Miracle-dz/UniFAR-FTUs huggingface dataset:
Activity recognition from a wearable camera

This paper proposes a novel activity recognition approach from video data obtained with a wearable camera. The objective is to recognise the user's activities from a tiny front-facing camera embedded in his/her glasses. Our system allows carers to remotely access the current status of a specified person, which can be broadly applied to those living with disabilities including the elderly who require cognitive assistance or guidance for daily activities. We collected, trained and tested our system on videos collected from different environmental settings. Sequences of four basic activities (drinking, walking, going upstairs and downstairs) are tested and evaluated in challenging real-world scenarios. An optical flow procedure is used as our primary feature extraction method, from which we downsize, reformat and classify sequence of activities using k-Nearest Neighbour algorithm (k-NN), LogitBoost (on Decision Stumps) and Support Vector Machine (SVM). We suggest the optimal settings of these classifiers through cross-validations and achieve an accuracy of 54.2% to 71.9%. Further smoothing using Hidden Markov Model (HMM) improves the result to 68.5%-82.1%.
Qualitative and Quantitative Reasoning and Instance-Based Learning in Spatial Orientation

This paper describes an experiment and a computational cognitive model involving a spatial orientation task. The experiment tests participants’ ability to identify their location on a map, given a view of the space. The model performs this task by applying an instantiation of a general strategy that has been shown to be effective in other spatial orientation tasks. It uses perceptual grouping to organize the space into recognizable elements (clusters), combined with qualitative (left versus right) and quantitative (egocentric bearing) information about those clusters to determine its response. The model is compared to the empirical data, showing good overall correspondence to human performance, including response times and errors.
Efficient search, mapping, and optimization of multi-protein genetic systems in diverse bacteria

Developing predictive models of multi-protein genetic systems to understand and optimize their behavior remains a combinatorial challenge, particularly when measurement throughput is limited. We developed a computational approach to build predictive models and identify optimal sequences and expression levels, while circumventing combinatorial explosion. Maximally informative genetic system variants were first designed by the RBS Library Calculator, an algorithm to design sequences for efficiently searching a multi-protein expression space across a >10,000-fold range with tailored search parameters and well-predicted translation rates. We validated the algorithm’s predictions by characterizing 646 genetic system variants, encoded in plasmids and genomes, expressed in six gram-positive and gram negative bacterial hosts. We then combined the search algorithm with system-level kinetic modeling, requiring the construction and characterization of 73 variants to build a sequence-expression-activity map (SEAMAP) for a biosynthesis pathway. Using model predictions, we designed and characterized 47 additional pathway variants to navigate its activity space, find optimal expression regions with desired activity response curves, and relieve rate-limiting steps in metabolism. Creating sequence-expression-activity maps accelerates the optimization of many-protein systems, and allows previous measurements to quantitatively inform future designs.
BARI+: A Biometric Based Distributed Key Management Approach for Wireless Body Area Networks

Wireless body area networks (WBAN) consist of resource constrained sensing devices just like other wireless sensor networks (WSN). However, they differ from WSN in topology, scale and security requirements. Due to these differences, key management schemes designed for WSN are inefficient and unnecessarily complex when applied to WBAN. Considering the key management issue, WBAN are also different from WPAN because WBAN can use random biometric measurements as keys. We highlight the differences between WSN and WBAN and propose an efficient key management scheme, which makes use of biometrics and is specifically designed for WBAN domain.
Immunomagnetic separation and DNA hybridization for detection of enterotoxigenic Escherichia coli in a piglet model

