Miracle-dz/UniFAR-FTUs huggingface dataset:
Distribution-Free Predictive Inference for Regression

ABSTRACT We develop a general framework for distribution-free predictive inference in regression, using conformal inference. The proposed methodology allows for the construction of a prediction band for the response variable using any estimator of the regression function. The resulting prediction band preserves the consistency properties of the original estimator under standard assumptions, while guaranteeing finite-sample marginal coverage even when these assumptions do not hold. We analyze and compare, both empirically and theoretically, the two major variants of our conformal framework: full conformal inference and split conformal inference, along with a related jackknife method. These methods offer different tradeoffs between statistical accuracy (length of resulting prediction intervals) and computational efficiency. As extensions, we develop a method for constructing valid in-sample prediction intervals called rank-one-out conformal inference, which has essentially the same computational efficiency as split conformal inference. We also describe an extension of our procedures for producing prediction bands with locally varying length, to adapt to heteroscedasticity in the data. Finally, we propose a model-free notion of variable importance, called leave-one-covariate-out or LOCO inference. Accompanying this article is an R package conformalInference that implements all of the proposals we have introduced. In the spirit of reproducibility, all of our empirical results can also be easily (re)generated using this package.
Pictures, patience, and practicalities: lessons learned from using photovoice in applied communication contexts

ABSTRACT The unforeseen trials of arts-based research may suppress creative and innovative explorations. Photovoice, a visual method of investigation employing participatory photography, presents a host of apprehensions. Yet, photovoice is both a valid and rich methodology that can greatly expand the reach and scope of applied communication research. In this essay, we – three applied communication researchers – discuss the complexities of photovoice, highlighting areas of consideration that interested researchers should contemplate before utilizing this methodology. Briefly exploring three of our research studies and their varying topics (i.e. long-term cancer survivors, domestic violence organizational advocacy, male body culture), we discuss three broad areas of difficulty in photovoice research: analysis, control, and exposure. Despite the unique obstacles that photovoice presents, we argue that it, along with other forms of arts-based research, are profitable and definitely worth the challenge.
Age‐specific seroprevalence of dengue infection in Hong Kong

A newly developed dengue virus vaccine (chimeric yellow fever virus‐tetravalent dengue vaccine [CYD‐TDV]) has recently been licensed for clinical use. The World Health Organization recommends vaccination for populations with seroprevalence of at least 70% to maximize public health impact. This study aimed to delineate the seroprevalence of dengue infection in Hong Kong. A total of 105 972 serum samples submitted for clinical testing during the period 2013‐2015 were age‐stratified and sex‐stratified. For each year of collection, 25 samples were randomly selected from each age‐sex group. Altogether, 2100 samples were tested for the dengue immunoglobulin G (IgG) antibody using a non‐type–specific ELISA kit. The overall dengue IgG‐positive rate was 4.6% and showed no significant change over the 3 years. The positive rate was not associated with sex, but a steep rise in seroprevalence for persons above 65 years (32.7%) was observed. The low dengue seroprevalence in Hong Kong does not support implementation of a national immunization program. Majority of the population in Hong Kong are susceptible to dengue infection, and a substantial proportion of persons older than 65 years could acquire secondary infection and are prone to develop severe dengue.
Comparison of Variational Bayes and Gibbs Sampling in Reconstruction of Missing Values with Probabilistic Principal Component Analysis

Lately there has been the interest of categorization and pat tern detection in large data sets, including the recovering of the dataset missing values. In this projec t the objective will be to recover the subset of missing values as accurately as possible from a movie rati ng data set. Initially the data matrix is preprocessed and its elements are divided in training and te st sets. Thereafter the resulting matrices are factorized and reconstructed according to probabilist ic principal component analysis (PCA). We compare the quality of reconstructions done with sampling a d variational Bayesian (VB) approach. The results of the experiments showed that sampling improve d th quality of the recovered missing values over VB-PCA typically after roughly 100 steps of Gibb s sampling.
Semantic Annotation of Heterogeneous Data Sources: Towards an Integrated Information Framework for Service Technicians

