Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of Medical Monitoring System based on Bluetooth Technique and GSM Communi-cation

This paper proposes a new portable medical monitoring system, which uses MSP430 as a micro controller. The system could carry out remote wirelessly monitoring based on Bluetooth and GSM, which can replace the wire communication. We introduce the Bluetooth technology and GSM. This monitoring system can monitor human bio-signals real-time, such as ECG, breath, blood pressure, pulse, temperature and it is fit for community medicine and domestic. The portable, minimizing, low-cost and wireless of the monitoring system will be widely applied in medicine and health field.
Comparison of two statistical models in predicting the outcome of female systemic lupus erythematosus patients in China

Objectives: Comparing two different statistical models to predict female SLE patients’ outcome and analyze some related factors. Methods: 1072 female SLE patients were from the Provincial Hospital of Anhui Province and The First Ancillary Hospital of Anhui Medical University from 1990 to 2000. Two types of statistical models including loglinear and Cox proportional hazard model were performed according to this data. Results: Marriage situation, family place, admission situation, whether coming from a different division, nosocomial infection, first occurrent or not and number of drug types had significant effects on LOS after fitting of a loglinear model. Related factors from Cox proportional hazard model were little more than those selected from loglinear model. Based on the former model, a female SLE patient could be predicted that how long she would stay in hospital. But from the latter model, we could predict the ratio of the probability of improvement between different groups of female SLE patients with different individual or clinical characteristics. Conclusions: Factors affecting the length of stay of female SLE patients could be selected from either loglinear model or Cox model. But these two models would be used to do different predictions.
Ranking-based Deep Cross-modal Hashing

Cross-modal hashing has been receiving increasing interests for its low storage cost and fast query speed in multi-modal data retrievals. However, most existing hashing methods are based on hand-crafted or raw level features of objects, which may not be optimally compatible with the coding process. Besides, these hashing methods are mainly designed to handle simple pairwise similarity. The complex multilevel ranking semantic structure of instances associated with multiple labels has not been well explored yet. In this paper, we propose a ranking-based deep cross-modal hashing approach (RDCMH). RDCMH firstly uses the feature and label information of data to derive a semi-supervised semantic ranking list. Next, to expand the semantic representation power of hand-crafted features, RDCMH integrates the semantic ranking information into deep cross-modal hashing and jointly optimizes the compatible parameters of deep feature representations and of hashing functions. Experiments on real multi-modal datasets show that RDCMH outperforms other competitive baselines and achieves the state-of-the-art performance in cross-modal retrieval applications.
Design of a Subtitle System for Internet TV Systems

Globalization was one of the most popular words in Korea about 20 years ago. Koreans believed that a nation cannot join the ranks of developed countries unless that nation is globalized. In order for Internet TV systems to be globalized and to spread throughout the world, broadcasters must provide a subtitling service in foreign languages. Otherwise, their programs cannot be understood by foreigners. There are authoring tools and automatic translators that can generate subtitles. This paper reviews techniques needed in subtitle system development, and introduces the design of a subtitle system for Internet TV. One of the distinguishing features of the design is that a machine translator is integrated into the system. A crawler is proposed as part of the translator to collect cultural and tourism proper nouns.
Prototype-driven alternations: The case of German weak nouns

Abstract Over the past years, multifactorial corpus-based explorations of alternations in grammar have become an accepted major tool in cognitively oriented corpus linguistics. For example, prototype theory as a theory of similarity-based and inherently probabilistic linguistic categorization has received support from studies showing that alternating constructions and items often occur with probabilities influenced by prototypical formal, semantic or contextual factors. In this paper, I analyze a low-frequency alternation effect in German noun inflection in terms of prototype theory, based on strong hypotheses from the existing literature that I integrate into an established theoretical framework of usage-based probabilistic morphology, which allows us to account for similarity effects even in seemingly regular areas of the grammar. Specifically, the so-called weak masculine nouns in German, which follow an unusual pattern of case marking and often have characteristic lexical properties, sporadically occur in forms of the dominant strong masculine nouns. Using data from the nine-billion-token DECOW12A web corpus of contemporary German, I demonstrate that the probability of the alternation is influenced by the presence or absence of semantic, phonotactic, and paradigmatic features. Token frequency is also shown to have an effect on the alternation, in line with common assumptions about the relation between frequency and entrenchment. I use a version of prototype theory with weighted features and polycentric categories, but I also discuss the question of whether such corpus data can be taken as strong evidence for or against specific models of cognitive representation (prototypes vs. exemplars).
Hypertension Treatment Using an Internet-based Lifestyle Intervention

