Miracle-dz/UniFAR-FTUs huggingface dataset:
Sleep Quality Estimation with Adversarial Domain Adaptation: From Laboratory to Real Scenario

Previous studies on EEG-based last-night sleep quality estimation mainly focus on evaluation with data from laboratory experiments. However, due to the reality gap con- stituted with device performance, subject groups, experiment settings and controlled conditions, the models trained solely on laboratory data cannot generalize well to real scenarios. In this work, we investigate the sleep quality estimation for high-speed train drivers as an instance of real-scenario application. Domain adaptation models are adopted to deal with the individual differences across subjects when modeling and testing with the real-scenario data. As it is usually difficult and costly to acquire data and annotate them in real scenarios, the high-quality data in laboratory conditions are used for model trainings. Knowledge from simulation is transferred to reality with domain adaptation methods. A novel approach called Domain Adversarial Neural Network (DANN) is adopted. DANN learns domain independent features through deep networks with an adversarial architecture. The experimental results indicate that DANN outperforms other state-of-the-art methods and achieves 19.55% and 23.50% im- provements in terms of accuracy on the cross-subject and cross- scenario tasks, respectively, in comparison with the baseline SVM model.
More Realistic and Efficient Face-Based Mobile Authentication using CNNs

In this work, we propose a more realistic and efficient facebased mobile authentication technique using CNNs. This paper discusses and explores an inevitable problem of using face images for mobile authentication, taken from varying distances with a front/selfie camera of the mobile phone. Incidentally, once an individual comes towards a certain distance from the camera, the face images get large and appear over-sized. Simultaneously sharp features of some portions of the face, such as forehead, cheek, and chin are changed completely. As a result, the face features change and the impact increases exponentially once the individual crosses a certain distance and gradually approaches towards the front camera. This work proposes a solution (achieving better accuracy and facial features, whereby face images were cropped and aligned around its close bounding box) to mitigate the aforementioned identified gap. The work investigated different frontier face detection and recognition techniques to justify the proposed solution. Among all the employed methods evaluated, CNNs worked best. For a quantitative comparison of the proposed method, manually cropped face images/annotations of the face images along with their close boundary were prepared. In turn, we have developed a database considering the above-mentioned scenario for 40 individuals, which will be publicly available for academic research purposes. The experimental results achieved indicate a successful implementation of the proposed method and the performance of the proposed technique is also found to be superior in comparison to the existing state-of-the-art.
Coupled Analysis Dictionary Learning to inductively learn inversion: Application to real-time reconstruction of Biomedical signals

This work addresses the problem of reconstructing biomedical signals from their lower dimensional projections. Traditionally Compressed Sensing (CS) based techniques have been employed for this task. These are transductive inversion processes; the problem with these approaches is that the inversion is time-consuming and hence not suitable for real-time applications. With the recent advent of deep learning, Stacked Sparse Denoising Autoencoder (SSDAE) has been used for learning inversion in an inductive setup. The training period for inductive learning is large but is very fast during application - capable of real-time speed. This work proposes a new approach for inductive learning of the inversion process. It is based on Coupled Analysis Dictionary Learning. Results on Biomedical signal reconstruction show that our proposed approach is very fast and yields result far better than CS and SSDAE.
A distinct strain of Arsenophonus symbiont decreases insecticide resistance in its insect host

Symbiotic bacteria are important drivers of phenotypic diversity in insects. One of the widespread symbionts to have emerged belongs to the genus Arsenophonus, however, its biological functions in most host insects remain entirely unknown. Here we report two distinct Arsenophonus strains in the brown planthopper (BPH), Nilaparvata lugens, a major pest insect in Asian countries that causes significant economic damage through rice crop destruction. Genomic resequencing data suggested that one Arsenophonus strain (S-type) negatively affected the insecticide resistance of the host. Indeed, replacement of the resident Arsenophonus with the S-type Arsenophonus significantly decreased host insecticide resistance. Transcriptome and metabolome analysis revealed down-regulation of xenobiotic metabolism and increased amino acid accumulation in the S-type Arsenophonus infected host. This study demonstrates how a symbiont-mediated phenotypic change can occur. The results of this study will aid in developing strategies that work through imposing an ecological disadvantage on insect pests, which will be of great value for pest control in agricultural industry.
Character-Level Intrusion Detection Based On Convolutional Neural Networks

