Miracle-dz/UniFAR-FTUs huggingface dataset:
Framing a Sustainable Architecture for Data Analytics Systems: An Exploratory Study

Data analytics systems (DASs) with big data capabilities have started playing a promising role in online service ecosystems and large-scaled interconnected systems of many enterprises. The rapid development of analytics models and technologies, along with affordable infrastructures and accumulated data repositories, leads to encouraging expectations on DAS, while also bringing challenges in terms of how to deal with the increased development complexity. However, systematic methodologies for designing a sustainable DAS are still missing. To harness the dynamics raised by technology evolution, ambiguous requirements, under-explored data environments, and so on, framing a sustainable software architecture turns out to be a critical task. By exploring the complex nature of DAS, we propose a novel approach, sustainable architecture development for DAS (SstAD-DAS), to provide practical guidelines for architecture development. A shock absorber mechanism is presented to harness the dynamics of DAS and facilitate the development of a sustainable architecture, the “long decision chain” challenges are handled with a generic process model, and collaborations and responsibilities of participants are suggested to enable better model implementation. SstAD-DAS allows architects to accommodate the long decision chain, leverage skill sets from multiple contributors, and evaluate architectural decisions continuously. Finally, this paper demonstrates the capability and usability of SstAD-DAS by sharing experiences and observations from the continuous development of an intelligence analysis system.
An Investigation of the Predictors of Comfortable and Fast Gait Speed in Community-Dwelling Older Adults.

BACKGROUND/PURPOSE To identify the unique predictors of comfortable and fast gait speed in community-dwelling older adults using measures of physical performance (eg, lower extremity strength and balance), self-reported balance confidence, and global cognitive function. METHODS Demographic information was collected from 60 healthy, community-dwelling older adults older than 60 years. Participants completed the following assessments: Mini-Mental State Examination; Activities-Specific Balance Confidence Scale; 30-second Chair Stand (30-SCS); Functional Reach (FR); and gait speed (comfortable and fast) using the GAITRite system. Hierarchical linear regression was used to examine the relationship of both fast and comfortable gait speeds with functional performance (CST and FR), cognition (Mini-Mental State Examination), Activities-Specific Balance Confidence Scale, and demographic information (age, gender, and body mass index). RESULTS AND DISCUSSION Functional performance measures (30-SCS and FR) explained 55.4% and 64.7%, respectively, of the variance in comfortable and fast gait speed. Unique predictors for comfortable gait speed included 30-SCS, FR, and body mass index. Unique predictors of fast gait speed included 30-SCS, FR, gender, body mass index, and Activities-Specific Balance Confidence Scale score. These predictors explained 68.5% and 80.4% of the total variance in comfortable and fast gait speed, respectively. Global cognition was not a unique predictor of gait speed when performance measures were statistically controlled. However, the current study measured global cognitive status rather than the specific cognitive constructs, such as processing speed or executive function. Further research is needed to determine the role of cognition in the composition of gait speed. CONCLUSIONS Gait speed is an important indicator for many outcomes such as fall risk, mortality, and functional status. Understanding that key variables of strength and balance comprise a large portion of gait speed allows clinicians to better direct their care and optimize rehabilitation outcomes. This study specifically used functional measures of strength and balance that can be easily implemented in the clinical setting.
Modified Monarch Butterfly Optimization Algorithm for RFID Network Planning

This paper presents an implementation of the modified monarch butterfly optimization algorithm for solving the RFID network planning problem. This problem is one of the most studied challenges in the RFID domain, and was not tackled with the monarch butterfly optimization metaheuristic before. RFID system consists of tags and readers, where readers access from certain distance the information that is stored in tags. By testing basic monarch butterfly algorithm, we noticed that the algorithm can be improved by enhancing exploitation in the late iterations. We tested our approach on the standard RFID model and data set, and conducted comparative analysis with other approaches. According to the performed experiments, we concluded that the modified monarch butterfly optimization can be successfully applied to the RFID network planning problem.
Power Optimization in Self-Organizing MEC Based Heterogeneous Small Cell Networks

Mobile edge computing (MEC) technology migrates the cloud computing platform to the edge of the mobile access network, which reduces the end-to-end delay of mobile service delivery and improves the user experience. However, since the number of the deployed small cell base stations (BSs) is larger than the macro BSs, automatic deployment can be used to control deployment costs. In this paper, we study the adaptive boundary algorithm of indoor small cell BSs and propose a power optimization scheme in self-organizing MEC-based heterogeneous small cell networks. According to the actual environment, the indoor small cell BS can be deployed quickly and flexibly. And the coverage of the small cell BS can be optimized by automatically adjusting the power parameters. Considering the coverage and spectral efficiency of the macro BS and the small cell BS, the performance indicators of the optimization program and results evaluation is proposed. Finally, the simulation results show that the proposed algorithm and scheme can control the deployment cost and improve the optimization coverage of macro BSs and small cell BSs under the control cost.
Topic Modeling Using Contextual Cues