Lifestyle change is important to blood pressure (BP) management. A health care challenge is to extend the reach of these lifestyle programs without over taxing health care resources. The rapid growth of Internet use presents an opportunity to tackle this challenge. However, meta-analytic data examining the efficacy of Internet-based lifestyle interventions to reduce BP in patients with hypertension is lacking. The optimal design strategies of effective Internet-based interventions also remain unclear; specifically, the effectiveness of clinical methods used (Uservs. Expert-driven protocols). Furthermore, self-monitoring of physical activity (PA) can help participants improve adherence to PA and reduce BP. Thus, it is important to find a user-friendly, low-cost and valid tool to measure PA that can be used in an Internet-based intervention. To address these questions, the first study examined the efficacy of Internet-based interventions in reducing BP in a meta-analysis and identified key components of an Internet-based intervention that are associated with BP control. The second study validated the XL-18 pedometer, which was a critical instrument in measuring daily steps used in the third study. The third study examined the influence of Uservs. expert-driven approaches used in an Internet-intervention aimed at promoting BP control and adherence to lifestyle changes.
Medical Interns', Residents' and Attending Physicians' Attitudes Towards Integrative Medicine and Recommended Treatments for Patients with Psoriasis

Background: Psoriasis is a multifactorial disease that effect approximately 7.5 million Americans. There are a variety of traditional treatments for psoriasis (e.g. topical medications, biologics) that for many, are effective in combating the disease. However, traditional treatments often have aversive side effects and may not be appropriate for every patient (e.g. pregnant women). Additionally, the efficacy of these medications are variable leaving some looking for adjunctive treatment options. There is increasing evidence to support the use of adjunctive treatments for psoriatic patients (e.g. psychotherapy, mindfulness). However, research is variable in regards to medical professionals’ attitudes towards integrative medicine (IM) as well as what types of adjunctive treatments they are recommending for psoriatic patients. Methods: A total of 105 allopathic (MD) and osteopathic (DO) interns, residents and attending physicians completed surveys to measure their attitudes towards IM and to determine what traditional and adjunctive treatments they recommend. Results: Results indicated no significant difference between MD and DO participants’ attitudes towards IM. There were no significant difference in attitudes towards IM between MD interns, residents and attending physicians. However, DO attending physicians had significantly more positive attitudes toward IM compared to DO residents. Notably, psychotherapy was the second highest recommended treatment in this study, second only to topical medications (78%). Conclusion: This study suggests no significant difference between MD and DO attitudes towards IM. However, the study suggest that time in the field may increase positive attitudes towards IM for the DO orientation. This study also suggests that medical professionals, both of the MD and DO orientation are open to recommending adjunctive treatments to their patients as 3 of the top 5 treatment recommendations in this study were adjunctive.
QoS provisioning using IPv6 flow label in the Internet

Today's Internet only provides "best-effort" service. Traffic is processed as quickly as possible, but with no commitment as to bandwidth or latency guarantee. This is inadequate for applications requiring timeliness such as video conference, video-on-demand (VOD) and voice over IP (VoIP), etc. For these applications to be widely used, Quality of Service (QoS) must be quantified and managed efficiently by the Internet. This paper focuses on QoS provisioning using IPv6 flow label field. In this paper, we first investigate the flow label specification and its usage in QoS support proposed in the literature, then propose an end-to-end QoS scheme using the IPv6 flow label field, which employs a hybrid approach for flow label specification and a " flow label option" for IPv4 packet. This proposed QoS scheme is especially designed for transitional IP network containing both IPv4 and IPv6 network nodes, which is the reality in the process of Internet transition from IPv4 to IPv6. Our simulation results show that the proposed QoS scheme can efficiently differentiate various types of traffic to meet differentiated QoS requirements in a transitional network containing IPv4 and IPv6 routers.
Collusions and Privacy in Rational-Resilient Gossip

