Miracle-dz/UniFAR-FTUs huggingface dataset:
Traffic Flow Prediction using Adaboost Algorithm with Random Forests as a Weak Learner

rely on a system of sensors, aim to describe in real-time traffic in urban areas using a set of parameters and estimating them. Though the state of the art focuses on data analysis, little is done in the sense of prediction. In this paper, we describe a machine learning system for traffic flow management and control for a prediction of traffic flow problem. This new algorithm is obtained by combining Random Forests algorithm into Adaboost algorithm as a weak learner. We show that our algorithm performs relatively well on real data, and enables, according to the Traffic Flow Evaluation model, to estimate and predict whether there is congestion or not at a given time on road intersections.
The Degenerate Primer Design Problem: Theory and Applications

A PCR primer sequence is called degenerate if some of its positions have several possible bases. The degeneracy of the primer is the number of unique sequence combinations it contains. We study the problem of designing a pair of primers with prescribed degeneracy that match a maximum number of given input sequences. Such problems occur when studying a family of genes that is known only in part, or is known in a related species. We prove that various simplified versions of the problem are hard, show the polynomiality of some restricted cases, and develop approximation algorithms for one variant. Based on these algorithms, we implemented a program called HYDEN for designing highly degenerate primers for a set of genomic sequences. We report on the success of the program in several applications, one of which is an experimental scheme for identifying all human olfactory receptor (OR) genes. In that project, HYDEN was used to design primers with degeneracies up to 10(10) that amplified with high specificity many novel genes of that family, tripling the number of OR genes known at the time.
Proceedings of the 2001 Winter Simulation Conference

Looking for appropriate dispatch rules for semiconductor fabrication facilities (wafer fabs), practitioners often intend to use the Shortest Processing Time First (SPTF) rule because it is said to reduce cycle times. In our study, we show, however, that this positive effect on cycle times can be achieved in single machine systems but not necessarily in complete wafer fabs. In addition, we discuss variants of the SPTF rule.
Manipulating the structure of natural scenes using wavelets to study the 1 functional architecture of perceptual hierarchies in the brain 2 Hierarchical model of natural images

Functional neuroimaging experiments that employ naturalistic stimuli (natural scenes, 25 films, spoken narratives) provide insights into cognitive function “in the wild”. Natural 26 stimuli typically possess crowded, spectrally dense, dynamic, and multimodal 27 properties within a rich multiscale structure. However, when using natural stimuli, 28 various challenges exist for creating parametric manipulations with tight experimental 29 control. Here, we revisit the typical spectral composition and statistical dependences 30 of natural scenes, which distinguish them from abstract stimuli. We then demonstrate 31 how to selectively degrade subtle statistical dependences within specific spatial scales 32 using the wavelet transform. Such manipulations leave basic features of the stimuli, 33 such as luminance and contrast, intact. Using functional neuroimaging of human 34 participants viewing degraded natural images, we demonstrate that cortical responses 35 at different levels of the visual hierarchy are differentially sensitive to subtle statistical 36 dependences in natural images. This demonstration supports the notion that 37 perceptual systems in the brain are optimally tuned to the complex statistical 38 properties of the natural world. The code to undertake these stimulus manipulations, 39 and their natural extension to dynamic natural scenes (films), is freely available.
Development and Validation of the VIOSync Sepsis Prediction Index: A Novel Machine Learning Model for Sepsis Prediction in ICU Patients

Background: Sepsis is the third leading cause of death worldwide and the main cause of in-hospital mortality. Despite decades of research, sepsis remains a major challenge faced by patients, clinicians, and medical systems worldwide. Early identification and prediction of patients at risk of sepsis and adverse outcomes associated with sepsis are critical. In this work, we aimed to develop an artificial intelligence algorithm that can predict sepsis early. Materials and Methods: We developed a predictive model for sepsis using data from the Physionet Cardiology Challenge 2019 ICU database. Our cohort consisted of adult patients who were admitted to the ICU. Sepsis diagnoses were determined using the Sepsis-3 criteria. The model, built with the XGBoost algorithm, was designed to anticipate sepsis prior to the appearance of clinical symptoms. An internal validation was conducted using a hold-off test dataset to evaluate the AI model's predictive performance. Results: We have developed the VIOSync Sepsis Prediction Index (SPI), an AI-based predictive model designed to forecast sepsis up to six hours before its clinical onset, as defined by Sepsis-3 criteria. The AI model, trained on a dataset comprising approximately 40,000 adult patients, integrates variables such as vital signs, laboratory data, and demographic information. The model demonstrated a high prediction accuracy rate of 97%, with a sensitivity of 87% and a specificity of 98% in predicting sepsis up to 6 hours before the onset. When compared to the established qSOFA score, which has a specificity of 89% for sepsis prediction, our VIOSync SPI algorithm significantly enhances predictive reliability, potentially reducing false positive rates by a factor of 5.5. Conclusions: The VIOSync SPI demonstrated superior prediction performance over current sepsis early warning scores and predictive algorithms for sepsis onset. To validate the generalizability of our method across populations and treatment protocols, external validation studies are essential.
Thrombotic and thromboembolic events, with or without thrombocytopenia, following viral vector-based COVID-19 vaccines administration: a systematic review protocol.

