Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection and Classification of Heart Sounds Using a Heart-Mobile Interface

An early detection of heart disease can save lives, caution individuals and also help to determine the type of treatment to be given to the patients. The first test of diagnosing a heart disease is through auscultation - listening to the heart sounds. The interpretation of heart sounds is subjective and requires a professional skill to identify the abnormalities in these sounds. A medical practitioner uses a stethoscope to perform an initial screening by listening for irregular sounds from the patient's chest. Later, echocardiography and electrocardiography tests are taken for further diagnosis. However, these tests are expensive and require specialized technicians to operate. A simple and economical way is vital for monitoring in homecare or rural hospitals and urban clinics. This dissertation is focused on developing a patient-centered device for initial screening of the heart sounds that is both low cost and can be used by the users on themselves, and later share the readings with the healthcare providers. An innovative mobile health service platform is created for analyzing and classifying heart sounds. Certain properties of heart sounds have to be evaluated to identify the irregularities such as the number of heart beats and gallops, intensity, frequency, and duration. Since heart sounds are generated in low frequencies, human ears tend to miss certain sounds as the high frequency sounds mask the lower ones. Therefore, this dissertation provides a solution to process the heart sounds using several signal processing techniques, identifies the features in the heart sounds and finally classifies them. This dissertation enables remote patient monitoring through the integration of advanced wireless communications and a customized low-cost stethoscope. It also permits remote management of patients' cardiac status while maximizing patient mobility. The smartphone application facilities recording, processing, visualizing, listening, and classifying heart sounds. The application also generates an electronic medical record, which is encrypted using the efficient elliptic curve cryptography and sent to the cloud, facilitating access to physicians for further analysis. Thus, this dissertation results in a patient-centered device that is essential for initial screening of the heart sounds, and could be shared for further diagnosis with the medical care practitioners.
Service business model framework and the service innovation scope

In this paper we present a framework for service business models. We build on three streams of research. The first stream is the service management and marketing literature that focuses on the specific challenges of managing a service business. The second stream consists of research on e-business models. The third and most recent stream of research includes frameworks and business models from strategic management and innovation management. The next step in our research is the development of a service business model framework and describing a set of core dimensions of innovation in a service business. The framework will be illustrated with recent case studies of service firms.
Connecting the Dots: Identifying Network Structure via Graph Signal Processing

Network topology inference is a significant problem in network science. Most graph signal processing (GSP) efforts to date assume that the underlying network is known and then analyze how the graph? s algebraic and spectral characteristics impact the properties of the graph signals of interest. Such an assumption is often untenable beyond applications dealing with, e.g., directly observable social and infrastructure networks; and typically adopted graph construction schemes are largely informal, distinctly lacking an element of validation. This article offers an overview of graph-learning methods developed to bridge the aforementioned gap, by using information available from graph signals to infer the underlying graph topology. Fairly mature statistical approaches are surveyed first, where correlation analysis takes center stage along with its connections to covariance selection and high-dimensional regression for learning Gaussian graphical models. Recent GSP-based network inference frameworks are also described, which postulate that the network exists as a latent underlying structure and that observations are generated as a result of a network process defined in such a graph. A number of arguably more nascent topics are also briefly outlined, including inference of dynamic networks and nonlinear models of pairwise interaction, as well as extensions to directed (di) graphs and their relation to causal inference. All in all, this article introduces readers to challenges and opportunities for SP research in emerging topic areas at the crossroads of modeling, prediction, and control of complex behavior arising in networked systems that evolve over time.
Order and Chaos in Young Adult Science Fiction: A Critical Stylistic Analysis

With the challenges and revolutionary changes in the world, it is essential that the sources of social power direct the communities towards the right path that leads to a brighter future, especially when it comes to young adults. Young adults represent a critical social group that needs special attention. Therefore, the present paper tackles one of the fascinating literary genres to young adults; young adult science fiction. The paper attempts to investigate how the social themes of order and chaos are delivered to young adults in young adult science fiction through conducting a critical stylistic analysis of certain extracts in selected young adult science fiction novels. The linguistic tool employed for the critical stylistic analysis is negation for its prevalent use in the discourse, in general, and for its textual effectiveness in rendering hidden ideologies, whether intended or unconscious.K
Office Rent Processes: The Case of U.S. Metropolitan Markets