This paper proposes a new solution for topic modeling using contextual cues by applying Automatic Term Recognition (ATR) to extract domain-specific terms in the text preprocessing step. The vocabularies used for topic modeling are constructed using linguistic patterns to determine the inner structure of each document analyzed and, by only taking into account the document's domain relevant terms and ignoring other words which do not bring any kind of informational gain, the proposed method manages to extract more coherent and human readable topics with a higher grade of separability as shown in our experiments. To evaluate our method, we use publicly available data sets containing scientific and news articles which are already labeled. These classes are used as ground truth for the clustering evaluation methods and as an evaluation technique for human readability. Moreover, the coherence of topics is evaluated by using the CV method proposed in the Palmetto framework.
Checkpoint Based Live Migration of Virtual Machine

Cloud computing is the blooming technology in the IT world now a days. Cloud computing enables the IT industries to cut down their infrastructural cost by providing its services. Virtualization is one of the primary tools through which cloud computing achieves its goals and live migration in virtual machines brings a lot of advantages like fault tolerance, manageability, performance improvement by reducing the downtime of machines. This paper introduces a pre-copy based live migration technique which not only reduces the downtime of machines but also takes care of the situation when a fault is introduced. The aim is achieved using checkpoint technique.
Facebook and MOOCs: a Comparative Analysis for a Collaborative Learning

The ubiquity of social media has marked today’s e-learning environment, especially Massive Open Online Courses (MOOCs) that have recently been gaining attention as a new form of teaching learning process. Despite their benefits, many learners have been disengaged or totally dropped out due to various factors such as lack of interaction and satisfaction.In this paper, we highlight a comparative analysis of MOOCs and Facebook through SWOT analysis. As an outcome, Facebook and MOOCs could have complementary functions in order to optimize learner’s engagement.
Numerical Investigation on Flexibility of Metal Cardiovascular Stent

In practical field, “ease of stent delivery” during PCI is important for cardiologists since the stent goes through a very complex passage of blood artery before arriving the plaque blockage. However, there was no standard yet for representing it and thus various opinions existed for among cardiologists. In the current study, “ease of stent delivery” would be presented by flexibility. The flexibility of cardiovascular stent is determined numerically by curvature-index which is ratio of rotation angle to the length of the stent. The curvature-index was depicted at crimped or non-expanded and inflated or expanded conditions. Bending moment was applied to the stent in respect to x and y axes. Numerical work was performed by software ABAQUS. A stent model was open cell design made of stainless steel 316L with size of 100mm thick, strut and link width of 80mm, 20 mm long and initial outer diameter of 1.237 mm. Results on flexibility simulation showed that there were proportional values of bending moments of less than 0.4 N-mm and curvature index of less than 0.01 rad/mm. Beyond that value, the curvature index increased steeply. However, in general the expanded stent showed more flexible compared to that of non-expanded. To improve its flexibility with remained design, it could be carried out by reducing its thickness for less than 100 mm of its strut and link width for less than 80 mm.
Masking Polarity Words in CNN

Sentiment Analysis (SA), also called Opinion Mining, has attracted more and more attentions in recent years. Convolutional Neural Networks (CNNs), which have a genius for extracting features, are commonly used in natural language processing (NLP) tasks. CNNs have been proven to be effective in SA tasks since sentiment is usually determined by some polarity words and phrases. In this work, we present a mask method to improve CNNs for SA tasks. We mask words according to their polarity scores and force the model to pay more attention to inapparent features. The model is supposed to extract not only the features with strong polarity but also the features with weak polarity, which is helpful for the classification. The effectiveness of the proposed mask method is demonstrated on five sentiment analysis datasets.
Global knowledge gaps in the prevention and control of bovine viral diarrhoea (BVD) virus

The significant economic impacts of bovine viral diarrhoea (BVD) virus have prompted many countries worldwide to embark on regional or national BVD eradication programmes. Unlike other infectious diseases, BVD control is highly feasible in cattle production systems because the pathogenesis is well understood and there are effective tools to break the disease transmission cycle at the farm and industry levels. Coordinated control approaches typically involve directly testing populations for virus or serological screening of cattle herds to identify those with recent exposure to BVD, testing individual animals within affected herds to identify and eliminate persistently infected (PI) cattle, and implementing biosecurity measures such as double-fencing shared farm boundaries, vaccinating susceptible breeding cattle, improving visitor and equipment hygiene practices, and maintaining closed herds to prevent further disease transmission. As highlighted by the recent DISCONTOOLS review conducted by a panel of internationally recognized experts, knowledge gaps in the control measures are primarily centred around the practical application of existing tools rather than the need for creation of new tools. Further research is required to: (a) determine the most cost effective and socially acceptable means of applying BVD control measures in different cattle production systems; (b) identify the most effective ways to build widespread support for implementing BVD control measures from the bottom-up through farmer engagement and from the top-down through national policy; and (c) to develop strategies to prevent the reintroduction of BVD into disease-free regions by managing the risks associated with the movements of animals, personnel and equipment. Stronger collaboration between epidemiologists, economists and social scientists will be essential for progressing efforts to eradicate BVD from more countries worldwide.
Attraction and Retention of Women in Engineering