Background Viral vector-based COVID-19 vaccines have proven to be effective and safe in clinical trials and post-authorization studies. Although infrequent, some serious thrombotic and thromboembolic events following immunization have emerged, and causality assessment committees must consider and critically assess different sources of evidence to inform their decisions about whether these events supposedly attributable to vaccination or immunization (ESAVI) are associated with the vaccine or are coincidental. Therefore, this systematic review aims to gather information on the association and biological mechanisms between thrombotic and thromboembolic events, with or without thrombocytopenia, and the administration of viral vector-based COVID-19 vaccines. Methods We will conduct a systematic review following the evidence synthesis framework proposed by the Pan American Health Organization to support the ESAVI causality assessment. We will search for primary clinical and preclinical studies in the Epistemonikos' COVID-19 L.OVE (Living Overview of the Evidence) repository, a comprehensive and validated source of COVID-19 evidence. We will include studies reporting any thrombotic or thromboembolic event, with or without thrombocytopenia, after the administration of a viral vector-based COVID-19 vaccine. The screening and data extraction will be performed by two independent authors. We will assess the risk of bias by two reviewers using the appropriate tool for each study design. Discrepancies will be discussed or resolved by a third author. We will use GRADE to assess the certainty of evidence for clinical studies and prepare summary of findings tables. For individual-based (case series and case reports) and preclinical studies, we will summarize the results in descriptive tables. Expected results and implications This will be the first systematic review using the evidence synthesis framework for ESAVI causality assessment, currently under validation by the Pan American Health Organization and the Epistemonikos Foundation. By gathering clinical and preclinical evidence, it is expected to inform about the risks of thromboembolic events following vaccination with viral vector-based COVID-19 vaccines, and also the possible underlying biological mechanisms. Policymakers, such as safe vaccination committees, and other evidence synthesis authors could replicate this novel methodology to strengthen the evidence-based ESAVI causality assessment.
Design and Management of Complex Technical Processes and Systems by means of Computational Intelligence Methods Benefits and Drawbacks for the Use of ε-Dominance in Evolutionary Multi-Objective Optimization

Using diversity mechanisms in evolutionary algorithms for multi-objective optimization problems is considered as an important issue for the design of successful algorithms. This is in particular the case for problems where the number of non-dominated feasible objective vectors is exponential with respect to the problem size. In this case the goal is to compute a good approximation of the Pareto front. We investigate how this goal can be achieved by using the diversity mechanism of ε-dominance and point out where this concept is provably helpful to obtain a good approximation of an exponentially large Pareto front in expected polynomial time. Afterwards, we consider the drawbacks of this approach and point out situations where the use of ε-dominance slows down the optimization process significantly.
Automated Guided Vehicles by Permanent Magnet Synchronous Motor: Future of In-house Logistics

Abstract Material handling and logistics management that involve transportation of work pieces on production floor are important aspects to manufacturing that affect productivity and efficiency. Tow vehicles that are manually driven are currently used for this purpose. These processes can be better performed through automation. Automated guided vehicle (AGV) is an apt solution. AGVs are unmanned autonomous vehicles that can be programmed to perform versatile tasks. AGVs available in market are imported and hence have high capital cost and increased lead time for spare parts. Proposed AGV is built with a capital cost that is less than half of the existing AGVs. Its design is made indigenously, with most of its parts locally sourced. It can achieve a speed of 0.83 m/s, with a pulling capacity of 1,300 kg. Its rechargeable batteries sustain four hours of continuous operation for one complete discharge. It has been tested and found to effectively replace tow vehicles.
Estimating Parameters in Physical Models using MathModelica

This paper describes a program which extends MathModelica so that model parameters can be estimated using measured data. Given initial values of the parameters, the parameter estimates are iteratively changed so that the sum of squared errors of the difference between the model output and the data is minimized. In each iteration an extended differential equation has to be simulated. The developed program imports the Modelica model into Mathematica and derives a symbolic expression for this extended differential equation. The extended model i converted to Modelica format and MathModelica is used to simulate it efficiently.
BNAIC/BeneLearn 2021 preproceedings

