Miracle-dz/UniFAR-FTUs huggingface dataset:
A performance based empirical study of the frequent itemset mining algorithms

Frequent itemset mining is one of the important domains in pattern mining. This deals with mining the frequent itemsets that occur in the dataset. Researches are still an ongoing process in this area. So far many algorithms have been proposed for mining frequent itemsets. Frequent itemsets are mined for framing association rules. Other than framing association rules, mining frequent itemsets leads to effective classification, clustering and predictive analysis. The commonly used algorithms are Apriori, FPGrowth and Eclat. Previously heaps of research works have been made using these three algorithms. Enhancements have also been made to improve the performance. This paper is based on the analysis of these three algorithms with four different datasets having varied number of transactions and size. Comparison is made using the parameters, time taken and the memory used by each algorithm to find the frequent patterns. The result paves way for the future research work in this field.
Intraocular bleeding in patients managed with novel oral anticoagulation and traditional anticoagulation: a network meta-analysis and systematic review

Background/aim To clarify the nature of the relationship between novel oral anticoagulants (NOACs) and traditional anticoagulation in respect to intraocular bleeding. Methods A comprehensive literature search up to October 2017 yielded 12 randomised controlled trials. Bayesian Markov chain Monte Carlo analysis was employed to investigate the relationship across multiple trials with varying NOACs. Random effects (informative priors) ORs were applied for the risk of intraocular bleeding due to various treatment measures. Mantel-Haenszel pairwise analyses were also performed. A total of 102 617 participants from 12 different randomised controlled trials. 11 746 received apixaban, 16 074 received dabigatran, 18 132 received edoxaban, 11 893 received rivaroxaban and 44 764 received warfarin. Results Edoxaban was significantly associated with a reduced risk of intraocular bleeding in comparison to warfarin (OR 0.59; 95% CI 0.34 to 0.98). All other findings were non-significant; however, apixaban was the only NOAC to trend with an increased event rate against warfarin. The Bayesian Markov chain Monte Carlo modelling indicated that edoxaban had the greatest chance of producing the lowest rate of bleeding (surface under the cumulative ranking curve 0.8642). Pooled pairwise analysis supported the network analysis results favouring edoxaban against warfarin (OR 0.59; 95% CI 0.39 to 0.90; p=0.02) as well as subgroup analysis of low-dose edoxaban versus warfarin (OR 0.43; 95% CI 0.24 to 0.78). Conclusion The analysis suggests that edoxaban may be the paramount agent in reducing intraocular bleeding rates. Given a paucity of reporting data for this rare event, future research and confirmation is strongly recommended.
Hypergraph  $p$ -Laplacian Regularization for Remotely Sensed Image Recognition

Graph-based and manifold-regularization (MR)-based semisupervised learning, including Laplacian regularization (LapR) and hypergraph LapR (HLapR), have achieved prominent performance in preserving locality and similarity information. However, it is still a great challenge to exactly explore and exploit the local structure of the data distribution. In this paper, we present an efficient and effective approximation algorithm of hypergraph <inline-formula> <tex-math notation="LaTeX">${p}$ </tex-math></inline-formula>-Laplacian and then propose hypergraph <inline-formula> <tex-math notation="LaTeX">${p}$ </tex-math></inline-formula>-LapR (HpLapR) to preserve the geometry of the probability distribution. In particular, hypergraph is a generalization of a standard graph while hypergraph <inline-formula> <tex-math notation="LaTeX">${p}$ </tex-math></inline-formula>-Laplacian is a nonlinear generalization of the standard graph Laplacian. The proposed HpLapR shows great potential to exploit the local structures. We integrate HpLapR with logistic regression for remote sensing image recognition. Experiments on UC-Merced data set demonstrate that the proposed HpLapR has superior performance compared with several popular MR methods including LapR and HLapR.
Identifying and Understanding the Gaps in Care Experienced by Adolescent and Young Adult Cancer Patients at the University of Iowa Hospitals and Clinics.

PURPOSE In an effort to counteract the differences in improvement in survival rates of adolescents and young adults (AYA), compared to other age groups with cancer, the University of Iowa Hospitals and Clinics established an AYA cancer program. This study was conducted to gather feedback from AYAs in an effort to generate actionable data for program development. METHODS The target population included patients aged 13-31 treated for malignancy in one of the following disease sites: central nervous system, leukemia, lymphoma, neuroendocrine, sarcoma, and thyroid. A series of four focus groups was held to identify and describe gaps in care and provide suggestions for program development. A convergent parallel mixed-methods design was used. Qualitative data were derived from focus group discussion and free-response survey questions, while quantitative data were derived from objective survey questions and electronic medical record data. RESULTS Across the four focus groups, 24 different AYAs participated, ranging from 8 to 19 individuals per session. Topics discussed included the following: communication, treatment experience, and overall AYA program; finances, work and school, and late effects; relationships, emotions, and spirituality; and body image, infertility, sexuality, risky behavior, and suicide. The results of the analyses corroborated what makes AYAs different from other age groups. The primary theme identified was the unique relationships of AYAs, which can be thought of along a continuum. CONCLUSIONS Information obtained from these analyses has been used to inform specific projects within the development of the AYA program to address patient-identified gaps. For AYAs, the importance of relationships along a continuum should be considered when developing an AYA program, in addition to potential policy or health service research utilization in the future.
Context aware algorithm

