Miracle-dz/UniFAR-FTUs huggingface dataset:
Information Labelling of Medical Forum Posts by Non-Clinical Text Information Retrieval

With the advent of web 2.0, modern societies produce a vast amount of data, and merely keeping up with storage and transmission is difficult; analyzing it to extract useful information has become further challenging. All the historical research in healthcare data processing is more concentrated on formal clinical data. There lies a lot of valuable yet idle lying data in the non-clinical information as well. The proposed study combines the state of the art methods within distributed computing, text retrieval, clustering methods, and finally, using a classification method to a computationally efficient system that can clarify cancer patient trajectories based on non-clinical and freely available online forum posts. The motivation is that informed patients, caretakers, and relatives often lead to better overall treatment outcomes due to enhanced possibilities of proper disease management. The resulting software prototype is fully functional and built to serve as a test bench for various text information retrieval and visualization methods. Via the prototype, we demonstrate a computationally efficient clustering of posts into cancer-types and subsequent within-cluster classification into trajectory related classes. The system also provides an interactive graphical user interface allowing end-users to mine and oversee the valuable information.
Scaling up data analytics in Python using multiple FPGAs

Big data applications are becoming more commonplace due to an abundance of digital data and increasingly powerful hardware. One of these classes of hardware devices are FPGAs, which are being used today in various ways such as data centers and embedded systems. High performance, power efficiency, and reprogrammability are the primary reasons behind their wide use. Another trend over the previous years has been to use distributed data processing frameworks such as Apache Spark to improve the performance of big data applications. Traditionally, such frameworks are deployed on commodity hardware to save costs. This approach is fairly popular, with organizations often having on-premise compute clusters or using a cloud provider to access a managed cluster. This project attempts to combine the above-mentioned worlds - FPGAs and distributed data processing. We have designed an architecture that allows us to use FP-GAs as end-devices in a compute cluster to perform the actual computation instead of CPUs. This architecture is designed by composing together several open source technologies and allows us to interact with an FPGA cluster using Python. Using a high-level programming language such as Python makes this system easy to use for software developers and data scientists, and also abstracts away the internal communication within the cluster. We have built prototypes based on this architecture for 3 hardware platforms (FPGA families) and 3 specific applications to demonstrate general applicability. We have observed noticeable performance gains in these applications by scaling up the FPGA cluster.
DocuBot : Generating ﬁnancial reports using natural language interactions

The ﬁnancial services industry perpetually processes an over-whelming amount of complex data. Digital reports are often created based on tedious manual analysis as well as visualization of the underlying trends and characteristics of data. Often, the accruing costs of human computation errors in creating these reports are very high. We present DocuBot , a novel AI-powered virtual assistant for creating and modifying content in digital documents by modeling natural language interactions as “skills” and using them to transform underlying data. DocuBot has the ability to agglomerate saved skills for reuse, enabling humans to automatically generate recurrent reports. DocuBot also has the capability to continuously learn domain-speciﬁc and user-speciﬁc vocabulary by inter-acting with the user. We present evidence that DocuBot adds value to the ﬁnancial industry and demonstrate its impact with experiments involving real and simulated users tasked with creating PowerPoint presentations.
Dietary Supplementation for Para-Athletes: A Systematic Review

The use of dietary supplements is high among athletes and non-athletes alike, as well as able-bodied individuals and those with impairments. However, evidence is lacking in the use of dietary supplements for sport performance in a para-athlete population (e.g., those training for the Paralympics or similar competition). Our objective was to examine the literature regarding evidence for various sport supplements in a para-athlete population. A comprehensive literature search was conducted using PubMed, SPORTDiscus, MedLine, and Rehabilitation and Sports Medicine Source. Fifteen studies met our inclusion criteria and were included in our review. Seven varieties of supplements were investigated in the studies reviewed, including caffeine, creatine, buffering agents, fish oil, leucine, and vitamin D. The evidence for each of these supplements remains inconclusive, with varying results between studies. Limitations of research in this area include the heterogeneity of the subjects within the population regarding functionality and impairment. Very few studies included individuals with impairments other than spinal cord injury. Overall, more research is needed to strengthen the evidence for or against supplement use in para-athletes. Future research is also recommended on performance in para-athlete populations with classifiable impairments other than spinal cord injuries.
Variable Selection with Rigorous Uncertainty Quantiﬁcation using Deep Bayesian Neural Networks

C Proofs for Bernstein-von Mises (BvM) Phenomenon 6 C.1 Background: Semi-parametric BvM Theorem for Smooth Functionals . 6 C.2 Preliminary I: Notations and Basic Setup . 7 C.3 Preliminary II: Proof Strategy and Preliminary Theorems . 8 C.4 Proof for Theorem 2 . 9 C.5 Proof for Theorem 3 . 11
Microbe-Mediated Biosynthesis of Nanoparticles: Applications and Future Prospects

