Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards a Cognitive Metric using Normalized Transfer Entropy

Investigation of functional brain networks using various complex network and inferential statistical techniques in various studies have provided insight into the intricacies of the patterns of structural and functional connectivity of human brain in recent years. Most of these studies have analysed the brain networks as being undirected where the direction of information flow between various brain regions has not been considered. The directions of information flow in the functional brain networks provide additional information on how one brain region influences the other and identify influential brain regions serving as network hubs during information processing. This study used information-theoretic concept of normalized transfer entropy on the EEG data to construct directed functional brain networks during five different brain states. Using a mix of signal processing, information and graph-theoretic techniques, the findings demonstrated that directed functional brain networks constructed using normalized transfer entropy is very sensitive to the changes in the cognitive tasks and this sensitivity can be used to develop a quantitative metric to measure cognition.
Based Point of Interest and Experience to Task Assignment on Location-Based Social Networks

In recent years, with the popularity of smart mobile devices, mobile Internet has rapidly developed. When the social network meets the localization technology, it gives birth to a Location-Based Social Network (LBSN). The situational awareness based on the location has become more research significance. However, how to combine the context awareness, mobile sensors and abundant users' historical location data to make the platform more efficient, how to ensure that the proceeds can improve the accuracy of recommendation and perceived task assignment, are still challenges in the location-based social network. In this paper, we demonstrate a model to use historical location data of the participants and analyze the point-of-interest (POI). Then we propose the user location and the empirical value algorithm PTHS based on the HITS algorithm. Through analyzing the interest points of the selected scene perception task, we find that those users have similar point-of-interest, and rank them by the location and experience PTHS algorithm. Finally, much more appropriate users are assigned to the tasks. Through theoretical analysis and extensive simulations, we validate that proposed method is effective and efficient.
Remote Sensing of 2000-2016 Alpine Spring Snowline Elevation in Dall Sheep Mountain Ranges of Alaska and Western Canada

The lowest elevation of spring snow (“snowline”) is an important factor influencing recruitment and survival of wildlife in alpine areas. In this study, we assessed the spatial and temporal variability of alpine spring snowline across major Dall sheep mountain areas in Alaska and northwestern Canada. We used a daily MODIS snow fraction product to estimate the last day of 2000–2016 spring snow for each 500-m pixel within 28 mountain areas. We then developed annual (2000–2016) regression models predicting the elevation of alpine snowline during mid-May for each mountain area. MODIS-based regression estimates were compared with estimates derived using a Normalized Difference Snow Index from Landsat-8 Operational Land Imager (OLI) surface reflectance data. We also used 2000–2009 decadal climate grids to estimate total winter precipitation and mean May temperature for each of the 28 mountain areas. Based on our MODIS regression models, the 2000–2016 mean May 15 snowline elevation ranged from 339 m in the cold arctic class to 1145 m in the interior mountain class. Spring snowline estimates from MODIS and Landsat OLI were similar, with a mean absolute error of 106 m. Spring snowline elevation was significantly related to mean May temperature and total winter precipitation. The late spring of 2013 may have impacted some sheep populations, especially in the cold arctic mountain areas which were snow-covered in mid-May, while some interior mountain areas had mid-May snowlines exceeding 1000 m elevation. We found this regional (>500,000 km2) remote sensing application useful for determining the inter-annual and regional variability of spring alpine snowline among 28 mountain areas.
A UDIO COMPRESSION AND ITS IMPACT ON EMOTION RECOGNITION IN AFFECTIVE COMPUTING

: Enabling a natural (human-like) spoken conversation with technical systems requires affective information, contained in spoken language, to be intelli-gibly transmitted. This study investigates the role of speech and music codecs for affect intelligibility. A decoding and encoding of affective speech was employed from the well-known EMO-DB corpus. Using four state-of-the-art acoustic codecs and different bit-rates, the spectral error and the human affect recognition ability in labeling experiments were investigated and set in relation to results of automatic recognition of base emotions. Through this approach, the general affect intelligibility as well as the emotion speciﬁc intelligibility was analyzed. Considering the results of the conducted automatic recognition experiments, the SPEEX codec conﬁgura-tion with a bit-rate of 6.6 kbit/s is recommended to achieve a high compression and overall good UARs for all emotions.
A quantitative method for selecting denoising filters, based on a new edge-sensitive metric