Enterotoxigenic Escherichia coli (ETEC) strains were detected by stool blot hybridization assays using different oligonucleotide probes for the colonization fimbrial antigen F4, heat-stable enterotoxin I (ST I), and heat-labile enterotoxin (LT I) genes. Forty-eight fecal samples and seven samples of intestinal content from ETEC-challenged newborn piglets were processed in two ways: (i) by direct inoculation of bacterial suspension onto nylon membranes overlaying blood agar and (ii) by immunomagnetic enrichment of F4+ ETEC using magnetic beads coated with F4 monoclonal antibodies before inoculation onto nylon membranes. In samples obtained from nondiarrheic piglets pre- and postchallenge, E. coli genes for F4, ST I, and LT I could be detected only after immunomagnetic enrichment. No difference between the two methods in detection of these E. coli genes was observed when stool blots from diarrheic piglets were examined. By using magnetic separation, it was easy to decrease background bacterial flora, intestinal cells, and fecal debris and thus produce purer specimens. The method evaluated in this animal model appeared simple and quick and increased the sensitivity of detection of ETEC strains 100-fold compared with the direct stool blot hybridization assays. Prior bacterial isolation and identification were not necessary.
Simulating Crop Evapotranspiration Using Insentek Soil Moisture for Maize Plants on Lysimeters

Crop evapotranspiration (ETc) simulation is important to modeling hydrological process in agro-ecosystem. In this study, ETc was monitored using a large-scale weighing lysimeter system, along with Insentek soil moisture probes at Xuchang Irrigation Experiment Station in 2015 and 2016. Relationships among daily ETc, leaf area index (LAI) and soil water content (SWC) were determined using the Levenberg–Marquardt Algorithm. Our results showed that ETc was significantly correlated with SWC in 0-40 cm soil layers, but not correlated with SWC below 40 cm soil layer. ETc was related to SWC in an exponential function combined with a quadratic function. Effects of SWC in 0-30 cm soil layers accounted for 32-53% variations of ETc. Averaging SWC across the 0-30 cm soil depth, it contributed 46% variations of ETc. Moreover, ETc was correlated with LAI in a negative linear manner, but its correlation coefficient is not significant (P>0.05). We concluded that SWC at top soil layer can be adopted to calibrate Insentek ETc simulation for maize plants.
Plasma α-synuclein predicts cognitive decline in Parkinson’s disease

Objective α-Synuclein is critical to the pathogenesis of Parkinson’s disease (PD). Few studies examined the plasma levels of α-synuclein due to the exceptionally low level of α-synuclein in plasma compared with cerebrospinal fluid. We aimed to investigate plasma α-synuclein in patients with PD of different disease severity. Methods There were total 114 participants, including 80 patients with PD and 34 controls, in the study. Participants received a complete evaluation of motor and non-motor symptoms, including cognitive function. We applied immunomagnetic reduction-based immunoassay to measure plasma levels of α-synuclein. Results Plasma levels of α-synuclein were significantly higher in patients with PD compared with controls (median: 1.56 pg/mL, 95% CI 1.02 to 1.98 pg/mL vs 0.02 pg/mL, 95% CI 0.01 to 0.03 pg/mL; p<0.0001). Although there was a significant increase in plasma α-synuclein levels in PD patients with a higher Hoehn-Yahr (H-Y) stage, there was no correlation with motor symptom severity, as assessed by Unified Parkinson’s Disease Rating Scale part III scores, after confounders (age, gender, and disease duration) were taken into account. However, plasma α-synuclein levels were significantly higher in PD patients with dementia (PDD) than in PD patients with mild cognitive impairment (PD-MCI) or normal cognition (0.42 pg/mL, (95% CI 0.25 to 0.93) for PD with normal cognition; 1.29 pg/mL (95% CI 0.76 to 1.93) for PD-MCI and 4.09 pg/mL (95% CI 1.99 to 6.19) for PDD, p<0.01) and were negatively correlated with Mini-Mental State Examination scores (R2-adjusted=0.3004, p<0.001), even after confounder adjustment. Conclusions Our data suggest that plasma α-synuclein level correlates with cognitive decline but not motor severity in patients with PD. Plasma α-synuclein could serve as a surrogate biomarker for patients at risk of cognitive decline.
Experimental processing of seaweeds for biofuels

