metallofullerenes have shown that they are potential emitters contributing to the observed mid-IR spectra in the fullerene-rich circumstellar environments of different types of evolved stars. Here we present the individual simulated mid-IR (∼5–50 μm) spectra of 28 metallofullerene species. Both neutral and charged endo- and exohedral metallofullerenes for seven different metals (Li, Na, K, Ca, Mg, Ti, and Fe) have been considered. The changes induced by the metal–C60 interaction in the intensity and position of the spectral features are highlighted using charge-density difference maps and electron-density partitioning. Our calculations identify the fundamental IR spectral regions in which, depending on the metal binding nature, there should be a major spectral contribution from each of the metallofullerenes. The IR spectra of the metallofullerenes are made publicly available to the astronomical community, especially users of the James Webb Space Telescope, for comparisons that could eventually lead to the detection of these species in space.
A h-D3QN-QMIX Design for Formation Decision in Air Combat

This paper addresses the formation decision problem in air combat. Based on the hierarchical reinforcement learning framework, a novel h-D3QN-QMIX algorithm is designed, where the D3QN term is used to determine the optimal formation according to the air combat state and at the same time the QMIX term is employed to achieve formation. The reward function is divided into the intrinsic and extrinsic rewards, where the former is designed based on the target position of the formation and the latter is designed based on the expected formation and combat results. It is showing that the win rate of the h-D3QN-QMIX is 71.4%, which is much higher than it of QMIX and rule-based method.
Teaching dynamical systems with Maxima

We report on our experience in teaching a dynamical systems course tosecond-year engineering students. The course has been taught with ahands-on approach, using a Computer Algebra System: Maxima. We havedeveloped some additional programs to help students explore thedynamics of the systems, in a more intuitive way and without loosingtoo much time with computer programming. The result has been veryencouraging; students are better motivated using this approach, ascompared to the more traditional methods that we used in the past.
A Study of Shared-Control With Bilateral Feedback for Obstacle Avoidance in Whole-Body Telelocomotion of a Wheeled Humanoid

Teleoperation has emerged as an alternative solution to fully-autonomous systems for achieving human-level capabilities on humanoids. Specifically, teleoperation with whole-body control is a promising hands-free strategy to command humanoids but requires more physical and mental demand. To mitigate this limitation, researchers have proposed shared-control methods incorporating robot decision-making to aid humans on low-level tasks, further reducing operation effort. However, shared-control methods for wheeled humanoid telelocomotion on a whole-body level has yet to be explored. In this work, we explore how whole-body bilateral feedback with haptics affects the performance of different shared-control methods for obstacle avoidance in diverse environments. A time-derivative Sigmoid function (TDSF) is implemented to generate more intuitive haptic feedback from obstacles. Comprehensive human experiments were conducted and the results concluded that bilateral feedback enhances the whole-body telelocomotion performance in unfamiliar environments but could reduce performance in familiar environments. Conveying the robot's intention through haptics showed further improvements since the operator can utilize the feedback for reactive short-distance planning and visual feedback for long-distance planning.
Virtual Screening-Accelerated Discovery of a Phosphodiesterase 9 Inhibitor with Neuroprotective Effects in the Kainate Toxicity In Vitro Model

In the central nervous system, some specific phosphodiesterase (PDE) isoforms modulate pathways involved in neuronal plasticity. Accumulating evidence suggests that PDE9 may be a promising therapeutic target for neurodegenerative diseases. In the current study, computational techniques were used to identify a nature-inspired PDE9 inhibitor bearing the scaffold of an isoflavone, starting from a database of synthetic small molecules using a ligand-based approach. Furthermore, docking studies supported by molecular dynamics investigations allowed us to evaluate the features of the ligand–target complex. In vitro assays confirmed the computational results, showing that the selected compound inhibits the enzyme in the nanomolar range. Additionally, we evaluated the expression of gene and protein levels of PDE9 in organotypic hippocampal slices, observing an increase following exposure to kainate (KA). Importantly, the PDE9 inhibitor reduced CA3 damage induced by KA in a dose-dependent manner in organotypic hippocampal slices. Taken together, these observations strongly support the potential of the identified nature-inspired PDE9 inhibitor and suggest that such a molecule could represent a promising lead compound to develop novel therapeutic tools against neurological diseases.
AutoSynth: Learning to Generate 3D Training Data for Object Point Cloud Registration

In the current deep learning paradigm, the amount and quality of training data are as critical as the network architecture and its training details. However, collecting, processing, and annotating real data at scale is difficult, expensive, and time-consuming, particularly for tasks such as 3D object registration. While synthetic datasets can be created, they require expertise to design and include a limited number of categories. In this paper, we introduce a new approach called AutoSynth, which automatically generates 3D training data for point cloud registration. Specifically, AutoSynth automatically curates an optimal dataset by exploring a search space encompassing millions of potential datasets with diverse 3D shapes at a low cost. To achieve this, we generate synthetic 3D datasets by assembling shape primitives, and develop a meta-learning strategy to search for the best training data for 3D registration on real point clouds. For this search to remain tractable, we replace the point cloud registration network with a much smaller surrogate network, leading to a 4056.43 times speedup. We demonstrate the generality of our approach by implementing it with two different point cloud registration networks, BPNet [13] and IDAM [34]. Our results on TUD-L [26], LINEMOD [23] and Occluded-LINEMOD [7] evidence that a neural network trained on our searched dataset yields consistently better performance than the same one trained on the widely used ModelNet40 dataset [65].
3D-intrusions transport active surface microbial assemblages to the dark ocean

Subtropical oceans contribute significantly to global primary production, but the fate of the picophytoplankton that dominate in these low nutrient regions is poorly understood. Working in the subtropical Mediterranean, we demonstrate that subduction of water at ocean fronts generates 3D intrusions with uncharacteristically high carbon, chlorophyll, and oxygen that extend below the sunlit photic-zone into the dark ocean. These contain “fresh” picophytoplankton assemblages that resemble the photic-zone regions where the water originated. Intrusions propagate depth-dependent seasonal variations in microbial assemblages into the ocean interior. Strikingly, the intrusions included dominant biomass contributions from non-photosynthetic bacteria and enrichment of enigmatic heterotrophic bacterial lineages. Thus, the intrusions not only deliver material that differs in composition and nutritional character from sinking detrital particles, but also drive shifts in bacterial community composition, organic matter processing, and interactions between surface and deep communities. Modeling efforts paired with global observations demonstrate that subduction can flux similar magnitudes of particulate organic carbon as sinking export, but is not accounted for in current export estimates and carbon cycle models. Intrusions formed by subduction are a particularly important mechanism for enhancing connectivity between surface and upper mesopelagic ecosystems in stratified subtropical ocean environments that are expanding due to the warming climate.