Service technicians in the domain of industrial maintenance require extensive technical knowledge and experience to complete their tasks. Some of the needed knowledge is made available as document-based technical manuals or reports from previous deployments. Unfortunately, due to the great amount of data, service technicians spend a considerable amount of working time searching for the correct information. Another challenge is posed by the fact that valuable insights from operation reports are not yet considered due to insufficient textual quality and content-wise ambiguity. In this work we propose a framework to annotate and integrate these heterogeneous data sources to make them available as information units through Linked Data technologies. We use machine learning to modularize and classify information from technical manuals together with ontology-based autocompletion to enrich reports with clearly defined concepts. By combining both approaches we can provide an unified and structured interface for manual and automated querying. We verify our approach by measuring precision and recall of information for typical retrieval tasks for service technicians, and show that our framework can provide substantial improvements for service and maintenance processes.
Integrating Multiplicative Features into Supervised Distributional Methods for Lexical Entailment

Supervised distributional methods are applied successfully in lexical entailment, but recent work questioned whether these methods actually learn a relation between two words. Specifically, Levy et al. (2015) claimed that linear classifiers learn only separate properties of each word. We suggest a cheap and easy way to boost the performance of these methods by integrating multiplicative features into commonly used representations. We provide an extensive evaluation with different classifiers and evaluation setups, and suggest a suitable evaluation setup for the task, eliminating biases existing in previous ones.
Effects of Exergaming in People with Dementia: Results of a Systematic Literature Review

Background: Physical exercise benefits functioning, health, and well-being. However, people living with dementia in particular hardly engage in exercise. Exergaming (exercise and gaming) is an innovative, fun, and relatively safe way of exercising in a virtual reality or gaming environment. It may help people living with dementia overcome barriers they can experience regarding regular exercise activities. Objective: This systematic literature review aims to provide an overview of the cost-effectiveness of exergaming and its effects on physical, cognitive, emotional, and social functioning, as well as the quality of life in people living with dementia. Methods: PubMed, Embase, Cinahl, PsycINFO, the Cochrane Library, and the Web of Science Core Collection were searched. Selection of studies was carried out by at least two independent researchers. Results: Three studies were found to be eligible and were included in this review. Two of these showed some statistically significant effects of exergaming on physical, cognitive, and emotional functioning in people living with dementia, although based on a very small sample. No articles were found about the cost-effectiveness of exergaming. Conclusions: Only a few controlled studies have been conducted into the effectiveness of exergaming, and these show very little significant benefits. More well-designed studies are necessary to examine the effects of exergaming.
Cryptococcus neoformans Is Resistant to Surfactant Protein A Mediated Host Defense Mechanisms

Initiation of a protective immune response to infection by the pathogenic fungus Cryptococcus neoformans is mediated in part by host factors that promote interactions between immune cells and C. neoformans yeast. Surfactant protein A (SP-A) contributes positively to pulmonary host defenses against a variety of bacteria, viruses, and fungi in part by promoting the recognition and phagocytosis of these pathogens by alveolar macrophages. In the present study we investigated the role of SP-A as a mediator of host defense against the pulmonary pathogen, C. neoformans. Previous studies have shown that SP-A binds to acapsular and minimally encapsulated strains of C. neoformans. Using in vitro binding assays we confirmed that SP-A does not directly bind to a fully encapsulated strain of C. neoformans (H99). However, we observed that when C. neoformans was incubated in bronchoalveolar fluid, SP-A binding was detected, suggesting that another alveolar host factor may enable SP-A binding. Indeed, we discovered that SP-A binds encapsulated C. neoformans via a previously unknown IgG dependent mechanism. The consequence of this interaction was the inhibition of IgG-mediated phagocytosis of C. neoformans by alveolar macrophages. Therefore, to assess the contribution of SP-A to the pulmonary host defenses we compared in vivo infections using SP-A null mice (SP-A-/-) and wild-type mice in an intranasal infection model. We found that the immune response assessed by cellular counts, TNFα cytokine production, and fungal burden in lungs and bronchoalveolar lavage fluids during early stages of infection were equivalent. Furthermore, the survival outcome of C. neoformans infection was equivalent in SP-A-/- and wild-type mice. Our results suggest that unlike a variety of bacteria, viruses, and other fungi, progression of disease with an inhalational challenge of C. neoformans does not appear to be negatively or positively affected by SP-A mediated mechanisms of pulmonary host defense.
Financial Aspect and Sentiment Predictions with Deep Neural Networks: An Ensemble Approach

