Miracle-dz/UniFAR-FTUs huggingface dataset:
Twitter Usage Across Industry: A Spatiotemporal Analysis

High resolution social media data presents an opportunity to better understand people's behavioural patterns and sentiment. Whilst significant work has been conducted in various targeted social contexts, very little is understood about differentiated behaviour in different industrial sectors. In this paper, we present results on how social media usage and general sentiment vary across the geographic and industry sector landscape. Unlike existing studies, we use a novel geocomputational approach to link location specific Twitter data with business sectors by leveraging the UK Standard Industrial Classification Code (SIC Code). Our baseline results for the Greater London area identifies Construction, Real Estate, Transport and Financial Services industries consistently have stronger Twitter footprints. We go on to apply natural language processing (NLP) techniques to understand the prevailing sentiment within each business sector and discuss how the evidence can contribute towards de-biasing Twitter data. We believe this research will prove a valuable surveillance tool for policy makers and service providers to monitor ongoing sentiment in different industry sectors, perceive the impact of new policies and can be used as a low cost alternative to survey methods in organisational studies.
Management Considerations in Infective Endocarditis: A Review

Importance Infective endocarditis occurs in approximately 15 of 100 000 people in the United States and has increased in incidence. Clinicians must make treatment decisions with respect to prophylaxis, surgical management, specific antibiotics, and the length of treatment in the setting of emerging, sometimes inconclusive clinical research findings. Observations Community–associated infective endocarditis remains the predominant form of the disease; however, health care accounts for one-third of cases in high-income countries. As medical interventions are increasingly performed on older patients, the disease incidence from cardiac implanted electronic devices is also increasing. In addition, younger patients involved with intravenous drug use has increased in the past decade and with it the proportion of US hospitalization has increased to more than 10%. These epidemiological factors have led to Staphylococcus aureus being the most common cause in high-income countries, accounting for up to 40% of cases. The mainstays of diagnosis are still echocardiography and blood cultures. Adjunctive imaging such as cardiac computed tomographic and nuclear imaging can improve the sensitivity for diagnosis when echocardiography is not conclusive. Serological studies, histopathology, and polymerase chain reaction assays have distinct roles in the diagnosis of infective endocarditis when blood culture have tested negative with the highest yield obtained from serological studies. Increasing antibiotic resistance, particularly to S aureus, has led to a need for different antibiotic treatment options such as newer antibiotics and combination therapy regimens. Surgery can confer a survival benefit to patients with major complications; however, the decision to pursue surgery must balance the risks and benefits of operations in these frequently high-risk patients. Conclusions and Relevance The epidemiology and management of infective endocarditis are continually changing. Guidelines provide specific recommendations about management; however, careful attention to individual patient characteristics, pathogen, and risk of sequela must be considered when making therapeutic decisions.
Design and shape optimization of PolyJet bellows actuators

With regard to future robotic systems, the combination of Additive Manufacturing (AM) and pneumatic actuation yields multiple opportunities. Bellows actuators are exceptionally suitable for AM as the required geometrical complexity can easily be obtained and their functionality is not affected by rough surfaces and small dimensional accuracy. In this paper, multi-material PolyJet printable linear bellows actuators are presented. A design strategy based on finite elements analysis and numerical shape optimization is proposed and validated by experimental testing under quasi-static and repeated loading conditions. The presented results are useful for researchers and engineers considering the application of AM soft material bellows actuators for robots and other dynamic systems.
Hybrid solution for scalable research articles recommendation

In text mining, clustering and classification are the most important techniques to extract information from textual data. These are techniques which allow us to identify similar groups of textual documents or build classification models based on some similarity. This paper presents a hybrid model based on clustering and classification techniques to recommend research articles to researchers. Since the process of literature review is time consuming, our aim is to automate this process and recommend the most relevant research articles based on users' research field preferences.
LEAP: A latency control protocol for multi-path data delivery with pre-defined QoS guarantees

Wireless Gigabit networks based on the 5G New Radio and WiGig standards are expected to enable new throughput-intensive applications such as Virtual/Augmented Reality, high-resolution video chats, and remote control of unmanned terrestrial and aerial vehicles. However, the interactive nature of such applications implies the need for reliable and timely delivery of data flows, i.e., latency-constrained data throughput will become the key determinant of application Quality of Service (QoS). In this context, we propose a new transport-layer protocol called Latency-controlled End-to-End Aggregation Protocol (LEAP) that enables an explicit enforcement of pre-defined QoS targets for an application data flow by jointly controlling its delivery throughput, latency and reliability. According to the LEAP protocol, the sender splits a QoS-constraint data flow into multiple subflows that are delivered in parallel over independent radio access technologies. Stochastic forecasting of the latency-constrained capacity of each path is used for throughput-maximizing congestion control, while cross-path payload encoding is used for controlling the minimum probability of timely data delivery within a pre-defined deadline. When compared with leading state-of-the-art transport protocols in live networks, LEAP exhibits a superior capacity to reliably provide stable throughput and probabilistic delay guarantees, both in wired and wireless scenarios.
Preliminary experience with a novel Multisensor algorithm for heart failure monitoring: The HeartLogic index