Context-Aware Algorithm has been developed for Human Physical Movement Activity Recognition. This Algorithm internally takes the data from the different sensor devices as an input and provides classified data as an output to the application layer. The Algorithm shall have the capability to contain different context aware algorithms. This Context-Aware Algorithm is flexible and modular enough to be ported to any platform with minimal efforts. The Algorithm has been tested and developed on TI Sensor Tag which is based on CC2650 wireless MCU. In this paper, we have made this algorithm to be able to run on Android Platform as nowadays smart-phone platforms are equipped with a diverse and powerful set of sensors. This work is significant because the Human Movement activity recognition permits us to gain useful knowledge about the habits of millions of users passively just by having them carry cell phones in their pockets. Our work has a wide range of applications enabled by activity recognition, like automatic customization of the mobile devices behavior based upon a user's activity (e.g., sending calls directly to voice-mail, if a user is running) and generating a daily activity profile to determine if a user is performing a healthy amount of exercise.
Emotion based social media text classification using optimized improved ID3 classifier

Emotion sensing is a complicated task of machine learning technology. That belongs to the Natural language processing (NLP) branch of artificial intelligence (AI). It is a broad domain of learning and analysis, the text classification and their emotional orientation discovery is also part of this domain. In this work the emotion based text classification is introduced. Thus previously developed data classification techniques are evaluated and a decision tree based text classification model is proposed. The key issues in traditional ID3 and its variants are to select optimal attribute from the training dataset for optimized learning. Also the performances of the previously developed algorithms are based on the size of training set which is another key issue. In this work the algorithm is designed such that its performance is almost independent of the size of the training data (size with minimum limit) as well as it tries to select more optimal attribute for decision tree. Therefore to improve learning ability modification on the attribute selection process is performed first. After improving the classifier's learning ability, the modified model is used with the NLP parsing tool for analyzing the text sentiments. Here the classification nature is binary classification. Additionally the comparative performance is also measured with the traditional ID3 algorithm and its variant classification algorithm. The experimental result based on parameters accuracy, error rate shows that the proposed technique outperforms as compared to its similar techniques implemented and gives more accurate sentiment orientations.
The Corpus Replication Task

In the field of Natural Language Processing (NLP), we revisit the well-known word embedding algorithm word2vec. Word embeddings identify words by vectors such that the words’ distributional similarity is captured. Unexpectedly, besides semantic similarity even relational similarity has been shown to be captured in word embeddings generated by word2vec, whence two questions arise. Firstly, which kind of relations are representable in continuous space and secondly, how are relations built. In order to tackle these questions we propose a bottom-up point of view. We call generating input text for which word2vec outputs target relations solving the Corpus Replication Task. Deeming generalizations of this approach to any set of relations possible, we expect solving of the Corpus Replication Task to provide partial answers to the questions.
Obstacle avoidance for mobile robot using RGB-D camera

Autonomous navigation of mobile robots in indoor cluttered environment is always a challenging task. This research paper represents the effectiveness of an RGB-D camera in order to achieve the task. With the help of the Kinect as a vision sensor and utilizing the open source Robot Operating System (ROS), this research aims to evaluate effectiveness of the system for Mobile robots. Research results show that within the typical range of the Kinect sensor, it performed very well with high accuracy. Experiment performed on a Raspberry Pi system shows minimum lag of the robot for avoiding obstacles, and verified the feasibility of the implemented algorithm.
CYP3A4 gene polymorphism is correlated with individual consumption of sufentanil

Pain is one of the major adverse clinical outcomes of cesarean section (CS). In the past few years, researchers and physicians have been optimizing post‐operative analgesic modalities, but the results are still undesirable for the parturient. The cytochrome P‐450 3A4 (CYP3A4) gene has been reported to contribute significantly to human liver microsomal oxidation of sufentanil and alfentanil.
A Group Key Management Protocol for Mobile Devices

