Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Parameterization of the Purine Metabolism Pathway through Discrete Event-based Simulation

Stochastic Petri Nets (SPN)are recognized as one of the standard formalisms to model metabolic networks. They allow incorporating randomness in the model and taking into account possible fluctuations and noise due to molecule interactions in the environment. Even though some frameworks have been proposed to implement and simulate SPN (e.g., Snoopy, Monalisa), they do not allow for automatic model parameterization, which is a crucial task to identify the network configurations that lead the model to satisfy certain biological properties. We present a framework to synthesize the SPN model of a metabolic network into executable code that can be simulated through a discrete event-based simulator. The framework allows the user to formally define the network properties to be observed and to automatically extrapolate, through Assertion-based Verification (ABV), the parameter configurations that lead the network to satisfy such properties. We applied the framework to model the purine metabolism and to reproduce the metabolomics data obtained from naive lymphocytes and autoreactive T cells implicated in the induction of experimental autoimmune disorders. We show system parameterization extrapolated by the framework to reproduce the experimental results and to simulate the model under different conditions.
Different modes of electron heating in dual-frequency capacitively coupled radio frequency discharges

A systematic study of different modes of electron heating in dual-frequency capacitively coupled radio frequency (CCRF) discharges is performed using a particle-in-cell simulation. Spatio-temporal distributions of the total excitation/ionization rates under variation of gas pressure, applied frequencies and gas species are discussed. Some results are compared qualitatively with an experiment (phase resolved optical emission spectroscopy) operated under conditions similar to a parameter set used in the simulation. Different modes of electron heating are identified and compared with α- and γ-mode operation of single-frequency CCRF discharges. In this context the frequency coupling and its relation to the ion density profile in the sheath are discussed and quantified. In light gases the ion density in the sheath is time modulated. This temporal modulation is well described by an analytical model and is found to affect the excitation dynamics via the frequency coupling. It is shown that the frequency coupling strongly affects the generation of beams of highly energetic electrons by the expanding sheath and field reversals caused by the collapsing sheath. The role of secondary electrons at intermediate and high pressures is clarified and the transition from α- to γ-mode operation is discussed. Depending on the gas and the corresponding cross sections for excitation/ionization the excitation does not generally probe the ionization as is usually assumed.
Balancing Student Learning and Community Relations in Software-Based Service Learning

Service-learning is a form of experiential learning that integrates academic learning objectives with community engagement. It can develop both technical and the broad professional skills needed in today’s global economy and is well-matched to the literature on diversity. In engineering and computing education, service-learning is typically project-based where students develop a project that adds value to the community partner. At a large Midwestern university, a design-based service-learning program engages students in teams that partner with local and global community partners to develop technological solutions to human, community and environmental needs. This article presents a case study of a software-based service-learning design class with multi-disciplinary students from varying academic levels, first-year to final year. Students are required to complete weekly reflections which were analyzed to attain insight into their experience. The study provides insights into how the students attempt to balance their own learning, project implementation, and community relations and impact while developing software solutions.
Bittorio revisited: structural coupling in the Game of Life

The notion of structural coupling plays a central role in Maturana and Varela’s biology of cognition framework and strongly influenced Varela’s subsequent enactive elaboration of this framework. Building upon previous work using a glider in the Game of Life (GoL) cellular automaton as a toy model of a minimal autopoietic system with which to concretely explore these theoretical frameworks, this article presents an analysis of structural coupling between a glider and its environment. Specifically, for sufficiently small GoL universes, we completely characterize the nonautonomous dynamics of both a glider and its environment in terms of interaction graphs, derive the set of possible glider lives determined by the mutual constraints between these interaction graphs, and show how such lives are embedded in the state transition graph of the entire GoL universe.
Digital neocolonialism and massive open online courses (MOOCs): colonial pasts and neoliberal futures