We report the first case of a patient in whom an implantable cardioverter‐defibrillator (ICD) endowed with HeartLogic, a novel algorithm for heart failure (HF) monitoring, was implanted in clinical practice. The good temporal association between HeartLogic index threshold crossings and HF hospitalizations confirms the high sensitivity in detecting gradual worsening of HF.
Enhancing the Matrix Addressing of Flexible Sensory Arrays by a Highly Nonlinear Threshold Switch

The increasing need for smart systems in healthcare, wearable, and soft robotics is creating demand for low‐power sensory circuits that can detect pressure, temperature, strain, and other local variables. Among the most critical requirements, the matrix circuitry to address the individual sensor device must be sensitive, immune to disturbances, and flexible within a high‐density sensory array. Here, a strategy is reported to enhance the matrix addressing of a fully integrated flexible sensory array with an improvement of 108 fold in the maximum readout value of impedance by a bidirectional threshold switch. The threshold switch shows high flexibility (bendable to a radius of about 1 mm) and a high nonlinearity of ≈1010 by using a nanocontact structure strategy, which is revealed and validated by molecular dynamics simulations and experiments at variable mechanical stress. Such a flexible electronic switch enables a new generation of large‐scale flexible and stretchable electronic and optoelectronic systems.
Network attack traffic detection using seed based sequential grouping model

Along with the development of high-speed Internet and smart devices, various attack methods were emerged, and attack traffic has also changed into various and complex forms. In order to provide reliable services and efficient management of network resources, it is essential to detect and analyze the attack traffic. While various application and attack traffic detection or classification methods have been studied, but signature-based methods are still mainstream of the most. In this paper, we propose the seed based sequential grouping model for attack traffic detection. Model consists of two main indices, which are similarity and connectivity index. In addition to model, we define the set of optimal thresholds of each index by using our balancing algorithm and define it as Guideline. By applying the proposed model to the actual attack traffic, we demonstrate that the model has high detection accuracy and completeness.
Color Transfer Pulse-Coupled Neural Networks for Underwater Robotic Visual Systems

With rapid developments in cloud computing, artificial intelligence, and robotic systems, ever more complex tasks, such as space and ocean exploration, are being implemented by intelligent robots. Here, we propose an underwater image enhancement scheme for robotic visual systems. The proposed algorithm and its implementation enhances and outputs an image captured by an underwater robot in real time. In this scheme, pulse-coupled neural network (PCNN)-based image enhancement and color transfer algorithms are combined to enhance the underwater image. To avoid color imbalance in the underwater image and enhance details while suppressing noise, color correction is first carried out on the underwater image before converting it into the hue–saturation–intensity domain and enhancing it by PCNN. The enhanced result improves the color and contrast of the source image and enhances the details and edges of darker regions. Experiments are performed on real world data to demonstrate the effectiveness of the proposed scheme.
Face Recognition based on Higher-Order Statistics

Identification of people by face is the most effective non-intrusive method in biometry. However, it is a great challenge for researchers because faces are complex and multidimensional. In addition, high level of difficulty is added by changes in illumination and/or pose. In this work, a face recognition method based on Higher-Order Statistics (HOS) is proposed. HOS has the important signal processing properties of: (i) handling colored Gaussian measurement noise more efficiently, (ii) extracting information due to deviations from Gaussianity, and (iii) detecting and characterizing nonlinear properties in signals. In this work, features based on HOS are used to build compact signature of faces. It is considered a Public Security scenario where the goal is to detect and identify individuals with criminal links, previously registered in a database. To select the most discriminant HOS-based features, the Fisher's Discriminant Ratio (FDR) was used and the linear correlation was applied to eliminate redundancy. Three classifiers (the Bayesian, Support Vector Machines (SVM) and the K-nearest neighbor (KNN)) were employed for final classification and their performances were compared. For performance evaluation, images from ORL dataset were used. The results showed detection and classification rates over 70% and indicates the potential of HOS on building face signatures.
Segnet-based gland segmentation from colon cancer histology images

