Miracle-dz/UniFAR-FTUs huggingface dataset:
Molecular Phylogenetic Identification of Actinobacteria

Molecular phylogenetics plays an important role in prokaryote taxonomy and identifica‐ tion. The content of this chapter is to introduce the common application of genetic criteria including 16S rRNA gene sequence nucleotide similarity and phylogeny, DNA G+C con‐ tent, and DNA–DNA hybridization. However, the genomics era might put forward some new criteria. This chapter emphasizes the methods and basic principles of molecular identification and taxonomy of actinobacteria.
CORPORATE GOVERNANCE MECHANISMS IN FAMILY FIRMS – A SOCIOEMOTIONAL WEALTH PERSPECTIVE

Family businesses are not only known as a decisive factor for economic growth and capital formation, but a considerable number of companies worldwide can be defined as family businesses (Spanos et al., 2008; Klein, 2000). This is one of the reasons why family businesses and their particularities in theory, practice and science have become increasingly important (Faccio & Lang, 2002; Colli et al., 2003; Dossena, 2009). The uncovering of advantages and disadvantages in family-owned companies in comparison to other companies can therefore be understood by examining the multitude of assumed dynamics and functions in the family-owned company (Kets de Vries, 1993; Chrisman et al., 2005). This makes the family one of the most valuable resources owned by family businesses (Cabrera-Suárez et al., 2001). Even when it comes to a universalistic definition, the binding factor of family businesses is the focus on the family business and the simultaneous use of two different levers: controlling family ownership and corporate management (Chua et al., 1999; Dyer & Whetten, 2006). Another aspect that is increasingly attracting attention in the global economy and the media is the behaviour of "good" corporate governance throughout the entire company. Due to accounting scandals, white-collar crime and other unethical behaviour, corporate governance principles are becoming increasingly important. These issues are important not only for large corporations, but also for SMEs and family businesses. However, the context of corporate governance focused on the individual components and the use of specified instruments in the context of family-owned companies is not widespread, especially since corporate governance is an issue that is relevant to every company regardless of its status. Based on the SEW theory, we investigate whether family-owned companies behave differently when using corporate governance mechanisms. The SEW theory draws attention to the characteristics of certain family-owned companies that can create or Abstract
The time-varying relationship between credit spreads and employment growth

ABSTRACT After the global financial crisis, several central banks introduced unconventional monetary policies, such as quantitative easing (QE). If QE increases asset prices, but does not boost the real economy to the same extent, the relationship between credit spreads and employment growth will weaken. This study investigates this issue for the U.S. in a moving-windows framework. Our results suggest that the link between credit spreads and employment growth is lower during bubbles and recessions. We also find that the relationship weakened after the Fed introduced QE.
Facilitation Strategy and Performance of Donor Assisted Water Supply and Sanitation Development Projects in Wajir County, Kenya

Facilitation strategy has an influence on the performance of donor assisted water supply and sanitation development projects Kenya. This study sought to determine the effect of facilitation strategy on the performance of donor assisted water supply and sanitation of development projects in Wajir County in Kenya. The specific objectives of the study are to:To establish the influence of funding on the performance of donor assisted water supply and sanitation development projects in Wajir County, to examine the influence of strategic partnerships on the performance of donor assisted water supply and sanitation development projects in WajirCounty, to determine the influence of system maintenance on the performance of donor assisted water supply and sanitation development projects in Wajir County and to assess the influence of stakeholders’ engagement on the performance of donor assisted water supply and sanitation development projects in Wajir County. The study was based on the descriptive survey research design. The target population was 51 donor assisted water supply and sanitation development projects and the 220 households in the study area which were Elnur, Lakoley, Wargadud and Sarman. Systematic sampling and purposive sampling techniques was applied in selecting a sample size of 111 heads of household and 51 project members from the target population. The study used questionnaires to collect data from the respondents. Validity of the questionnaire was ensured through content validity and face validity while reliability of the instrument was ensured through a test-retest procedure. Quantitative data was analyzed using correlation analysis and regression analysis. The findings of the study revealed that most of the water supply and sanitation development projects in received funding. It also found that there was presence of strategic partnerships and these partnerships helped the project leadership to do the work more efficiently. Further findings indicated that most of the water supply and sanitation development projects lacked or had improper system maintenance practices. Another finding revealed that there was involvement of stakeholders in several aspect of the project. The study therefore recommends that there is need to ensure timely disbursement of project funds; all organizations that intend to enter into a relationship put in place mechanisms that will ensure that they adhere to them otherwise the partnership will not be successful; management of the water and sanitation facility should devise ways of reducing the high maintenance costs, and that project leaders should be communicating with and involving all the key stakeholders in any donor funded projects.
Dynamic optimal management of a hybrid microgrid based on weather forecasts

