Miracle-dz/UniFAR-FTUs huggingface dataset:
LossLeaP: Learning to Predict for Intent-Based Networking

Intent-Based Networking mandates that high-level human-understandable intents are automatically interpreted and implemented by network management entities. As a key part in this process, it is required that network orchestrators activate the correct automated decision model to meet the intent objective. In anticipatory networking tasks, this requirement maps to identifying and deploying a tailored prediction model that can produce a forecast aligned with the specific –and typically complex– network management goal expressed by the original intent. Current forecasting models for network demands or network management optimize generic, non-flexible, and manually designed objectives, hence do not fulfil the needs of anticipatory Intent-Based Networking. To close this gap, we propose LossLeaP, a novel forecasting model that can autonomously learn the relationship between the prediction and the target management objective, steering the former to minimize the latter. To this end, LossLeaP adopts an original deep learning architecture that advances current efforts in automated machine learning, towards a spontaneous design of loss functions for regression tasks. Extensive experiments in controlled environments and in practical application case studies prove that LossLeaP outperforms a wide range of benchmarks, including state-of-the-art solutions for network capacity forecasting.
Deep Pianist (Category: Generative Modeling)

Songwriting indeed requires unexplainably intuitive methods to create melody, rhythms, and chord progressions. However, there clearly exist noticeable structural patterns within a genre, works by the same artist, or even music in general. We have created a Long-Short Term Memory (LSTM)-based generative model for music generation, aiming to capture this structural pattern in the process of creating a musical piece. Our model trained on a MIDI ﬁle-based text representation of music, to generate musical sequences. We show that this model successfully generates musical sequences but with room for improvement in terms of listenability
Imbalanced Satellite Image Classification using Positive Unlabeled Generative Adversarial Network (Generative Modeling)

Many social and geological studies require numerous satellite images to yield accurate result for weather prediction. However, it is expensive to collect images directly from the satellites when the ground is covered by clouds. Hence we will have much more satellites samples without cloud than samples with clouds. The imbalanced dataset causes inaccurate prediction in weather classification task. In this paper, we explore both the generative adversarial neural network and positive unlabelled generative adversarial neural network to generate more cloudy satellite images in order to up-sample the underrepresented class in our dataset. Our method helps balancing the dataset and yields a higher classification accuracy.
Self-supervised Short-text Modeling through Auxiliary Context Generation

Short text is ambiguous and often relies predominantly on the domain and context at hand in order to attain semantic relevance. Existing classification models perform poorly on short text due to data sparsity and inadequate context. Auxiliary context, which can often provide sufficient background regarding the domain, is typically available in several application scenarios. While some of the existing works aim to leverage real-world knowledge to enhance short-text representations, they fail to place appropriate emphasis on the auxiliary context. Such models do not harness the full potential of the available context in auxiliary sources. To address this challenge, we reformulate short-text classification as a dual channel self-supervised learning problem (that leverages auxiliary context) with a generation network and a corresponding prediction model. We propose a self-supervised framework, Pseudo-Auxiliary Context generation network for Short-text Modeling (PACS), to comprehensively leverage auxiliary context and it is jointly learned with a prediction network in an end-to-end manner. Our PACS model consists of two sub-networks: a Context Generation Network (CGN) that models the auxiliary context’s distribution and a Prediction Network (PN) to map the short-text features and auxiliary context distribution to the final class label. Our experimental results on diverse datasets demonstrate that PACS outperforms formidable state-of-the-art baselines. We also demonstrate the performance of our model on cold-start scenarios (where contextual information is non-existent) during prediction. Furthermore, we perform interpretability and ablation studies to analyze various representational features captured by our model and the individual contribution of its modules to the overall performance of PACS, respectively.
Development of a time projection chamber with a sheet-resistor field cage

A new-concept time projection chamber (TPC) using a commercial resistive sheet, sheet-resistor micro-TPC SR-microPIC, was developed and its performance was measured. SR-microTPC has the potential to create a more uniform electric field than conventional TPCs with resistor-chains owing to its continuous sheet resistivity, and its production would be easier than that of conventional TPCs. The material used in this study, Achilles-Vynilas, was found to be thin, transparent, and low-radioactive. The performance test with cosmic muons showed very promising results, including the demonstration of a good tracking-performance. This type of TPC field cage can offer an alternative for the widely used conventional field cages
Care Policy in Slovenia: Divergent Trends and Convergent Attitudes