The following paper provides an overview of the potential uses of seaweeds derived both from artificial cultivation as well as from eutrophic reservoirs as the feedstock for biofuels production. This review presents biochemical (anaerobic digestion [AD] and fermentation), chemical (extraction and transesterification) and thermochemical conversions (combustion, liquefaction, gasification and pyrolysis) of seaweeds for biofuels with special attention being paid to seaweeds processing such as pretreatment techniques, production methods and experimental conditions, and so forth. Seaweeds are considered as a suitable source for biogas and bioethanol production due to their high carbohydrate content. This review explores also the possibility of the application of oil extracted from seaweeds for biodiesel production. Since the chemical composition of seaweeds significantly differs from land biomass, a new comprehensive approach is required. Many macroalgal components are recalcitrant to bioconversion and pose microbiological challenges. The text deals with advantages and disadvantages of seaweeds as a feedstock for biofuels. Since macroalgae exploitation only for energy production is too expensive, seaweed integrated biorefineries were proposed as a solution which enables a development of high‐value algal bioproducts. Algae appear to be a promising source of biofuels. Utilization of waste algal biomass constitutes a link between the pollution abatement and energy production.
An interactive multimedia software house simulation for postgraduate software engineers

The Open University's M880 Software Engineering is a postgraduate distance education course aimed at software professionals. The case study element of the course (approximately 100 hours of study) is presented through an innovative interactive multimedia simulation of a software house Open Software Solutions (OSS). The student 'joins' OSS as an employee and performs various tasks as a member of the company's project teams. The course is now in its sixth presentation and has been studied by over 1500 students. The authors present the background to the development, and a description of the environment and student tasks.
Zero-Shot Transfer Learning for Event Extraction

Most previous supervised event extraction methods have relied on features derived from manual annotations, and thus cannot be applied to new event types without extra annotation effort. We take a fresh look at event extraction and model it as a generic grounding problem: mapping each event mention to a specific type in a target event ontology. We design a transferable architecture of structural and compositional neural networks to jointly represent and map event mentions and types into a shared semantic space. Based on this new framework, we can select, for each event mention, the event type which is semantically closest in this space as its type. By leveraging manual annotations available for a small set of existing event types, our framework can be applied to new unseen event types without additional manual annotations. When tested on 23 unseen event types, our zero-shot framework, without manual annotations, achieved performance comparable to a supervised model trained from 3,000 sentences annotated with 500 event mentions.
Developing A Regional Land Use Drought Index In Florida

Drought is a natural phenomenon that occurs when a significant decrease of water availability during a significant period of time and over a larger area. Drought indices can be a useful tool to assess and respond to drought. However, current drought indices could not fully show the land use effects and they have limitation in data sources. ENSO influence the climate of Florida; where El Nino years tend to be cooler and wetter, and La Nina years tend to be warmer and drier than normal in the fall through the spring, with the strongest effect in the winter. Both prolonged heavy rainfall and drought potentially have impacts on land uses and many aspects of Florida's economy and quality of life. Hence, understanding local ENSO patterns on regional scales and developing a new land use drought index in Florida are critical and necessary in agriculture and water resources planning and managements. This paper presents a 32 km high resolution land use adapted drought index on five different land uses (lake, urban, forest, wetland, and agriculture) in Florida based on the National Centers for Environmental Prediction (NCEP) North American Regional Reanalysis (RR) data from 1979 to 2002. The new regional land use drought indices were developed from normalized Bowen ratio and the results show that they could reflect not only the level of severity in drought events resulting from land use effects, but also La Nina driven drought impacts.
Color recognition with compact color features

