Miracle-dz/UniFAR-FTUs huggingface dataset:
Blackberry Yellow Vein Disease (BYVD) Complex and Associated Viruses

Blackberry and raspberry are members of the family Rosaceae. They are classified in the genus Rubus, which comprises hundreds of species and has a center of origin in the Far East. Rubus is divided into 15 subgenera with blackberries classified in the Rubus (formerly Eubatus) and raspberries in the Idaeobatus subgenera (41). Blackberry plants are generally larger and more vigorous than raspberries and are classified in three broad categories based on growth habit: trailing, semi-erect, and erect. Blackberry and raspberry canes typically have spines, commonly called thorns for blackberry and black raspberry. Rubus species are propagated vegetatively and are subject to infection by viruses during development, propagation, and fruit production stages. Reports of initial detection and symptoms of more than 30 viruses, virus-like diseases, and phytoplasmas affecting Rubus spp. were reviewed more than 20 years ago (17,18). Initially, most of the virus diseases of Rubus were described based upon symptoms on the virus indicators Rubus occidentalis (black raspberry), R. henryii, and R. idaeus ‘Lloyd George’ and ‘Malling Landmark’ following graft inoculation (114). Since the last review on Rubus viruses (18), significant progress has been made in the molecular characterization of many of the viruses that infect Rubus spp. Vectors of the newly described virus can be inferred based on its phylogenetic relationships with better-characterized viruses that are closely related, but in most cases they still need to be confirmed in transmission studies. Prior to 2000, complete or partial molecular data existed only for some of the nematode-transmitted viruses that infect Rubus (Arabis mosaic virus [ArMV; 63], Raspberry ringspot virus [RpRSV; 104], Strawberry latent ringspot virus [SLRSV; 29,59], Tobacco ringspot virus [TRSV; 14,94,139], and Tomato ringspot virus [ToRSV; 95,96,138]). Currently, reverse transcription–polymerase chain reaction (RT-PCR) detection methods are available for most of the viruses known to infect Rubus. There are two caveats to this statement: (i) for many of the recently identified viruses, only a few isolates have been studied, and thus, assay reliability is unknown given the limited knowledge of virus diversity; and (ii) many new viruses of Rubus have been characterized since 2005, and it is likely that more, previously unknown viruses, are yet to be identified. The goals of this article are to update the knowledge on previously characterized viruses of Rubus, highlight recently described viruses, review the virus-induced symptoms, describe the advances made in their detection, and discuss our knowledge about several virus complexes that cause serious diseases in Rubus. Virus complexes have been identified recently as the major cause of diseases in blackberries and raspberries. The names, acronyms, vectors, classification, currently recommended laboratory-based detection methods, and distribution of these viruses discussed below are summarized in Table 1.
Health outcomes and adherence to a healthy lifestyle after a multimodal intervention in people with multiple sclerosis: Three year follow-up