The morphology of glands is the main basis of colon cancer diagnosis and accurate segmentation of glands from histology images is a prerequisite for correct clinical diagnosis. A gland segmentation method based on Segnet is proposed in this paper. First, the Warwick-QU dataset is used and augmented for training Segnet. Second, the network parameters are optimized according to the training results. Finally, the trained Segnet is used to perform segmentation test on both Parts A and B of Warwick-QU. The results show that the presented method achieves segmentation accuracy of 0.882 on Part A and 0.8636 on Part B, and shape similarity 106.6471 on Part A and 102.5729 on Part B, respectively. Compared with the existing methods with respect to the same dataset, our method reaches the highest accuracy on whole.
Azithromycin Inhibits Biofilm Formation by Staphylococcus xylosus and Affects Histidine Biosynthesis Pathway

Staphylococcus xylosus, a coagulase-negative, non-pathogenic bacterium, responsible for opportunistic infections in humans and bovine mastitis, has the ability to form biofilms, which are responsible for persistent infections and antibiotic resistance. In our study, azithromycin significantly inhibited biofilm formation by altering protein expression. Of the 1764 proteins measured by the isobaric Tag for Relative and Absolute Quantification (iTRAQ) technique, only 148 proteins showed significantly different expression between the azithromycin-treated and untreated cells. Most ribosomal proteins were markedly up-regulated, and the expression of the proteins involved in histidine biosynthesis, which, in turn, influence biofilm formation, was down-regulated, particularly imidazole glycerophosphate dehydratase (IGPD). Previously, we had observed that IGPD plays an important role in biofilm formation by S. xylosus. Therefore, hisB expression was studied by real-time PCR, and the interactions between azithromycin and IGPD were predicted by molecular docking analysis. hisB was found to be significantly down-regulated, and six bond interactions were observed between azithromycin and IGPD. Many active atoms of azithromycin did not interact with the biologically active site of IGPD. Surface plasmon resonance analysis used to further study the relationship between IGPD and azithromycin showed minimum interaction between them. Histidine content in the azithromycin-treated and untreated groups was determined. We noted a slight difference, which was not consistent with the expression of the proteins involved in histidine biosynthesis. Therefore, histidine degradation into glutamate was also studied, and we found that all proteins were down-regulated. This could be the reason why histidine content showed little change between the treated and untreated groups. In summary, we found that azithromycin is a potential inhibitor of S. xylosus biofilm formation, and the underlying mechanism was preliminarily elucidated in this study.
Descriptive data in different paper-based cognitive assessments in elderly from the community Stratification by age and education

ABSTRACT Cognitive aging is dynamic and heterogeneous in elderly, thus adequate tools such as paper-based tests are relevant to describe the cognitive profile of this population. Objective: To describe different paper-based cognitive assessments tests in elderly people stratified by age and education. Methods: A cross-sectional study of 667 elderly (≥60 years) living in the community was conducted. Sociodemographic information was collected. Global cognition was assessed by the Addenbrooke's Cognitive Examination-Revised (ACE-R), Mini Addenbrooke’s Cognitive Examination (M-ACE) and Mini-Mental State Examination (MMSE). The data were analyzed using descriptive statistics, the t-test and Pearson’s Correlation Coefficient. Results: The findings showed a predominance of women (53.8%), mean age of 71.3 (±7.7) years and 3.6 (±3.5) years of education. The best global cognitive performance and cognitive domain assessment scores were found in the group with higher formal educational level. Each year of education was associated with an increase of up to 10% in scores on the M-ACE and MMSE and up to 11% in ACE-R scores. The mean values of the scores varied according to age, where the 60-69 years group had better scores than other age groups. The correlation matrix between the cognitive tests showed that near perfect correlations (r=1) were frequent in the subgroup with higher education. Conclusion: Younger elderly and those with higher educational level had greater global and domain scores. This study describes the scores of elderly for different strata of education and age. In practice, it is important to choose the most suitable screening instrument, considering the characteristics of the elderly.
Security analysis and psychological study of authentication methods with PIN codes

Touch screens have become ubiquitous in the past few years, like for instance in smartphones and tablets. These devices are often the entry door to numerous information systems, hence having a secure and practical authentication mechanism is crucial. In this paper, we examine the complexity of different authentication methods specifically designed for such devices. We study the widely spread technology to authenticate a user using a Personal Identifier Number code (PIN code). Entering the code is a critical moment where there are several possibilities for an attacker to discover the secret. We consider the three attack models: a Bruteforce Attack (BA) model, a Smudge Attack (SA) model, and an Observation Attack (OA) model where the attacker sees the user logging in on his device. The aim of the intruder is to learn the secret code. Our goal is to propose alternative methods to enter a PIN code. We compare such different methods in terms of security. Some methods require more intentional resources than other, this is why we performed a psychological study on the different methods to evaluate the users' perception of the different methods and their usage.
OpenFlow-based virtual TAP using open vSwitch and DPDK