ABSTRACT Through evaluating dominant MOOC platforms created by Western universities, I argue that MOOCs on such platforms tend to embed Western-centric epistemologies and propagate this without questioning their global relevance. Consequently, such MOOCs can be detrimental when educating diverse and complex participants as they erode local and indigenous knowledge systems. Arguing that the digital divide is an exacerbation of historical inequalities, I draw parallels between colonial education, specifically across Sub-Saharan Africa, and ‘digital neocolonialism’ through Western MOOC platforms. I analyse similarities in ideology, assumptions, and methods of control. Highlighting evolving forms of coloniality, I include contemporary problems created by neoliberal techno-capitalist agendas, such as the commodification of education. Balance is needed between the opportunities offered through MOOCs and the harms they cause through overshadowing marginalised knowledges and framing disruptive technologies as the saviour. While recommending solutions for inclusion of marginalised voices, further problems such as adverse incorporation are raised.
Visual context for verb sense disambiguation and multilingual representation learning

Every day billions of images are uploaded to the web. To process images at such a large scale it is important to build automatic image understanding systems. An important step towards understanding the content of the images is to be able to understand all the objects, scenes and actions depicted in the image. These systems should be capable of integrating with natural language or text to be able to query and interact with humans for tasks such as image retrieval. Verbs play a key role in the understanding of sentences and scenes. Verbs express the semantics of an actions as well as the interactions between objects participating in an event. Thus understanding verbs is central to both language and image understanding. However, verbs are known for their variability in meaning with context. Many studies in psychology have shown that contextual information plays an important role in semantic understanding and processing in the human visual system. We use this as intuition and understand the role of textual or visual context in tasks that combine language and vision. Our research presented in this thesis focuses on the problems of integrating visual and textual contexts for: (i) automatically identifying verbs that denote actions depicted in the images; (ii) fine-grained analysis of how visual context can help disambiguate different meanings of verbs in a language or across languages; (iii) the role played by the visual and multilingual context in learning representations that allow us to query information across modalities and languages. First, we propose the task of visual sense disambiguation, an alternative way of addressing the action recognition task. Instead of identifying the actions directly, we develop a two step process: identifying the verb that denotes the action being depicted in an image and then disambiguate the meaning of the verb based on the visual and textual context associated with the image. We first build a image-verb classifier based on the weak signal from image description data and analyse the specific regions that model focuses on while predicting the verb. We then disambiguate the meaning of the verb shown in the image using image features and sense-inventories. We test the hypothesis that visual and textual context associated with the image contribute to the disambiguation task. Second, we ask whether the predictions made by such models correspond to human intuitions about visual verbs or actions. We analyse whether the image regions a verb prediction model identifies as salient for a given verb correlate with the regions fixated
Seq2Seq or Perceptrons for Robust Lemmatization. An Empirical Examination

We propose a morphologically-informed neural Sequence to Sequence (Seq2Seq) architecture for lemmatization. We evaluate the architecture on German and compare it to a log-linear state-of-the-art lemmatizer based on edit trees. We provide a type-based evaluation with an emphasis on robustness against noisy input and uncover irregularities in the training data. We find that our Seq2Seq variant achieves state-of-the-art performance and provide insight in advantages and disadvantages of the approach. Specifically, we find that the log-linear model has an advantage when dealing with misspelled words, whereas the Seq2Seq model generalizes better to unknown words.
Human Detection, Tracking and Segmentation in Surveillance Video

This dissertation addresses the problem of human detection and tracking in surveillance videos. Even though this is a well-explored topic, many challenges remain when confronted with data from real world situations. These challenges include appearance variation, illumination changes, camera motion, cluttered scenes and occlusion. In this dissertation several novel methods for improving on the current state of human detection and tracking based on learning scene-specific information in video feeds are proposed. Firstly, we propose a novel method for human detection which employs unsupervised learning and superpixel segmentation. The performance of generic human detectors is usually degraded in unconstrained video environments due to varying lighting conditions, backgrounds and camera viewpoints. To handle this problem, we employ an unsupervised learning framework that improves the detection performance of a generic detector when it is applied to a particular video. In our approach, a generic DPM human detector is employed to collect initial detection examples. These examples are segmented into superpixels and then represented using Bag-of-Words (BoW) framework. The superpixel-based BoW feature encodes useful color features of the scene, which provides additional information. Finally a new scene-specific classifier is trained using the BoW features extracted from the new examples. Compared to previous work, our method learns scenespecific information through superpixel-based features, hence it can avoid many false detections typically obtained by a generic detector. We are able to demonstrate a significant improvement in the performance of the state-of-the-art detector. Given robust human detection, we propose a robust multiple-human tracking framework using a part-based model. Human detection using part models has become quite popular, yet its extension in tracking has not been fully explored. Single camera-based multiple-person tracking is often hindered by difficulties such as occlusion and changes in appearance. We address such problems by developing an online-learning tracking-by-detection method. Our approach learns
Evaluating image quality measures to assess the impact of lossy data compression applied to climate simulation data