Nanotechnology is the science of nano-sized particles/structures (~100 nm) having a high surface-to-volume ratio that can modulate the physical, chemical and biological properties of the chemical compositions. In last few decades, nanoscience has attracted the attention of the scientific community worldwide due to its potential uses in the pharmacy, medical diagnostics and disease treatment, energy, electronics, agriculture, chemical and space industries. The properties of nanoparticles (NPs) are size and shape dependent. These characteristic features of nanoparticles can be explored for various other applications such as computer transistors, chemical sensors, electrometers, memory schemes, reusable catalysts, biosensing, antimicrobial activity, nanocomposites, medical imaging, tumor detection and drug delivery. Therefore, synthesizing nanoparticles of desired size, structure, monodispersity and morphology is crucial for the aforementioned applications. Recent advancements in nanotechnology aim at the synthesis of nanoparticles/materials using reliable, innoxious and novel ecofriendly techniques. In contrast to the traditional methods, the biosynthesis of nanoparticles of a desired nature and structure using the microbial machinery is not only quicker and safer but more environmentally friendly. Various microbes, including bacteria, actinobacteria, fungi, yeast, microalgae and viruses, have recently been explored for the synthesis of metal, metal oxide and other important NPs through intracellular and extracellular processes. Some bacteria and microalgae possess specific potential to fabricate distinctive nanomaterials such as exopolysaccharides, nanocellulose, nanoplates and nanowires. Moreover, their ability to synthesize nanoparticles can be enhanced using genetic engineering approaches. Thus, the use of microorganisms for synthesis of nanoparticles is unique and has a promising future. The present review provides explicit information on different strategies for the synthesis of nanoparticles using microbial cells; their applications in bioremediation, agriculture, medicine and diagnostics; and their future prospects.
Preliminary study on the effects of movement velocity training of the upper limbs on gait ability in older adults: a nonrandomized controlled trial in Aging

Purpose: Movement velocity of the limbs or trunk plays an important determinant of gait speed in older adults. Movement velocity-focused training of the lower limbs or trunk has recently been shown to be an effective intervention to improve gait ability. Because movement velocities of various body regions are signi ﬁ cantly correlated, movement velocity training of the upper limbs may also be effective for improving gait speed. Therefore, the purpose of this study was to investigate whether movement velocity training of the upper limbs in a seated position is effective for improving gait ability. Patients and methods: This study was a nonrandomized controlled trial. The participants were older adults residing in geriatric health service facilities. They were assigned to the movement velocity training of the upper limbs group (n=26) or control group (n=15). The participants in the training group performed exercises (three times per week for 10 weeks) to move the upper limbs as quickly as possible. The outcomes were gait speed, movement velocity, and quadriceps strength. These measurements were performed preintervention and 4, 8, and 10 weeks after intervention. Results: A signi ﬁ cant time – group interaction was found for maximum gait speed and movement velocity of the upper limbs. Bonferroni post-hoc test showed signi ﬁ cant improvement in gait speed between preintervention and 10 weeks after intervention in the training group. The movement velocity of the upper limbs was signi ﬁ cantly improved between preintervention and 4, 8, and 10 weeks after intervention. Conclusion: Movement velocity training of the upper limbs showed signi ﬁ cant and clinically relevant improvements in maximum gait speed at 10 weeks after intervention. This training is a potentially useful intervention and can be safely performed.
Methods for Automatic Sentiment Detection

Semantic analysis has great potential applications in various fields of science and the national economy . Much of the information in the world is not structured, so there is the problem of processing and extracting useful data. Natural language processing is a very complex process. On November 23, 2017, as part of the 2017 Runet Prize award ceremony, the HOT-LIST 2018 was presented, that identified the main digital trends of 2018 and presented a list of trendsetters in 10 technology, innovation and business areas.There were included 10 leading companies in 10 areas and the first area is AI technologies. AI technology has become a key technology trend in 2018, and the volume of global investment in these technologies and products based on them in excess of $ 1 billion. [25] More than 180 private companies working on projects in the field of AI technologies have been purchased during the period 2011-2018. According to IDC Customer Insights & Analysis. [25] During the period 2011-2018 it was purchased more than 180 private companies working on projects of AI technologies. According to forecasts of Frost & Sullivan, by 2022 artificial intelligence market will grow to $ 10 billion using machine learning technologies and natu-ral language recognition in advertising, retail, finance and health. [25]
Analyzing the Applicability of Kubernetes for the Deployment of an IoT Publish/Subscribe System