Currently, server (host) virtualization technology that brings effective use of server resources to a data center is promising as cloud services are being prevalent with increasing traffic volumes and requirements for higher service quality. Proposed network TAP, named vTAP (Virtual Test Access Port), overcomes the problem that existing hardware TAP devices cannot be utilized for virtual network links to monitor traffic among virtual machines (VMs) at a packet level. vTAP can be implemented by a virtual switch that gives network connectivity to VMs by switching packets over the virtual network links. The port mirroring feature of a virtual switch can be a naive solution to provide packet level monitoring among VMs. However, using the feature in an environment that needs to treat large volume of network traffic with low delay such as NFV (Network Function Virtualization) incurs performance degradation in packet switching capability of the switch and error-prone manual configurations. This paper provides design and implementation approaches to vTAP using Open vSwitch with DPDK (Data Plane Development Kit) and an OpenFlow SDN (Software-Defined Networking) controller to overcome the problems. DPDK can accelerate overall packet processing operations needed in vTAP, and OpenFlow controller can provide a centralized and flexible way to apply and manage TAP policies in an SDN network. This paper also provides performance comparisons of the proposed vTAP and the naive method, port mirroring.
Secure mobile crowdsourcing for WLAN indoor localization

At present, the significantly-developed indoor Location-based services (LBSs) have raised the serious problem of the disclosure of users' location privacy. The existing studies working on indoor localization mainly focus on the localization accuracy and energy efficiency, which normally ignore the security aspect during the localization process. The fingerprint calibration based user-side and trilateration based network-side localization techniques have been widely-used by the state-of-the-art indoor localization systems, whereas neither of them is able to well protect the users' location privacy. In response to this compelling problem, this paper proposes a new secure mobile crowdsourcing approach for WLAN indoor localization. The experimental results demonstrate that the proposed approach is capable of achieving satisfactory localization accuracy as well as avoiding the leakage of users' location privacy in the anonymous WLAN indoor environment.
Simultaneous diagnosis of multiple parametric faults based on differential evolution algorithm

This paper addresses analysis and design of multiple fault diagnosis for a class of Lipschitz nonlinear system. In order to automatically estimate multi-fault parameters efficiently, a new method of multi-fault diagnosis based on the differential evolution algorithm (DE) is proposed. Finally, a series of experiments validate the feasibility and effectiveness of the proposed method. The simulation show the high accuracy of the proposed strategies in multiple abrupt faults diagnosis.
Improving cognitive functions of dyslexies using multi-sensory learning and EEG neurofeedback

AutoTrainBrain is a neurofeedback and multi-sensory based mobile phone software application, designed in Sabancı University laboratory with the aim of improving the cognitive functions of dyslexic children. It reads electroencephalography (EEG) signals from 14 channels of eMotiv EPOC+ and processes these signals to provide neurofeedback to child for improving the brain signals with visual and auditory cues in real time. AutoTrainBrain software has been applied to a 14-year old dyslexic child, 10 minutes per week for 9 consecutive weeks. The EEG data has been analyzed by using the following three approaches: estimation of single-channel EEG complexity levels (entropy), spectral brain connectivity between two-channels (coherence), single channel relative Alpha band power ratio. Our experimental analysis shows that the proposed brain training system offers improvements based on the measures used in the three approaches mentioned above. This suggests such training may help increase the number of active cortical neurons and improve regional brain connectivity.
Rhodiola rosea Improves Lifespan, Locomotion, and Neurodegeneration in a Drosophila melanogaster Model of Huntington's Disease

Huntington's disease (HD) is a dominant, late-onset disease characterized by choreiform movements, cognitive decline, and personality disturbance. It is caused by a polyglutamine repeat expansion in the Huntington's disease gene encoding for the Huntingtin protein (Htt) which functions as a scaffold for selective macroautophagy. Mutant Htt (mHtt) disrupts vesicle trafficking and prevents autophagosome fusion with lysosomes, thus deregulating autophagy in neuronal cells, leading to cell death. Autophagy has been described as a therapeutic target for HD, owing to the key role Htt plays in the cellular process. Rhodiola rosea, a plant extract used in traditional medicine in Europe and Asia, has been shown to attenuate aging in the fly and other model species. It has also been shown to inhibit the mTOR pathway and induce autophagy in bladder cancer cell lines. We hypothesized that R. rosea, by inducing autophagy, may improve the phenotype of a Huntington's disease model of the fly. Flies expressing HttQ93 which exhibit decreased lifespan, impaired locomotion, and increased neurodegeneration were supplemented with R. rosea extract, and assays testing lifespan, locomotion, and pseudopupil degeneration provided quantitative measures of improvement. Based on our observations, R. rosea may be further evaluated as a potential therapy for Huntington's disease.