Miracle-dz/UniFAR-FTUs huggingface dataset:
Hard Disk Drive Failure Prediction Method Based On A Bayesian Network

The ability to predict failures in Hard Disk Drives (HDD) is a major objective of HDD manufacturers since avoiding unexpected failures may prevent data loss. As a consequence, failure prediction in HDDs became a topic that attracted much attention in recent years. Nowadays, most HDDs are equipped with a threshold-based monitoring system named Self-Monitoring, Analysis and Reporting Technology (SMART). The system collects several performance parameters and detects anomalies that may indicate incipient failures. Although the SMART system is very popular, it achieves failure detection rates of 3% to 10%. Moreover, SMART works as an incipient failure detection method and does not provide an estimate of the remaining life of the HDD. In this paper, we propose a failure prediction method using SMART attributes and a Bayesian Network. The proposed method, named Bayesian network based Method for Failure prediction in HDDs (BNFH) uses a subset of the SMART attributes and a set of SMART trend related attributes to provide remaining life estimates of HDDs. To demonstrate practical usefulness, this method was applied to a dataset consisting of 49,056 hard drives from Backblaze's data centers.
Geometry and Generation of a New Graph Planarity Game

We introduce a new abstract graph game, SWAP PLANARITY, where the goal is to reach a state without edge intersections and a move consists of swapping the locations of two vertices connected by an edge. We analyze this puzzle game using concepts from graph theory and graph drawing, computational geometry, and complexity. Furthermore, we specify what good levels look like and we show how they can be generated. We also report on experiments that show how well the generation works.
Requirements Engineering for Consensus-Oriented Technical Specifications

Technical standards are established as a kind of norm or general requirement with respect to the development of technical systems. They shall ensure a uniform way of engineering, to gain benefits when it comes to product comparability, interoperability, assurance of product quality, reliability, safety, and similar properties. Usually, technical standards are developed iteratively, where text documents are used as work products which are subject to continuous negotiations. In this paper, we retrace the requirement-driven development process of the Distributed Co-Simulation Protocol~(DCP) specification. It is the main output of the ACOSAR ("Advanced Co-Simulation Open System Architecture") project. The DCP focuses on the integration of real-time systems into simulation environments. Therefore it poses a significant contribution to development and test of many technical systems. Typical examples are automotive advanced driver assistance system (ADAS), test beds for vehicles and engines, as well as components like electronic control units (ECU). The DCP specification is intended for standardization with a recognized standardization body. Unlike most other technical standards, the DCP specification was engineered on the foundation of written requirements, formulated by an inhomogeneous group of stakeholders. This paper highlights the applied development process, provides examples of requirements, and reports the overall outcome, including benefits and drawbacks.
A Social Science-based Approach to Explanations for (Game) AI

The current AI revolution provides us with many new, but often very complex algorithmic systems. This complexity does not only limit understanding, but also acceptance of e.g. deep learning methods. In recent years, explainable AI (XAI) has been proposed as a remedy. However, this research is rarely supported by publications on explanations from social sciences. We suggest a bottom-up approach to explanations for (game) AI, by starting from a baseline definition of understandability informed by the concept of limited human working memory. We detail our approach and demonstrate its application to two games from the GVGAI framework. Finally, we discuss our vision of how additional concepts from social sciences can be integrated into our proposed approach and how the results can be generalised.
A 250 μm × 57 μm Microscale Opto-electronically Transduced Electrodes (MOTEs) for Neural Recording

Recording neural activity in live animals <italic>in vivo</italic> with minimal tissue damage is one of the major barriers to understanding the nervous system. This paper presents the technology for a tetherless opto-electronic neural interface based on 180 nm CMOS circuits, heterogeneously integrated with an AlGaAs diode that functions as both a photovoltaic and light emitting diode. These microscale opto-electrically transduced electrodes (MOTEs) are powered by and communicate through an optical interface, simultaneously enabling high temporal-resolution electrical measurements without a tether or a bulky RF coil. The MOTE presented here is 250 <italic>μ</italic>m × 57 <italic>μ</italic>m, consumes 1 <italic>μ</italic>W of electrical power, and is capable of capturing and encoding neural signals before transmitting the encoded signals. The measured noise floor is as low as 15 <italic>μ</italic>V<sub>RMS</sub> at a 15 kHz bandwidth.
Risk factors for venous thromboembolism (VTE) recurrences in Thai patients without cancer