Deep learning models are increasingly applied in the intrusion detection system (IDS) and propel its development nowadays. In this paper, a new character-level IDS is proposed based on convolutional neural networks and obtains better performance. Different from other models which are in the feature level, this model is in the character level, which views network traffic records as sequences of characters. Each character of a record is encoded into a vector based on an alphabet. The vectors of the characters in the record are aggregated into a matrix as the input of the convolutional neural networks with an improved structure. This model simplifies the preprocessing without considering the priori knowledge about the data. Experiments on the dataset NSL-KDD show that the binary classification and the multi-classification of our model have good performance. Compared with other machine learning algorithms in term of accuracy, our model outperforms those algorithms. The comparison with the model using convolutional neural networks with the preprocessing in the feature level reveals that our model performs much better with high accuracy, high detection rate and low false alarm rate.
Eigenspectrum Shape Based Nyström Sampling

Spectral clustering has shown a superior performance in analyzing the cluster structure. However, its computational complexity limits its application in analyzing large-scale data. To address this problem, many low-rank matrix approximating algorithms are proposed, including the Nyström method - an approach with proven approximate error bounds. There are several algorithms that provide recipes to construct Nyström approximations with variable accuracies and computing times. This paper proposes a scalable Nyström-based clustering algorithm with a new sampling procedure, Centroid Minimum Sum of Squared Similarities (CMS3), and a heuristic on when to use it. Our heuristic depends on the eigenspectrum shape of the dataset, and yields competitive low-rank approximations in test datasets compared to the other state-of-the-art methods.
Facilitating the Informed Consent Process Using Teleconsent: Protocol for a Feasibility and Efficacy Study

Background Informed consent is among the biggest challenges in recruiting participants for clinical research studies. Researchers face many challenges in conducting clinical trials, some of which include budgetary restrictions, lack of trained personnel, and difficulty recruiting study participants—particularly minorities and participants from rural communities. Objective The objective of this study is to utilize telemedicine to improve the informed consent process for clinical trials and studies. We aim to assess the feasibility and efficacy of the teleconsent intervention among residents in urban and rural settings. Methods This study will be conducted separately yet concurrently at two institutions, the Medical University of South Carolina and the University of North Carolina at Chapel Hill, to compare results within and across institutions. Results Enrollment for Phase 1 began in March of 2018 and concluded in May 2018. Data transcription and analysis will be conducted through June and September of 2018. Conclusions In this paper, we present a novel approach for conducting informed consent using a new telemedicine modality, namely, teleconsent. Teleconsent presents the ability to conduct a live interaction among clinical research coordinators and potential participants while synchronously presenting the consent form on the screen and obtaining participant’s signature through doxy.me, the teleconsent system. Teleconsent provides potential to improve obtaining informed consent from potential clinical trial participants. Registered Report Identifier RR1-10.2196/11239
Fuzzy Classification of Bariatric Post-surgery Effectiveness

The expected post-operatory weight loss is not always achieved after bariatric surgery. Efforts have been done to describe the causes. Recently, total weight loss (%TWL) has been pointed out to better assess weight loss in bariatric patients. However, there is no cut off point that delimits the patients who successfully achieve their weight goals after a bariatric surgery. In this work, a method based on fuzzy modeling is implemented to help clinicians setting up the best cut-off point in %TWL for a specific population. The best boundary to delimit success and failure will be selected based on the predictive performance of the assessed cut-off points: 25, 30, 35 and 40%TWL after one and two years of surgery. Area under the receiver operating characteristic curve (AUC) values of 0.70 and 0.75 were achieved for the first and second post-surgery periods, respectively. Further, features not previously described as predictors of weight loss were identified as good predictors of the outcome.
The Application of Classification Trees to Pharmacy School Admissions

In recent years, the American Association of Colleges of Pharmacy (AACP) has encouraged the application of big data analytic techniques to pharmaceutical education. Indeed, the 2013-2014 Academic Affairs Committee Report included a “Learning Analytics in Pharmacy Education” section that reviewed the potential benefits of adopting big data techniques.1 Likewise, the 2014-2015 Argus Commission Report discussed uses for big data analytics in the classroom, practice, and admissions.2 While both of these reports were thorough, neither discussed specific analytic techniques. Consequently, this commentary will introduce classification trees, with a particular emphasis on their use in admission. With electronic applications, pharmacy schools and colleges now have access to detailed applicant records containing thousands of observations. With declining applications nationwide, admissions analytics may be more important than ever.3
Alcohol consumption and sleep deprivation among Ghanaian adults: Ghana Demographic and Health Survey