Gossip-based content dissemination protocols are a scalable and cheap alternative to centralised content sharing systems. However, it is well known that these protocols suffer from rational nodes, i.e., nodes that aim at downloading the content without contributing their fair share to the system. While the problem of rational nodes that act individually has been well addressed in the literature, textit{colluding} rational nodes is still an open issue. In addition, previous rational-resilient gossip-based solutions require nodes to log their interactions with others, and disclose the content of their logs, which may disclose sensitive information. Nowadays, a consensus exists on the necessity of reinforcing the control of users on their personal information. Nonetheless, to the best of our knowledge no privacy-preserving rational-resilient gossip-based content dissemination system exists.The contributions of this thesis are twofold.First, we present AcTinG, a protocol that prevents rational collusions in gossip-based content dissemination protocols, while guaranteeing zero false positive accusations. AcTing makes nodes maintain secure logs and mutually check each others' correctness thanks to verifiable but non predictable audits. As a consequence of its design, it is shown to be a Nash-equilibrium. A performance evaluation shows that AcTinG is able to deliver all messages despite the presence of colluders, and exhibits similar scalability properties as standard gossip-based dissemination protocols.Second, we describe PAG, the first accountable and privacy-preserving gossip protocol. PAG builds on a monitoring infrastructure, and homomorphic cryptographic procedures to provide privacy to nodes while making sure that nodes forward the content they receive. The theoretical evaluation of PAG shows that breaking the privacy of interactions is difficult, even in presence of a global and active opponent. We assess this protocol both in terms of privacy and performance using a deployment performed on a cluster of machines, simulations involving up to a million of nodes, and theoretical proofs. The bandwidth overhead is much lower than existing anonymous communication protocols, while still being practical in terms of CPU usage.
Who's on the Line? Managing student communications in distance learning using a one-window approach

Information technology (IT) has become a common component in the process of designing and delivering distance-learning courses of study. Many researchers have addressed the technical and pedagogical issues surrounding IT-enabled or IT-enhanced distance-learning systems. However, studies of the management of the student's learning experience as it is influenced by the emerging virtual organization would assist practitioners and researchers as they seek to understand the important challenges of current practice in distance-learning systems. This paper reports on the operation of a one-window approach to serving students, using the organizational structure and technological innovation of a call centre. Before the call centre was established, it was hypothesized that the call-centre approach would be well suited to routine tasks of supporting student learning. In practice, it was found that most student inquiries were indeed in the category of routine tasks and that only a small proportion of student inquiries could be categorised as uncertain events that required the specialised knowledge of the professor or other academic expert. Implications for the management of other distance learning systems are considered.
A tumor microenvironment-related risk model for predicting the prognosis and tumor immunity of breast cancer patients

Background This study aimed to construct a tumor microenvironment (TME)-related risk model to predict the overall survival (OS) of patients with breast cancer. Methods Gene expression data from The Cancer Genome Atlas was used as the training set. Differentially expressed gene analysis, prognosis analysis, weighted gene co-expression network analysis, Least Absolute Shrinkage and Selection Operator regression analysis, and Wald stepwise Cox regression were performed to screen for the TME-related risk model. Three Gene Expression Omnibus databases were used to validate the predictive efficiency of the prognostic model. The TME-risk-related biological function was investigated using the gene set enrichment analysis (GSEA) method. Tumor immune and mutation signatures were analyzed between low- and high-TME-risk groups. The patients’ response to chemotherapy and immunotherapy were evaluated by the tumor immune dysfunction and exclusion (TIDE) score and immunophenscore (IPS). Results Five TME-related genes were screened for constructing a prognostic signature. Higher TME risk scores were significantly associated with worse clinical outcomes in the training set and the validation set. Correlation and stratification analyses also confirmed the predictive efficiency of the TME risk model in different subtypes and stages of breast cancer. Furthermore, immune checkpoint expression and immune cell infiltration were found to be upregulated in the low-TME-risk group. Biological processes related to immune response functions were proved to be enriched in the low-TME-risk group through GSEA analysis. Tumor mutation analysis and TIDE and IPS analyses showed that the high-TME-risk group had more tumor mutation burden and responded better to immunotherapy. Conclusion The novel and robust TME-related risk model had a strong implication for breast cancer patients in OS, immune response, and therapeutic efficiency.
Vision-based human action recognition using machine learning techniques