ABSTRACT Introduction: Studies in Western countries show that VTE recurrent rates are lower in the presence of a transient provoking factor, older age, female sex and/or hormonal use, while thrombophilia (factor V Leiden or prothrombin mutation) has no predictive role. This study aimed to determine the incidence and risk factors of recurrent VTE in Asian patients. Method: This is a retrospective cohort study in adult VTE patients who were diagnosed during 2004–2014, had no active cancer and followed-up for at least 1 year. Results: There were 198 patients. The mean age was 58.2 ± 17.6 years and 68.2% were female. The diagnoses were deep vein thrombosis (56%), pulmonary embolism (32%) or both (12%). They were provoked by major surgery in 21.7%. Thrombophilia (antiphospholipid syndrome or protein C or protein S deficiency) was found in 11.6%. The recurrent rate during the median follow-up time of 52 months was 2.2 (95% confidence interval [CI] 1.46–3.31) per 100 patient-years. Presence of thrombophilia was independently associated with recurrences with the adjusted hazard ratio (aHR) of 3.52 (95% CI 1.32–9.42, p = 0.01). There were 144 patients who discontinued anticoagulants. During the median time of 55 months after stopping anticoagulants, the recurrent rate was 4.3 per 100 patient-years. Thrombophilia was associated with recurrences with aHR of 4.00 (95% CI 1.42–11.31, p = 0.01), while male sex, younger age, provoking conditions and hormone use showed no significant association. Conclusion: VTE recurrent rate in Asians is comparable to Caucasians, but thrombophilia is the strongest risk factor.
Topic recommendation using Doc2Vec

The ever-increasing number of electronic content stored in digital libraries requires a significant amount of effort in cataloguing and has led to self-deposit solutions where the authors submit and publish their own digital records. Even in self-deposit, going through the abstract and assigning subject terms or keywords is a time consuming and expensive process, yet crucial for the metadata quality of the record that affects retrieval. Therefore, an automatic, or even a semi-automatic process that can recommend topics for a new entry is of huge practical value. A system that can address that has to rely basically on two components, one component for efficiently representing the relevant information of the new document and one component for recommending an appropriate set of topics based on the representation of the previous stage. In this work, different candidate solutions for both components are investigated and compared. For the first stage both distributed Document to Vector (doc2vec) and conventional Bag of Words (BoW) components are employed, while for the latter two different transformation approaches from the field of multi-label classification are compared. For the comparison, a collection of Ph.D. abstracts (∼19000 documents) from the MIT Libraries Dspace repository is used suggesting that different combinations can provide high quality solutions.
FraudNE: a Joint Embedding Approach for Fraud Detection

Detecting fraudsters is a meaningful problem for both users and e-commerce platform. Existing graph-based approaches mainly adopt shallow models, which cannot capture the highly non-linear relationship between vertexes in a bipartite graph composed of users and items. To address this issue, in this paper we propose a joint deep structure embedding approach FraudNE for fraud detection that (a) can preserve the highly non-linear structural information of networks, (b) is robust to sparse networks, (c) embeds different types of vertexes jointly in the same latent space. It is worth mentioning that we can detect multiple fraudulent groups without the number of groups as a priori. Compared with baselines, our method achieved significant accuracy improvement.
Bioinformatics Analysis and Functional Prediction of Transmembrane Proteins in Entamoeba histolytica