Noise removal is a fundamental requirement for many image processing applications. Its goal is filtering noise from images while preserving integrity of fine details and edges. In order to assess the quality of image denoising algorithms, it is of paramount importance to use suitable metrics. In other words, the use of an appropriate Image Quality Assessment (IQA) procedure is a key factor for selecting the most suitable image denoising filter and, hence, it has a major impact on the quality of the final image. This paper demonstrates that currently used IQA metrics are not suitable for selecting optimal image denoising filters when edge preservation must be evaluated, because they provide quality figures for the entire image, without specifically addressing edges. From this fact, a method for selecting the optimal filter in this context is proposed, based on minimizing a novel metric that accounts for filter response in edges. Smoothing and sharpening algorithms have been applied to different noisy images and then evaluated using popular metrics and the proposed one. Results show that the new metric is more suitable to assess final image quality when edge deterioration effects have to be accounted for, while offering a simple and understandable approach.
Card Sorts to Acquire Requirements

This paper deals with a requirement acquisition techniques card sorts. One reason is that requirements projects are often constrained by software solutions such as components, off-the-shelf packages, and application features. More and more projects need to acquire requirements to select between available solutions. This is where card sorts come in. During a card sort, one or more stakeholders work with a set of cards or objects, each of which rep resents an item. A card sort treats the item names as given, and the stakeholders should already be familiar with the items. As the name suggests, stakeholders sort the item (cards) into categories. Card sorts encourage stakeholders to verbalize information about categories of items and criteria that discriminate between them.
Cardiac Simulation to Personalize Cardiac Resynchronization Therapy.

Personalized medicine of heart failure patients has become the holy grail of ongoing research. Identification of responders to medical or device therapy before initiation of the therapy would be ideal to improve survival and reduce cardiovascular events.1 Particularly, in the field of heart failure, selection of patients who will benefit from cardiac resynchronization therapy (CRT) has attracted much attention. Left ventricular (LV) dyssynchrony, extent and location of myocardial scar, and position of the LV pacing lead, among other clinical variables, have shown to be strong determinants of the therapeutic response and outcomes.2,3 Although current guidelines do not include imaging criteria to select patients for CRT,4,5 many centers use imaging modalities (echocardiography, magnetic resonance imaging, computed tomography, and nuclear imaging) to select the patients and tailor the therapy to optimize the clinical results. Echocardiography is the imaging technique of first choice because of its versatility and widespread availability. It can provide information on LV dyssynchrony and site of latest mechanical activation and indicate the regions with a significant extent of myocardial scar tissue. Magnetic resonance imaging using late gadolinium enhancement provides information on scar extent and location with superior spatial resolution over echocardiography and can depict cardiac venous anatomy with similar accuracy as with computed tomography. Initial experience with fusion imaging, overlaying magnetic resonance imaging data on myocardial scar location, and cardiac venous anatomy onto fluoroscopy or positron emission tomography and computed tomography have been developed to tailor CRT delivery and avoid LV lead placement in areas of scar tissue.6,7 Despite these efforts, the rate of nonresponse to CRT remains unchanged, and the use of sequential imaging may lead to increased costs. In an ideal situation, the information obtained from each patient could be used to generate a cardiac model that …
Metrics for Evaluation of Word-level Machine Translation Quality Estimation

The aim of this paper is to investigate suitable evaluation strategies for the task of word-level quality estimation of machine translation. We suggest various metrics to replace F 1 -score for the “BAD” class, which is currently used as main metric. We compare the metrics’ performance on real system outputs and synthetically generated datasets and suggest a reliable alternative to the F 1 -BAD score — the multiplication of F 1 -scores for different classes. Other metrics have lower discriminative power and are biased by unfair labellings.
A Community Contribution Framework for Sharing Materials Data with Materials Project

As scientific discovery becomes increasingly data-driven, software platforms are needed to efficiently organize and disseminate data from disparate sources. This is certainly the case in the field of materials science. For example, Materials Project has generated computational data on over 60,000 chemical compounds and has made that data available through a Web portal and REST interface. However, such portals must seek to incorporate community submissions to expand the scope of scientific data sharing. In this paper, we describe MPContribs, a computing/software infrastructure to integrate and organize contributions of simulated or measured materials data from users. Our solution supports complex submissions and provides interfaces that allow contributors to share analyses and graphs. A RESTful API exposes mechanisms for book-keeping, retrieval and aggregation of submitted entries, as well as persistent URIs or DOIs that can be used to reference the data in publications. Our approach isolates contributed data from a host project's quality-controlled core data and yet enables analyses across the entire dataset, programmatically or through customized web apps. We expect the developed framework to enhance collaborative determination of material properties and to maximize the impact of each contributor's dataset. In the long-term, MPContribs seeks to make Materials Project an institutional, and thus community-wide, memory for computational and experimental materials science.
Predicting mortality among a general practice-based sample of older people with heart failure

