Miracle-dz/UniFAR-FTUs huggingface dataset:
An Introduction to Probabilistic Record Linkage with a Focus on Linkage Processing for WTC Registries

Since its post-World War II inception, the science of record linkage has grown exponentially and is used across industrial, governmental, and academic agencies. The academic fields that rely on record linkage are diverse, ranging from history to public health to demography. In this paper, we introduce the different types of data linkage and give a historical context to their development. We then introduce the three types of underlying models for probabilistic record linkage: Fellegi-Sunter-based methods, machine learning methods, and Bayesian methods. Practical considerations, such as data standardization and privacy concerns, are then discussed. Finally, recommendations are given for organizations developing or maintaining record linkage programs, with an emphasis on organizations measuring long-term complications of disasters, such as 9/11.
Distributed and Fault-Tolerant Construction of Low Stretch Spanning Tree

Spanning trees are widely used as a communication backbone over some given infrastructure and help network designers achieve a low-cost communication overhead. Spanning trees are generally designed, keeping in mind some optimizing metric (most general being sum of edge weights in a Minimum Spanning Tree) with respect to the underlying graph. For applications that require preserving shortest path distances between nodes of the weighted underlying graph in the abstracted spanning tree, we look to minimize a parameter known as stretch. Stretch is defined as the ratio of the distance between two nodes in the tree to its shortest path distance in the communication graph.To make spanning-tree constructions resilient to edge failures in an error-prone environment, we consider what is called the All Best Swap Edges (ABSE) problem. Since every edge in a tree is a bridge edge, a single edge failure disconnects the tree into two connected components. In the ABSE problem, for each edge e in the spanning tree, we compute a swap edge f corresponding to e, that is activated when e fails. f helps to restore the communication in the tree by connecting the disconnected components.In this paper, we give a novel distributed algorithm to efficiently construct a low average stretch spanning tree and make it robust against edge failures by finding a swap edge for every edge in the constructed tree. This is the first known deterministic distributed algorithm for constructing a low stretch tree that is also edge fault-tolerant. The distributed ABSE computation in our case equals the state-of-the-art running time of O(h) rounds, where h is the height of the tree.
Impacts of Mobility Models on RPL-Based Mobile IoT Infrastructures: An Evaluative Comparison and Survey

With the widespread use of IoT applications and the increasing trend in the number of connected smart devices, the concept of routing has become very challenging. In this regard, the IPv6 Routing Protocol for Low-power and Lossy Networks (PRL) was standardized to be adopted in IoT networks. Nevertheless, while mobile IoT domains have gained significant popularity in recent years, since RPL was fundamentally designed for stationary IoT applications, it could not well adjust with the dynamic fluctuations in mobile applications. While there have been a number of studies on tuning RPL for mobile IoT applications, but still there is a high demand for more efforts to reach a standard version of this protocol for such applications. Accordingly, in this survey, we try to conduct a precise and comprehensive experimental study on the impact of various mobility models on the performance of a mobility-aware RPL to help this process. In this regard, a complete and scrutinized survey of the mobility models has been presented to be able to fairly justify and compare the outcome results. A significant set of evaluations has been conducted via precise IoT simulation tools to monitor and compare the performance of the network and its IoT devices in mobile RPL-based IoT applications under the presence of different mobility models from different perspectives including power consumption, reliability, latency, and control packet overhead. This will pave the way for researchers in both academia and industry to be able to compare the impact of various mobility models on the functionality of RPL, and consequently to design and implement application-specific and even a standard version of this protocol, which is capable of being employed in mobile IoT applications.
Question-Answering using Keyword Entries in the Oil&Gas Domain

To improve the efficiency and accuracy of knowledge access in the oil industry, we propose to build a question-answer system for domain users. The system applies the state-of-the-art natural language processing techniques to analyze the domain knowledge in the form of keywords and their explanations. To this end, we collect over 20,000 keyword entries from reliable resources in the oil domain. The system then uses four different feature extraction techniques, including deep learning models, over these keyword entries to generate features, which are used to compute similarity between any given user question and keyword entries. Eventually, the keyword entries with the highest similarity scores are chosen as the answers and presented to the user. Our empirical evaluation shows that the traditional TF-IDF methods for feature extraction outperforms the other methods in our testing dataset.
Finding Emotion from Multi-lingual Voice Data