With the increased availability and affordability of miniature computing devices, such as sensors, the era of Internet of Things (IoT) has arrived. Meanwhile, the developments of the cellular mobile technologies and emerge of 5G accelerated the adoption of IoT scenarios within mobile networks. Rapid growth in the number of IoT devices has resulted in greater volumes of data being generated and exchanged between various entities. This highlights the need for efficient data transmission between data producers and consumers. To this goal, Nokia Bell Labs has developed a distributed data dissemination system based on Publish/Subscribe messaging protocol and according to the micro-service architecture. Currently, the system is deployed on virtual machines (VM), where the corresponding compiled Java file (Jar file) of each micro-service is running on a separate VM. This approach for deployment might cause a problem, named ’Dependency Hell’ for the continuous integration and continuous development (CI/CD) workflow. Moreover, one of the system’s limitations is that no service discovery is present. Instead, services are connected through hard-coded IP addresses, defined on their corresponding configuration files. Hence, IPs must be changed manually per deployment according to the infrastructure. To solve the present limitations, this thesis proposes a deployment based on Kubernetes. Kubernetes is a container orchestration framework that introduces several benefits for the deployment process including automation, management, monitoring and scaling of multi-container packaged applications, such as the current Pub/Sub IoT system. A proof-of-concept solution for the deployment of the system using Kubernetes is presented and its implications on the system’s efficiency and scalability are discussed.
A National Programmable Infrastructure to Experiment with Next-Generation Networks

We have set up the first multi-domain P4programmable network in the Netherlands, consisting of six different sites interconnected by SURF’s optical network. The purpose of the network is to experiment with novel inter-domain network functions and emerging internet architectures, in particular to improve the transparency, accountability, and controllability of the Internet and future networks. We provide an overview of the testbed’s set-up, examples of how we have used it, and our lessons learned.
Improving the efficiency of KDD cup 1999 data by using Make Density Based Clusterer algorithm in Intrusion Detection system by removing the count attribute

Abstract—An Intrusion Detection System screens the network traffic and looks for dubious or untrustworthy movement and known intimidation on the network, and sends up the caution if it comes across such an item. Intrusion detection (ID) as a radiance remains censorial in digital security. To comprehend intrusion detection, initially realize what intrusion is? According to Heady et al., it is defined as “any action that attempts to negotiate the integrity, privacy or accessibility of a resource "for example gaining illegal access, attacking and rendering a system out of service, etc. With the end goal of this article, here it describes intrusion as any unbowed framework or merriment on (at least one) PC or set of connections of computers. This is a delineation of a legal client of a framework attempting to strengthen his advantages to acquire more prominent access to the framework that he presently depended, or a similar client attempting to interface with an unapproved remote port of a server. These are the interruptions that can incite from the rest of the world, a wronged ex-worker who was terminated recently, from your devoted staff. In this section, the average data is revealed as incursion when the case is a false positive. Here they are concentrating on this dilemma with delineation and offering one answer for a similar issue. The KDD CUP 1999 data set is used. In the result of this analysis, it can be seen that if a class has a higher number of counts then this class is opined as an anomaly class. But it will be counted as an anomaly if the right individual is passing the threshold value. An elucidation is proposed to detect the true person and to get rid of fake positives.
The role of group status and history of the conflict on intergroup discrimination strategies

According to Tajfel Social Identity and Intergroup Relations Theory, perception of higher status in intergroup conflict is claimed to elicited both stronger Statements of ingroup identification and stronger ingroup favouritism than perceptions of lower status. It was hipothesized that the degree to which both higher and lower status conflicting groups assert ingroup identity and display ingroup favouritism also depends on the history of the conflict. This variable reflects the perceived ratio of gains and losses in the past, as well as the present expectations of positive or negative change of the status rela­ tions. A linear history of conflict (without a significant change on groups’ status position) is expected to induce weaker comparisons between groups as well as lower expectations of change, thus eliciting lower ingroup favouritism and ingroup identification assertions in both groups. Non-linear history of the conflict (with significant reversal of group’s status position in the mid-run) is expected, on the contrary, to enhance comparability between groups and expectation of change, thus eliciting stronger group categorizations, stronger assertations of group identification and higher ingroup favouritism. An experimental study was design to study these predictions. The experiment was a two factor design experiment (high vs. low status; linear vs. non-linear history of the conflict) where college students participated in a simulated conflict between two induced groups. Perceived legitimacy and stability of the status relations were controlled and group identification and intergroup differentation strategies were observed. Predictions about the importance of the conflict to moderate ingroup identification assertations and ingroup bias were confirmed.
The relationship between CHRNA5/A3/B4 gene cluster polymorphisms and lung cancer risk

