Miracle-dz/UniFAR-FTUs huggingface dataset:
PENGGUNAAN MODEL NOISE PADA METODE ITERATIVE FEEDBACK TUNING UNTUK PENGHILANGAN GANGGUAN SISTEM PENGENDALIAN

Tulisan ini bertujuan membicarakan tiga metode perancangan pengendalian dasar yang digunakan untuk menghilangkan gangguan (disturbance) pada sistem pengendalian. Ide utamanya adalah menggunakan model noise sederhana waktu kontinu tetap sebagai suatu variabel perancangan dalam mendapatkan sifat-sifat lup tertutup yang diharapkan. Ketiga metode perancangan pengendali (controller) berbasis model noise yang masing-masing saling berhubungan erat tersebut adalah Minimum Variance Control, Weighted Minimum Variance Control dan Loop Shaping Design Procedure . Kemudian akan dibahas proses penalaan parameter-parameter pengendali yang dilakukan dengan menggunakan metode Iterative Feedback Tuning (IFT). Pada metode IFT, parameter-parameter pengendali akan didapatkan dengan cara meminimisasi suatu cost function tertentu menggunakan teknik pencarian gradient.
Robust Control System Networks

From the researcher who was one of the first to identify and analyze the infamous industrial control system malware "Stuxnet", comes a book that takes a new, radical approach to making Industrial control systems safe from such cyber attacks: design the controls systems themselves to be "robust". Other security experts advocate risk management, implementing more firewalls and carefully managing passwords and access. Not so this book: those measures, while necessary, can still be circumvented. Instead, this book shows in clear, concise detail how a system that has been set up with an eye toward quality design in the first place is much more likely to remain secure and less vulnerable to hacking, sabotage or malicious control. It blends several well-established concepts and methods from control theory, systems theory, cybernetics and quality engineering to create the ideal protected system. The book's maxim is taken from the famous quality engineer William Edwards Deming, "If I had to reduce my message to management to just a few words, I'd say it all has to do with reducing variation". Highlights of this title include: an overview of the problem of "cyber fragility" in industrial control systems; how to make an industrial control system "robust", including principal design objectives and overall strategic planning; and, why using the methods of quality engineering like the Taguchi method, SOP and UML will help to design more "armored" industrial control systems.
Fish Species Classification with Data Augmentation

The demand for seafood has grown rapidly over the last few decades. Given this growth, there is an increasing need for more efficient and sustainable fishing practices. A prime challenge in this regard is monitoring fisheries to prevent harmful fishing practices. Therefore, commercial fishing boats have started carrying electronic monitoring (EM) systems to mitigate these practices. However, as the volume of video footage from EM grows, reviewing the footage manually becomes infeasible. Therefore, we seek to address this limitation by developing a convolutional neural network model to classify images of fish in EM footage of fisheries. To make our model efficient and effective, we utilize transfer learning, wherein pre-trained image classification models are built upon to improve model training speed, and data augmentation, wherein images are modified to create a larger training dataset. For evaluation, we utilize the FishNet Open Image Database with over 85,000 images from EM footage of fisheries in the western and central Pacific Ocean. We train two models to classify 10 species of fish: one without and one with data augmentation. Our results show that using data augmentation improves our model’s ability to deal with more difficult fish image classification scenarios. By using deep learning, we can achieve a high classification accuracy and quickly identify species in EM footage.
Transformer-Based Global Zenith Tropospheric Delay Forecasting Model