Slovenia makes a compelling case for care policy analysis since it is marked by extreme dichotomy in care. Therefore, placing Slovenia on a continuum of care regimes ranging from defamilialised to familialised with respect to care is difficult, with care for children being highly defamilialised, and care for older people highly familialised. The country’s childcare policies build on a historically well-developed system of public childcare provision and generous leave policies, together with a well-developed social protection system targeting families. These have been retained and, in some cases, were expanded, still following the 2009 economic crisis, certain austerity measures were introduced. On the other hand, care policies for older people started to develop later and after the initial growth they relatively stagnated (especially the social homecare system). A comprehensive long-term care system has yet to be developed and become a subject of ongoing political debates. Further, unlike in childcare, the increasing role of private actors can be observed in this sector. In the article, we discuss these care policy developments in Slovenia in terms of the role of relevant actors (state, family, private actors) with an emphasis on the views of people regarding care, based on data gathered within an innovative method of democratic forums. The article reveals that the dichotomy of care policies, as well as the differing recent trends in family policies for children and care for older people, is not present in people’s attitudes and their preferences for the arrangement of such policies.
Covid Cases Forecast Using Twitter Data

This paper details an approach to leveraging Covid related Tweets to forecast State-level Covid case numbers. The forecast is constrained to the United States. Inputs to the model include the daily averaged Glove Vectors of Covid related tweets per state, the state (encoded into a numerical class), and the state’s population. The final model consists of a two layer LSTM model with a fully connected output layer. The model was able to attain an r 2 value of 0.99, as well as a MAPE (mean absolute persentage error) score of 0.11. The strong performance of the model suggests that similar data and model architectures can be leveraged to forecast Covid cases at higher geographic granularity, and potentially provide farther in future forecasts.
A General Equilibrium Quantification of the Impact of Fraunhofer on the German Economy A General Equilibrium Quantication of the Impact of Fraunhofer on the German Economy

I construct a general equilibrium model with endogenous development and adoption of technologies at the rm level. The model recognizes the dual role of Fraunhofer as an institution that conducts public R&D and that helps some rms close their technological gap with the frontier through research contracts. I use a calibrated version of the model to calculate the long-run contribution of FhG to the German economy. The key nding is that FhG leads to a long-run level of GDP in Germany 3.65% higher than if Fraunhofer did not existed. The magnitude of this e¤ect is robust. Both the public R&D and research contracts contribute to this e¤ect, though the e¤ect of FhG research on GDP via the increase in public R&D is much greater than the e¤ect of research contracts on technology adoption. Keywords: Fraunhofer, technology adoption, innovation, reserach contracts, publis R&D, general equilibrium. JEL Classi cation: E3, O3.
Evaluation of Containment Boxes as a Fire Mitigation Method in Elevated Oxygen Conditions

NASA performed testing to evaluate the efficacy of fire containment boxes without forced ventilation. Configurational flammability testing was performed on a simulation avionics box replicating critical design features and filled with materials possessing representative flammability characteristics. This paper discusses the box's ability, under simulated end-use conditions, to inhibit the propagation of combustion to surrounding materials. Analysis was also performed to evaluate the potential for the fire containment box to serve as an overheat/ignition source to temperature sensitive equipment (such as items with lithium-ion batteries). Unrealistically severe combustion scenarios were used as a means to better understand the fire containment mechanism. These scenarios were achieved by utilizing materials/fuels not typically used in space vehicles due to flammability concerns. Oxygen depletion, during combustion within the fire containment boxes, drove self-extinguishment and proved an effective method of fire containment
基於Histogram of Oriented Gradients之課堂舉手辨識研究

In recently classroom environment, there are more and more teachers using electronic devices to help them easy to understand what students think and how students act. In all gestures, raising hand is the most popular way that students interacting with teachers. In this research, we provided a raising hand recognition system to help teacher to handle all students’ behavior. We use camera in complex background and monitor multiple people. To propose a system that can satisfy all environments and will not re-train after changing environments, we separate the system into two parts: people segmentation and gesture recognition. In people segmentation part, we use k-means clustering to extract skin color and then use motion to remove skin-liked background. In gesture recognition part, we use histogram of oriented gradient to get the gesture feature and then use SVM to classify. Finally in experimental part, we test 3 scenes to verify our method. When we use the same case to train and test, the correct rate is average 91%. Even we use different day for training/testing, the correct rate can also reach 80%.
Molecular identification of three endophytic fungi from eucalyptus leaves and their pathogenicity

