Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring User Needs and Preferences for Mobile Apps for Sleep Disturbance: Mixed Methods Study

Background Mobile health (mHealth) apps demonstrate promise for improving sleep at scale. End-user engagement is a prerequisite for sustained use and effectiveness. Objective We assessed the needs and preferences of those with poor sleep and insomnia to inform the development of an engaging sleep app. Methods We triangulated results from qualitative (focus groups and app reviews) and quantitative (online survey) approaches. A total of 2 focus groups were conducted (N=9). An online survey tested themes identified from the focus groups against a larger population (N=167). In addition, we analyzed 434 user reviews of 6 mobile apps available on app stores. Results Common focus group themes included the need to account for diverse sleep phenotypes with an adaptive and tailored program, key app features (alarms and sleep diaries), the complex yet condescending nature of existing resources, providing rationale for information requested, and cost as a motivator. Most survey participants (156/167, 93%) would try an evidence-based sleep app. The most important app features reported were sleep diaries (148/167, 88%), sharing sleep data with a doctor (116/167, 70%), and lifestyle tracking (107/167, 64%). App reviews highlighted the alarm as the most salient app feature (43/122, 35%) and data synchronization with a wearable device (WD) as the most commonly mentioned functionality (40/135, 30%). Conclusions This co-design process involving end users through 3 methods consistently highlighted sleep tracking (through a diary and WD), alarms, and personalization as vital for engagement, although their implementation was commonly criticized in review. Engagement is negatively affected by poorly designed features, bugs, and didactic information which must be addressed. Other needs depend upon the type of user, for example, those with severe insomnia.
Applications and Technology of Electronic Nose for Clinical Diagnosis

Rapid advances in sensor technologies have facilitated the development of high-performance electronic noses that can detect and discriminate volatile compounds in situ. The research and development of electronic noses has resulted in a new qualitative and semi-quantitative detection approach in the field of clinical diagnostics. Electronic noses have a clear potential to be a non-invasive, simple and rapid but above all accurate early diagnostic screening tool. This review collates existing knowledge of recent advances in electronic nose technologies and applications.
Modeling the Effects of Avian Flu (H5N1) Vaccination Strategies on Poultry

The work in this article addresses a problem posed by Dr. Maria Salvato to the CODEE community. The task was to model costs associated with varying vaccination strategies for the Avian Flu virus (H5N1) on chicken populations. The vaccination strategies proposed included vaccination varying proportions of the flock with live virus vaccine, dead virus vaccine, and no vaccination. This article encompasses the construction of a model for the problem using a modification to the SIER model and the subsequent analysis of that model. The analysis of the model revealed the most cost effective vaccination strategy to be vaccination of half the flock with dead virus vaccine.
Non Bayesian Conditioning and Deconditioning

In this paper, we present a Non-Bayesian condition- ing rule for belief revision. This rule is truly Non-Bayesian in the sense that it doesn't satisfy the common adopted princip le that when a prior belief is Bayesian, after conditioning by X, Bel(X|X) must be equal to one. Our new conditioning rule for belief revision is based on the proportional conflict redist ribution rule of combination developed in DSmT (Dezert-Smarandache Theory) which abandons Bayes' conditioning principle. Such Non-Bayesian conditioning allows to take into account judiciously the level of conflict between the prior belief available and the conditional evidence. We also introduce the deconditioning problem and show that this problem admits a unique solution in the case of Bayesian prior; a solution which is not possible to obtain when classical Shafer and Bayes conditioning rules are used. Several simple examples are also presented to compare the results between this new Non-Bayesian conditioning and the classical one.
Committee Scoring Rules: Axiomatic Classification and Hierarchy

We consider several natural classes of committee scoring rules, namely, weakly separable, representation-focused, top-k-counting, OWA-based, and decomposable rules. We study some of their axiomatic properties, especially properties of monotonicity, and concentrate on containment relations between them. We characterize SNTV, Bloc, and k-approval Chamberlin-Courant, as the only rules in certain intersections of these classes. We introduce decomposable rules, describe some of their applications, and show that the class of decomposable rules strictly contains the class of OWA-based rules.
Preventing converted speech spoofing attacks in speaker verification