Hybrid microgrids containing both renewable and conventional power sources are becoming increasingly attractive for a variety of reasons. However, intermittency of renewable power production and uncertainty in future load prediction increase risks of electric grid instability and, by consequence, restrict the portion of renewable power production in microgrids. In order, to prefigure the upcoming renewable power production, particularly, wind power and photovoltaic power, we suggest using weather forecasts. In addition to illustrating short term renewable power prediction based on ensemble weather forecasts, this paper focuses on optimizing the management of distributed power generation, power storage, and power exchange with the commercial electric grid. Establishing an optimal operating plan for the grid makes the integration of the predicted renewable production more efficient. The optimization problem is formulated as an integer linear program. The operating plan is initially optimized over the upcoming day and then regularly updated and incremented to cover a longer time horizon. After analyzing the obtained plans, we test and evaluate them relative to the observed weather conditions. Finally, we investigate some practical problems that had arisen and we apply a time cascade optimization technique to mitigate the effects of initial conditions and end-of-horizon effects, as well as to take advantage of updated forecasts.
Analyzing and interpreting variations of public sentiments on social network

Social networks have become one of the most popular means of communication on the internet, as a result of which internet users have grown rapidly. Millions of messages are regularly available on websites that offer web services such as Facebook, Tweeter, WhatsApp and LinkedIn. Millions of users share their personal opinions or views on various topics and discuss recent news on social websites, making it an important basis for tracking and analyzing the sentimental perception of the community. A social site is just as innovative as a small blog platform with more than one million unique weekly guests. On social sites, each user placed a message with the name tweeter or blog, which is visible to everyone. Such monitoring or analysis can provide important information for making decisions and evaluating opinions in different domains. In this work, we went a step further to interpret the mood swings. We have established that the emerging topics within the periods of mood variation are strongly related to the real reasons for the variations. We propose a model based on latent Dirichlet allocation, LDA for foreground and background (FB-LDA) to distill the foreground themes and filter the age-old background themes. These foreground themes help to interpret method swings in social networks. The feeling analysis, also known as Opinion Mining, plays a crucial role in determining the feelings in different web content. The analysis of opinions is very important to make decisions. Example, if you want to buy a new phone, a competent buyer of the web will always first assess the opinions to make a purchasing decision based on other experiences. The analysis of feelings extracts opinions, feelings, and emotions from the text and analyzes them. This information is very useful for governments, companies, and individuals. Although this content might be useful for analyzing most of the content generated by the user, it is difficult and time-consuming. Sentiment analysis is the automatic extraction of opinions, perspectives, and emotions from data sources via NLP.
The Buckley-Osthus model and the block preferential attachment model: statistical analysis and application

This paper is concerned with statistical estimation of two preferential attachment models: the Buckley-Osthus model and the hierarchical preferential attachment model. We prove that the maximum likelihood estimates for both models are consistent. We perform simulation studies to corroborate our theoretical findings. We also apply both models to study the evolution of a real-world network. A list of open problems are presented.
VALIDATION OF THE TURKISH VERSION OF THE WHOQOL-AGE AND A PROPOSED ALTERNATIVE SCALE STRUCTURE

Methods: Internal consistency, item–total correlations, and item success were analyzed taking the original structure into account. The validity tests consisted of construct validity and criterion validity analyses. The original scale structure was compared with a proposed new scale structure, comprised of two domains and based on the exploratory and confirmatory factor analysis, in terms of goodnessof-fit measures.
A Study on Blockchain Networking for Internet of Things

High expectations are posed on the blockchain-based internet of things (IoT), in which IoT and blockchain technology is combined to obtain trust in the Internet, where trust appears impossible to obtain. However, applications of current blockchain-based IoT technology to real-world scenarios appears to be significantly more difficult owing to limitations regarding scalability and security. In this paper, the difficulties to implement blockchain networking technologies for IoT and digital businesses are investigated and practical solutions such as sharding, off-chain, de-idetification and P2P crypto-currency exchange are explored. In further work, a blockchain platform for IoTs which provides scalability and security will be implemented according to this research results, and compared with conventional blockchain platforms
AI Encountering Interactive Systems: A Deep Learning Reinforced Musical Composition Model