Eucalyptus leaves have antibacterial, antiviral, and antifungal components and have been use to treat colds, influenza, rhinitis, sinusitis, and other respiratory infections. Endophytic fungi can produce secondary metabolites similar to their hosts. The research was performed to assess the pathogenicity of species-specific endophytic fungi isolated from Eucalyptus leaves on Eucalyptus citriodora plantlets through in vitro inoculation and molecular identification. This research was conducted at the Indonesian Spice and Medicinal Crop Research Institute, Plant Protection Laboratory, from July 2020 – May 2021. Isolation of endophytic fungi was done by direct planting method, whereas purification and characterization of fifty isolates were conducted on Potato Dextrose Agar (PDA) media for their activity as antimicrobial. Based on ITS sequence analysis, the DNA sequences of isolate A-TKR1 were identical to Aspergillus flavus, isolate E-TKR3 to Penicillium citrinum, and isolate ECT- TKR7 to Neofusicoccum parvum. The similarity of those isolates was 100%, 99.81%, 99.82%, respectively. The re-isolation analysis of infected tissues showed that A. flavus, P. citrinum, and N. parvum matched Koch’s postulates and confirmed endophytes’ presence within living tissues.
Viability and vigor of sugarcane (Saccharum spp. Hybrid) seeds from intrageneric and intergeneric crosses at various storage periods

Sugarcane is one of the essential crops with high economic value in Indonesia, because it is the primary source of sugar. Sugarcane is propagated vegetatively for commercial purposes, but for genetic improvement, crosses carried out to produce seeds with the new genetic combinations. This study aims to determine the viability and vigor of seeds from intrageneric and intergeneric crosses at various storage periods. This study used seed samples from 19 intrageneric and intergeneric crosses conducted by ISRI from 2006 to 2016. The seeds were kept in aluminum foil bags in the freezer at -20oC. Viability and vigor of seeds were determined by evaluating germination, growth speed, growth synchronously, vigor index, and maximum growth potential. The results showed that the type of crosses and the storage time of seeds had a very significant effect on germination, growth speed, growth synchronously, vigor index and maximum growth potential. However, the longer storage of sugarcane seeds was not always accompanied by a decrease in seed viability and vigor, both seeds resulting from intergeneric and intrageneric crosses. In addition, the viability and vigor of seeds from intergeneric crosses was lower than the viability and vigor of seeds from intrageneric crosses.
Peripheral Collisions of Ice-covered Silica Dust Grains

Collisions with ice-covered silica grains are studied using molecular-dynamics simulation, with a focus on the influence of the impact parameter on the collision dynamics. The ice mantle induces an attractive interaction between the colliding grains, which is caused by the melting of the mantles in the collision zone and their fusion. For noncentral collisions, this attractive interaction leads to a deflection of the grain trajectories and, at smaller velocities, to the agglomeration (“sticking”) of the colliding grains. The bouncing velocity, which is defined as the smallest velocity at which grains bounce off each other rather than stick, shows only a negligible dependence on the impact parameter. Close to the bouncing velocity, a temporary bridge builds up between the colliding grains, which, however, ruptures when the collided grains separate and relaxes to the grains. At higher velocities, the ice in the collision zone is squeezed out from between the silica cores, forming an expanding disk, which ultimately tears and dissolves into a multitude of small droplets. An essential fraction of the ice cover in the collision zone is then set free to space. Astrophysical implications include the possibility that organic species that might be present in small concentrations on the ice surface or at the ice–silica interface are liberated to space in such noncentral collisions.
Circulating tumor cells isolated from colorectal cancer patients can form spheroids spontaneously: a small study in Ciptomangunkusumo Hospital, Indonesia [version 1; peer review: awaiting peer review]

Background: Circulating Tumor Cells (CTC) are tumor cells that have been separated from the primary site and enter the blood or lymphatic circulation of cancer patients. CTCs are known to have tumorigenic properties and are thought to have a relationship with metastasis. One of the tests that can be used to assess tumorigenesis is the spheroid formation test. This study was conducted to determine CTCs’ ability to form spheroids. Methods: A 1 ml sample of blood was taken from two patients with metastatic colorectal cancer and placed in separate heparin tubes. The CTCs were then isolated using the DirectCTC enrichment kit according to the manufacturer's procedure. A total of 10,000 cells were grown in nuncsphera u plates in Dulbecco’s Modified Eagle Medium (DMEM) for three days under standard culture conditions. Spheroid or aggregation was then evaluated using an inverted microscope. Results: We identified a CTC culture derived from each of the patients with metastatic colorectal cancer, namely CTC ID001 and CTC 002 respectively. CTC ID001 formed spheroids spontaneously while CTC ID002 did not. CTC ID001 appears to aggregate spontaneously and resembles primary tumor cells in their ability to form spheroids. Conclusions: We observed that CTCs can mimic primary tumors to form spheroids spontaneously, though some CTCs are not able to form spheroids. We suspected that this was related to the tumorigenic nature of CTC in these patients. Open Peer Review Reviewer Status AWAITING PEER REVIEW Any reports and responses or comments on the article can be found at the end of the article. Page 1 of 7 F1000Research 2022, 11:75 Last updated: 02 FEB 2022
QB-13005-GM 71..90