Entamoeba histolytica is an invasive, pathogenic parasite causing amoebiasis. Given that proteins involved in transmembrane (TM) transport are crucial for the adherence, invasion, and nutrition of the parasite, we conducted a genome-wide bioinformatics analysis of encoding proteins to functionally classify and characterize all the TM proteins in E. histolytica. In the present study, 692 TM proteins have been identified, of which 546 are TM transporters. For the first time, we report a set of 141 uncharacterized proteins predicted as TM transporters. The percentage of TM proteins was found to be lower in comparison to the free-living eukaryotes, due to the extracellular nature and functional diversification of the TM proteins. The number of multi-pass proteins is larger than the single-pass proteins; though both have their own significance in parasitism, multi-pass proteins are more extensively required as these are involved in acquiring nutrition and for ion transport, while single-pass proteins are only required at the time of inciting infection. Overall, this intestinal parasite implements multiple mechanisms for establishing infection, obtaining nutrition, and adapting itself to the new host environment. A classification of the repertoire of TM transporters in the present study augments several hints on potential methods of targeting the parasite for therapeutic benefits.
Optimal Computational Power Allocation in Multi-Access Mobile Edge Computing for Blockchain

Blockchain has emerged as a decentralized and trustable ledger for recording and storing digital transactions. The mining process of Blockchain, however, incurs a heavy computational workload for miners to solve the proof-of-work puzzle (i.e., a series of the hashing computation), which is prohibitive from the perspective of the mobile terminals (MTs). The advanced multi-access mobile edge computing (MEC), which enables the MTs to offload part of the computational workloads (for solving the proof-of-work) to the nearby edge-servers (ESs), provides a promising approach to address this issue. By offloading the computational workloads via multi-access MEC, the MTs can effectively increase their successful probabilities when participating in the mining game and gain the consequent reward (i.e., winning the bitcoin). However, as a compensation to the ESs which provide the computational resources to the MTs, the MTs need to pay the ESs for the corresponding resource-acquisition costs. Thus, to investigate the trade-off between obtaining the computational resources from the ESs (for solving the proof-of-work) and paying for the consequent cost, we formulate an optimization problem in which the MTs determine their acquired computational resources from different ESs, with the objective of maximizing the MTs’ social net-reward in the mining process while keeping the fairness among the MTs. In spite of the non-convexity of the formulated problem, we exploit its layered structure and propose efficient distributed algorithms for the MTs to individually determine their optimal computational resources acquired from different ESs. Numerical results are provided to validate the effectiveness of our proposed algorithms and the performance of our proposed multi-access MEC for Blockchain.
Simple Regret Minimization for Contextual Bandits

There are two variants of the classical multi-armed bandit (MAB) problem that have received considerable attention from machine learning researchers in recent years: contextual bandits and simple regret minimization. Contextual bandits are a sub-class of MABs where, at every time step, the learner has access to side information that is predictive of the best arm. Simple regret minimization assumes that the learner only incurs regret after a pure exploration phase. In this work, we study simple regret minimization for contextual bandits. Motivated by applications where the learner has separate training and autonomous modes, we assume that, the learner experiences a pure exploration phase, where feedback is received after every action but no regret is incurred, followed by a pure exploitation phase in which regret is incurred but there is no feedback. We present the Contextual-Gap algorithm and establish performance guarantees on the simple regret, i.e., the regret during the pure exploitation phase. Our experiments examine a novel application to adaptive sensor selection for magnetic field estimation in interplanetary spacecraft, and demonstrate considerable improvement over algorithms designed to minimize the cumulative regret.
RAGE in the pathophysiology of skeletal muscle

Emerging evidence suggests that the signalling of the Receptor for Advanced Glycation End products (RAGE) is critical for skeletal muscle physiology controlling both the activity of muscle precursors during skeletal muscle development and the correct time of muscle regeneration after acute injury. On the other hand, the aberrant re‐expression/activity of RAGE in adult skeletal muscle is a hallmark of muscle wasting that occurs in response to ageing, genetic disorders, inflammatory conditions, cancer, and metabolic alterations. In this review, we discuss the mechanisms of action and the ligands of RAGE involved in myoblast differentiation, muscle regeneration, and muscle pathological conditions. We highlight potential therapeutic strategies for targeting RAGE to improve skeletal muscle function.
A Comparison of the Cortical Structure of the Bowhead Whale (Balaena mysticetus), a Basal Mysticete, with Other Cetaceans