Zenith tropospheric delay (ZTD) plays an important role in high-precision global navigation satellite system (GNSS) positioning and meteorology. At present, commonly used ZTD forecasting models comprise empirical, meteorological parameter, and neural network models. The empirical model can only fit approximate periodic variations, and its accuracy is relatively low. The accuracy of the meteorological parameter model depends heavily on the accuracy of the meteorological parameters. The recurrent neural network (RNN) is suitable for short-term series data prediction, but for long-term series, the ZTD prediction accuracy is clearly reduced. Long short-term memory (LSTM) has superior forecasting accuracy for long-term ZTD series; however, the LSTM model is complex, cannot be parallelized, and is time-consuming. In this study, we propose a novel ZTD time-series forecasting utilizing transformer-based machine-learning methods that are popular in natural language processing (NLP) and forecasting global ZTD, the training parameters provided by the global geodetic observing system (GGOS). The proposed transformer model leverages self-attention mechanisms by encoder and decoder modules to learn complex patterns and dynamics from long ZTD time series. The numeric results showed that the root mean square error (RMSE) of the forecasting ZTD results were 1.8 cm and mean bias, STD, MAE, and R 0.0, 1.7, 1.3, and 0.95, respectively, which is superior to that of the LSTM, RNN, convolutional neural network (CNN), and GPT3 series models. We investigated the global distribution of these accuracy indicators, and the results demonstrated that the accuracy in continents was superior to maritime space transformer ZTD forecasting model accuracy at high latitudes superior to that at low latitude. In addition to the overall accuracy improvement, the proposed transformer ZTD forecast model also mitigates the accuracy variations in space and time, thereby guaranteeing high accuracy globally. This study provides a novel method to estimate the ZTD, which could potentially contribute to precise GNSS positioning and meteorology.
Mitochondrial metabolic determinants of multiple myeloma growth, survival, and therapy efficacy

Multiple myeloma (MM) is a plasma cell dyscrasia characterized by the clonal proliferation of antibody producing plasma cells. Despite the use of next generation proteasome inhibitors (PI), immunomodulatory agents (IMiDs) and immunotherapy, the development of therapy refractory disease is common, with approximately 20% of MM patients succumbing to aggressive treatment-refractory disease within 2 years of diagnosis. A large emphasis is placed on understanding inter/intra-tumoral genetic, epigenetic and transcriptomic changes contributing to relapsed/refractory disease, however, the contribution of cellular metabolism and intrinsic/extrinsic metabolites to therapy sensitivity and resistance mechanisms is less well understood. Cancer cells depend on specific metabolites for bioenergetics, duplication of biomass and redox homeostasis for growth, proliferation, and survival. Cancer therapy, importantly, largely relies on targeting cellular growth, proliferation, and survival. Thus, understanding the metabolic changes intersecting with a drug’s mechanism of action can inform us of methods to elicit deeper responses and prevent acquired resistance. Knowledge of the Warburg effect and elevated aerobic glycolysis in cancer cells, including MM, has allowed us to capitalize on this phenomenon for diagnostics and prognostics. The demonstration that mitochondria play critical roles in cancer development, progression, and therapy sensitivity despite the inherent preference of cancer cells to engage aerobic glycolysis has re-invigorated deeper inquiry into how mitochondrial metabolism regulates tumor biology and therapy efficacy. Mitochondria are the sole source for coupled respiration mediated ATP synthesis and a key source for the anabolic synthesis of amino acids and reducing equivalents. Beyond their core metabolic activities, mitochondria facilitate apoptotic cell death, impact the activation of the cytosolic integrated response to stress, and through nuclear and cytosolic retrograde crosstalk maintain cell fitness and survival. Here, we hope to shed light on key mitochondrial functions that shape MM development and therapy sensitivity.
Towards Accurate Latency Prediction of DNN Layers Inference on Diverse Computing Platforms