We propose a Centralized Tree based Diffie-Hellman (CTDH) protocol for wireless mesh networks, which take into account the characteristics of mesh network operations, wireless routers and mobile devices. Performance analysis shows that CTDH is more efficient than the Tree-Based Group Diffie-Hellman Protocol (TGDH).
An empirical study of application layer protocols for IoT

IoT has enormous potential of growth. The application layer protocols used are key drivers of internet traffic. Appropriately chosen protocols can reduce network traffic, improve reliability. Here in this paper are discussing performance of three application layer protocols, Constrained Application Protocol (CoAP), Message Queuing Telemetry Transport (MQTT) and REST (Representational State Transport). These protocols are implemented on arm based device Raspberry Pi3 which as as Gateway. Performance is measured experimentally and compared in terms of bandwidth consumed and time taken. Two different networks are used: cellular 4G and High speed broadband connection. Results show that CoAP is best performer for small payloads and bad performer for bigger payloads.
Decentralized Implementation of Unit Commitment With Analytical Target Cascading: A Parallel Approach

This paper presents a decentralized solution algorithm for network-constrained unit commitment (NCUC) in multiregional power systems. The proposed algorithm is based on our previous work in which a local NCUC was formulated for each control entity (i.e., region) and an analytical target cascading (ATC) based distributed but partially parallelized algorithm requiring a central coordinator was presented. The primary objective of this paper is to present a decentralized approach that relaxes the need for any form of central coordinator in ATC and allows fully parallelized solutions of the local NCUCs. To achieve this objective, we formulate a bilevel optimization problem for each control entity. While the upper level solves the NCUC problem of the control entity, the lower level seeks to coordinate the control entity with its neighboring regions. The lower level is a convex optimization, which can be further replaced in the upper level problem by the Karush–Kuhn–Tucker conditions. The control entities communicate directly with each other and synchronously solve their local NCUCs. Having no need for any form of central coordinator, the proposed algorithm is potentially less vulnerable to cyber-attacks and communication failures than the distributed methods utilizing a coordinator.
Reagent Validation to Facilitate Experimental Reproducibility

Many results reported in the biomedical research literature cannot be independently reproduced, undermining the basic foundations of science. This overview is intended for researchers who are committed to improving the quality and integrity of biomedical science by raising awareness of both the sources of irreproducibility, and activities specifically targeted to address the issue. The irreproducibility of biomedical research is due to a variety of factors, known and unknown, that markedly influence experimental outcomes. Among the known factors are inadequate training or mentoring, or experimenter incompetence. These may result in flawed experimental design and execution that reflect a lack of planning, leading to an underpowering of a study, an absence of appropriate controls, selective data analysis, inappropriate statistical analysis, and/or investigator bias or fraud. Another frequently overlooked source of irreproducibility is failure to ensure that the equipment used is performing up to manufacturer specifications. Failure to validate/authenticate the experimental reagents is another source of irreproducibility. These include compounds, cell lines, antibodies, and the animal models used in a study. This overview discusses how a lack of validation of research reagents negatively impact experimental reproducibility, and provides guidelines to avoid these pitfalls. © 2018 by John Wiley & Sons, Inc.
Enhanced security for data sharing in clouds through policy and access control management

Cloud records sharing protection, presents a enhanced computing, with the traits of essential use of assets. In this computing, cloud service-carriers provide a concept of endless garage area for clients to host information. It can assist customers reduce their economic overhead of facts managements through migrating the neighborhood managements gadget into cloud servers. Recommend a secure statistics sharing scheme, which could reap secure key distribution and data sharing for dynamic group. In this assignment, existing scheme is capable of assist dynamic organizations efficaciously, while other user joins inside the organization or a consumer is revoked from the organization, the personal keys of the opposite customers do now not need to be recomputed and up to date. Moreover, scheme can gain comfortable consumer revocation; the revoked customers cannot be able to getting unique information files once they're revoked although they combine with the untrusted cloud. To avoid these hazards, Data Anti collusion is a way for removing reproduction copies of statistics, and has been extensively used in cloud garage to reduce storage space and upload bandwidth and at ease cloud information stored. The proposed convergent encryption model has been considerably followed for comfortable Anti collusion and to correctly and reliably control a large variety of convergent keys.
SVM-based Fault Diagnosis System for Materials Change Detection

A systematic procedure to develop a fault diagnosis system for materials change detection is presented. The system is used to detect the necessity for changing wear parts of production machines and to predict the remaining life time of the supervised part as a prewarning in a condition-based maintenance strategy. The system is based on support vector machine classification as a powerful signal-based diagnosis technique. Measurements were processed to select the most reliable characteristics to depend on. A related change index is presented based on a decision value of the SVM to predict the remaining life time.
Ownership Structure of Cable Networks and Competition in Local Access