Background Modifiable risk factors such as smoking and sedentary lifestyle adversely affect multiple sclerosis (MS) progression. Few multimodal behavioural interventions have been conducted for people with MS, and follow-up beyond 1 year is rare for lifestyle interventions. This study assessed adoption and adherence to healthy lifestyle behaviours and health outcomes 3 years after a lifestyle modification intervention, using generalized estimating equation models to account for within-participant correlation over time. Methods 95 people with MS completed baseline surveys before participating in 5-day MS lifestyle risk-factor modification workshops. 76 and 78 participants completed the 1-year and 3-year follow-up surveys respectively. Mean age at 3-year follow-up was 47 years, 72% were female, most (62.8%) had MS for 5 years or less, and 73% had relapsing remitting MS (RRMS). Results Compared to baseline, participants reported clinically meaningful increases in physical (mean difference (MD): 8.0, 95% Confidence Interval (CI): 5.2–10.8) and mental health (MD: 9.2, CI: 5.8–12.6) quality of life (QOL) at 1-year, and physical (MD: 8.7, CI: 5.3–12.2) and mental health (MD: 8.0, CI: 4.2–11.8) QOL at 3-year follow-up. There was a small decrease in disability from baseline to 1-year follow-up (MD: 0.9, CI: 0.9,1.0) and to 3-year follow-up (MD: 1.0, CI: 0.9,1.0), which was not clinically meaningful. Of those with RRMS, compared to baseline, fewer had a relapse during the year before 1-year follow-up (OR: 0.1, CI 0.0–0.2) and 3-year follow-up (OR: 0.15, CI 0.06–0.33). Participants’ healthy diet score, the proportion meditating ≥1 hours a week, supplementing with ≥ 5000IU vitamin D daily, and supplementing with omega-3 flaxseed oil increased at 1-year follow-up and was sustained, although slightly lower at 3-year follow-up. However, there was no evidence for a change in physical activity and not enough smokers to make meaningful comparisons. Medication use increased at 1-year follow-up and at 3-year follow-up. Conclusion The results provide evidence that lifestyle risk factor modification is feasible and sustainable over time, in a small self-selected and motivated sample of people with MS. Furthermore, participation in a lifestyle intervention is not associated with a decrease in MS medication use.
A low cost IoT based crowd management system for public transport

With the ever growing global population, crowding in public transport is becoming an increasing menace. Public transport systems around the world have remained largely the same over the past several decades although the population they serve has burgeoned. This paper aims to demonstrate a low cost IoT based solution to the crowding problem by using smart seats that can detect and display the seat occupancy status in real time over an internet or mobile application. The feasibility of the project was assessed and simulated using the NETSIM simulation software. The results of the software simulation showed promise and hence a hardware prototype was built using the IEEE 802.15. 4 standard on the Arduino — Raspberry Pi — nRF platform. The prototype results are positive and show a fully functional IoT system that can be implemented in buses and trains.
Estimation of Temporal Gait Parameters Using a Human Body Electrostatic Sensing-Based Method

Accurate estimation of gait parameters is essential for obtaining quantitative information on motor deficits in Parkinson’s disease and other neurodegenerative diseases, which helps determine disease progression and therapeutic interventions. Due to the demand for high accuracy, unobtrusive measurement methods such as optical motion capture systems, foot pressure plates, and other systems have been commonly used in clinical environments. However, the high cost of existing lab-based methods greatly hinders their wider usage, especially in developing countries. In this study, we present a low-cost, noncontact, and an accurate temporal gait parameters estimation method by sensing and analyzing the electrostatic field generated from human foot stepping. The proposed method achieved an average 97% accuracy on gait phase detection and was further validated by comparison to the foot pressure system in 10 healthy subjects. Two results were compared using the Pearson coefficient r and obtained an excellent consistency (r = 0.99, p < 0.05). The repeatability of the purposed method was calculated between days by intraclass correlation coefficients (ICC), and showed good test-retest reliability (ICC = 0.87, p < 0.01). The proposed method could be an affordable and accurate tool to measure temporal gait parameters in hospital laboratories and in patients’ home environments.
DM_NLP at SemEval-2018 Task 8: neural sequence labeling with linguistic features

This paper describes our submissions for SemEval-2018 Task 8: Semantic Extraction from CybersecUrity REports using NLP. The DM_NLP participated in two subtasks: SubTask 1 classifies if a sentence is useful for inferring malware actions and capabilities, and SubTask 2 predicts token labels (“Action”, “Entity”, “Modifier” and “Others”) for a given malware-related sentence. Since we leverage results of Subtask 2 directly to infer the result of Subtask 1, the paper focus on the system solving Subtask 2. By taking Subtask 2 as a sequence labeling task, our system relies on a recurrent neural network named BiLSTM-CNN-CRF with rich linguistic features, such as POS tags, dependency parsing labels, chunking labels, NER labels, Brown clustering. Our system achieved the highest F1 score in both token level and phrase level.
Segmenting human trajectory data by movement states while addressing signal loss and signal noise

