Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Network Coding with Interference-Awareness and Neighbor States Updating in Wireless Networks

Network coding is emerging as a promising technique that can provide significant improvements in the throughput of Internet of Things (IoT). Previous network coding schemes focus on several nodes, regardless of the topology and communication range in the whole network. Consequently, these schemes are greedy. Namely, all opportunities of combinations of packets in these nodes are exploited. We demonstrate that there is still room for whole network throughput improvement for these greedy design principles. Thus, in this paper, we propose a novel network coding scheme, ECS (Efficient Coding Scheme), which is designed to achieve a higher throughput improvement with lower computational complexity and buffer occupancy compared to current greedy schemes for wireless mesh networks. ECS utilizes the knowledge of the topologies to minimize interference and obtain more throughput. We also prove that the widely used expected transmission count metric (ETX) in opportunistic listening has an inherent error ratio that would lead to decoding failure. ECS therefore exploits a more reliable broadcast protocol to decrease the impact of this inherent error ratio in ETX. Simulation results show that ECS can greatly improve the performance of network coding and decrease buffer occupancy.
IMPROVING ACCURACY IN SENTIMENT ANALYSIS FOR MALAY LANGUAGE

Accurately determining the intent of a person from their writings is one of the key challenges in developing Sentiment Analysis methods and tools. Among the various proposed methodologies in the literature, Machine Learning based approaches are widely used, especially for multi-lingual text content. This paper presents a hybrid approach of a Knowledge Base approach combined with Machine Learning, implemented in Mi-Intelligence Sentiment Analysis system. The proposed approach supports multilingualism and is applied on text articles written in Malay (Bahasa Melayu) language. A dataset in Malay language is manually annotated with sentiment values and used for performance evaluation. The results are compared with other Machine Learning tools and analysed. The proposed approach achieved an accuracy of 94.34% and outperformed the traditional pattern matching approaches by being able to take into consideration the meaning of the text.
The role of the reef flat in coral reef trophodynamics: Past, present, and future

Abstract The reef flat is one of the largest and most distinctive habitats on coral reefs, yet its role in reef trophodynamics is poorly understood. Evolutionary evidence suggests that reef flat colonization by grazing fishes was a major innovation that permitted the exploitation of new space and trophic resources. However, the reef flat is hydrodynamically challenging, subject to high predation risks and covered with sediments that inhibit feeding by grazers. To explore these opposing influences, we examine the Great Barrier Reef (GBR) as a model system. We focus on grazing herbivores that directly access algal primary productivity in the epilithic algal matrix (EAM). By assessing abundance, biomass, and potential fish productivity, we explore the potential of the reef flat to support key ecosystem processes and its ability to maintain fisheries yields. On the GBR, the reef flat is, by far, the most important habitat for turf‐grazing fishes, supporting an estimated 79% of individuals and 58% of the total biomass of grazing surgeonfishes, parrotfishes, and rabbitfishes. Approximately 59% of all (reef‐wide) turf algal productivity is removed by reef flat grazers. The flat also supports approximately 75% of all grazer biomass growth. Our results highlight the evolutionary and ecological benefits of occupying shallow‐water habitats (permitting a ninefold population increase). The acquisition of key locomotor and feeding traits has enabled fishes to access the trophic benefits of the reef flat, outweighing the costs imposed by water movement, predation, and sediments. Benthic assemblages on reefs in the future may increasingly resemble those seen on reef flats today, with low coral cover, limited topographic complexity, and extensive EAM. Reef flat grazing fishes may therefore play an increasingly important role in key ecosystem processes and in sustaining future fisheries yields.
Evaluation of PLC Channel Capacity and ABER Performances for OFDM-Based Two-Hop Relaying Transmission

Powerline network is recognized as a favorable infrastructure for Smart Grid to transmit information in the network thanks to its broad coverage and low cost deployment. The existing works are trying to improve and adapt transmission techniques to reduce Powerline Communication (PLC) channel attenuation and exploit the limited bandwidth to support high data rate over long distances. Two-hop relaying BroadBand PLC (BB-PLC) system, in which Orthogonal Frequency Division Multiplexing (OFDM) is used, is considered in this paper. We derive and compare the PLC channel capacity and the end-to-end Average BER (ABER) for OFDM-based direct link (DL) BB-PLC system and for OFDM-based two-hop relaying BB-PLC system for Amplify and Forward (AF) and Decode and Forward (DF) protocols. We analyze the improvements when we consider the direct link in a cooperative communication when the relay node only transmits the correctly decoded signal. Maximum ratio combining is employed at the destination node to detect the transmitted signal. In addition, in this paper, we highlight the impact of the relay location on the channel capacity and ABER for AF and DF transmission protocols. Moreover, an efficient use of the direct link was also investigated in this paper.
Handling Missing Values using Decision Trees with Branch-Exclusive Splits