Deep Neural Networks (DNNs) have been intensively deployed in a variety of mobile and edge applications. These applications are constrained by latency constraints. Therefore, collaborative inference has recently become a research hotspot, in which the basis of collaborative inference is to accurately predicate the execution latency of each layer in a DDN. Unfortunately, it is laborious and difficult to predict the inference latency of DNN layers. The prediction-based approaches seem to address this problem, but it still faces challenges in the current solutions, such as (1) the existing prediction strategies do not consider the change of a load of platforms when estimating the computation latency; (2) most of existing prediction approaches consider only CNN networks and do not consider the RNN model. To this end, in this paper, we propose a DNN layers latency prediction framework to accurately estimate the inference of each layer in a DNN in the end-edge-cloud computing environment. Specifically, first, a latency prediction model is built to estimate the layer-wise execution latency of CNN models and RNN models on different heterogeneous computing platforms, which use neural networks to learn non-linear features related to inference latency. Second, a more comprehensive configuration is proposed to improve the predictive performance, which includes static (i.e., FLOPs, memory features, size of the parameter) and dynamic configurations (i.e., CPU, memory, and GPU). Finally, we conduct extensive experiments and the experimental results show that our latency prediction models can improve the prediction accuracy by about 27.2% on average compared with the four baseline approaches.
Structure of the DM Tau Outer Disk: Probing the vertical kinetic temperature gradient

We use high angular and spectral resolution (${\sim} 1''$ & 0.1 km s -1 ) images in $^{12}{\rm CO}$, $^{13}{\rm CO}$ $J=2\! \rightarrow\! 1$, $J=1\! \rightarrow\! 0$ and ${\rm C}^{18}{\rm O}$ $J=2\! \rightarrow\! 1$ coming from the IRAM interferometer to probe the vertical temperature distribution in the disk of DM Tau. We investigate here a new method based on the different opacities of the CO isotopomers to sample the temperature disk structure at various vertical scales inside the disk. Typically, the $^{12}{\rm CO}$ transitions are sampling at 2–4 scale heights, while the $^{13}{\rm CO}$ data are more likely tracing 1 scale height for the $J=2$–1, and the disk mid-plane for the $J=1$–0 line. At the disk scale at which the IRAM observations are sensitive ($R\geq 50$–60 AU), the analysis reveals a vertical temperature gradient. The outer layers where τ ($^{12}{\rm CO}$ $J=2\! \rightarrow\! 1$ $) \simeq 1$ are warmer (${\sim} 30$ K) than the inner layers probed by $^{13}{\rm CO}$ and ${\rm C}^{18}{\rm O}$ data (${\sim} 13$–20 K). These findings are consistent with the structure expected for flared irradiated disks around TTauri stars. We also observe that the outer radius of the disk is smaller in $^{13}{\rm CO}$ and ${\rm C}^{18}{\rm O}$ than in $^{12}{\rm CO}$. These differences in truncation radius are in agreement with photodissociation effects. We also note that the dynamical mass determination from CO is weakly affected by the temperature gradient.
Fundamentals of queueing theory

Praise for the Third Edition: "This is one of the best books available. Its excellent organizational structure allows quick reference to specific models and its clear presentation . solidifies the understanding of the concepts being presented. "IIE Transactions on Operations EngineeringThoroughly revised and expanded to reflect the latest developments in the field, Fundamentals of Queueing Theory, Fourth Edition continues to present the basic statistical principles that are necessary to analyze the probabilistic nature of queues. Rather than presenting a narrow focus on the subject, this update illustrates the wide-reaching, fundamental concepts in queueing theory and its applications to diverse areas such as computer science, engineering, business, and operations research.This update takes a numerical approach to understanding and making probable estimations relating to queues, with a comprehensive outline of simple and more advanced queueing models. Newly featured topics of the Fourth Edition include:Retrial queuesApproximations for queueing networksNumerical inversion of transformsDetermining the appropriate number of servers to balance quality and cost of serviceEach chapter provides a self-contained presentation of key concepts and formulae, allowing readers to work with each section independently, while a summary table at the end of the book outlines the types of queues that have been discussed and their results. In addition, two new appendices have been added, discussing transforms and generating functions as well as the fundamentals of differential and difference equations. New examples are now included along with problems that incorporate QtsPlus software, which is freely available via the book's related Web site.With its accessible style and wealth of real-world examples, Fundamentals of Queueing Theory, Fourth Edition is an ideal book for courses on queueing theory at the upper-undergraduate and graduate levels. It is also a valuable resource for researchers and practitioners who analyze congestion in the fields of telecommunications, transportation, aviation, and management science.
Fair and Optimal Decision Trees: A Dynamic Programming Approach