ABSTRACT This paper considers the problem of partitioning an individual GPS trajectory data into homogeneous, meaningful segments such as stops and trips. Signal loss and signal noise are highly prevalent in human trajectory data, and it is challenging to deal with uncertainties in segmentation algorithms. We propose a new trajectory segmentation algorithm that detects stop segments in a noise-robust manner from GPS data with time gaps. The algorithm consists of three steps that impute time gaps, split data into base segments and estimate states over a base segment. The state-dependent path interpolation was proposed as a framework for gap imputation to deal with locational and temporal uncertainties associated with signal loss. A spatiotemporal clustering-based trajectory segmentation was proposed to detect spatiotemporal clusters of any shape regardless of density to cut a trajectory into internally similar base segments. Fuzzy inference was employed to deal with borderline cases in determining states over base segments based on input features. The proposed algorithm was applied to detect stop/move episodes from raw GPS trajectories that were collected from 20 urban and 19 suburban participants. Sensitivity analysis was conducted to guide the choice of parameters such as the temporal and spatial definitions of a stop. Experimentation results show that the proposed method correctly identified 92% of stop/move episodes, and correctly estimated 98% of episode duration. This study indicates that a sequence of state-dependent gap imputation, clustering-based data segmentation and fuzzy-set-based state estimation can satisfactorily deal with uncertainty in processing human GPS trajectory data.
Zur Berechnung von Dipolmomenten aus Messungen an verdünnten Lösungen

Die zahlreichen, zur Auswertung von Dipolmomentmessungen an verdünnten Lösungen nach DCM in der Literatur angegebenen Extrapolationsmethoden auf unendliche Verdünnung werden unter einem einheitlichen Gesichtspunkt zusammengestellt, und es wird gezeigt, daß sie physikalisch gleichwertig sind. Für die einfachste Auswertung nach Guggenheim und Smith wird eine übersichtliche Ableitung angegeben, aus der hervorgeht, daß für die Anwendbarkeit dieser Methode nur eine, meist mit genügender Annäherung erfüllte Voraussetzung besteht.
DMCB at SemEval-2018 Task 1: Transfer Learning of Sentiment Classification Using Group LSTM for Emotion Intensity prediction

This paper describes a system attended in the SemEval-2018 Task 1 “Affect in tweets” that predicts emotional intensities. We use Group LSTM with an attention model and transfer learning with sentiment classification data as a source data (SemEval 2017 Task 4a). A transfer model structure consists of a source domain and a target domain. Additionally, we try a new dropout that is applied to LSTMs in the Group LSTM. Our system ranked 8th at the subtask 1a (emotion intensity regression). We also show various results with different architectures in the source, target and transfer models.
MR-Mafia: Parallel Subspace Clustering Algorithm Based on MapReduce for Large Multi-dimensional Datasets

The mission of subspace clustering is to find hidden clusters exist in different subspaces within a dataset. In recent years, with the exponential growth of data size and data dimensions, traditional subspace clustering algorithms become inefficient as well as ineffective while extracting knowledge in the big data environment, resulting in an emergent need to design efficient parallel distributed subspace clustering algorithms to handle large multi-dimensional data with an acceptable computational cost. In this paper, we introduce MR-Mafia: a parallel mafia subspace clustering algorithm based on MapReduce. The algorithm takes advantage of MapReduce's data partitioning and task parallelism and achieves a good tradeoff between the cost for disk accesses and communication cost. The experimental results show near linear speedups and demonstrate the high scalability and great application prospects of the proposed algorithm.
An implementation of automatic container number recognition system