In this article we propose a new decision tree construction algorithm. The proposed approach allows the algorithm to interact with some predictors that are only defined in subspaces of the feature space. One way to utilize this new tool is to create or use one of the predictors to keep track of missing values. This predictor can later be used to define the subspace where predictors with missing values are available for the data partitioning process. By doing so, this new classification tree can handle missing values for both modelling and prediction. The algorithm is tested against simulated and real data. The result is a classification procedure that efficiently handles missing values and produces results that are more accurate and more interpretable than most common procedures.
Automated classification of content components in technical communication

Automated classification is usually not adjusted to specialized domains due to a lack of suitable data collections and insufficient characterization of the domain‐specific content and its effect on the classification process. This work describes an approach for the automated multiclass classification of content components used in technical communication based on a vector space model. We show that differences in the form and substance of content components require an adaption of document‐based classification methods and validate our assumptions with multiple real‐world data sets in 2 languages. As a result, we propose general adaptions on feature selection and token weighting, as well as new ideas for the measurement of classifier confidence and the semantic weighting of XML‐based training data. We introduce several potential applications of our method and provide prototypical implementation. Our contribution beyond the state of the art is a dedicated procedure model for the automated classification of content components in technical communication, which outperforms current document‐centered or domain‐agnostic approaches.
DETERMINANTS OF EARLY MARRIAGE AND MODEL OF MATURING MARRIAGE AGE POLICY

This study aims to analyze factors affecting early marriages and to formulate a model of maturing marriage age policy in Jambi Province, Indonesia. Using binary logistic model and interpretive structural modeling, we found that: firstly, factors affecting women’s decision to get married early could come from both individual and their parents charateristics. Secondly, the appropriate model for maturing marriage age policy involved six main elements of the system. Those are objectives, institutions, needs, constraints, community involvement, and assessment benchmarks. We come to the conclusion that maturing marriage age is not only determined by individual characteristics, but also other factors
Feedback control goes wireless: guaranteed stability over low-power multi-hop networks

Closing feedback loops fast and over long distances is key to emerging applications; for example, robot motion control and swarm coordination require update intervals of tens of milliseconds. Low-power wireless technology is preferred for its low cost, small form factor, and flexibility, especially if the devices support multi-hop communication. So far, however, feedback control over wireless multi-hop networks has only been shown for update intervals on the order of seconds. This paper presents a wireless embedded system that tames imperfections impairing control performance (e.g., jitter and message loss), and a control design that exploits the essential properties of this system to provably guarantee closed-loop stability for physical processes with linear time-invariant dynamics. Using experiments on a cyber-physical testbed with 20 wireless nodes and multiple cart-pole systems, we are the first to demonstrate and evaluate feedback control and coordination over wireless multi-hop networks for update intervals of 20 to 50 milliseconds.
The efficacy of video-assisted thoracoscopic surgery for anterior release and fusion in the management of pediatric spinal deformities.

PURPOSE This study examined clinical and radiological outcomes following video-assisted thoracoscopic surgery (VATS) for anterior release and fusion in the correction of pediatric scoliotic deformities. METHODS We undertook a detailed chart and radiographic review to determine the degree of correction and perioperative morbidity and complications, if any, of a sequential group of patients who underwent VATS between 2000 and 2004 at British Columbia's Children's Hospital. We used patients who underwent open thoracotomy immediately before the adoption of the VATS technique at the same hospital to evaluate the relative efficacy of VATS. RESULTS There were 19 patients in each group, 17 with idiopathic scoliosis in the VATS group and 16 in the open thoracotomy group. Mean age, weight at surgery and preoperative Cobb angle were similar (p = 1.0, 0.8 and 0.05, respectively). There was no significant difference in operative time per level between the VATS group and the open thoracotomy group (37.2 v. 34.5 min, p = 0.2) or total blood loss (908 v. 823 mL, p = 0.5). There were no major complications encountered in the VATS group. One patient in the open thoracotomy group experienced atelectasis and subsequent lower lobe collapse. CONCLUSIONS VATS has the potential to decrease postoperative morbidity while still allowing the same degree of correction as traditional open thoracotomies and is a good alternative in the pediatric population.
Feedback synthesis for underactuated systems using sequential second-order needle variations

This paper derives nonlinear feedback control synthesis for general control affine systems using second-order actions, the second-order needle variations of optimal control, as the basis for choosing each control response to the current state. A second result of this paper is that the method provably exploits the nonlinear controllability of a system by virtue of an explicit dependence of the second-order needle variation on the Lie bracket between vector fields. As a result, each control decision necessarily decreases the objective when the system is nonlinearly controllable using first-order Lie brackets. Simulation results using a differential drive cart, an underactuated kinematic vehicle in three dimensions, and an underactuated dynamic model of an underwater vehicle demonstrate that the method finds control solutions when the first-order analysis is singular. Finally, the underactuated dynamic underwater vehicle model demonstrates convergence even in the presence of a velocity field.
Established and potential echocardiographic markers of embolism and their therapeutic implications in patients with ischemic stroke