In this paper, we discuss the role of cable television networks and their ownership structure in promoting competition in the local access market. First, we show that the dual ownership of a local telephone network and a cable network, compared with separate ownership, may increase or decrease incentives to invest in upgrading the cable television network. Second, we argue that separate ownership of the two networks is important to promote competition in local access.
Collective strong coupling in multimode cavity QED

We study an atom-cavity system in which the cavity has several degenerate transverse modes. Mode-resolved cavity transmission spectroscopy reveals well-resolved atom-cavity resonances for several cavity modes, a signature of collective strong coupling for the different modes. Furthermore, the experiment shows that the cavity modes are coupled via the atomic ensemble contained in the cavity. The experimental observations are supported by a detailed theoretical analysis. The work paves the way to the use of interacting degenerate modes in cavity-based quantum information processing, where qubits corresponding to different cavity modes interact via an atom shared by the two modes. Our results are also relevant to the experimental realization of quantum spin glasses with ultracold atoms.
The microRNA expression profiles in extracellular vesicles from HeLa cancer cells in response to cationic lipid- or polyethylenimine-mediated gene delivery

Abstract Intercellular communication is known to be involved in various stages of tumour development and metastasis through the secretion of extracellular vesicles (EVs) containing messengers such as microRNAs (miRNAs). Therefore, this study explored miRNA profiles in cancer cell-derived EVs after non-viral gene delivery in order to better understand the molecular information of intercellular communication in cancer cells after gene delivery. Two commonly used non-viral vectors (Lipofectamine 2000 and jet polyethylenimine) were used for the delivery of gene fluorescent protein plasmid in HeLa cancer cells. EVs were extracted and the contents of their RNA were subjected to the next-generation sequencing. In order to illustrate the common characteristics of non-viral vectors in the cancer cells, two overlapped up-regulated miRNAs (hsa-miR-143-3p and hsa-miR-193b-3p) were confirmed by real-time quantitative reverse transcriptase-polymerase chain reaction in the secreted EVs in response to both lipoplexes and polyplexes. The prediction of target genes and molecular pathways involved in these two miRNAs were determined, and the protein expressions related to the pathways of cell death and stress in HeLa cells were identified. Hsa-miR-143-3p and hsa-miR-193b-3p were found to be up-regulated by the use of different non-viral vectors and can thus serve as potential targets of non-viral cancer gene therapy.
Object of interest detection in video sequence using co-segmentation: A new era in video surveillance

In the field of video surveillance, one of the existing problems is the detection of object(s) in the video sequence. In this paper, detection of the particular object of interest in the video sequence with more accuracy has been focused. The main objective of this work is to detect the object which belongs to both query image and video sequence. The necessity of this work lies in the field of surveillance, main interest is in monitoring the activity of the pre-specified person or the pre-defined object. For this purpose co-segmentation based, common object detection technique has been implemented to detect the particular object of interest in the video sequence. The main goal of this paper is to establish a system to detect the query or the pre-defined object(s) in the video sequence. The work has been performed with a consideration that all the video frames containing the single object of interest (OI), which is to be detected. The effectiveness of the proposed work has been compared with the existing detection and tracking algorithms in the literature. Utility of the proposed system proves much effective and trustworthy, where lies the hope that the results will stand more accurate in detecting the pre-defined object of interest.
Extreme hydrological changes in the southwestern US drive reductions in water supply to Southern California by mid century

The Southwestern United States has a greater vulnerability to climate change impacts on water security due to a reliance on snowmelt driven imported water. The State of California, which is the most populous and agriculturally productive in the United States, depends on an extensive artificial water storage and conveyance system primarily for irrigated agriculture, municipal and industrial supply and hydropower generation. Here we take an integrative high-resolution ensemble modeling approach to examine near term climate change impacts on all imported and local sources of water supply to Southern California. While annual precipitation is projected to remain the same or slightly increase, rising temperatures result in a shift towards more rainfall, reduced cold season snowpack and earlier snowmelt. Associated with these hydrological changes are substantial increases in the frequency and the intensity of both drier conditions and flooding events. The 50 year extreme daily maximum precipitation and runoff events are 1.5–6 times more likely to occur depending on the water supply basin. Simultaneously, a clear deficit in total annual runoff over mountainous snow generating regions like the Sierra Nevada is projected. On one hand, the greater probability of drought decreases imported water supply availability. On the other hand, earlier snowmelt and significantly stronger winter precipitation events pose increased flood risk requiring water releases from control reservoirs, which may potentially decrease water availability outside of the wet season. Lack of timely local water resource expansion coupled with projected climate changes and population increases may leave the area in extended periods of shortages.