This paper presents a design and implementation of automatic container number recognition (ACNR) system for an oversea logistic purpose. Usually in import/export industry in Thailand, the container number which is used for logistic management and also customs duties is manually inspected and recorded by human at each gate of port. However, the manual operation by human causes of error and slow speed of operation from bottle neck, especially customs clearance verification process which customs officers and terminal operators must deal with containers individually as they enter and leave terminals via a gate. By these reasons, an automatic system is required. The cameras capture containers and by using image processing and optical characteristic recognition (OCR) techniques, a container number will be achieved and then customs clearance verification can be automatically checked via database system including other logistic management. The proposed ACNR system can reduce the bottle neck issue, enhance time utilization, accuracy rate, and data management efficiency. From an experiment, total samples of 218 containers are tested, the proposed system can give an accuracy rate of 95.41% with time utilization < 10 seconds per/container.
Machine learning gait analysis algorithm for ontogenetic features compensation

From the very first time when knowledge of exact human gait cycle phase was needed, numerous gait identification algorithms were proposed. Because of relatively simple nature in comparison to other phenomenons, some of those methods were based on foot-ground contact zone reactions. Regrettably, human gait is drastically complex process that differs from one person to another, not infrequently significantly, which causes numerous problems in creating its mathematical model. Due to this fact it is hard to create rehabilitation device control system based on traditional algorithms suitable for each person. In this paper we present comparison of signal similarities algorithms and adaptive neuro fuzzy inference system based gait phase classifier designed to counteract ontogenetic characteristics. Data sets composed of foot pressure sensors readings are provided to both algorithms to determine differences in each approach.
KVLMM: A Trajectory Prediction Method Based on a Variable-Order Markov Model With Kernel Smoothing

With the dramatic proliferation of global positioning system (GPS) devices, a rich range of research has been conducted on the analysis of GPS trajectories. Research on trajectory prediction uses historical trajectory data to forecast future positions. The typical method is to use a statistical model based on the Markov chain. However, existing models are inefficient in two aspects. The methods of using lower-order Markov models use only current information and ignore historical information, degrading the prediction accuracy. In contrast, higher-order Markov models can improve the prediction accuracy but incur increased time and space complexity. Here, we propose the kernel variable length Markov model (KVLMM), a variable-order Markov model based on kernel smoothing, which combines sequence analysis with the Markov statistical model. The KVLMM can adaptively train trajectory data and learn rules from the training results. When training a large data sample, the KVLMM can rapidly execute training in linearly complex time and space. Moreover, this model uses kernel smoothing when training fewer data samples. In other words, the KVLMM improves the prediction accuracy and reduces the overhead of the data process. Our experimental results show that KVLMM has a lower algorithm complexity and a higher prediction accuracy.
Pressure from dark matter annihilation and the rotation curve of spiral galaxies

The rotation curves of spiral galaxies are one of the basic predictions of the cold dark matter paradigm, and their shape in the innermost regions has been hotly debated over the last decades. The present work shows that dark matter annihilation into electron–positron pairs may affect the observed rotation curve by a significant amount. We adopt a model-independent approach, where all the electrons and positrons are injected with the same initial energy E0 ∼ mdmc 2 in the range from 1 MeV to 1 TeV and the injection rate is constrained by INTEGRAL, Fermi and HESS data. The pressure of the relativistic electron–positron gas is determined by solving the diffusion-loss equation, considering inverse Compton scattering, synchrotron radiation, Coulomb collisions, bremsstrahlung and ionization. For values of the gas density and magnetic field that are representative of the Milky Way, it is estimated that pressure gradients are strong enough to balance gravity in the central parts if E0 < 1 GeV. The exact value depends somewhat on the astrophysical parameters, and it changes dramatically with the slope of the dark matter density profile. For very steep slopes, as those expected from adiabatic contraction, the rotation curves of spiral galaxies would be affected on ∼kpc scales for most values of E0. By comparing the predicted rotation curves with observations of dwarf and low surface brightness galaxies, we show that the pressure from dark matter annihilation may improve the agreement between theory and observations in some cases, but it also imposes severe constraints on the model parameters (most notably, the inner slope of halo density profile, as well as the mass and the annihilation cross-section of dark matter particles into electron–positron pairs).
Econometric Evidence on the Depreciation of Innovations