In this paper, we describe our ensemble approach for sentiment and aspect predictions in the financial domain for a given text. This ensemble approach uses Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) with a ridge regression and a voting strategy for sentiment and aspect predictions, and therefore, does not rely on any handcrafted feature. Based on 5-cross validation on the released training set, the results show that CNNs overall perform better than RNNs on both tasks, and the ensemble approach can boost the performance further by leveraging different types of deep learning approaches.
Mobilization Therapy in the Pediatric Intensive Care Unit: A Multidisciplinary Quality Improvement Initiative

Background Mobilization is safe and associated with improved outcomes in critically ill adults, but little is known about mobilization of critically ill children. Objective To implement a standardized mobilization therapy protocol in a pediatric intensive care unit and improve mobilization of patients. Methods A goal‐directed mobilization protocol was instituted as a quality improvement project in a 20‐bed cardiac and medical‐surgical pediatric intensive care unit within an academic tertiary care center. The mobilization goal was based on age and severity of illness. Data on severity of illness, ordered activity limitations, baseline functioning, mobilization level, complications of mobilization, and mobilization barriers were collected. Goal mobilization was defined as a ratio of mobilization level to severity of illness of 1 or greater. Results In 9 months, 567 patient encounters were analyzed, 294 (52%) of which achieved goal mobilization. The mean ratio of mobilization level to severity of illness improved slightly but nonsignificantly. Encounters that met mobilization goals were in younger (P = .04) and more ill (P < .001) patients and were less likely to have barriers (P < .001) than encounters not meeting the goals. Complication rate was 2.5%, with no difference between groups (P = .18). No serious adverse events occurred. Conclusions A multidisciplinary, multiprofessional, goaldirected mobilization protocol achieved goal mobilization in more than 50% of patients in this pediatric intensive care unit. Undermobilized patients were older, less ill, and more likely to have mobilization barriers at the patient and provider level.
Prevalence and Prediction of Exercise-Induced Oxygen Desaturation in Patients with Chronic Obstructive Pulmonary Disease

Background: Previous studies with small sample sizes reported contradicting findings as to whether pulmonary function tests can predict exercise-induced oxygen desaturation (EID). Objective: To evaluate whether forced expiratory volume in one second (FEV<sub>1</sub>), resting oxygen saturation (Sp<smlcap>o</smlcap><sub>2</sub>) and diffusion capacity for carbon monoxide (D<smlcap>l</smlcap><smlcap>c</smlcap><smlcap>o</smlcap>) are predictors of EID in chronic obstructive pulmonary disease (COPD). Methods: We measured FEV<sub>1</sub>, D<smlcap>l</smlcap><smlcap>c</smlcap><smlcap>o</smlcap>, Sp<smlcap>o</smlcap><sub>2</sub> at rest and during a 6-min walking test as well as physical activity by an accelerometer. A drop in Sp<smlcap>o</smlcap><sub>2</sub> of >4 to <90% was defined as EID. To evaluate associations between measures of lung function and EID univariate and multivariate analyses were used and positive/negative predictive values were calculated. Receiver operating characteristic curve analysis was performed to determine the most useful threshold in order to predict/exclude EID. Results: We included 154 patients with COPD (87 females). The mean FEV<sub>1</sub> was 43.0% (19.2) predicted and the prevalence of EID was 61.7%. The only independent predictor of EID was FEV<sub>1</sub> and the optimal cutoff value of FEV<sub>1</sub> was at 50% predicted (area under ROC curve, 0.85; p < 0.001). The positive predictive value of a threshold of FEV<sub>1</sub> <50% was 0.83 with a likelihood ratio of 3.03 and the negative predicting value of a threshold of FEV<sub>1</sub> ≥80% was 1.0. The severity of EID was correlated with daily physical activity (r = –0.31, p = 0.008). Conclusions: EID is highly prevalent among patients with COPD and can be predicted by FEV<sub>1</sub>. EID seems to be associated with impaired daily physical activity which supports its clinical importance.
VOCs monitoring using microwave capacitive resonator and conductive polymer — MWCNTs nanocomposites for environmental applications