For color images, color histograms are generally used as the color feature vectors for classifying the colors of objects. To achieve a higher success rate in color classification, feature vectors with a higher dimension are required, yet this causes a low efficiency with regard to the computation time and memory usage. Therefore, this paper proposes a method of reducing the feature vector dimension by a factor of 170 based on combining two techniques: (i) projecting a color histogram generated in 3D color space into 2D color planes and (ii) converting the color histograms to class histograms using a naive Bayesian classifier. The resulting feature vectors are then classified using a support vector machine method and template matching method to recognize the object colors. With both classification methods, a better recognition rate is achieved than when using the original large feature vectors. Copyright © 2011 John Wiley & Sons, Ltd.
Decoding of interleaved Reed-Solomon codes using improved power decoding

We propose a new partial decoding algorithm for m-interleaved Reed-Solomon (IRS) codes that can decode, with high probability, a random error of relative weight 1 − Rm/m+1 at all code rates R, in time polynomial in the code length n. For m > 2, this is an asymptotic improvement over the previous state-of-the-art for all rates, and the first improvement for R > 1/3 in the last 20 years. The method combines collaborative decoding of IRS codes with power decoding up to the Johnson radius.
Topology optimization of a fully compliant prosthetic finger: Design and testing

Traditional prosthetic fingers use rigid links and kinematic joints, which lead to the fingers that lack adaptability. This paper presents a new design of fingers which are fully compliant for prosthetic applications. A home-based topology optimization method was used for the structural synthesis and dimensional analysis in order to determine the topology and geometry of the finger. A prototype was manufactured and experimented for its performance. In order to evaluate the performance of the prosthetic finger, the forces and displacements of the input end and output were measured. A spring was attached at the output end to mimic the stiffness of the work-piece in order to evaluate the grasping ability. Finite element analysis was also performed to compare with the experimental results. It was found that the compliant prosthetic finger met the design requirements and overcome some problems present in the traditional prosthetic fingers. The home-made topology optimization method is reliable for the design of prosthetic finger.
Multivariate statistical modeling for multi-temporal SAR change detection using wavelet transforms

In this paper, we propose a new method for automatic change detection in multi-temporal SAR images based on statistical wavelet subband modeling. The image is decomposed into multiple scales using wavelet transform and the probability density function of the sliding window coefficients of each subband is assumed to be multivariate Gaussian distribution. Kullback-Leibler similarity measures are computed between two corresponding subbands of the same scale and used to generate the change map. The multivariate statistical model is considered here to better model the spatial information given by texture than that given by a univariate statistical model. The proposed method is compared to the classical method based on univariate Gaussian distribution. Test on real data show that our approach outperforms the conventional approach.
Sample-efficient Nonstationary Policy Evaluation for Contextual Bandits

We present and prove properties of a new offline policy evaluator for an exploration learning setting which is superior to previous evaluators. In particular, it simultaneously and correctly incorporates techniques from importance weighting, doubly robust evaluation, and nonstationary policy evaluation approaches. In addition, our approach allows generating longer histories by careful control of a bias-variance tradeoff, and further decreases variance by incorporating information about randomness of the target policy. Empirical evidence from synthetic and realworld exploration learning problems shows the new evaluator successfully unifies previous approaches and uses information an order of magnitude more efficiently.
Endoscopic papillectomy

Purpose of review Although few controlled trials exist in the field, endoscopic papillectomy has evolved over the recent years with new data on preoperative staging and improved methods for its safe and successful completion. In 2006, a consensus guideline was published by the American Society of Gastrointestinal Endoscopy evaluating the role of endoscopy in managing ampullary adenomas. Recent findings The recent literature of endoscopic papillectomy has focused on the preoperative management of ampullary tumors, with a paper evaluating the role of endoscopic ultrasound. Also, a randomized controlled trial has shown that the use of pancreatic duct stents is associated with less incidence of postendoscopic papillectomy pancreatitis, although the study was probably underpowered. Several methods can be used to help locate the pancreatic duct postendoscopic papillectomy (endoscopic ultrasound-guided rendezvous and methylene blue injection). The recurrence and complication rate in more recent papers continue to be acceptable, at about 30 and 20%, respectively. Summary Endoscopic papillectomy is a reasonable alternative to transduodenal surgical excision, but more controlled studies with long-term data are needed to evaluate preoperative staging accuracy and recurrence rates.
Visual tracking via adaptive observation weight