Interpretable and fair machine learning models are required for many applications, such as credit assessment and in criminal justice. Decision trees offer this inter-pretability, especially when they are small. Optimal decision trees are of particular interest because they offer the best performance possible for a given size. However, state-of-the-art algorithms for fair and optimal decision trees have scalability issues, often requiring several hours to find such trees even for small datasets. Previous research has shown that dynamic programming (DP) performs well for optimizing decision trees because it can exploit the tree structure. However, adding a global fairness constraint to a DP approach is not straightforward, because the global constraint violates the condition that subproblems should be independent. We show how such a constraint can be incorporated by introducing upper and lower bounds on final fairness values for partial solutions of subproblems, which enables early comparison and pruning. Our results show that our model can find fair and optimal trees several orders of magnitude faster than previous methods, and now also for larger datasets that were previously beyond reach. Moreover, we show that with this substantial improvement our method can find the full Pareto front in the trade-off between accuracy and fairness.
A NOTE ON X-RAYS OF PERMUTATIONS AND A PROBLEM OF BRUALDI AND FRITSCHER

The subject of this note is a challenging conjecture about X-rays of permutations which is a special case of a conjecture regarding Skolem sequences. In relation to this, Brualdi and Fritscher [Linear Algebra and its Applications, 2014] posed the following problem: Determine a bijection between extremal Skolem sets and binary Hankel X-rays of permutation matrices. We give such a bijection, along with some related observations. 1. Skolem sequences Skolem sequences originates from the work by Thoralf Skolem in 1957 [8] on the construction of Steiner triple systems. Skolem proved that the set {1, 2, . , 2n} can be partitioned in n pairs (si, ti) such that ti − si = i for i = 1, 2, . , n, if and only if n ≡ 0, 1 (mod 4). This result can be reformulated as: There is a sequence with two copies of every element k in A = {1, 2, . , n} such that the two copies of k are placed k places apart in the sequence, if and only if n ≡ 0, 1 (mod 4). For example, the set {1, 2, 3, 4} can be used to form the sequence 42324311, but the set {1, 2, 3} cannot be used to form such a sequence. For more information on Skolem sequences and generalizations thereof, see the survey [7]. A natural generalization is when the set of differences A is any set or multiset of positive integers. If A is a multiset, the sequences are called multi Skolem sequences, and the corresponding existence question is that of deciding for which multisets A = {a1, . , an} there is a partition of {1, . , 2n} into the differences in A. A set A such that there is a partition of {1, . , 2n} into the differences in A is called a multi Skolem set. In my MSc thesis [4], I identified (rather obvious) parity and density conditions that are necessary for A to be a multi Skolem set. These conditions were far from sufficient (unsurprisingly as the existence question for multi Skolem sets turns out to be NP-complete [6]). But, surprisingly, I discovered that when A is an ordinary set (i.e., not a multiset), then these simple necessary conditions seem to be sufficient. Conjecture 1 ([4]). A set A = {a1, a2, . , an} with a1 < a2 < · · · < an is a Skolem set if and only if the number of even ai’s is even, and ∑n i=m ai ≤ n −(m−1) for each 1 ≤ m ≤ n. A particularly interesting special case of Conjecture 1 emerge when the set of differences A = {a1, a2, . , an} is as sparse as possible (in the sense that adding 1 to any element in A force A to violate the density condition), i.e., ∑n i=1 ai = n . Such sets (multisets) A = {a1, a2, . , an} satisfying ∑n i=1 ai = n 2 are called extremal. Conjecture 2 ([5]). A set A = {a1, a2, . , an} with a1 < a2 < · · · < an and ∑n i=1 ai = n 2 is an extremal Skolem set if and only if ∑n i=m ai ≤ n 2 − (m− 1) for each 1 ≤ m ≤ n. Note that the parity condition in Conjecture 1 is implied by ∑n i=1 ai = n , and that the conjecture is invalid for extremal multisets. A minimal counterexample is A = {4, 4, 4, 8, 8, 8}.
An Investigation of Recurrent Neural Architectures for Drug Name Recognition Clinical Text Prediction with Numerically Grounded Conditional Language Models Modelling Radiological Language with Bidirectional Long Short-term Memory Networks Data Resource Acquisition from People at Various Stages of Co