This paper synthesizes elements of the traditional and contemporary theory of real estate markets to formulate an empirical framework for exploring metropolitan office rent processes. Such a framework is then applied to the analysis of office rents across eighteen U.S. office markets during 1986-1995. The empirical results underscore the sluggishness of rental adjustments, highlight the extent of rental disequilibria across markets, and uncover the role of office employment factors--size, diversity, spatial organization, growth rates, and volatility--, construction costs, interest rates, amenities, and zoning in shaping interarea differentials in the equilibrium component of office rents.
Extended open shop scheduling with resource constraints: Appointment scheduling for integrated practice units

Abstract An Integrated Practice Unit (IPU) is a new approach to outpatient care in which a co-located multidisciplinary team of clinicians, technicians, and staff provide treatment in a single patient visit. This article presents a new integer programming model for an extended open shop problem with application to clinic appointment scheduling for IPUs. The advantages of the new model are discussed and several valid inequalities are introduced to tighten the linear programming relaxation. The objective of the problem is to minimize a combination of makespan and total job processing time, or in terms of an IPU, to minimize a combination of closing time and total patient waiting time. Feasible solutions are obtained with a two-step heuristic, which also provides a lower bound that is used to judge solution quality. Next, a two-stage stochastic optimization model is presented for a joint pain IPU. The expected value solution is used to generate two different patient arrival templates. Extensive computations are performed to evaluate the solutions obtained with these templates and several others found in the literature. Comparisons with the expected value solution and the wait-and-see solution are also included. For the templates derived from the expected value solution, the results show that the average gap between the feasible solution and lower bound provided by the two-step heuristic is 2% for 14 patients. They also show that either of the two templates derived from the expected value solution is a good candidate for assigning appointment times when either the clinic closing time or the patient waiting time is the more important consideration. Sensitivity analysis confirmed that the optimality gap and clinic statistics are stable for marginal changes in key resources.
Company Stock in Defined Contribution Plans: Evidence from Proxy Voting

I examine whether firms’ decisions to offer company stock in defined contribution (DC) plans are explained by managers’ corporate control motives. Using a large sample of proxy voting outcomes, I find that employee ownership in DC plans is significantly and positively associated with the level of voting support for management-sponsored proposals. This suggests that managers encourage employee DC holdings in company stock in order to receive higher voting support in favor of management. The effects of employee ownership on voting outcomes are significantly greater in specific subsamples where employee vote is more important to management.
Precoder Design For Multi-Group Multicasting With a Common Message

This paper considers precoding for multi-group multicasting with a common message. The multiple antenna base station communicates with $K$ clusters, each with $L$ users. There is a common message destined to all users and a private multicast message for each cluster. We study the weighted sum rate (WSR) maximization problem for two different schemes: 1) the base station transmits the superposition of common and multicast messages and 2) the base station concatenates the multicast message vector with the common message. We also formulate a second problem, weighted minimum mean square error (WMMSE) minimization, and prove that WSR maximization and WMMSE minimization are equivalent at the optimal solution. Inspired by the WMMSE problem, we suggest a suboptimal algorithm, based on alternating optimization. We apply this algorithm to the two transmission schemes and understand that there is a fundamental difference between the two. We compare the results with maximal ratio transmission (MRT) and zero-forcing (ZF) precoding and investigate the effects of the number of base station antennas, the number of groups, and the number of users in a group. Finally, we study imperfect successive interference cancellation (SIC) at the receivers and show that the first transmission scheme is more robust.
Arbitration in International Trade

The great majority of international contracts provides that any disputes which may arise will be decided by arbitration. Legal scholars argue that international arbitration is leading to the development of legal doctrine attuned to the needs of business and independent of national laws. This paper studies international arbitration as a beautiful example of the role of private trade in shaping international institutions. We review the provisions and the practice of international arbitration, and present a general equilibrium model of the relationship between the expansion of trade and the adoption of arbitration. The model focuses on the heterogeneity existing among economic agents in terms of their legal needs. It shows how arbitration alters the size and composition of markets, while at the same time responding to exogenous change in trade. In addition, it shows how the legal services provided by the courts deteriorate in the presence of arbitration and predicts that the share of traders using arbitration should rise as markets expand. Overall, the model does remarkably well in generating results commonly discussed in the legal literature.
Foreign Direct Investment and Economic Development in Guangdong: Problems and Prospects