With the advance of digital society it is imperative to attract and retain women specialists in engineering. To identify gender patterns of career choice in Russia and the problems Russian women specialists face while adapting to work environments in STEM, we conducted a series of expert interviews with young women engineers in the spring of 2018 and compared the data with the results of the 2013-2014 studies. We came to the conclusion that although some of the professional gender-related stereotypes still persist, they also undergo active transformation. Women’s success in undergraduate STEM education makes them feel more empowered to continue their post-graduate studies and to pursue careers in engineering. We also found that the current policy of attracting more women to the field of engineering, high-tech industries and innovation has had a certain effect on women’s choice of profession and career plans. The results of our research can be used for designing more gender balanced personnel policies and strategies to help women overcome structural barriers in their professional development and career advancement in STEM.
Cognitive-motor interference in persons with multiple sclerosis during five upper limb motor tasks with different complexity

Background: Cognitive-motor interference in multiple sclerosis has been well examined during walking, but not during upper limb (UL) performance. Objectives: To examine the dual-task cost (DTC) in persons with multiple sclerosis (pwMS) and healthy controls (HC) in various type and complexity of UL motor tasks. Method: In total, 30 pwMS without major UL impairment and 30 HC performed five different UL tasks, in single condition and combined with the phonemic word list generation task. The percent change in performances was evaluated by the motor, cognitive, and combined DTC. The motor tasks consisted of four unimanual (sustained hand grip strength, box-and-block test, Purdue pegboard test, finger tapping task) and one bimanual task (Purdue pegboard test). Group and task differences were analyzed with unpaired and paired t-tests, respectively, and overall effect with a multivariate analysis of variance. Results: The motor DTC ranged between 10% (Purdue pegboard bimanual) and 35% (box-and-block test). The cognitive DTC ranged between −8% (finger tapping test) and +21% (bimanual Purdue pegboard). The magnitude of the combined DTC did not differ significantly between pwMS and HC in any task. Conclusion: DTC is influenced by the complexity of the UL task, but was not significantly different between HC and cognitive intact, but mildly motor disabled pwMS.
Comparative screening of recombinant antigen thermostability for improved leptospirosis vaccine design

Recombinant antigens exhibit targeted protectiveproperties and offer important opportunities in the development of therapeutic technologies. Biophysical and structural methods have become important tools for the rational design and engineering of improved antigen‐based vaccines. Vaccines containing Leptospira immunoglobulin‐like (Lig) protein‐derived antigens are currently the most promising candidates for protective immunity against the globally prevalent bacterial pathogen, Leptospira interrogans; however, vaccine trials using these domains have produced inconsistent results. Here, we compare the thermostability of domains from the main immunogenic regions from major leptospiral antigens, LigA and LigB. By measuring temperature‐dependent fluorescence decay of the hydrophobic core tryptophan, 17 individual Lig protein immunoglobulin‐like (Ig‐like) domains were shown to display a broad range of unfolding temperatures. For a majority of the domains, stability issues begin to occur at physiologically relevant temperatures. A set of chimeric Ig‐like domains was used to establish the ability of transplanted domain regions to enhance thermostability. Further insights into the determinants for domain stabilization were explored with nuclear magnetic resonance dynamics and mutational analysis. The current study has yielded a set of thermostable Ig‐like domain scaffolds for use in engineering antigen‐based vaccines and demonstrates the importance of incorporating thermostability screening as a design parameter.
FERONIA regulates auxin‐mediated lateral root development and primary root gravitropism

The Arabidopsis FERONIA (FER) receptor kinase is a key hub of cell signaling networks mediating various hormone, stress, and immune responses. Previous studies have shown that FER functions correlate with auxin responses, but the underlying molecular mechanism is unknown. Here, we demonstrate that the primary root of the fer‐4 mutant displays increased lateral root branching and a delayed gravitropic response, which are associated with polar auxin transport (PAT). Our data suggest that aberrant PIN2 polarity is responsible for the delayed gravitropic response in fer‐4. Furthermore, the diminished F‐actin cytoskeleton in fer‐4 implies that FER modulates F‐actin‐mediated PIN2 polar localization. Our findings provide new insights into the function of FER in PAT.
Soluble Urokinase Plasminogen Activator Receptor (suPAR) is predictive of Non-AIDS Events during Antiretroviral Therapy-mediated Viral Suppression.