Human reasoning under uncertainty is conjectured to use Mental Models as a representation format. Each Mental Model characterizes a possible state of the world based on and constrained by the available information. Conclusion about the world must hold in each of these Mental Models. An important task in human reasoning is the construction these Mental Models using the available information. This paper investigates whether it is possible to design a neural network architecture that enables the construction of Mental Model, similarly to the conjectured way that humans reason. The paper investigates different architectures in an incremental way. The final architecture not only produces the correct mental models but also learns correct mental models for intermediate representation without being explicitly trained to do so. This contributes to the explainability of the approach.
for Scalable

Massively parallel processors introduce new demands on software systems with respect to performance, scalability, robustness and portability. The increased complexity of the memory systems and the increased range of problem sizes for which a given piece of software is used, poses serious challenges to software developers. The Connection Machine Scienti(cid:12)c Software Library, CMSSL, uses several novel techniques to meet these challenges. The CMSSL contains routines for managing the data distribution and provides data distribution independent functionality. High performance is achieved through careful scheduling of arithmetic operations and data motion, and through the automatic selection of algorithms at run{time. We discuss some of the techniques used, and provide evidence that CMSSL has reached the goals of performance and scalability for an important set of applications.
Sentiment Vector Space Model for Lyric-Based Song Sentiment Classification

Song sentiment analysis attracts much attention in research areas such as acoustic signal processing (ASP) and natural language processing (NLP). The text-based efforts in the NLP community are found to be interesting. As a popular text-based solution, the lyric-based sentiment classification approach adopts the vector space model (VSM) to represent lyric text and assigns songs sentiment labels such as light-hearted and heavy-hearted. Four problems render the term-based VSM model ineffective. Firstly, many words within song lyrics contribute little to expressing sentiment, but they are equally considered as term features. Secondly, nouns and verbs being used to express sentiment are ambiguously used in natural language text. But the ambiguity is not addressed. Thirdly, negations and modifiers accompanying the sentiment keywords make particular contributions to sentiment expressing, but the contributions are not reflected. Lastly, song lyric is usually very short and data sparseness problem is serious. To address these problems, the sentiment vector space model (s-VSM) is proposed in this paper to represent song lyric document based on sentiment unit. With the s-VSM model, the support vector machines (SVM) classification algorithm is applied to perform song sentiment analysis. Experimental results show that lyric is helpful to song sentiment analysis and the s-VSM model outperforms the VSM model significantly in performing the task of song sentiment analysis.
Computer Communications And Networks

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A statistical approach to rule learning

This dissertation investigates rule learning as a statistical classification problem. Rule learning has a long history in machine learning and is known for inducing interpretable and comprehensible classifiers. Methods from statistical machine learning, on the other hand, have traditionally focused on predictive accuracy, often at the expense of interpretability. The goal of this work is to combine the approaches to obtain highly predictive, yet interpretable classifiers. Unlike many of the heuristics applied in traditional rule learning, statistical principles allow for a better theoretical investigation of rule learning systems, possibly leading to valuable insights. To this end, we investigate two types of rule sets, propositional logic formulae in disjunctive normal form (DNF formulae), and weighted rule sets. For DNF formulae, we investigate the NP-hard problem of empirical risk minimization of DNF formulae of at most k terms. We propose a stochastic local search (SLS) algorithm to solve this problem efficiently. Then, we apply the SLS algorithm in a structural risk minimization procedure to gain small yet predictive rule sets. Alternatively, we combine SLS with an ensemble approach to reduce the estimation part of the prediction error and to upper-bound it analytically. For weighted rule sets, we devise a novel convex optimization criterion, Margin Minus Variance (MMV), a feasible relaxation of the infeasible empirical risk minimization problem. For capacity control we derive a novel concentration inequality. An implementation of these ideas, the Rumble system, yields favorable results in experiments dealing with structure-activity relationships of small molecules. Finally, for multirelational learning, we propose and use a framework to assess and compare learning systems according to the flow of information. We evaluate different strategies analytically and experimentally and present a novel rule generation criterion that aims at highly diverse rule sets.
Fast-CLOCs: Fast Camera-LiDAR Object Candidates Fusion for 3D Object Detection – Supplementary Materials