Voice conversion (VC) techniques, which modify a speaker's voice to sound like another's, present a threat to automatic speaker verification (SV) systems. In this paper, we evaluate the vulnerability of a state-of-the-art SV system against a converted speech spoofing attack. To overcome the spoofing attack, we implement state-of-the-art converted speech detectors based on short- and long-term features. We propose a new converted speech detector using a compact feature representation and a discriminative modeling approach. We experiment pairing converted speech detectors based on short- and long-term features to improve converted speech detection. The results indicate that the proposed converted speech detector pair outperforms state-of-the-art ones, achieving a detection accuracy of 97.9% for natural utterances and 98.0% for converted utterances. We include the anti-spoofing mechanism in our SV system as a post-processing module for accepted trials and reevaluate its performance, comparing it with the performance of an ideal system. Our results show that the SV system's performance returns to acceptable values, with less than 1.6% equal error rate (EER) change.
Expandable indexes vs. sequential menus for searching hierarchies on the World Wide Web

An experiment is reported that compared expandable indexes providing full menu context with sequential menus providing only partial context. Menu depth was varied using hierarchies of two, three and four levels deep in an asymmetric structure of 457 root level items. Menus were presented on the World Wide Web within a browser. Participants searched for specific targets. Results suggest that reducing the depth of hierarchies improves performance in terms of speed and search efficiency. Surprisingly, expandable indexes resulted in poorer performance with deeper hierarchies than did sequential menus.
The MASH Pipeline for Protein Function Prediction and an Algorithm for the Geometric Refinement of 3D Motifs

The development of new and effective drugs is strongly affected by the need to identify drug targets and to reduce side effects. Resolving these issues depends partially on a thorough understanding of the biological function of proteins. Unfortunately, the experimental determination of protein function is expensive and time consuming. To support and accelerate the determination of protein functions, algorithms for function prediction are designed to gather evidence indicating functional similarity with well studied proteins. One such approach is the MASH pipeline, described in the first half of this paper. MASH identifies matches of geometric and chemical similarity between motifs, representing known functional sites, and substructures of functionally uncharacterized proteins (targets). Observations from several research groups concur that statistically significant matches can indicate functionally related active sites. One major subproblem is the design of effective motifs, which have many matches to functionally related targets (sensitive motifs), and few matches to functionally unrelated targets (specific motifs). Current techniques select and combine structural, physical, and evolutionary properties to generate motifs that mirror functional characteristics in active sites. This approach ignores incidental similarities that may occur with functionally unrelated proteins. To address this problem, we have developed Geometric Sieving (GS), a parallel distributed algorithm that efficiently refines motifs, designed by existing methods, into optimized motifs with maximal geometric and chemical dissimilarity from all known protein structures. In exhaustive comparison of all possible motifs based on the active sites of 10 well-studied proteins, we observed that optimized motifs were among the most sensitive and specific.
Higher Arf Functions and Topology of the Moduli Space of Higher Spin Riemann Surfaces

We prove that any connected component of the space of m-spin structures on compact Riemann surfaces with finite number of punctures and holes is homeomorphic to a quotient of the vector space R^d by a discrete group action. Our proof is based on the representation of the space of m-spin structures on a Riemann surface as a finite affine space of Z/mZ-valued functions on the fundamental group of the surface.
Slicing Timed Systems

This paper presents a method of slicing timed systems to create reduced models for model checking verification. The reduction is made at the very beginning of the verification process and this makes it beneficial and effective in handling the state explosion problem. The method uses techniques of static analysis to examine the syntax of a program and to remove irrelevant fragments of the code. The timed extension of the classical slicing definition is given and applied to Intermediate Language of the verification system Verics and Estelle specification language.
Classification Based on Specific Vocabulary

Assuming a binomial distribution for word occurrence, we propose computing a standardized Z score to define the specific vocabulary of a subset compared to that of the entire corpus. This approach is applied to weight terms characterizing a document (or a sample of texts). We then show how these Z score values can be used to derive an efficient categorization scheme. To evaluate this proposition we categorize speeches given by B. Obama as either electoral or presidential. The results tend to show that the suggested classification scheme performs better than a Support Vector Machine scheme, and a Naive Bayes classifier (10-fold cross validation).
Efficiently Acquiring Communication Traces for Large-Scale Parallel Applications

Communication patterns of parallel applications are important to optimize application performance and design better communication subsystems. Communication patterns can be extracted from communication traces. However, existing approaches to generate communication traces need to execute the entire parallel applications on full-scale systems that are time consuming and expensive. We propose a novel technique, called Fact, which can perform FAst Communication Traces collection for large-scale parallel applications on small-scale systems. Our idea is to reduce the original program to obtain a program slice through static analysis, and to execute the program slice to acquire the communication traces. Our idea is based on an observation that most computation and message contents in parallel applications are not relevant to their spatial and volume communication attributes, and therefore can be removed for the purpose of communication trace collection. We have implemented Fact and evaluated it with NPB programs and Sweep3D. The results show that Fact can reduce resource consumptions by two orders of magnitude in most cases.
Pathologic and Protective Roles for Microglial Subsets and Bone Marrow- and Blood-Derived Myeloid Cells in Central Nervous System Inflammation