Objective: To identify factors available to general practitioners (GPs) that are predictive of mortality within a general practice-based population of heart failure patients, and to report the sensitivity and specificity of prognostic information from GPs. Methods: Five hundred and forty-two heart failure patients aged >60 years were recruited from 16 UK GP surgeries. Patients completed quality-of-life and services use questionnaires every 3 months for 24 months or until death. Factors with independent significant association with survival were identified using Cox proportional hazards regression analysis. Results: Women had a 58% lower risk of death. Patients self-reporting New York Heart Association Classification III or IV had an 81% higher risk of death. Patients aged 85+ years had over a five-fold risk of death as compared with those aged <65 years. Patients with a co-morbidity of cancer had a 78% higher risk of death. Of the 14 patients who died in a 12-month period, the GPs identified 11 (sensitivity 79%). They identified 133 of the 217 who did not die (specificity 61%). Discussion: Predictors readily available to GPs, such as patient characteristics, are easy to adapt to use in general practice, where most heart failure patients are diagnosed and treated. Identifying factors likely to influence death is useful in primary care, as this can initiate discussion about end-of-life care.
Resistance training for adults with Alzheimer's disease and related dementias : feasibility of program implementation, appropriateness of participant engagement, and effects on physical performance and quality of life

Coupled with normal age-related regression in muscle mass, adults with cognitive impairment are at high risk for exacerbated declines in muscle strength, associated psychological well-being, and overall independence. Working from the environmental press model, a 12-week strength training intervention was designed to both support participants continuing abilities and meet varied needs. Tailoring the environment helps optimize participation, which is essential if participants are to experience the greatest possible gains from a group-based exercise program. The intervention was a group-based, progressive strength training program designed specifically for adults with dementia at two dementia care centers. The exercises were performed three times each week and the sessions were led by the centers activities leaders. Participants used hand-held barbells when performing the upper-body exercises. Findings indicated that individuals retain the capability to enhance their own quality of life through active participation in a therapeutic intervention. This is illustrated by the consistent effort of exercising participants to perform appropriately during the exercise intervention. Not only were adults able to demonstrate effort to appropriately participate, but the intervention supported high levels of correct performance of the exercise repetitions which is important for achieving physical gains. Exercisers did not experience improvement in physical abilities nor did they significantly differ at posttest from non-exercising participants in measures of physical
Recommendations for a Brief International Cognitive Assessment for Multiple Sclerosis (BICAMS)

Background: Cognitive impairment in MS impacts negatively on many patients at all disease stages and in all subtypes. Full clinical cognitive assessment is expensive, requiring expert staff and special equipment. Test versions and normative data are not available for all languages and cultures. Objective: To recommend a brief cognitive assessment for multiple sclerosis (MS) that is optimized for small centers, with one or few staff members, who may not have neuropsychological training and constructed to maximize international use. Methods: An expert committee of twelve members representing the main cultural groups that have so far contributed considerable data about MS cognitive dysfunction was convened. Following exhaustive literature review, peer-reviewed articles were selected to cover a broad spectrum of cultures and scales that targeted cognitive domains vulnerable to MS. Each was rated by two committee members and candidates scales were rated on psychometric qualities (reliability, validity, and sensitivity), international application, ease of administration, feasibility in the specified context, and acceptability to patients. Results: The committee recommended the Symbol Digit Modalities Test, if only 5 minutes was available, with the addition of the California Verbal Learning Test – Second Edition and the Brief Visuospatial Memory Test – Revised learning trials if a further 10 minutes could be allocated for testing. Conclusions: A brief cognitive assessment for MS has been recommended. A validation protocol has been prepared for language groups and validation studies have commenced.
Regioselectivity and Tautomerism of Novel Five-Membered Ring Nitrogen Heterocycles Formed via Cyclocondensation of Acylthiosemicarbazides