Few studies exist of the bowhead whale brain and virtually nothing is known about its cortical cytoarchitecture or how it compares to other cetaceans. Bowhead whales are one of the least encephalized cetaceans and occupy a basal phylogenetic position among mysticetes. Therefore, the bowhead whale is an important specimen for understanding the evolutionary specializations of cetacean brains. Here, we present an overview of the structure and cytoarchitecture of the bowhead whale cerebral cortex gleaned from Nissl‐stained sections and magnetic resonance imaging (MRI) in comparison with other mysticetes and odontocetes. In general, the cytoarchitecture of cetacean cortex is consistent in displaying a thin cortex, a thick, prominent layer I, and absence of a granular layer IV. Cell density, composition, and width of layers III, V, and VI vary among cortical regions, and cetacean cortex is cell‐sparse relative to that of terrestrial mammals. Notably, all regions of the bowhead cortex possess high numbers of von Economo neurons and fork neurons, with the highest numbers observed at the apex of gyri. The bowhead whale is also distinctive in having a significantly reduced hippocampus that occupies a space below the corpus callosum within the lateral ventricle. Consistent with other balaenids, bowhead whales possess what appears to be a blunted temporal lobe, which is in contrast to the expansive temporal lobes that characterize most odontocetes. The present report demonstrates that many morphological and cytoarchitectural characteristics are conserved among cetaceans, while other features, such as a reduced temporal lobe, may characterize balaenids among mysticetes. Anat Rec, 2018. © 2018 Wiley Periodicals, Inc. Anat Rec, 302:745–760, 2019. © 2018 Wiley Periodicals, Inc.
Human-Centric Scene Understanding from Single View 360 Video

In this paper, we propose an approach to indoor scene understanding from observation of people in single view spherical video. As input, our approach takes a centrally located spherical video capture of an indoor scene, estimating the 3D localisation of human actions performed throughout the long term capture. The central contribution of this work is a deep convolutional encoder-decoder network trained on a synthetic dataset to reconstruct regions of affordance from captured human activity. The predicted affordance segmentation is then applied to compose a reconstruction of the complete 3D scene, integrating the affordance segmentation into 3D space. The mapping learnt between human activity and affordance segmentation demonstrates that omnidirectional observation of human activity can be applied to scene understanding tasks such as 3D reconstruction. We show that our approach using only observation of people performs well against previous approaches, allowing reconstruction of occluded regions and labelling of scene affordances.
Evaluation of Iron Overload Between Age Groups Using Magnetic Resonance Imaging and Its Correlation with Iron Profile in Transfusion-dependent Thalassemia.

BACKGROUND routine blood transfusion in transfusion-dependent-thalassemia (TDT) causes iron accumulation in various organ. Serum markers of iron overload, serum ferritin and transferrin saturation, are sensitive but not specific. MRI T2-star (T2*) is valuable for detecting iron level in organs. This study aimed to explore the degree of iron overload in various organs, iron deposition difference between children and adults, also its correlation with serum marker of iron overload. METHODS this was a cross-sectional study of TDT patients who had been evaluated by MRI T2* examination in Cipto Mangunkusumo Hospital from 2014 to 2018. RESULTS a total of 546 subjects was included in this study. The number of subjects between children and adults was almost equal. Most of subjects had normal cardiac iron deposition. The difference of cardiac iron overload between children and adults was significant (p=0.009). Liver evaluation showed that most of subjects had moderate to severe iron overload. This difference between children and adults was significant (p=0.017). Pancreas evaluation showed that either children or adults mostly had mild pancreatic iron overload. Analysis of T2* showed that pancreatic iron deposition progressed with increasing age. Serum ferritin had weak correlation with heart T2* MRI, moderate correlation with pancreas and liver T2* MRI. Relationship between transferrin saturation and T2* MRI was extremely weak. CONCLUSION cardiac and hepatic iron deposition between children and adults differ significantly. Liver has the greatest iron overload, followed by pancreas and heart. Iron deposition in liver and pancreas has been started from earlier age. Pancreatic iron deposition rises with increasing age. Serum ferritin and transferrin saturation should not be used solely to predict iron overload in various organs. We suggest that MRI evaluation must be conducted at least once to assess iron deposition in organs.
Extended LBP based Facial Expression Recognition System for Adaptive AI Agent Behaviour