Abstract Heavy consumption of alcohol has shown to be associated with sleep disturbances among adult and elderly people in high income settings. So far, the relationship between alcohol drinking and sleeping pattern has not been studied in an African setting. Therefore, in this study we investigated whether alcohol consumption has any influence on sleeping hours among adult men and women in Ghana. Methods Data for this survey were extracted from Ghana Demographic and Health Survey (GDHS 2008). GDHS is the only cross-sectional survey conducted on men and women aged above 15 years that collected information on variables such as sleeping hours and alcohol consumption. The analysis was controlled for various demographic, socioeconomic, household level factors, and smoking. Results Prevalence of sleeping 1–3 h, 4–6 h, and >7 h was respectively 1.5% (1.2–2.0), 14.1% (12–16.5), and 84.4% (82.1–86.4), and that of alcohol use was 26.9% (23.4–30.6). In the multivariable regression analysis, compared with non-drinkers, those reported drinking had significantly lower odds of sleeping for at least 7 h. In the adjusted model, drinkers had 0.8 times (adjusted OR = 0.803, (95% CI [0.690–0.935])) lower odds of sleeping for at least 7 h. The odds for sleeping 4–6 h were not statistically significant. In the stratified analysis, the odds of sleeping for at least seven were comparatively lower among women (adjusted OR = 0.657, (95% CI [0.509–0.849]) then among men (adjusted OR = 0.867, (95% CI [0.740–0.965]). Conclusion Men and women who reported consuming alcohol had significantly lower odds of getting adequate sleep (>7 h). The sleep-disrupting effect of alcohol appeared to be more prominent among women than among men. Currently there is not sufficient evidence on alcohol consumption and sleep disorder among Ghanaian population or any other country in the region. Further studies are required to understand sleeping patterns and the burden of alcohol drinking in this population to design intervention programs.
Thin water films and particle morphology evolution in nanocrystalline MgO

Abstract A key question in the field of ceramics and catalysis is how and to what extent residual water in the reactive environment of a metal oxide particle powder affects particle coarsening and morphology. With X‐ray Diffraction (XRD) and Transmission Electron Microscopy (TEM), we investigated annealing‐induced morphology changes on powders of MgO nanocubes in different gaseous H2O environments. The use of such a model system for particle powders enabled us to describe how adsorbed water that originates from short exposure to air determines the evolution of MgO grain size, morphology, and microstructure. While cubic nanoparticles with a predominant abundance of (100) surface planes retain their shape after annealing to T = 1173 K under continuous pumping with a base pressure of water p(H2O) = 10−5 mbar, higher water partial pressures promote mass transport on the surfaces and across interfaces of such particle systems. This leads to substantial growth and intergrowth of particles and simultaneously favors the formation of step edges and shallow protrusions on terraces. The mass transfer is promoted by thin films of water providing a two‐dimensional solvent for Mg2+ ion hydration. In addition, we obtained direct evidence for hydroxylation‐induced stabilization of (110) faces and step edges of the grain surfaces.
Robust Finite-Time Bounded Controller Design of Time-Delay Conic Nonlinear Systems Using Sliding Mode Control Strategy

The finite-time sliding mode controller design problem of a class of conic-type nonlinear systems with time-delays and mismatched external disturbance is studied. The time-delay conic nonlinearities are considered to lie in a known hypersphere with an uncertain center. A scalar selection criterion dependent sliding mode control (SMC) law is constructed to drive the state trajectories onto the specified sliding surface during any assigned short time interval. By using slack matrix approach, a delay-dependent sufficient condition is derived to ensure the finite-time boundedness of the closed-loop systems over the finite-time interval. Then, the algorithm for designing the finite-time SMC law is established. Finally, two examples related to the time-delayed Chua’s circuit is given to demonstrate the effectiveness of the developed methods.
Efficiency and Effectiveness of Requirements Elicitation Techniques for Children

[Context] The market for software targeting children, both for education and entertainment, is growing. Existing work, mainly from HCI, has considered the effectiveness of elicitation techniques for eliciting requirements from children as part of a design process. [Objective] However, we are lacking work which compares requirements elicitation techniques when used with children. [Methods] This study compares five elicitation techniques, taking into consideration the effectiveness and efficiency of each technique. Techniques were used with a total of 54 children aged 8-13, eliciting requirements for a museum flight simulator. We compare techniques by looking at the number and type of requirements discovered, perceived participant satisfaction, resources required, perceived usefulness, and requirements coverage of domain specific categories. [Conclusions] We observed notable differences between the techniques, including the effectiveness of observations and relative ineffectiveness of questionnaires. We present a set of guidelines to aid industry in eliciting requirements for child-friendly software.
Measuring Program Comprehension: A Large-Scale Field Study with Professionals