Human-Machine interaction through audio or speech is always challenging because of the difficulties in emotion detection from audio data. Emotion is a natural way to express the individual's mental state. When a person is in an unusual mental state, the voice changes accordingly in his or her subconsciousness. Emotion detection from continuous speech with multi-lingual platform is more difficult due to complicated feature extraction and feature modeling process. Voice features change due to physical, background and channel noise or sue to other articulatory obstacles. In spite of all these uncertainties, researchers find that among glottal wave forms (consists of glottal excitation and vocal tract filters), glottal pulses carry basic information of voice because these pulses are out of articulatory effects or inner-mouth changes. In this research, we capture real-time speech signal, separate the glottal pulses and buffer the signal into specified frames to identify the phonemes those are affected mostly in emotionally unstable conditions. In these frames, we identify the basic voice controlling features like pitch, intensity, jitter to get these phonemes. For this scheme, we use a real-life continuous dataset which is a multilingual corpus of emotional speech. After feature extraction from the dataset, different classification methodologies have been applied to predict the emotional state and we find our prediction model with around 83% accuracy.
Parallel multi-head dot product attention for video summarization

The dominant video summarization deep learning models are based on recurrent or convolutional neural network with a complex architecures. The best performing models also use attention mechanism. We propose a novel method for supervised, keyframes based video summarization by applying a well known Transformer architecture. Current state of the art method based on self-attention netwok. These network is simple and robust but still lower than humman level. We propose model for video summarization based on a powerfull language architecture Transformer which performs a single feed forward and backward pass during training for the entire video sequence to frame score transformation. This approach was adapted for the sequential regression followed by binarized evaluation which allowed to compare predicted summary with human level. Experiments show that model is competitive in performance and have a good parallel training ability also. Our model achieves F1 48.1 and 50.19 for SumMe in cannonical and augmented setting and 60.12 and 61.5 for TVSum.
Bases moléculaires de la susceptibilité aux xénobiotiques : aspects métaboliques.

La susceptibilite individuelle aux xenobiotiques (polluants, medicaments, composes alimentaires.) auxquels nous sommes necessairement exposes, depend, entre autres, de leur metabolisme et donc de l'expression des enzymes du metabolisme des xenobiotiques. Pour chacun d'entre nous, cette expression est tres variable en fonction de facteurs genetiques, environnementaux et physiopathologiques, rendant compte ainsi de la grande diversite de la reponse pharmacologique ou toxique.
Association of Prenatal Alcohol Exposure With Psychological, Behavioral, and Neurodevelopmental Outcomes in Children From the Adolescent Brain Cognitive Development Study.

OBJECTIVE Data on the neurodevelopmental and associated behavioral effects of light to moderate in utero alcohol exposure are limited. This retrospective investigation tested for associations between reported maternal prenatal alcohol use and psychological, behavioral, and neurodevelopmental outcomes in substance-naive youths. METHODS Participants were 9,719 youths (ages 9.0 to 10.9 years) from the Adolescent Brain Cognitive Development Study. Based on parental reports, 2,518 (25.9%) had been exposed to alcohol in utero. Generalized additive mixed models and multilevel cross-sectional and longitudinal mediation models were used to test whether prenatal alcohol exposure was associated with psychological, behavioral, and cognitive outcomes, and whether differences in brain structure and resting-state functional connectivity partially explained these associations at baseline and 1-year follow-up, after controlling for possible confounding factors. RESULTS Prenatal alcohol exposure of any severity was associated with greater psychopathology, attention deficits, and impulsiveness, with some effects showing a dose-dependent response. Children with prenatal alcohol exposure, compared with those without, displayed greater cerebral and regional volume and greater regional surface area. Resting-state functional connectivity was largely unaltered in children with in utero exposure. Some of the psychological and behavioral outcomes at baseline and at the 1-year follow-up were partially explained by differences in brain structure among youths who had been exposed to alcohol in utero. CONCLUSIONS Any alcohol use during pregnancy is associated with subtle yet significant psychological and behavioral effects in children. Women should continue to be advised to abstain from alcohol consumption from conception throughout pregnancy.
Credit Card Fraud Detection Based on Multilayer Perceptron and Extreme Learning Machine Architectures

Due to the increasing digitalization of banking services and the predominance of mobile banking applications, the rate of credit card payments is increasing every year, among billions of transactions identified as fraudulent. Data mining algorithms have played a fundamental role in detecting fraudulent transactions, through combating fraudster’s attacks working around classical fraud prevention systems. In this paper, we try to detect fraudulent transactions using two artificial neural network classifiers, Multilayer Perceptron (MLP) and Extreme Learning Machine (ELM), applied on the credit card fraud dataset. The performance of these classifiers is evaluated based on accuracy, recall, precision, and classification time. The results show that the accuracy of MLP and ELM classifiers achieves respectively 97.84% and 95.46%. Otherwise, ELM is very fast for predicting new fraudulent transactions.
Synergy between 15-lipoxygenase and secreted PLA2 promotes inflammation by formation of TLR4 agonists from extracellular vesicles