Automatic facial expression recognition is widely used for various applications such as health care, surveillance and human-robot interaction. In this paper, we present a novel system which employs automatic facial emotion recognition technique for adaptive AI agent behaviour. The proposed system is equipped with kirsch operator based local binary patterns for feature extraction and diverse classifiers for emotion recognition. First, we nominate a novel variant of the local binary pattern (LBP) for feature extraction to deal with illumination changes, scaling and rotation variations. The features extracted are then used as input to the classifier for recognizing seven emotions. The detected emotion is then used to enhance the behaviour selection of the artificial intelligence (AI) agents in a shooter game. The proposed system is evaluated with multiple facial expression datasets and outperformed other state-of-the-art models by a significant margin.
Early Prediction of Acute Kidney Injury using Serum Phosphorus as a Biomarker in Pediatric Cardiac Surgical Patients

Background: Acute kidney injury (AKI) is a strong predictor of morbidity and mortality after cardiac surgery. Lack of valid early biomarkers for predicting AKI has hampered the ability to take therapeutic measures for preventive cause. Hyperphosphatemia that occurs in AKI due to renal excretion defect was not studied in this context and could be simple marker of AKI. Therefore, we tested role of serum phosphorus in prediction of AKI as a biomarker after cardiac surgery in children. Methodology: We prospectively evaluated 51 children aged between 3 weeks and 12 years undergoing elective cardiac surgery. Serum creatinine and phosphorus were measured preoperatively and postoperatively at 24 and 48 h. As per the Kidney Disease Improving Global Outcomes criteria, patients were grouped into AKI and non-AKI on the basis of the development of AKI within 48 h postsurgery. The postoperative diagnostic performance of phosphorus thresholds was analyzed by the area under receiver operating characteristic curves (AUC-ROC). Results: From 51 children included, 10 developed AKI. In AKI group, serum phosphorus increased significantly from 4.47 ± 0.43 baseline to 6.29 ± 0.32 at 24 h postsurgery (P = 0.01) while serum creatinine increased from baseline 0.33 (0.24–0.46) to 0.49 (0.26–0.91) at 24 h which is statistically insignificant (P = 0.16). ROC analysis showed that serum phosphorus at 24 h, the AUC was 0.84 with sensitivity 0.75 and specificity 0.93 for a cutoff value of 6.4 mg/dl. Whereas serum phosphorus at 48 h, the AUC was 0.86 with sensitivity 66.67% and specificity 97.62% for a cutoff value of 5.4 mg/dl. Conclusion: Serum phosphorus can be an alternative biomarker as early as 24 h for early prediction of AKI in pediatric cardiac surgery.
Failure prediction with reporting delay

We consider a parametric framework for predicting the future number of failures based on censored and right truncated field data. This type of data regularly occurs in industrial field data with reporting delays. This article presents methods to account for the truncation in this prediction problem. Our simulations show the influence of truncation on the coverage of the resulting prediction interval. The coverage properties of the introduced methods are evaluated. The adjusted prediction intervals show improved coverage properties compared with the unmodified prediction interval based on the truncated data.
Hierarchical Autoencoder for Collaborative Filtering

In recent years autoencoder based collaborative filtering for recommender systems have shown promise. In the past, several variants of the basic autoencoder based approach has been proposed - marginalized denoising autoencoder and stacked denoising autoencoder. However, these are not new developments; just applications of existing machine learning techniques on collaborative filtering. In this work we propose a fundamentally new architecture of hierarchical autoencoder. In a normal stacked denoising autoencoder, reconstruction only happens at the final layer, the intermediate layers are not directly responsible. In our proposed hierarchical model every layer reconstructs; each layer provides complimentary information. The output from all the layers are fused to yield the final result. Experiments of benchmark collaborative filtering datasets show the superiority of our technique over the state-of-art.