To solve the problem of poor robustness and low effectiveness of visual tracking in complex scenes, a novel target tracking algorithm based on adaptive observation weight is proposed in this paper. First of all, a weighted observation model is established by linear visual tracking representation. Then an iterative optimization algorithm is proposed to obtain the parameters of the model, and adaptively update the weight matrix to eliminate negative influences of observation outliers. Finally, an effective likelihood evaluation function is adopted to track the object accurately and robustly. Analyzing both qualitatively and quantitatively on several challenging video sequences demonstrate that the algorithm outperforms other state-of-the-art tracking algorithms in tracking accuracy and robustness.
A CRY–BIC negative‐feedback circuitry regulating blue light sensitivity of Arabidopsis

Cryptochromes are blue light receptors that regulate various light responses in plants. Arabidopsis cryptochrome 1 (CRY1) and cryptochrome 2 (CRY2) mediate blue light inhibition of hypocotyl elongation and long-day (LD) promotion of floral initiation. It has been reported recently that two negative regulators of Arabidopsis cryptochromes, Blue light Inhibitors of Cryptochromes 1 and 2 (BIC1 and BIC2), inhibit cryptochrome function by blocking blue light-dependent cryptochrome dimerization. However, it remained unclear how cryptochromes regulate the BIC gene activity. Here we show that cryptochromes mediate light activation of transcription of the BIC genes, by suppressing the activity of CONSTITUTIVE PHOTOMORPHOGENIC 1 (COP1), resulting in activation of the transcription activator ELONGATED HYPOCOTYL 5 (HY5) that is associated with chromatins of the BIC promoters. These results demonstrate a CRY-BIC negative-feedback circuitry that regulates the activity of each other. Surprisingly, phytochromes also mediate light activation of BIC transcription, suggesting a novel photoreceptor co-action mechanism to sustain blue light sensitivity of plants under the broad spectra of solar radiation in nature.
Problem Solving Environment for Medical Image Analysis

The development of Medical Image Analysis (MIA) applications that can successfully be applied in clinical practice is difficult for several reasons, one of them being the large amount and variety of resources involved (people, data, methods, computing). The application goes through several phases (development, parameter optimization, evaluation and clinical deployment) usually supported by different systems. The lack of support for information flow from phase to phase puts extra logistics burden on the lifecycle of MIA applications. In this paper we describe our first efforts to develop a Problem Solving Environment (PSE) for MIA applications using the three systems available at the proof-of-concept environment of the Virtual Laboratory for e-Sciences project. The proposed PSE implements data provenance mechanisms that support information flow among systems, facilitating navigation across phases of the application lifecycle.
Simulating Timescale Dynamics of Network Traffic Using Homogeneous Modeling

Simulating and understanding traffic dynamics in large networks are difficult and challenging due to the complexity of such networks and the limitations inherent in simulation modeling. Typically, simulation models used to study traffic dynamics include substantial detail representing protocol mechanisms across several layers of functionality. Such models must be restricted in space and time in order to be computationally tractable. We propose an alternative simulation approach that uses homogeneous modeling with an increased level of abstraction, in order to explore networks at larger space-time scales than otherwise feasible and to develop intuition and insight about the space-time dynamics of large networks. To illustrate the utility of our approach, we examine some current understandings of the timescale dynamics of network traffic, and we discuss some speculative results obtained with homogeneous modeling. Using a wavelet-based technique, we show correlation structures, and changes in correlation structures, of network traffic under variations in traffic sources, transport mechanisms, and network structure. Our simulation results justify further investigation of our approach, which might benefit from cross-verifications against more detailed simulation models.