This paper presents estimates of the depreciation rate of innovations using survey data on revenues associated with Australian patents. Its novelty is twofold. First, it relies on direct observation of the revenue streams of inventions. This is in sharp contrast with previous studies, which all rely on models based on indirect observation and require strong identifying assumptions. Second, it presents estimates of the effect of patent protection on the depreciation rate. Results suggest that the depreciation rate is in 2–7% range. Inventions for which a patent is granted are associated with a 1–2 percentage point reduction in the depreciation rate.
OFDM Performance Improvement Based on Active Constellation Extension and Active Interference Cancelation

Orthogonal Frequency Division Multiplexing (OFDM) is a transceiver technology able to achieve spectrally efﬁcient and high data rate wireless transmissions. It is also able to transmit in a non-contiguous (NC) fashion by utilizing several separate spectral whitespaces. Cognitive radio systems based on non-contiguous orthogonal frequency division multiplexing (NC-OFDM) is a promising technique that can provide efcient bandwidth utilization for high data rate wireless communication systems under frequency selective fading environments. However, the high peak to average power ratio (PAPR) and large spectrum sidelobe power are the two major limitations at the transmitter side of the NC-OFDM based Cognitive Radio (CR) system, which may significantly reduce the power efficiency and performance of the system. In this paper, we present a joint method for the PAPR reduction and sidelobe suppression based on Active Constellation Extension(ACE) and Active Interference Cancelation(AIC). The key idea of the proposed method is to dynamically extend part of the constellation points on the secondary user (SU) subcarriers based on ACE and add several SC symbols on the primary user (PU) subcarriers based on AIC to generate the appropriate cancelation signal for joint PAPR reduction and sidelobe suppression. Since the convex optimization is computationally complex in nature, a suboptimal method is also proposed whose simulation result shows that there is a significant improvement in terms of PAPR and sidelobe reduction.
Screening and Identification of Genotypes for Natural Color from Grain Amaranth (Amaranth sp.)

Nature exhibits a wide range of colours in the form of plant pigments that are used as dyes. Anthocyanin is one such water soluble pigment responsible for an attractive red to blue colour and is known to possess high antioxidant activity. Research on grain amaranth thus far has been focussed on production and productivity besides its nutritive quality. The present study highlights on extraction of anthocyanin from grain amaranth, an underutilized crop. Total anthocyanin content (TAC) (mg/g) in leaf, inflorescence and stem peel was estimated and evaluated statistically (1% and 5% level of significance). Leaves, stem-peel and inflorescence of grain amaranth considered as a waste material after seed extraction is apotential source of anthocyanin adds value.
Turbo-coded parallel modem techniques for personal communications

We study the performance of the wideband orthogonal frequency division multiplexing (OFDM) system in conjunction with channel coding based on turbo codes over a range of wideband Rayleigh fading channels. Due to their diversity effect wideband propagation channels provide similar gains for OFDM modems to those of equalised narrowband channels, resulting in substantial coding gains, when combined with turbo coding.
Digital Operatives at SemEval-2018 Task 8: Using dependency features for malware NLP

The four sub-tasks of SecureNLP build towards a capability for quickly highlighting critical information from malware reports, such as the specific actions taken by a malware sample. Digital Operatives (DO) submitted to sub-tasks 1 and 2, using standard text analysis technology (text classification for sub-task 1, and a CRF for sub-task 2). Performance is broadly competitive with other submitted systems on sub-task 1 and weak on sub-task 2. The annotation guidelines for the intermediate sub-tasks create a linkage to the final task, which is both an annotation challenge and a potentially useful feature of the task. The methods that DO chose do not attempt to make use of this linkage, which may be a missed opportunity. This motivates a post-hoc error analysis. It appears that the annotation task is very hard, and that in some cases both deep conceptual knowledge and substantial surrounding context are needed in order to correctly classify sentences.