Significance Oxidative stress, which accompanies sterile inflammation, induces release of extracellular vesicles (stressEVs), which activate Toll-like receptor 4 (TLR4), resulting in a pattern of gene expression distinct from response triggered by bacterial lipopolysaccharide. The synergy between 15-lipoxygenase and secreted phospholipase A2, both of which are induced by stress, is necessary for the formation of oxidized lysophospholipids, TLR4 endogenous agonists. Moreover, TLR4 was necessary for sPLA2 promoted K/BxN serum-induced arthritis. As the formation of TLR4 agonists is enzyme driven, it provides an opportunity to inhibit these enzymes to limit sterile inflammation. Damage-associated endogenous molecules induce innate immune response, thus making sterile inflammation medically relevant. Stress-derived extracellular vesicles (stressEVs) released during oxidative stress conditions were previously found to activate Toll-like receptor 4 (TLR4), resulting in expression of a different pattern of immune response proteins in comparison to lipopolysaccharide (LPS), underlying the differences between pathogen-induced and sterile inflammation. Here we report that synergistic activities of 15-lipoxygenase (15-LO) and secreted phospholipase A2 (sPLA2) are needed for the formation of TLR4 agonists, which were identified as lysophospholipids (lysoPLs) with oxidized unsaturated acyl chain. Hydroxy, hydroperoxy, and keto products of 2-arachidonoyl-lysoPI oxidation by 15-LO were identified by mass spectrometry (MS), and they activated the same gene pattern as stressEVs. Extracellular PLA2 activity was detected in the synovial fluid from rheumatoid arthritis and gout patients. Furthermore, injection of sPLA2 promoted K/BxN serum-induced arthritis in mice, whereby ankle swelling was partially TLR4 dependent. Results confirm the role of oxidized lysoPL of stressEVs in sterile inflammation that promotes chronic diseases. Both 15-LO and sPLA2 enzymes are induced during inflammation, which opens the opportunity for therapy without compromising innate immunity against pathogens.
Enhancing GPSR routing protocol based on Velocity and Density for real-time urban scenario

The main goal of VANET is to improve the traffic safety, by minimizing the number of road accidents. Hence, a reliable and rapid communication is crucial for vehicles. However, the high dynamic topology of VANETs, the frequently link breakage problem and the high speed of vehicles have brought great challenges to the routing design of VANETs. It is difficult for existing routing protocols for WSN and MANETs to adapt the high dynamics of vehicular networks. Therefore, various number of routing protocols have been proposed to deal with the specific characteristics of VANETs. GPSR for Greedy Perimeter Stateless Routing is the most popular protocol, but it still suffers from frequent link breakages issue due to the high-mobility of vehicles, which cause a low PDR and throughput. In this investigation, we introduce two new strategies to enhance the classical GPSR protocol, by reducing the problem of link breakages and getting a stable route that improve the PDR and throughput in addition to reduce the routing overhead. The two proposed routing protocols E-GPSR and DVA-GPSR guide the selection of the next hop node based not only on the position but also on other important metrics of the participating nodes. Simulation results show that DVA-GPSR and E-GPSR protocols perform remarkably GPSR in terms of PDR, throughput, and routing overhead by varying the number of vehicles in a real urban scenario.
Optical Wireless Channel Simulation for Communications Inside Aircraft Cockpits

Communications inside an aircraft cockpit are currently based on wired connections especially for the audio headsets used by the pilots. A wireless headset would be an advantage in terms of comfort and flexibility but the use of classical radio frequencies is limited by interference and security issues. Optical wireless communication technology is an option for headset connectivity. Indeed, as optical beams are confined, this technology provides robustness against the risk of hacking, thus increasing security. In addition, the use of optical waves ensures the absence of radio-frequency disturbances. Using simulation, this article presents a thorough study of the optical wireless channel behavior inside the cockpit of an aircraft by considering a headset worn by a pilot possibly in motion and an access point at the ceiling. The impact of the characteristics of the environment model, such as the level of geometric description, the reflectivity of materials and for the first time, the ambient noise induced by the sun, is highlighted. System performance is evaluated in terms of optimal half-power angles and the necessary optical power of the light sources.
On Delay Bounds and Measurements: A COTS Testbed for Network Performance Experimentation

In many application domains such as the automotive and the avionics sector, networks need to fulfill different non-functional requirements. Among them is the strict demand to provide predictable and deterministic worst-case communication performance. Without formal verification of this property, an entire system embedding the network may not be certified and thus not be operated. A methodology for formal verification of communication performance is the Deterministic Network Calculus (DNC). DNC is under active development to better model real systems and to reduce pessimism required to provide verifiably correct bounds on communication delays. This previous work is mostly of theoretical nature. In our paper, we aim at providing a testbed design to benchmark bounds against measurements. Our design is composed of off-the-shelf components that we accompany with a custom software stack for taking measurements (network configuration and time stamping) as well as deriving delay bounds with DNC (system modeling and analysis). We also provide lessons-learned as well as first results that compare delay measurements and DNC-derived bounds.
A Risk Averse Stochastic Optimization Model for Wind Power Plants Portfolio Selection