Fig 1 shows some qualitative results of our proposed fusion method on KITTI test set. Red bounding boxes represent false positive detections from PV-RCNN [4] that are deleted by our Fast-CLOCs, green bounding boxes are true positive detections that are confirmed by Fast-CLOCs. Incorporating image visual information can help remove LiDAR false positive detections and confirm LiDAR true positive detections, as shown in Fig 1. The image projection region of a false positive detection in the LiDAR point cloud, will be classified and usually rejected as a detection through our 3D-Q-2D detector. Then Fast-CLOCs can leverage this inconsistency to remove the false positive. Objects that are double confirmed by LiDAR detector and 3D-Q-2D image detector will be kept by Fast-CLOCs.
Comparison of CBIR Systems with Different Number of Feature Vector Components

Content-based image retrieval (CBIR) systems with user relevance feedback are considered. The influence of the type and the number of feature vector (FV) components on the retrieval efficiency was investigated. We compared a CBIR system with a very small number of FV components (only 25 components describing color and texture) with a system with a high-dimensional FV inspired by MPEG-7 (556 coordinates describing color, texture and line directions), as well as with a system using feature vector reduction (FVR) of about 90% (with about 50 FV components from the full-length 556-component FVs). The systems are tested over the annotated Corel IK and Corel 60K datasets. Simulation results show that a decreased number of FV components does not have significant influence on the quality of image retrieval, while the processing time is reduced compared to CBIR with full-length FV and/or FVR.
Forschungsberichte der Fakultät IV – Elektrotechnik und Informatik Conformance Analysis of Organizational Models in a new Enterprise Modeling Framework using Algebraic Graph Transformation-Extended Version

Organizational models play a key role in today's enterprise modeling. These models often show up as partial models produced by people with di erent conceptual understandings in a usually decentralized organization, where they are modeled in a distributed and non-synchronized fashion. For this reason, there is a rst major need to organize partial organizational models within a suitable modeling framework, and there is a second major need to check their mutual conformance. This builds the basis to integrate the partial organizational models later on into one holistic model of the organization. Moreover, the partial models can be used for model checking certain security, risk, and compliance constraints. In order to satisfy the two major needs, this paper presents two mutually aligned contributions. The rst one is a new enterprise modeling framework the EM-Cube. The second contribution is a new approach for checking conformance of models that are developed based on the suggested formal modeling technique associated with the proposed framework. In addition to that, we evaluate our potential solution against concrete requirements derived from a real-world scenario coming out of the nance industry.
Comparison of prokaryotic communities among fields exhibiting different disinfestation effects by anaerobic soil disinfestation

Anaerobic soil disinfestation (ASD) is a chemical-independent fumigation method used for reducing the abundance of pathogens at soil depths of <40 cm. However, its disinfestation efficiency is unstable under field conditions. The microbial community reflects the soil environment and is a good indicator of soil health. Therefore, soil with a good disinfestation efficiency may have a unique microbial community. The aim of the present study was to compare the prokaryotic communities among soils obtained from 17 geographically different greenhouses that experienced tomato bacterial wilt but exhibited different disinfestation efficiencies after ASD treatment with the same substrate. In the present study, soil prokaryotic communities in the field, which indicate difference in disinfestation effects after ASD treatment among several fields, were compared using next-generation sequencing. The prokaryotic communities in the fields showing different disinfestation effects were roughly separated into sampling fields. The relative abundances of Betaproteobacteria and Clostridia were significantly increased in well-disinfested fields. Overall, 25 operational taxonomic units (OTUs) were specifically increased in various well-disinfested soils and 18 OTUs belonged to phylogenetically diversified Clostridia. Other OTUs belonged to aerobic bacteria and were not previously detected in sample collected from ASD-treated fields. The results showed that the changes to the prokaryotic communities did not affect ASD efficiency, whereas changes in the abundance of specific microbes in the community were related to disinfestation.
Approximation Algorithms and Heuristics for Classical Planning

Automated planning has been an active area of research in theoretical computer science and Artificial Intelligence (AI) for over 40 years. Planning is the study of general purpose algorithms that accept as input an initial state, a set of desired goal states, and a planning domain model that describes how actions can transform the state. The problem is to find a sequence of actions that transforms the initial state into one of the goal states. Planning is widely applicable, and has been used in such diverse application domains as spacecraft control [MNPW98], planetary rover operations [BJMR05], automated nursing aides [MP02], image processing [GPNV03], computer security [BGHH05] and automated manufacturing [RDF05]. Planning is also the subject of continued and lively ongoing research. In this chapter, we will present an overview of how approximations and related techniques are used in automated planning. We focus on classical planning problems, where states are conjunctions of propositions, all state information is known to the planner, and all action outcomes are deterministic. Classical planning is nonetheless a large problem class that generalizes many combinatorial problems including bin-packing ∗Universities Space Research Association †The authors gratefully acknowledge Sailesh Ramakrishnan, Ronen Brafman and Michael Freed for reviewing our early drafts