Supplemental Digital Content is available in the text Abstract Background: Genetic polymorphisms in the 15q25 region have been associated with the risk of lung cancer (LC). However, studies have yielded conflicting results. Methods: Searches were conducted in databases, including PubMed, EMbase, Web of Science, CNKI, and Wanfang, for case-control studies up to August 1, 2019. After retrieving eligible studies and data extraction, we calculated pooled odds ratios with 95% confidence intervals. In the meta-analysis, we included 32 publications with a total of 52,795 patients with LC and 97,493 control cases to evaluate the polymorphisms in the CHRNA5/A3/B4 gene cluster in the 15q25 region. Results: Data of the meta-analysis showed a significantly increased risk of LC in the presence of genetic polymorphisms (rs1051730, rs16969968, rs8034191). In the smoking subgroup, the CHRNA3 rs1051730 polymorphism was found to contribute to LC risk using following 5 models: the allelic model, the homozygous model, the heterozygous model, the dominant model, and the recessive model. Thus, the rs1051730 polymorphism may modify LC susceptibility under the condition of smoking. Stratification studies for CHRNA5-rs8034191 showed that Caucasian groups with the wild-type genotype (C/C) may be susceptible to LC in all 5 models. No significant relationship between CHRNA3 rs6495309 or rs3743073 and LC susceptibility was found. However, Asians with the rs3743037 B-allele showed an obviously higher risk of LC susceptibility than the Caucasian population, observed via allelic, heterozygous, and dominant models. Conclusions: The 3 polymorphisms of rs1051730, rs16969968 and rs8034191 in the CHRNA5/A3/B4 gene cluster in the 15q25 region were associated with LC risk, which might be influenced by ethnicity and smoking status.
AdCube: WebVR Ad Fraud and Practical Confinement of Third-Party Ads

Web technology has evolved to offer 360-degree immersive browsing experiences. This new technology, called WebVR, enables virtual reality by rendering a three-dimensional world on an HTML canvas. Unfortunately, there exists no browser-supported way of sharing this canvas between different parties. Assuming an abusive ad service provider who exploits this absence, we present four new ad fraud attack methods. Our user study demonstrates that the success rates of our attacks range from 88.23% to 100%, conﬁrming their effectiveness. To mitigate the presented threats, we propose AdCube, which allows publishers to specify the behaviors of third-party ad code and enforce this speciﬁcation. We show that AdCube is able to block the presented threats with a small page loading latency of 236 msec and a negligible frame-per-second (FPS) drop for nine WebVR ofﬁcial demo sites.
The Association Between Social Adjustment and Work-Family Conflict and Organizational Citizenship Behavior in Clinical Staff of the Khatam-al-Anbia Hospital in Salmas in 2019

Background: Clinical staff are particularly prone to work-family conflict due to their professional nature and require interdisciplinary collaboration and social adjustment to increase their job motivation and achieving success in teamwork. Objectives: The current study aimed to investigate the association between social adjustment and workfamily conflict as well as organizational citizenship behavior (OCB) in hospital clinical staff. Methods: This descriptive correlational study was conducted on all (n=115) nurses and midwives working in Khatam-al-Anbia Hospital in Salmas in 2019. Data were collected using 4 questionnaires of (1) demographic information, (2) social adjustment, (3) work-family conflict, and (4) organizational citizenship behavior. Data were analyzed using SPSS version 16 by Pearson correlation, independent t-test, and one-way ANOVA. Results: Most of the employees were female (84.5%), married (67.3%), had a bachelor's degree (90%) and had work experience of 8.77±7.18 years. There was a significant indirect association between social adjustment and work-family conflict (r=-0>.21, p=0.05). There was no significant association between social adjustment and OCB (r=-0.06, p=0.65). Conclusion: This study demonstrated an indirect association between social adjustment and work-family conflict in hospital clinical staff. Therefore, nursing managers can improve the quality of patient care by developing employees' adaptation skills programs intended to reduce their work-family conflict.
Improved Confidence Bounds for the Linear Logistic Model and Applications to Bandits

We propose improved fixed-design confidence bounds for the linear logistic model. Our bounds significantly improve upon the state-of-the-art bound by Li et al. (2017) via recent developments of the self-concordant analysis of the logistic loss (Faury et al., 2020). Specifically, our confidence bound avoids a direct dependence on $1/\kappa$, where $\kappa$ is the minimal variance over all arms' reward distributions. In general, $1/\kappa$ scales exponentially with the norm of the unknown linear parameter $\theta^*$. Instead of relying on this worst-case quantity, our confidence bound for the reward of any given arm depends directly on the variance of that arm's reward distribution. We present two applications of our novel bounds to pure exploration and regret minimization logistic bandits improving upon state-of-the-art performance guarantees. For pure exploration, we also provide a lower bound highlighting a dependence on $1/\kappa$ for a family of instances.