Background Despite effective antiretroviral therapy (ART), HIV infection remains associated with higher morbidity/mortality, driven- in part -by increased inflammation. The objective of this study is to identify associations between levels of plasma biomarkers of chronic inflammation, microbial translocation, and monocyte activation, measured prior to and during suppressive ART, with occurrence of non-AIDS events. Methods Participants (141 cases, 310 matched controls) were selected from the longitudinal observational ACTG ALLRT trial; all were virally suppressed on ART at year 1, and thereafter. Soluble urokinase plasminogen activator receptor (suPAR), lipopolysaccharide binding protein (LBP), beta-D-glucan (BDG), intestinal fatty acid binding protein (I-FABP), oxidized (ox)LDL, and soluble CD163 (s)CD163 were measured pre-ART, after 1-year of ART, and pre-event. At each timepoint, conditional logistic regression analysis assessed associations of the biomarkers with events, and adjusted for relevant covariates to calculate odds ratios (OR) according to one interquartile range (IQR) differences. Results At all time points, higher levels of suPAR were associated with increased risk of non-AIDS events (OR per one IQR 1.7 before ART-initiation, OR 2.0 after 1-year of suppressive ART and OR 2.1 pre-event). Higher levels of BDG and LBP at year one and pre-event (but not at baseline) were associated with increased risk of non-AIDS events. No associations were observed for other biomarkers. Conclusions Elevated levels of suPAR were strongly, consistently and independently predictive of non-AIDS events at every measured time point. Interventions that target the suPAR pathway should be investigated to explore its role in the pathogenesis of non-AIDS-related outcomes in HIV-infection.
Personal Documents Identification System Development Using Neural Network

The problem of analyzing and classification of digital images, that contain the documents, is very common for finance and economic spheres, for example, for bank and government agencies. Analyzing the documents differs from photos classification since the documents very often have low contrast and are similar to each other. For the personal documents classification, the structure of the convolutional neural network has been designed and developed. To achieve high accuracy of the recognition, that is very important, working with documents, the optimal parameters for the designed network were determined experimentally. The problems, that occur using such system, are mentioned. A method, used to avoid overfitting of the network, is described. The experimental network has been approved by practical experiments, their results are presented.
Dog Breed Identification Using Deep Learning

The current paper presents a fine-grained image recognition problem, one of multi-class classification, namely determining the breed of a dog in a given image. The presented system employs innovative methods in deep learning, including convolutional neural networks. Two different networks are trained and evaluated on the Stanford Dogs dataset. The usage/evaluation of convolutional neural networks is presented through a software system. It contains a central server and a mobile client, which includes components and libraries for evaluating on a neural network in both online and offline environments.
Packet Subscriptions for Programmable ASICs

In this paper, we explore how programmable data planes can provide a higher-level of service to user applications via a new abstraction called packet subscriptions. Packet subscriptions generalize forwarding rules, and can be used to express both traditional routing and more esoteric, content-based approaches. We describe a compiler for packet subscriptions that uses a novel BDD-based algorithm to efficiently translate predicates into P4 tables that can support O(100K) expressions. Using our compiler, we've built a proof-of-concept pub/sub financial application for splitting market feeds (e.g., Nasdaq's ITCH protocol) with line-rate message processing, using the full switch bandwidth of 6.5Tbps.
Improving hospital food menu quality: an experimental approach.

PURPOSE The purpose of this paper is to examine whether the negative impressions consumers hold toward institutional food can be remedied by subtle changes in menu descriptions. While an expectancy-disconfirmation explanation would suggest this, a negativity bias explanation would suggest otherwise. DESIGN/METHODOLOGY/APPROACH The authors test the research question using an experimental 2 × 2 full factorial design, with data collected from 100 university students. FINDINGS The results show that when hospital menus are made somewhat fancier in their description, consumers evaluate the food as more attractive and menu variation to be greater. This implies that the judgments are more likely to be based on an expectancy-disconfirmation process that on being subject to negativity bias. RESEARCH LIMITATIONS/IMPLICATIONS The authors study perceived attractiveness and menu variation, but future research should include taste perceptions and consumption volume. PRACTICAL IMPLICATIONS Institution managers could improve consumer perceptions of how attractive the food being served is, and the perceived variation in their menus, by subtly changing the course descriptions to become fancier. However, as such, a strategy based on an expectancy-disconfirmation process, institution managers should beware not to sweeten the pill too much, i.e., making promises they cannot keep may backfire if one makes the menus too fancy compared to what is delivered. ORIGINALITY/VALUE The authors extend current knowledge on menu label effects by addressing them for food suppliers, which are inherently associated with low food quality. The authors also show that when managers apply such strategies, the effect is due to a disconfirmation process rather than a negativity bias.