Foreign direct investment has made an important contribution to Guangdong's impressive economic growth in the past 18 years and to its current leading position in China. Now, without preferential policies and some other advantages, Guangdong is facing more difficulties than before to attract FDI. However, with the achieved economic progress, better infrastructure, a more educated and skilled labour force, well-developed economic integration with Hong Kong and the close ties with overseas Chinese, if the provincial government's new policies of attracting FDI are carried out effectively, Guangdong will be able to keep its top position in China for hosting FDI and maintaining rapid economic growth for at least another decade.
A vehicle logo recognition method based on improved SIFT feature and bag-of-words model

Vehicle logo, as the key information of vehicle, combined with other vehicle characteristics will make vehicle management more effective in the intelligent transportation system. However, it is still a challenging task to extract effective features for vehicle logo recognition, largely due to its variations in illumination and low resolution. Aiming at improving the recognition rate of vehicle logo recognition, this paper proposes a new vehicle logo recognition method. First, in the aspect of vehicle logo feature extraction, a vehicle logo feature extraction algorithm based on the fusion of SIFT features and Dense-SIFT features was put forward to generate local feature descriptors. Then Bag-of-words model was used to describe vehicle logo features and form visual dictionary histogram. Considering that bag-of-words model ignores spatial structure information of objects, we introduced spatial pyramid model into bag-of-words model. In the aspect of vehicle logo recognition, vehicle logo was classified by using Support Vector Machine (SVM) based on one-against-the-rest multiclassification structure. Finally, our method was verified effectively through the experiment compared to other methods.
Multi-robot Simultaneous Localization and Mapping based on Pose Graphs Fusion

This paper describes an efficient method for multi-robot simultaneous localization and mapping (SLAM), which establishes a joint model of multi-robot pose graphs based on several special inner and outer loop points called conjunction points. An optimization algorithm based on the fusion of pose graphs is proposed. The rapid fusion of multiple local sub graphs is realized by using the robot encounter information, thus solving the problem of indeterminate positional relationship of the robot and realizing the optimization of the global map.
Simulation of Stylized Facts in Agent-Based Computational Economic Market Models

We study the qualitative and quantitative appearance of stylized facts in several agent-based computational economic market (ABCEM) models. We perform our simulations with the SABCEMM (Simulator for Agent-Based Computational Economic Market Models) tool recently introduced by the authors (Trimborn et al. 2018a). The SABCEMM simulator is implemented in C++ and is well suited for large scale computations. Thanks to its object-oriented software design, the SABCEMM tool enables the creation of new models by plugging together novel and existing agent and market designs as easily as plugging together pieces of a puzzle. We present new ABCEM models created by recombining existing models and study them with respect to stylized facts as well. The code is available on GitHub (Trimborn et al. 2018b), such that all results can be reproduced by the reader.
Economic Regulation of Domestic Air Transport Theory and Policy. George W. Douglas and James C. Miller III. The Brookings Institution, 1775 Massachusetts Avenue, N.W., Washington, D.C. 20036. 1974. 211p

Short historical summary of regulations of U.S. domestic airlines. Examination of the present status of regulations. Establishment of a model describing how air transportation is operating under these regulations. Using this model a comparison is made of air transportation as it is against what it would be if economic efficacy was its aim and driving force. A critique of tariffs, pricing and regulations is made. Suggestions for better returns are given.
MEDIA SOCIALIZATION OF INDIVIDUALS IN MODERN INFORMATIONAL SOCIETY

The article deals with the analysis of the essential characteristics of media socialization of individuals in the informational society. Based on the theoretical analysis held, the author arrives at the conclusion that media socialization is a topical aspect of the social development of individuals in the social and cultural space of the informational society and is an important component of the modern process of individuals’ socialization. In conditions of modern challenges there increases scholars’ attention to the problems of youths’ socialization – young men being at the stage of the development of their personalities as future professional, they being engaged in the virtual reality of the modern social and cultural space. In this context, it is hard to overestimate the role of institutions of higher education as the leading agents of a socially controlled socialization of individuals in modern social and cultural environment.
Fuzzy Deformable Based Fusion Approach for Tumor Segmentation and Classification in Brain MRI Images