Applying lossy data compression to climate model output is an attractive means of reducing the enormous volumes of data generated by climate models. However, because lossy data compression does not exactly preserve the original data, its application to scientific data must be done judiciously. To this end, a collection of measures is being developed to evaluate various aspects of lossy compression quality on climate model output. Given the importance of data visualization to climate scientists interacting with model output, any suite of measures must include a means of assessing whether images generated from the compressed model data are noticeably different from images based on the original model data. Therefore, in this work we conduct a forced‐choice visual evaluation study with climate model data that surveyed more than one hundred participants with domain relevant expertise. In addition to the images created from unaltered climate model data, study images are generated from model data that is subjected to two different types of lossy compression approaches and multiple levels (amounts) of compression. Study participants indicate whether a visual difference can be seen, with respect to the reference image, due to lossy compression effects. We assess the relationship between the perceptual scores from the user study to a number of common (full reference) image quality assessment (IQA) measures, and use statistical models to suggest appropriate measures and thresholds for evaluating lossily compressed climate data. We find the structural similarity index (SSIM) to perform the best, and our findings indicate that the threshold required for climate model data is much higher than previous findings in the literature.
Medical Image Compression Based on Region of Interest

Medical images show a great interest since it is needed in various medical applications. In order to decrease the size of medical images which are needed to be transmitted in a faster way; Region of Interest (ROI) and hybrid lossless compression techniques are applied on medical images to be compressed without losing important data. In this paper, a proposed model will be presented and assessed based on size of the image, the Peak Signal to Noise Ratio (PSNR),and the time that is required to compress and reconstruct the original image.The major objective of the proposed model is to minimize the size of image and the transmission time. Moreover, improving the PSNR is a critical challenge.The results of the proposed model illustrate that applying hybrid losslesstechniques on the ROI of medical images reduces size by 39% and gives better results in terms of the compression ratio and PSNR.
Mitigating the harmful effects of technostress: inducing chaos theory in an experimental setting

ABSTRACT This research investigates the role of users’ proactive personality when they mitigate the adverse impacts of ICT's (information and communication technologies’) technostress in an experimental setting. Further, this study examines genuinely two types of individuals’ proactive personality, i.e. confront and transform. Both types can handle ICT's technostress creators by different attitudes and behaviours. This article contributes to a novelty by inducing chaos theory. This inducement laid in the experimental setting that the users had been in a chaotic situation. Results of this research show that users’ proactive personality mitigates the negative impact of ICT's technostress on their performance and satisfaction. This study found that the user's proactive personality, rested in a chaotic situation, does mitigate greatly. This research finds that users’ performance and satisfaction are higher to the proactive personality verging to the transform than that of confront. It implies that information system managers should consider the ICT user's characteristics. Besides, it means that ICT's developer should make users do their innovativeness.
Locally Confined Modality Fusion Network With a Global Perspective for Multimodal Human Affective Computing

In this paper, we propose a novel multimodal fusion framework, called the locally confined modality fusion network (LMFN), that contains a bidirectional multiconnected LSTM (BM-LSTM) to address the multimodal human affective computing problem. In the LMFN, we introduce a generic fusion structure that explores both local and global fusion to obtain an integral comprehension of information. Specifically, we partition the feature vector corresponding to each modality into multiple segments and learn every local interaction through a tensor fusion procedure. Global interaction is then modeled by learning the dependence between local tensors via an originally designed BM-LSTM architecture, establishing a direct connection of cells and states of local tensors that are several time steps apart. With the LMFN, we achieve advantages over other methods in the following aspects: 1) local interactions are successfully modeled using a feasible vector segmentation procedure that can explore cross-modal dynamics in a more specialized manner; 2) global interactions are modeled to obtain an integral view of multimodal information using BM-LSTM, which guarantees an adequate flow of information; and 3) our general fusion structure is highly extendable by applying other local and global fusion methods. Experiments show that the LMFN yields state-of-the-art results. Moreover, the LMFN achieves higher efficiency compared to other models by applying the outer product as the fusion method.
Digital Amplifier: A Power-Efficient and Process-Scaling Amplifier for Switched Capacitor Circuits