ii Preface The Seventh International Workshop on Health Text Mining and Information Analysis provides an interdisciplinary forum for researchers interested in automated processing of health documents. Health documents encompass electronic health records, clinical guidelines, spontaneous reports for pharmacovigilance, biomedical literature, health forums/blogs or any other type of health-related documents. The LOUHI workshop series fosters interactions between the Computational Linguistics, Medical Informatics and Artificial Intelligence communities. Following the six previous edition of the workshop which were co-The aim of the LOUHI 2016 workshop is to bring together research work on topics related to health documents, particularly emphasizing multidisciplinary aspects of health documentation and the interplay between nursing and medical sciences, information systems, computational linguistics and computer science. The topics include, but are not limited to, the following Natural Language Processing techniques and related areas: • Classification and text mining applications (e.g. diagnostic classifications such as ICD-10 and nursing intensity scores) and problems (e.g. handling of unbalanced data sets) • Text representation, including dealing with data sparsity and dimensionality issues • Syntactic, semantic and pragmatic analysis of health documents • Anonymization/de-identification of health records and ethics • Supporting the development of medical terminologies and ontologies • Individualization of content, consumer health vocabularies, summarization and simplification of text • NLP for supporting documentation and decision making practices iii • Predictive modeling of adverse events, e.g. adverse drug events and hospital acquired infections The call for papers encouraged authors to submit papers describing substantial and completed work but also focus on a contribution, a negative result, a software package or work in progress. We also encouraged to report work on low-resourced languages, addressing the challenges of data sparsity and language characteristic diversity. We received 21 submissions. Each submission went through a double-blind review process which involved three program committee members. Based on comments and rankings supplied by the reviewers, we accepted 13 papers. The overall acceptance rate is 62%. During the workshop, 6 papers will be presented orally, and 7 papers will be presented as posters. Our special thanks go to Nigel Collier for accepting to give an invited talk. Finally, we would like to thank the members of the program committee for the quality of theirs reviews in a very short period, and the authors for their submissions and the quality of their work. NLP and Online Health Reports: What do we say and what do we mean? Abstract Drug name recognition (DNR) …
A Lightweight Mitigation Approach for Addressing DDAO Attacks in RPL-based Internet of Things

There are many Internet of Things (IoT) applications which demand an infrastructure that requires significantly less energy and supports longer operation time. Such an infrastructure is provided by Low-Power and Lossy Networks (LLN) that run on resource-constrained devices. Achieving an energy-efficient routing on such networks is a significant challenge which is addressed by the Routing Protocol for Low-power and Lossy Networks (RPL). The specifications of RPL are depicted in RFC 6550 by IETF. Although RPL gives many benefits to LLN, at the same time, it also attracts many security issues. One such security issue is the Dropping Destination Advertisement Object (DDAO) attack. In RPL, a client node uses Destination Advertisement Object (DAO) control messages to pass the destination information to the root node. An attacker may exploit the DAO passing mechanism of RPL to deliberately drop DAO received from child nodes and send fake Destination Advertisement Object-Acknowledgement (DAO-ACK) messages to perform a DDAO attack. The existing research work provides only a detection algorithm to detect the DDAO attack. Therefore, in this research paper, we proposed a defense technique, i.e., lightweight Acknowledgement (ACK) authentication, based on a challenge-response strategy to mitigate the attack. For this, we have used a Prime Sequence Code Matrix (PSCM) and a modified version of the DAO and DAO-ACK control packet of the standard RPL. Our proposed technique reduces the impact of attack and restores network performance significantly.