Inflammation is a series of processes designed for eventual clearance of pathogens and repair of damaged tissue. In the context of autoimmune recognition, inflammatory processes are usually considered to be pathological. This is also true for inflammatory responses in the central nervous system (CNS). However, as in other tissues, neuroinflammation can have beneficial as well as pathological outcomes. The complex role of encephalitogenic T cells in multiple sclerosis and its animal model experimental autoimmune encephalomyelitis (EAE) may derive from heterogeneity of the myeloid cells with which these T cells interact within the CNS. Myeloid cells, including resident microglia and infiltrating bone marrow-derived cells, such as dendritic cells (DC) and monocytes/macrophages [bone marrow-derived macrophages (BMDM)], are highly heterogeneous populations that may be involved in neurotoxicity and also immunoregulation and regenerative processes. Better understanding and characterization of myeloid cell heterogeneity is essential for future development of treatments controlling inflammation and inducing neuroprotection and neuroregeneration in diseased CNS. Here, we describe and compare three populations of myeloid cells: CD11c+ microglia, CD11c− microglia, and CD11c+ blood-derived cells in terms of their pathological versus protective functions in the CNS of mice with EAE. Our data show that CNS-resident microglia include functionally distinct subsets that can be distinguished by their expression of CD11c. These subsets differ in their expression of Arg-1, YM1, iNOS, IL-10, and IGF-1. Moreover, in contrast to BMDM/DC, both subsets of microglia express protective interferon-beta (IFNβ), high levels of colony-stimulating factor-1 receptor, and do not express the Th1-associated transcription factor T-bet. Taken together, our data suggest that CD11c+ microglia, CD11c− microglia, and infiltrating BMDM/DC represent separate and distinct populations and illustrate the heterogeneity of the CNS inflammatory environment.
Design of Experiment for In-Situ Synthesis of Waste Vegetable Oils and Synthetic Based Polyurethane (PU) Foam Composites

This study focused on fabrication of polyurethane (PU) foams and its composites based on hydroxylated bioepoxy (B) and petroleum based synthetic epoxy (E), crosslinker and wood fillers. The fabrication of foams: B and E with different wood filler size such as flake (L) and powder (P) on different composition ratios of 5%, 10%, 15% and 20%. The fabrication techniques used in this research are open moulding technique. The physical properties such as morphological pore structure and its homogeneity of wood filler distribution in foam polymer composites were measured. The effect of different size of wood filler loading in PU foam composites were analyzed and discussed in detail such as gelling time, main pore size, interconnected pore and struts thickness. The operatory conditions of in-situ PU foams synthesis were optimized using a design of experiment (DOE). The correlations between factors (X1, X2, X3) and the responses (Y1, Y2, Y3, Y4) for B, E as well as BL, BF, EL or EF foams were further analyzed using Minitab software.
Effects of Brazilian Propolis on Dental Plaque and Gingiva in Patients with Oral Cleft Malformation Treated with Multibracket and Removable Appliances: A Comparative Study

Orthodontic appliances modify the local environment of the oral cavity, increase the accumulation of dental plaque, and affect the condition of the gingiva. The aim of this study is assessment of Brazilian propolis toothpaste's effect on plaque index (PLI) and gingival index (GI) in patients with CL/CLP treated using orthodontic appliances in the 35-day study period. The study population included 96 patients of an Orthodontic Outpatient Clinic, ACSiMS in Bytom. All the patients participated in the active phase of orthodontic treatment using buccal multibracket appliances or removable appliances. During the first examination, each patient was randomly qualified to the propolis group or control group. A statistically significant decrease in GI and PLI in the entire propolis group (P < 0.01) was shown during repeated examination. Insignificant change in GI was in the entire control group during the repeated examination compared to the baseline. Similar result was obtained in patients treated with multibracket and removable appliances. The orthodontic appliance type did not affect the final dental plaque amount and gingival condition in patients using the propolis toothpaste. These results may be clinically useful to improve prevention and control oral infectious diseases during orthodontic treatment patients with oral cleft.
“Vamos a la Escuelita Arte Terapia”: An Art Therapy Protocol for Promoting Resilience with Latino Farm Worker Children