A series of 1-acyl-4-phenyl/(acridin-9-yl)thiosemicarbazides 3, including four new compounds, were prepared in order to study substituent effects on cyclization reactions with oxalyl chloride (producing imidazolidine-4,5-diones 4), dimethyl acetylenedicarboxylate (to give thiazolidin-4-ones 7 and 8) and autocondensation under alkaline conditions (to yield 1,2,4-triazoles 9). A positional isomer, 10 of compound 3f was also prepared. Altogether, twenty new compounds characterized and identified by IR, UV, 1H, 13C and 2D NMR and quantum chemical calculations are described. The tautomerism of the products and regioselectivity of the reactions were evaluated. Compounds 3f–h, 3h∙2HCl, 7b,d and 10 were screened for cytotoxic activity against the L1210 leukemia cell line and all compounds, except for 3f, exhibited promising inhibitions of cell growth.
Acoustical and lexical based confidence measures for a very large vocabulary telephone speech hypothesis-verification system

In the context of large vocabulary speech recognition system, it’s of major interest to classify every utterance as being correctly or incorrectly recognised. In this paper we are presenting a preliminary study on a wordlevel confidence estimation system based on the output of a neural network. We use a combination of multiple features extracted from the acoustical and lexical decoders of our reference system, those available in the hypothesis stage of a hypothesis-verification very large vocabulary telephone speech recognition system. We will show the system architecture, describe the experiments leading to the selection of the set of parameters to be used by the NN and the final performance, showing promising results as compared with the use of standard log-likelihood ratio techniques for confidence scoring.
Accelerating Random Forest training process using FPGA

Random Forest (RF) is one of the state-of-art supervised learning methods in Machine Learning and inherently consists of two steps: the training and the evaluation step. In applications where the system needs to be updated periodically, the training step becomes the bottleneck of the system, imposing hard constraints on its adaptability to a changing environment. In this work, a novel FPGA architecture for accelerating the RF training step is presented, exploring key features of the device. By combing a fine-grain data-flow processing at low-level and by exploiting parallelism available at high level inherent in the algorithm, significant acceleration factors are achieved. Key to the above gains is a novel FPGA FIFO based merge sorter module, a core component in the architecture, that exhibits high efficiency in memory utilisation; as well as a batch training strategy that enable full exploitation of the high memory bandwidth offered by the on-chip memory featured on FPGA devices. The proposed system achieves speed-up factors of up to 230x over a 3GHz Intel Core i5 processor when an Altera Stratix IV device is utilised under classification problems drawn from VOC2007.
Context-Sensitive Affect Recognition for a Robotic Game Companion

Social perception abilities are among the most important skills necessary for robots to engage humans in natural forms of interaction. Affect-sensitive robots are more likely to be able to establish and maintain believable interactions over extended periods of time. Nevertheless, the integration of affect recognition frameworks in real-time human-robot interaction scenarios is still underexplored. In this article, we propose and evaluate a context-sensitive affect recognition framework for a robotic game companion for children. The robot can automatically detect affective states experienced by children in an interactive chess game scenario. The affect recognition framework is based on the automatic extraction of task features and social interaction-based features. Vision-based indicators of the children’s nonverbal behaviour are merged with contextual features related to the game and the interaction and given as input to support vector machines to create a context-sensitive multimodal system for affect recognition. The affect recognition framework is fully integrated in an architecture for adaptive human-robot interaction. Experimental evaluation showed that children’s affect can be successfully predicted using a combination of behavioural and contextual data related to the game and the interaction with the robot. It was found that contextual data alone can be used to successfully predict a subset of affective dimensions, such as interest toward the robot. Experiments also showed that engagement with the robot can be predicted using information about the user’s valence, interest and anticipatory behaviour. These results provide evidence that social engagement can be modelled as a state consisting of affect and attention components in the context of the interaction.
Multiple-source localization using line-of-bearing measurements: Approaches to the data association problem

In this paper, we consider the data association problem of multiple-source localization using line-of-bearing measurements. More specifically, we propose two approaches that circumvent the very high complexity of a brute-force solution to this problem. The first approach relies on statistical clustering of the intersections of line-of-bearing measurements and the second approach exploits cyclostationary features of the received signals to allow for line-of-bearing measurement separation based on each signalpsilas transmission modes. These two proposed approaches have reduced time and computational complexity, while maintaining a reasonable amount of accuracy, and are of great value for dense environments with large numbers of sensors and/or targets.
Deep Mixture of Experts with Diverse Task Spaces

In this paper, a deep mixture algorithm is developed to support large-scale visual recognition (e.g., recognizing tens of thousands of object classes) by seamlessly combining a set of base deep CNNs (AlexNet) with diverse task spaces, e.g., such base deep CNNs (i.e., diverse experts) are trained to recognize different subsets of tens of thousands of object classes rather than the same set of object classes. Our experimental results have demonstrated that our deep mixture algorithm can achieve very competitive results on large-scale visual recognition.