Cardiogenic strokes comprised 11% of all strokes and 25% of ischemic strokes. An accurate identification of the cause of stroke is necessary in order to prepare an adequate preventive strategy. In this review the confirmed and potential causes of embolic strokes are presented, which can be detected in echocardiography in the context of present treatment guidelines and gaps in evidence. There remains a need for further studies assessing the meaning of potential cardiac sources of embolism and establishment of rules for optimal medical prevention (antiplatelet therapy [APT] vs. oral anticoagulation [OAC]) and interventional procedures to reduce the incidence of ischemic strokes. Currently available data does not provide definitive evidence on the comparative benefits of OAC vs. APT in patients with cryptogenic stroke or embolic stroke of undetermined source. There is a lack of antithrombotic treatment scheme in the time between stroke and the completed diagnosis of potential sources of thromboembolism.
Voluntary Disclosure and Information Asymmetry: Evidence from the 2005 Securities Offering Reform

In 2005, the SEC enacted the Securities Offering Reform (Reform), which relaxes ‘gun jumping’ restrictions, thereby allowing firms to more freely disclose information before equity offerings. We examine the effect of the Reform on voluntary disclosure behavior before equity offerings and the associated economic consequences. We find that firms provide significantly more pre-offering disclosures after the Reform. Further, we find that these pre-offering disclosures are associated with a decrease in information asymmetry and a reduction in the cost of raising equity capital. Our findings not only inform the debate on the market effect of the Reform, but also speak to the literature on the relation between voluntary disclosure and information asymmetry by examining the effect of quasi-exogenous changes in voluntary disclosure on information asymmetry, and thus a firm’s cost of capital.
Apparent absence of Cryptosporidium, Giardia and Toxoplasma gondii in three species of penguins along the Antarctic Peninsula

Abstract We carried out a study to investigate the presence of some protozoan parasites (Cryptosporidium sp., Giardia sp., Toxoplasma gondii) on three species of Antarctic penguins: Adélie (Pygoscelis adeliae), gentoo (Pygoscelis papua) and chinstrap (Pygoscelis antarctica) from different locations along the Antarctic Peninsula and the South Shetland Islands. Swabs and faeces samples were analysed by PCR assay for Cryptosporidium sp. and Giardia sp. while Toxoplasma was studied using serological methods from blood samples. We did not detect the presence of these organisms in the species studied. However, based on the upper values of the confidence intervals of the observed prevalence, their presence cannot be completely excluded.
Fast and Scalable Learning of Neuro-Symbolic Representations of Biomedical Knowledge

In this work we address the problem of fast and scalable learning of neuro-symbolic representations for general biological knowledge. Based on a recently published comprehensive biological knowledge graph (Alshahrani, 2017) that was used for demonstrating neuro-symbolic representation learning, we show how to train fast (under 1 minute) log-linear neural embeddings of the entities. We utilize these representations as inputs for machine learning classifiers to enable important tasks such as biological link prediction. Classifiers are trained by concatenating learned entity embeddings to represent entity relations, and training classifiers on the concatenated embeddings to discern true relations from automatically generated negative examples. Our simple embedding methodology greatly improves on classification error compared to previously published state-of-the-art results, yielding a maximum increase of $+0.28$ F-measure and $+0.22$ ROC AUC scores for the most difficult biological link prediction problem. Finally, our embedding approach is orders of magnitude faster to train ($\leq$ 1 minute vs. hours), much more economical in terms of embedding dimensions ($d=50$ vs. $d=512$), and naturally encodes the directionality of the asymmetric biological relations, that can be controlled by the order with which we concatenate the embeddings.
A Pilot Study of the Efficacy of a Mindfulness Program for Children Newly Diagnosed with Attention-Deficit Hyperactivity Disorder: Impact on Core Symptoms and Executive Functions