This paper presents a chemical microwave flexible sensor based on a resonant electromagnetic transducer in micro-strip technology with poly (3,4-ethylenedioxythiophene) polystyrene sulfonate and multi wall carbon nanotubes (PEDOT:PSS-MWCNTs) as sensitive material for Volatile Organic Compounds (VOCs) detection. The results show a high sensitivity to ethanol and toluene vapors on the S parameters of a passive resonator over a large frequency range. It is equal to −1.59 kHz/ppm and −1.45 kHz/ppm for ethanol and toluene vapors respectively. This kind of sensor can be integrated into real-time multi-sensing platform adaptable for the Internet of Things (IoT).
Distributed Ledger Technology: Blockchain Compared to Directed Acyclic Graph

In addition to blockchain, a new paradigm is gaining momentum in the filed of distributed ledger technology—directed acyclic graphs. This paper compares the two paradigms focusing on features relevant to distributed systems using the following representative implementations: Bitcoin, Ethereum and Nano. We examine the applied data structures for maintaining the ledger, consensus mechanisms, transaction confirmation confidence, ledger size, and scalability.
ESPRES: transparent SDN update scheduling

Network forwarding state undergoes frequent changes, in batches of forwarding rule modifications at multiple switches. Installing or modifying a large number of rules is time consuming given the performance limits of current programmable switches, which are also due to economical factors in addition to technological ones. In this paper, we observe that a large network-state update typically consists of a set of sub-updates that are independent of one another w.r. t. the traffic they affect, and hence sub-updates can be installed in parallel, in any order. Leveraging this observation, we treat update installation as a scheduling problem and design ESPRES, a runtime mechanism that rate-limits and reorders updates to fully utilize processing capacities of switches without overloading them. Our early results show that compared to using no scheduler, our schemes yield 2.17-3.88 times quicker sub-update completion time for 20th percentile of sub-updates and 1.27-1.57 times quicker for 50th percentile.
Applying DCOP_MST to a Team of Mobile Robots with Directional Sensing Abilities: (Extended Abstract)

DCOP_MST is an extension of the DCOP framework for representing and solving dynamic multi-agent applications that include teams of mobile sensing agents. Local search algorithms, enhanced with exploration methods were recently found to produce high quality solutions for DCOP_MST in software simulations. Applying DCOP_MST to robots with directed sensors (e.g., cameras) requires addressing limitations, which were not part of the original design of the DCOP_MST model, e.g., limited angle of the field of vision, collisions between robots etc. In this paper we contribute to the ongoing effort of applying DCOPs to real world applications by addressing the challenges one faces when applying the DCOP_MST model to a team of mobile sensing robots with directed sensors. We integrate the required adjustments into a new model, DCOP_MSTR, which is the modified version of DCOP_MST for such a real world robot application with directed sensors. The proposed revised model was implemented and evaluated both in software simulations and on a team of robots carrying cameras. Our evaluation of existing algorithms revealed the need to combine actions that change the location of a robot with actions that change its sensing direction in order to achieve effective exploration when solving DCOP_MSTR.
xJsnark: A Framework for Efficient Verifiable Computation