This work focuses on the wind power plants portfolio selection. We developed a stochastic optimization model whose objective function considers the financial risk and return, weighted by the risk aversion profile of the decision-maker. The financial risk is measured by the Conditional Value-at-Risk metric. The agent risk aversion profile brings important implications for resource allocation strategy definition. We apply the model in a case study considering sixteen locations in Brazil, where optimal portfolios compositions are analyzed under risk aversion levels, from the perspective of a risk-neutral agent (decision based only on the Expected Revenue) up to risk-averse (decision based only on CVaR), considering intermediate levels of these extremes. The results showed relevant changes in the portfolio allocation under different risk aversion levels. The model application contributes to discussions on this relevant subject and for mapping potential strategic association between wind power plants in Brazil. The model can be effortlessly adapted for applications in any location worldwide.
ClothFace: Battery-Free User Interface Solution Embedded into Clothing and Everyday Surroundings

This paper introduces ClothFace, a passive ultrahigh frequency (UHF) radio frequency identification (RFID) -based user interface solution, which can be embedded into clothing and into our everyday surroundings. The user interface platform consists of RFID tags, each of which has a unique ID. All the tags are initially readable to an external RFID reader. A specific tag can be switched off by covering it with a hand, which change can then be used as a digital input to any connected device. Because of the used passive UHF RFID technology, there is no need for embedded energy sources, but the interface platform gets all the needed energy from the external RFID reader. In this study, two test setups were created to an office environment: For the Body Test, the interface was integrated into a cotton shirt and into an item. For the Table Test, the interface was integrated into a wooden table. A gamelike testing software was created for both setups and two male test subjects tested the platform. The achieved results were very promising: success rates of 99–100 % and 94–98 % were reached in the Body Test and in the Table Test, respectively. Based on these promising preliminary results, we can envision the employment of ClothFace for developing multi-modal interfaces that can provide on-body gestural controls in body-based serious game applications.
Taxi Demand Prediction using an LSTM-Based Deep Sequence Model and Points of Interest

Nowadays, urban mobility plays an important role in modern cities for city planning, navigation, and other mobility services. Taxicabs are vital public services in large cities that are taken by passengers thousands of times every day. Reducing the number of vacant vehicles on the streets will help service providers to raise drivers' incomes, reduce energy consumption, optimize traffic efficiency, and control air pollution problems in large cities. Since drivers do not have enough information about the location of passengers and other taxis, most of them might drive to the same area. Due to the lack of passenger information, they often end up without picking up any passengers while there are highly demanded areas in their neighborhood. To address these issues, machine learning techniques can be applied to analyze mobility data acquired from the IoT sensors and help companies to organize the taxi fleet or minimize the wait-time for both passengers and drivers in the city. In this paper, an LSTM-based deep sequence learning model is applied to forecast taxi-demand in a particular urban area in a smart city. For this purpose, points of interest (POIs) in the city are extracted from Google Maps and integrated with the mobility data sources. Given a real-world dataset and two evaluation metrics, we observed that taxi-demand in each urban area can be influenced by external factors such as neighborhood locations and the POIs located in that area. The results show that the proposed method outperforms the vanilla LSTM model and has less average error than baseline methods in terms of the Mean Squared Error (MSE) and Symmetric Mean Absolute Percentage Error (SMAPE).
Photosensitivity During Quinolone Therapy in Diabetic Foot Patients May Falsely Indicate Treatment Failure

Acute bacterial skin and skin structure infections (ABSSSI), also referred to as skin and soft tissue infections, or skin and skin structure infections, with or without osteomyelitis (OM) in diabetic foot are complications of diabetes. Quinolones are a widely used class of antibiotics in ABSSSI and OM, and photosensitivity is among their adverse reactions. Its appearance may falsely indicate treatment failure. We describe 2 such cases. The first patient is a 49-year-old male with type 2 diabetes mellitus (DM), who presented with left lower limb ulcer with fever (39 °C) over a week. He began treatment with ciprofloxacin, clindamycin, and linezolid. Although his clinical condition and laboratory tests improved, the redness of the left lower limb increased. Clinical examination revealed redness on the face and the parts of the body that had been exposed to sunlight. The patient continued the same antimicrobial therapy and was given instructions to avoid exposure to sunlight. Redness and infection improved, and the patient was discharged. The second case is a 72-year-old male with a history of type 2 DM, admitted to hospital because of an infected ulcer of the first toe of the right limb. The patient received intravenous treatment with levofloxacin and clindamycin. On the fourth hospital day, the patient presented redness (with eczematous plaques) on his left lower limb. Clinical examination revealed that the patient’s bed was placed near a window and his left limb was exposed to direct sunlight. Patient’s bed was changed to avoid sun exposure. Symptoms began to improve over the next few days.