The rapid technological developments following the Human Genome Project have made possible the availability of personalized genomes. As the focus now shifts from characterizing genomes to making personalized disease associations, in combination with the availability of other omics technologies, the next big push will be not only to obtain a personalized genome, but to quantitatively follow other omics. This will include transcriptomes, proteomes, metabolomes, antibodyomes, and new emerging technologies, enabling the profiling of thousands of molecular components in individuals. Furthermore, omics profiling performed longitudinally can probe the temporal patterns associated with both molecular changes and associated physiological health and disease states. Such data necessitates the development of computational methodology to not only handle and descriptively assess such data, but also construct quantitative biological models. Here we describe the availability of personal genomes and developing omics technologies that can be brought together for personalized implementations and how these novel integrated approaches may effectively provide a precise personalized medicine that focuses on not only characterization and treatment but ultimately the prevention of disease.
Zoom In on Agents: Interpreting Deep Reinforcement Learning Models

Despite the successes of deep neural networks for solving reinforcement learning environments, these models’ transparency remains highly limited, making it harder to ensure their reliability. In recent years, a number of new approaches to interpreting neural networks have been proposed, yet applications have focused on other machine learning applications. This paper builds on earlier techniques by creating tools for saliency mapping, data sampling, weights visualization, and input optimization, and by applying these tools to interpret a deep Q-learning network pre-trained for playing Atari Breakthrough. Focusing on the first and fourth layers, I find that layer-1 neurons tend to be interpretable as polyseman-tic representations of one of several distinct objects in the game Breakthrough, identifying particular objects’ locations, their direction of motion, and–in some cases–their destruction. Additionally, I find that layer-4 neurons are often used more narrowly, for identifying very specific high-level game states, to be directly used for action selection.
Analysis of the Effect of Gender, High School Origin, Middle School Department, College Motivation on Academic Achievement of Students of Accounting and Finance Department: Case Study on One of Private University in Indonesia

This research aims to know the learning patterns and student’s abilities in education performance by identify and analyze the influence of gender, the high school origin, high school programs, motivation on academic achievement. The methods of data collection are carried out through the following methods of questionnaires, literature studies. This study is quantitative explanatory and retrieves important accounting items. Based on the data analysis was conducted, the results showed that gender, high school programs, college motivation are not significantly affected in the GPA While the origin of high school significantly affects the GPA. This study suggested that future studies adopt other methods of data analysis and take the dependent variable other than academic achievement.
The Elements of Journalism

The book “The Elements of Journalism” written by Kovach and Rosenstiel attempts to propagate the „ideal‟ elements of journalism. What this book captures better than any single book on the practice and principles of journalism is the weaving together reasons and scenarios why media audiences have fled and why new technology and large corporate ownership are putting journalism at risk. The book is also a product of interaction with practicing journalists under the auspices of the “Committee of Concerned Journalists Traveling Curriculum” in the United States. Written in eleven chapters, the book attempts to weave in the various strands and argument, alas, within the United States alone, on what constitutes elements of journalism, why they are controversial and what is practicable within the book‟s geographic area of coverage.
The problems and countermeasures of computer-assisted Mandarin Chinese Test in colleges

With the development of technology,the Mandarin Chinese Test is in the new eras.Computer-assisted Mandarin Chinese Test has become the tendency of the development of Mandarin Chinese Test.By analyzing the problems exiting in the Computer-assisted Mandarin Chinese Test of college,the corresponding countermeasures are put forward,which provides references to the designer of system developing and the related personnel in colleges.
Dynamic Phasor Based Analysis and Control in Renewable Energy Integration

The objective of this dissertation is to carry out dynamic modeling, analysis and control of power systems with Renewable Energy Sources (RES) such as: Photovoltaic (PV) power sources and wind farms. The dissertation work is mainly focused on microgrid since it plays a major role in modern power systems and tend to have higher renewable power penetration. Two main theoretical concepts, dynamic phasor and impedance modeling have been adopted to model and analyze the power systems/mocrogrids with RES. The initial state calculation which is essential for small signal analysis of a system is carried out as the first step of the dissertation work. Dynamic phasor and impedance modeling techniques have been utilized to model and analyze power systems/micogrids as the second phase of the work. This part consists of two main studies. First case investigates the impedance modeling of Thyristor Controller Series Capacitor (TCSC) for subsynchronous resonance (SSR) analysis where a wind farm is connected to a power system through series compensated line. Second case utilizes the dynamic phasor concept to model a microgrid in unbalanced condition. Here the unbalance is caused by a single phase PV connected to the microgrid. Third Phase of the dissertation work includes upper level control of the microgrid. Here prediction and optimization control for a microgrid with a wind farm, a PV system, an energy storage system and loads is evaluated. The last part of the dissertation work focuses on real time modeling and hardware in loop simulation test bed for microgrid applications. This dissertation has led to four journal papers (three accepted, one submitted) and five conference papers.