The focus of this thesis is on automatic recognition of human actions in videos. Human action recognition is defined as automatic understating of what actions occur in a video performed by a human. This is a difficult problem due to the many challenges including, but not limited to, variations in human shape and motion, occlusion, cluttered background, moving cameras, illumination conditions, and viewpoint variations. To start with, The most popular and prominent state-of-the-art techniques are reviewed, evaluated, compared, and presented. Based on the literature review, these techniques are categorized into handcrafted feature-based and deep learning-based approaches. The proposed action recognition framework is then based on these handcrafted and deep learning based techniques, which are then adopted throughout the thesis by embedding novel algorithms for action recognition, both in the handcrafted and deep learning domains. First, a new method based on handcrafted approach is presented. This method addresses one of the major challenges known as “viewpoint variations” by presenting a novel feature descriptor for multiview human action recognition. This descriptor employs the region-based features extracted from the human silhouette. The proposed approach is quite simple and achieves state-of-the-art results without compromising the efficiency of the recognition process which shows its suitability for real-time applications. Second, two innovative methods are presented based on deep learning approach, to go beyond the limitations of handcrafted approach. The first method is based on transfer learning using pre-trained deep learning model as a source architecture to solve the problem of human action recognition. It is experimentally confirmed that deep Convolutional Neural Network model already trained on large-scale annotated dataset is transferable to action recognition task with limited training dataset. The comparative analysis also confirms its superior performance over handcrafted feature-based methods in terms of accuracy on same datasets. The second method is based on unsupervised deep learning-based approach. This method employs Deep Belief Networks (DBNs) with restricted Boltzmann machines for action recognition in unconstrained videos. The proposed method automatically extracts suitable feature representation without any prior knowledge using unsupervised deep learning model. The effectiveness of the proposed method is confirmed with high recognition results on a challenging UCF sports dataset. Finally, the thesis is concluded with important discussions and research directions in the area of human action recognition.
Neural network model controlling saccade based on probabilistic expectation

Psychophysical experiment and analysis of the data by a linear neuronal model are presented to investigate mechanisms of expectation on probabilistic stimuli. The results suggest that the expectation is held by synaptic weight of the neuron and the neural mechanism controlling saccade based on expectation consists of two pathways working in parallel. One facilitates saccades based on expectation. The other executes saccades in response to the saccade signal. The neural mechanism is consistent with some neurophysiological findings.
Steroid implants for the induction of vitellogenesis in feminized European silver eels (Anguilla anguilla L.)

Assisted propagation of the European eel will lead to a closed production cycle supplying the aquaculture industry with juvenile glass eels. Females require long-term weekly treatment with pituitary extract (PE), which is stressful and causes abnormalities in oogenesis. We tested the effects of 17α-methyltestosterone (17 MT), as potent androgen activating the androgen receptor, and 17β-estradiol (E2), as an inducer of vitellogenesis, to shorten the duration of PE treatment.Four groups of feminized eels were subjected to a simulated migration and subsequent injection with implants containing 17 MT (17 MT-group), E2 (E2-group) or 17 MT plus E2 (17 MT + E2-group) to test for synergistic effects, or without any steroids as controls (C-group). The effects of a 2-months treatment were investigated by determining the eye index (EI), hepatosomatic and gonadosomatic index (HSI and GSI, respectively), plasma steroid concentrations by liquid chromatography mass spectrometry (LCMS), gonadal histology, expression of androgen receptors a and b (ara, arb); estrogen receptor 1 (esr1); FSH receptor (fshr); vitellogenin receptor (vtgr) and aromatase (cyp19), and the required number of weekly PE injections to fully mature. For many parameters, both the 17 MT and E2 groups showed an increase vs. controls, with the 17 MT + E2 group showing a synergistic effect, as seen for EI, GSI (3.4 for 17 MT and for E2, 6.6 for 17 MT + E2), oocyte diameter and ara, arb and esr1 expression. Concentrations of almost all focal steroids decreased with simulated migration and steroid treatment. Only eels of the 17 MT-group showed increased expression of cyp19 and of fshr, while fshr expression increased 44-fold in the 17 MT + E2 group, highlighting that co-implantation is most effective in raising fshr mRNA levels. Specific for eels of the E2 groups were vitellogenesis-associated changes such as an increase of HSI, plasma E2, and presence of yolk in the oocytes. Steroid treatments reduced the duration of PE treatment, again synergistically for co-implantation. In conclusion, E2 is necessary to start vitellogenesis, but 17 MT has specific effects on cyp19 and fshr expression. The combination is necessary for synergistic effects and as such, steroid implants could be applied in assisted reproduction protocols for European eel to improve oocyte quality leading to the production of more vital larvae.
REDUCING COST OF POWER CONSUMPTION USING GOAL PROGRAMMING OPTIMIZATION AND RENEWABLE ENERGY SOURCE

The demand of cloud computing grows rapidly with the rapid increase in IT infrastructure. Cloud computing now a day, are widely used by industry, organization and society to deliver IT services. This rapid growth leads to the creation of large data centers. These data centers requires enormous amount of electrical power for its operation and thus, result in high operational cost and carbon-dioxide emission. In this work the key idea is to reduce the requirement of power consumption by efficient task allocation and its cost. Datacenters are connected to conventional power grid as well as to renewable energy source. We performed this work in two phases: first used goal programming optimization for energy efficient task allocation to reduce power consumption and then analyzed the reduction in cost if we use RES for power supply. We used solar power panel as renewable energy source and analyzed the significant reduction in cost.