To realize high-resolution pipelined and pipelined-SAR analog-to-digital-converters (ADCs), an accurate residue amplifier is necessary. However, realizing such an amplifier in scaled CMOS is challenging due to the worsened transistor characteristics. Prior works focused on gain calibration techniques to mitigate the use of low-gain amplifiers, in return of system complexity and prolonged startups. In this paper, we introduce a digital amplifier (DA) technique to realize power-efficient and accurate amplification in scaled CMOS. DA cancels out all errors (i.e., gain error, nonlinearity, incomplete settling, power supply noise, and thermal noise) of the low-gain amplifier by feedback based on successive approximation. The DA accuracy can be arbitrary set by configuring the number of bits in the DA capacitor digital-to-analog-converter; the amplifier gain is decoupled from the transistor intrinsic gain which is suitable for scaled CMOS integration. We also show that the power efficiency can be enhanced over conventional opamp-based designs with relaxed settling error requirements of DA-based multiplying digital-to-analog-converters (MDACs). Moreover, the circuit design of DA-based MDACs is further discussed. Measurement results of the calibration-free 0.7-V 12-bit 160-MS/s pipelined-SAR ADC implemented in 28-nm CMOS are reported. Without calibration, the ADC achieves signal-to-noise-and-distortion-ratio = 61.1 dB, figure-of-merit = 12.8 fJ/conv. , which is over $3\times $ improvement compared with conventional calibration-free high-speed pipelined ADCs. In addition, we evaluate the DA’s process scalability by comparing the measured results of the DA-based MDAC prototyped in 65- and 28-nm CMOS. We observe $2\times - 3\times $ improvement in speed, power, and area mainly resulting from the DA’s process scalability.
Utilization Of Mobile Phones To Apply Blended Learning At Higher Education: Computer Subject at State Vocational High School 1 BENGKULU

The application of blended learning at vocational high schools is one of the solutions to increase interest in learning to students. The preparation of software is easily accessible on gadgets, thus the delivery of material can be done online. Tasks and practices may be applied in class according to teacher's instruction. By data in the form of the amount of gadgets used by students reaching 100 percent, the delivery of material by video, voice recordings to learning materials can be received easily. The task and practice plan project is delivered at the beginning of lesson with targets determined in accordance with the applicable curriculum. By applying the blended learning method by utilizing gadgets, it can be one alternative for developing and increasing interest in learning to students
Curated Model Development Using NEUROiD: A Web-Based NEUROmotor Integration and Design Platform

Decades of research on neuromotor circuits and systems has provided valuable information on neuronal control of movement. Computational models of several elements of the neuromotor system have been developed at various scales, from sub-cellular to system. While several small models abound, their structured integration is the key to building larger and more biologically realistic models which can predict the behavior of the system in different scenarios. This effort calls for integration of elements across neuroscience and musculoskeletal biomechanics. There is also a need for development of methods and tools for structured integration that yield larger in silico models demonstrating a set of desired system responses. We take a small step in this direction with the NEUROmotor integration and Design (NEUROiD) platform. NEUROiD helps integrate results from motor systems anatomy, physiology, and biomechanics into an integrated neuromotor system model. Simulation and visualization of the model across multiple scales is supported. Standard electrophysiological operations such as slicing, current injection, recording of membrane potential, and local field potential are part of NEUROiD. The platform allows traceability of model parameters to primary literature. We illustrate the power and utility of NEUROiD by building a simple ankle model and its controlling neural circuitry by curating a set of published components. NEUROiD allows researchers to utilize remote high-performance computers for simulation, while controlling the model using a web browser.