Mindfulness ha emergido como un nuevo enfoque para el tratamiento de los trastornos mentales. El objetivo de este estudio es investigar la eficacia preliminar de la formacion de meditacion de atencion plena basada en grupos para ninos en sintomas basicos, funcionamiento ejecutivo y sintomas de comorbilidad en una muestra de trastorno por deficit de atencion con hiperactividad (TDAH). Cinco ninos de 7-12 anos recien diagnosticados con TDAH recibieron un tratamiento de ocho semanas basado en el grupo. El programa consistio en sesiones de una vez por semana con una duracion de 75 minutos y tareas diarias. Las evaluaciones del estudio incluyeron la medida previa y posterior de los sintomas psiquiatricos y el funcionamiento cognitivo. Los datos se analizaron utilizando Wilcoxon firma-rango de prueba. Se observaron mejoras antes del post en los sintomas del TDAH y el rendimiento de las pruebas en las tareas que miden el funcionamiento del ejecutivo. Se observaron resultados significativos en los sintomas de ADHD total (p = .042), sintomas de ansiedad (p = .042), inhibicion cognitiva (p = .042) y velocidad de procesamiento (p = .043). En resumen, aunque el tamano de la muestra era pequeno, nuestro estudio piloto sugiere que el entrenamiento de la atencion plena para los ninos es una intervencion factible en al menos un subconjunto de ninos con TDAH y puede mejorar las deficiencias conductuales y neurocognitivas. Como citar este documento: Huguet A, Miguel-Ruiz D, Haro JM, y Alda JA (2017). Home Idiomas Ingresar a Epistemonikos Busqueda avanzada Eficacia de un programa de atencion plena para ninos recien diagnosticados con trastorno de hiperactividad con deficit de atencion. Impacto en los Sintomas Basicos y las Funciones Ejecutivas: Un Estudio Piloto. Revista Internacional de Psicologia y Terapia Psicologica, 17, 305-316.
Got political Islam? Are politically moderate Muslims really different from radicals?

Is making an explicit distinction between politically moderate devout Muslims and political radicals empirically valid? If yes, in what ways do political moderates differ from political radicals? By systematically examining cross-national Muslim attitudes, this article scrutinizes the distinctiveness of politically moderate and politically radical Islam against the weight of empirical evidence. By drawing from extant theoretical linkages, we conduct a confirmatory factor analysis of cross-national survey data from 13 Muslim-majority states to test the fit of two widely theorized factors—moderate and radical Islamism. The findings suggest that support for politically moderate Islam is distinctively different from support for politically radical Islam. This article makes two key contributions. First, this study introduces a systematic empirical operationalization of Political Islam, and a more nuanced measurement thereof for empirical research. Second, the findings help advance our understanding of the variation in politically divergent religious attitudes in the Islamic world.
Generative Temporal Models with Spatial Memory for Partially Observed Environments

In model-based reinforcement learning, generative and temporal models of environments can be leveraged to boost agent performance, either by tuning the agent's representations during training or via use as part of an explicit planning mechanism. However, their application in practice has been limited to simplistic environments, due to the difficulty of training such models in larger, potentially partially-observed and 3D environments. In this work we introduce a novel action-conditioned generative model of such challenging environments. The model features a non-parametric spatial memory system in which we store learned, disentangled representations of the environment. Low-dimensional spatial updates are computed using a state-space model that makes use of knowledge on the prior dynamics of the moving agent, and high-dimensional visual observations are modelled with a Variational Auto-Encoder. The result is a scalable architecture capable of performing coherent predictions over hundreds of time steps across a range of partially observed 2D and 3D environments.
Understanding Power and Rules of Thumb for Determining Sample Sizes

This article addresses the definition of power and its relationship to Type I and Type II errors. We discuss the relationship of sample size and power. Finally, we offer statistical rules of thumb guiding the selection of sample sizes large enough for sufficient power to detecting differences, associations, chi‐square, and factor analyses.
Predictors of Broad Dimensions of Psychopathology among Patients with Panic Disorder after Cognitive-Behavioral Therapy

Background Many patients with panic disorder meet criteria for at least one other diagnosis, most commonly other anxiety or mood disorders. Cognitive-behavioral therapy is the best empirically supported psychotherapy for panic disorder. There is now evidence indicating that cognitive-behavioral therapy for panic disorder yields positive benefits upon comorbid disorders. Objectives The present study aimed to examine the predictors of broad dimensions of psychopathology in panic disorder after cognitive-behavioral therapy. Methods Two hundred patients affected by panic disorder were treated with manualized group cognitive-behavioral therapy. We examined if the baseline personality dimensions of NEO Five Factor Index predicted the subscales of Symptom Checklist-90 Revised at endpoint using multiple regression analysis based on the intention-to-treat principle. Results Conscientiousness score of NEO Five Factor Index at baseline was a predictor of four Symptom Checklist-90 Revised subscales including obsessive-compulsive (β = −0.15, P < 0.01), depression (β = −0.13, P < 0.05), phobic anxiety (β = −0.15, P < 0.05), and Global Severity Index (β = −0.13, P < 0.05). Conclusion Conscientiousness at baseline may predict several dimensions of psychopathology in patients with panic disorder after cognitive-behavioral therapy. For the purpose of improving a wide range of psychiatric symptoms with patients affected by panic disorder, it may be useful to pay more attention to this personal trait at baseline.