In recent years, the automatic identification and classification of tumor regions have gained more interest due to accuracy and reduced time complexity. One of the important strategies in tumor identification is segmenting the image as tumor and nontumor region, and this helps the researchers more significantly, as the MRI image comes in different modalities. This work introduces novel optimization based strategy for segmenting and classifying the image. Initially, the MRI images in the database are subjected to pre-processing and given to the segmentation process. For segmentation, this work utilizes the deformable model, and Fuzzy C Means (FCM) algorithm and the resultant segmented images are hybridized through proposed Dolphin based Sine Cosine Algorithm, preferred to be Dolphin-SCA. After segmentation, the tumor and non tumor-related features are extracted using the power LBP operator. The extracted features are subjected to Fuzzy Naive Bayes classifier for the classification, and finally, the classifier finds the suitable tumor class labels. Here, the entire experimentation is done by taking the MRI images from the BRATS database, and evaluated based on sensitivity, specificity, accuracy and ROC metrics. The simulation results reveal the dominance of proposed scheme over other comparative models, and the proposed scheme achieved 95.249% accuracy.
IS2MoD: an interactive system based on expert systems and Kinect devices to support the motor rehabilitation and development of children with disabilities

Several authors such as Piaget, suggest that sensory and motor experiences are considered the base of intellectual functioning during the first two years of a person’s life. In this line, the fine and gross motor skills are tightly related with the cognitive development of a child. For these reasons, in this paper, we present an interactive system aimed at supporting exercises and activities for both rehabilitation and development of motor skills in children with disabilities. Our proposal relies on a Kinect device and an expert system that provides a guide of activities that must be carried out by a child according to his/her profile. We have tested the system in two stages, one to determine how well the expert system provides suggestions on the base of one first level of granularity: areas and general interactive activities configuration. Moreover, in the second stage, we worked in real therapy sessions with six children with different types of disabilities. The results show high levels of motivation by children side, and 90% of precision in the generation of therapy plans.
Biomedical relation extraction with reduced manual effort

Biomedical relation extraction is an critical text-mining task that concerns automatic extraction of related bio-entities in text. Rule-based and machine learning methods are two main approaches for relation extraction. While these two methods can be used to develop high-performance relation extraction system, considerable amount of manual effort is required by both methods in different phases of the system development. This hinders fast application of both methods to extract new types of relations. This dissertation focuses on developing techniques to assist the development of biomedical relation extraction systems using rule-based and machine learning methods. For rule-based systems, one main component requiring manual effort is pattern design. Domain experts often need to examine considerable amount of documents and exhaustively collect every pattern that can be used to extract relations. We leverage various linguistic knowledge to automatically generate a comprehensive set of patterns. Our first approach is instantiated to develop miRTex, a system that extracts three kinds of miRNA-gene relations that regulate a wide range of biological processes and are involved with diseases. Only a small number of triggers and rules are needed to achieve the state-of-the-art performance. Our second approach is to translate the ideas in Lexicalized Tree Adjoining Grammar to dependency graph for pattern generation, and adopt Extended Dependency Graph as an abstract sentence representation. This approach is applied to extract five type of post-translational modifications, a class of relations that plays an important role in cellular functions. Evaluations on BioNLP 2011 EPI task show that the resulting system achieves state-of-the-art performance. For machine learning systems, a sizable training corpus is needed to train the extraction model, while the annotation of the corpus is timeand labor-intensive. We
Traditional Chinese Medicine (TCM) Diagnosis Model Building Based on Multi-label Classification

In the study, we propose a TCM diagnosis model that can be used for multi-label classification and give clear diagnosis, as well as the basis for diagnosis and differentiation when the symptoms correspond to multiple diseases or syndromes. The implementation of the model is divided into three steps. Firstly, choose the machine learning algorithm to train the TCM diagnosis model. The features of the training data are symptoms and the labels are diseases or syndromes. Secondly, give the number α (α>1, α∈Z+) , the model will output the diagnoses with the top α highest probability according to the input symptoms as candidate diagnoses. Finally, the rules of differential diagnosis are designed to determine which candidate diagnoses should be reserved, thereby complete the multi-label classification. In our test dataset, by 10-fold cross-validation, the average accuracy of the single label classification was 0.882; the average precision was 0.974; the average recall was 1.000; the average f1 score was 0.967; the average accuracy of the multi-label classification was 0.706; the average micro precision was 0.934; the average micro recall was 0.941 and the average hamming loss was 0.060. Through the test we can know that this model had a good potential for auxiliary decision making in clinical diagnosis and treatment.