Many cloud and cryptocurrency applications rely on verifying the integrity of outsourced computations, in which a verifier can efficiently verify the correctness of a computation made by an untrusted prover. State-of-the-art protocols for verifiable computation require that the computation task be expressed as arithmetic circuits, and the number of multiplication gates in the circuit is the primary metric that determines performance. At the present, a programmer could rely on two approaches for expressing the computation task, either by composing the circuits directly through low-level development tools; or by expressing the computation in a high-level program and rely on compilers to perform the program-to-circuit transformation. The former approach is difficult to use but on the other hand allows an expert programmer to perform custom optimizations that minimize the resulting circuit. In comparison, the latter approach is much more friendly to non-specialist users, but existing compilers often emit suboptimal circuits. We present xJsnark, a programming framework for verifiable computation that aims to achieve the best of both worlds: offering programmability to non-specialist users, and meanwhile automating the task of circuit size minimization through a combination of techniques. Specifically, we present new circuit-friendly algorithms for frequent operations that achieve constant to asymptotic savings over existing ones; various globally aware optimizations for short-and long-integer arithmetic; as well as circuit minimization techniques that allow us to reduce redundant computation over multiple expressions. We illustrate the savings in different applications, and show the framework's applicability in developing large application circuits, such as ZeroCash, while minimizing the circuit size as in low-level implementations.
The Intensity of Victimization: Associations with Children's Psychosocial Well-Being and Social Standing in the Classroom

The association between experienced victimization and students' psychological and social adjustment depends on the intensity of victimization. We examined how frequency and multiplicity of victimization, and the number of bullies involved, account for differences in students’ psychosocial well-being and social standing in the classroom. Multilevel analyses were conducted on the control group of an intervention study among students in grades 3–6 of Dutch elementary schools (N = 2859 students from 124 classes and 33 schools; ages 8–12; 49.6% boys). It was found that victims of frequent and multiple victimization, and victims who were victimized by several bullies, had higher levels of psychosocial adjustment problems than victims of less frequent and non-multiple victimization, and victims with only one bully. Moreover, these more severe victims turned out to be least accepted and most rejected among their classmates. The findings illustrate that it can be fruitful to use several measures of victimization so that (differences in) adjustment problems can be better understood. Moreover, the results suggest that it is important to find out who is victimized, in what ways, and by whom. Anti-bullying interventions should provide resources to do this.
BlendCAC: A BLockchain-Enabled Decentralized Capability-Based Access Control for IoTs

The prevalence of Internet of Things (IoT) allows heterogeneous embedded smart devices to collaboratively provide smart services with or without human intervention. While leveraging the large-scale IoT-based applications like Smart Gird or Smart Cities, IoT also incurs more concerns on privacy and security. Among the top security challenges that IoT face, access authorization is critical in resource sharing and information protection. One of the weaknesses of today's access control (AC) is the centralized authorization server, which can be the performance bottleneck or the single point of failure. In this paper, BlendCAC, a blockchain-enabled decentralized capability-based AC is proposed for the security of IoTs. The BlendCAC aims at an effective access control processes to devices, services and information in large scale IoT systems. Based on the blockchain network, a capability delegation mechanism is suggested for access permission propagation. A robust identity-based capability token management strategy is proposed, which takes advantage of a smart contract for registration, propagation and revocation of the access authorization. In the proposed BlendCAC scheme, IoT devices are their own master to control their resources instead of being supervised by a centralized authority. Implemented and tested on a Raspberry Pi device and on a local private blockchain network, the experimental results demonstrate the feasibility of the proposed BlendCAC approach to offer a decentralized, scalable, lightweight and fine-grained AC solution to IoT systems.