During software development and maintenance, developers spend a considerable amount of time on program comprehension activities. Previous studies show that program comprehension takes up as much as half of a developer’s time. However, most of these studies are performed in a controlled setting, or with a small number of participants, and investigate the program comprehension activities only within the IDEs. However, developers’ program comprehension activities go well beyond their IDE interactions. In this paper, we extend our ActivitySpace framework to collect and analyze Human-Computer Interaction (HCI) data across many applications (not just the IDEs). We follow Minelli et al.’s approach to assign developers’ activities into four categories: navigation, editing, comprehension, and other. We then measure the comprehension time by calculating the time that developers spend on program comprehension, e.g., inspecting console and breakpoints in IDE, or reading and understanding tutorials in web browsers. Using this approach, we can perform a more realistic investigation of program comprehension activities, through a field study of program comprehension in practice across a total of seven real projects, on 78 professional developers, and amounting to 3,148 working hours. Our study leverages interaction data that is collected across many applications by the developers. Our study finds that on average developers spend $\sim$ 58 percent of their time on program comprehension activities, and that they frequently use web browsers and document editors to perform program comprehension activities. We also investigate the impact of programming language, developers’ experience, and project phase on the time that is spent on program comprehension, and we find senior developers spend significantly less percentages of time on program comprehension than junior developers. Our study also highlights the importance of several research directions needed to reduce program comprehension time, e.g., building automatic detection and improvement of low quality code and documentation, construction of software-engineering-specific search engines, designing better IDEs that help developers navigate code and browse information more efficiently, etc.
Nearly Optimal Space Efficient Algorithm for Depth First Search

We design a space-efficient algorithm for performing depth-first search traversal(DFS) of a graph in $O(m+n\log^* n)$ time using $O(n)$ bits of space. While a normal DFS algorithm results in a DFS-tree (in case the graph is connected), our space bounds do not permit us even to store such a tree. However, our algorithm correctly outputs all edges of the DFS-tree. The previous best algorithm (which used $O(n)$ working space) took $O(m \log n)$ time (Asano, Izumi, Kiyomi, Konagaya, Ono, Otachi, Schweitzer, Tarui, Uehara (ISAAC 2014) and Elmasry, Hagerup, Krammer (STACS 2015)). The main open question left behind in this area was to design faster algorithm for DFS using $O(n)$ bits of space. Our algorithm answers this open question as it has a nearly optimal running time (as the DFS takes $O(m+n)$ time even if there is no space restriction).
A Novel Low-Complexity VLSI Architecture for an EEG Feature Extraction Platform

This paper presents a novel low-complexity VLSI architecture for an EEG Feature Extraction Platform for wearable health monitoring systems. It integrates a processing core unit, preprocessing FIR filter block and an FFT accelerator. The platform supports 8 scalp EEG channels and it can be scaled up to 16 or 32 EEG channels. In this paper, we analyze several architectural optimizations for hardware implementation of features such as Power Spectral Density, Discrete Wavelet Transform, Autoregression etc. The proposed architecture is easily scalable to new feature extractors. An FFT accelerator is proposed to improve the flexibility of the platform. We provide a comparison with software models for each feature extractor and also validate the platform by implementing a seizure detection algorithm on CHB-MIT Scalp EEG database with an accuracy of over 95%.
Predicting Drug Targets from Heterogeneous Spaces using Anchor Graph Hashing and Ensemble Learning

The in silico prediction of potential drug-targetinteractions is of critical importance in drug research. Existing computational methods have achieved remarkable prediction accuracy, however usually obtain poor prediction efficiency due to computational problems. To improve the prediction efficiency, we propose to predict drug targets based on inte- gration of heterogeneous features with anchor graph hashing and ensemble learning. First, we encode each drug as a 5682- bit vector, and each target as a 4198-bit vector using their heterogeneous features respectively. Then, these vectors are embedded into low-dimensional Hamming Space using anchor graph hashing. Next, we append hashing bits of a target to hashing bits of a drug as a vector to represent the drug-target pair. Finally, vectors of positive samples composed of known drug-target pairs and randomly selected negative samples are used to train and evaluate the ensemble learning model. The performance of the proposed method is evaluated on simulative target prediction of 1094 drugs from DrugBank. Ex- tensive comparison experiments demonstrate that the proposed method can achieve high prediction efficiency while preserving satisfactory accuracy. In fact, it is 99.3 times faster and only 0.001 less in AUC than the best literature method “Pairwise Kernel Method”.