ABSTRACT Children of Latino farm worker families risk experiencing emotional and behavioral issues. Yet mental health resources are often inaccessible because of transportation and communication disparities. Subsequently, there is a need for accessible therapy-based programs which cross language and cultural barriers. The pilot study, la Escuelita Arte Terapia, sought to address the compounding needs of the children by running a six-week art therapy protocol within a Latino farm worker community in southern Georgia. The average attendance was 95 percent, with approximately 25 children between the ages of 2 and 7 attending each session. Observational data revealed that several therapeutic goals were successful in building internal resiliency while working to reduce risk factors and improve the participant’s school-readiness. Specifically, participants displayed increased developments in their fine and gross motor skills, creative development, emotional expression, behavioral focus, and social engagement. Several factors contributed to the success of la Escuelita Arte Terapia, which included how the promotores acted as mediators between the art therapists and the families, the art therapists’ supportive role, the art therapy milieu, and the community connections. It is hoped that future endeavors will build on the intervention proposed to help determine how art therapy can address emotional and behavioral disparities with Latino farm worker children.
State-Action Based Link Layer Design for IEEE 802.11b Compliant MATLAB-Based SDR

Software defined radio (SDR) allows unprecedented levels of flexibility by transitioning the radio communication system from a rigid hardware platform to a more user-controlled software paradigm. However, it can still be time consuming to design and implement such SDRs as they typically require thorough knowledge of the operating environment and a careful tuning of the program. In this work, we describe a systems contribution and outline strategies on how to create a state-action based design in implementing the CSMA/CA/ACK MAC layer in MATLAB® that runs on the USRP® platform, a commonly used SDR. Our design allows optimal selection of the parameters so that all operations remain functionally compliant with the IEEE 802.11b standard (1Mbps specification). The code base of the system is enabled through the Communications System ToolboxTM and incorporates channel sensing and exponential random back-off for contention resolution. The current work provides a testbed to experiment with and enables creation of new MAC protocols starting from the fundamental IEEE 802.11b compliant standard. Our system design approach guarantees the consistent performance of the bi-directional link and we include the experimental results for the three node system to demonstrate the robustness of the MAC layer in mitigating packet collisions and enforcing fairness among nodes.
A novel adaptive learning method for low-sidelobe step frequency waveform designing

For a step frequency waveform, high range resolution can be achieved by inter-pulse sysnthesis processing and its sidelobe level can be controled by weighting in frequency domain. Howerver, high range sidelobe arise when the frequeuy steps is sparsely spaced. For this, low sidelobe can be obtained in two ways. One is by signal processing after receiving. And the other is to design the waveform with low sidelobe. In order to avoid the SNR loss, The latter is more effective. In this paper, a novel adatpive learning method is given. The method can adaptively adjust the frequencys by max sidelobe discrepancy between the matched filtering out of present and the ideal one. The result of simulation shows that it can approach the optimal frequencys for which the max sidelobe is lowest.
Corruption and the Size Distribution of Firms ∗

Empirical evidence suggest that the degree of size heterogeneity across firms is greater for less developed countries (LDCs) than in developed economies. In this paper, we argue that the interaction of bribes and heterogeneity in firm’s productivity induces patterns in firm’s growth which can deliver such a peculiar size distribution of firms. Entrepreneurs who would benefit from scaling-up employment may refrain from doing so in order to remain informal and avoid tax liabilities and bribes. While waiting for a sufficiently large productivity shock, firms substitute capital for labor. Using data for a developing country (Uganda) we document that such a substitution is taking place. We then build a dynamic general equilibrium model where bribes and taxes act as a selection mechanism which restricts the growth of all but the most productive firms. Under some simplifying assumptions we solve our model analytically and conduct a comparative analysis. We then calibrate the full-blown version of our model with capital investment and use this simulated counterfactual to analyze the impact of tax evasion and corruption on welfare.
Trusting those who trust you: A study on trust and privacy on Facebook

Over more than a decade, IS research has examined the role of trust in the context of technology adoption such as website acceptance (Gefen et al. 2003), successful online interactions (Coppola et al. 2004) and recommendation agents usage (Wang and Benbasat 2005). The primary antecedent of trust in the line of research is often trustee’s trustworthiness. However, Dashiti et al. (2011) articulated the role of trust- received (trustor’s felt trust from trustee) will cause trust-given (trustor’s trust in trustee) and later the trusting behaviors in the e-government context. Drawing on the recent development, this study aims to investigate the context of online social networks (OSNs) and to test whether trust- received plays a similar role in forming the interpersonal trust among Facebook user’s interactions. We aim to find out (i) whether users are more willing to trust others and share their personal information with them when individuals experienced trust-received and (ii) what IT features influence trust-received.