In this paper, we present an artificial intelligence-based musical composition algorithm for generating harmonic and various arpeggios based on given chords in real-time, which incorporates a recurrent neural network (RNN) with the gated recurrent unit (GRU) and a tetrahedral context-sensitive L-system (TCSL). RNN can play a significant role of learning inherent harmony from arpeggio datasets and providing probabilistic prediction which suggests the selection of the next note and the lengths of the selected notes. We also establish TCSL model based on a tetrahedron model utilizing seven interval operators and production rules to increase the variety of generation. TCSL is responsible for generating one note at each iteration by respectively requesting probabilistic predication from RNN, calculating optional notes and determining target note. Our experiments where we trained two RNNs for TCSL generation model indicated that the proposed algorithm has advantages in overcoming the obstacles of achieving inherent global harmony as well as the variety of generation in current computer-aided arpeggio composition algorithms. Our research attempts to extend deep learning model (DLM) towards the design space for interactive composition systems.
Minimum Startup Delay Approach to Coordination of Multiple Mobile Robots Motion Along Predefined Paths

This paper is concerned with the collision free motion coordination of multiple mobile robots sharing the same workspace. To successfully coordinate motion of multiple mobile robots, a common approach is to assign priority level to each mobile robot. A mobile robot with highest priority takes into account only static obstacles while other mobile robots have to take into account both static obstacles and dynamic obstacles, which are the mobile robots with higher priorities. Avoiding dynamic obstacles is based on avoiding time-obstacles in the collision map. A mobile robot efficiently avoids the time-obstacles by minimum startup delay time of its original trajectory along the predefined path. By applying the same principle to all lower priority robots a collision free motion coordination of multiple mobile robots can be achieved. A proposed method is fast and more intuitive then delaying a nonlinear traveling length versus sampling time curve or mixed integer linear programming formulation, which checks overlap between two intervals of any collision-time interval pair.
Curvature-corrected learning dynamics in deep neural networks

Deep neural networks exhibit complex learning dynamics due to their non-convex loss land-scapes. Second-order optimization methods facilitate learning dynamics by compensating for ill-conditioned curvature. In this work, we investigate how curvature correction modiﬁes the learning dynamics in deep linear neural networks and provide analytical solutions. We derive a generalized conservation law that preserves the path of parameter dynamics from curvature correction, which shows that curvature correction only modiﬁes the temporal proﬁles of dynamics along the path. We show that while curvature correction accelerates the convergence dynamics of the input-output map, it can also negatively affect the generalization performance. Our analysis also reveals an undesirable effect of curvature correction that compromises stability of parameters dynamics during learning, especially with block-diagonal approximation of natural gradient descent. We introduce fractional curvature correction that resolves this problem while retaining most of the acceleration beneﬁts of full curvature correction.
A random subspace based conic functions ensemble classifier

: Classifiers overfit when the data dimensionality ratio to the number of samples is high in a dataset. This problem makes a classification model unreliable. When the overfitting problem occurs, one can achieve high accuracy in the training; however, test accuracy occurs significantly less than training accuracy. The random subspace method is a practical approach to overcome the overfitting problem. In random subspace methods, the classification algorithm selects a random subset of the features and trains a classifier function trained with the selected features. The classification algorithm repeats the process multiple times, and eventually obtains an ensemble of classifier functions. Conic functions based classifiers achieve high performance in the literature; however, these classifiers cannot overcome the overfitting problem when it is the case data dimensionality ratio to the number of samples is high. The proposed method fills the gap in the conic functions classifiers related literature. In this study, we combine the random subspace method and a novel conic function based classifier algorithm. We present the computational results by comparing the new approach with a wide range of models in the literature. The proposed method achieves better results than the previous implementations of conic function based classifiers and can compete with the other well-known methods.
A NoSQL data management infrastructure for bridge monitoring

Advances in sensor technologies have led to the instrumentation of sensor networks for bridge monitoring and management. For a dense sensor network, enormous amount of sensor data are collected. The data need to be managed, processed, and interpreted. Data management issues are of prime importance for a bridge management system. This paper describes a data management infrastructure for bridge monitoring applications. Specifically, NoSQL database systems such as MongoDB and Apache Cassandra are employed to handle time-series data as well the unstructured bridge information model data. Standard XML-based modeling languages such as OpenBrIM and SensorML are adopted to manage semantically meaningful data and to support interoperability. Data interoperability and integration among different components of a bridge monitoring system that includes on-site computers, a central server, local computing platforms, and mobile devices are illustrated. The data management framework is demonstrated using the data collected from the wireless sensor network installed on the Telegraph Road Bridge, Monroe, MI.
Making Tea in Place: Experiences of Women Engaged in a Japanese Tea Ceremony

This paper reports a small-scale qualitative study involving women who participated in the occupations of tea ceremony; an essential part of Japanese culture. Interviewing the women revealed the importance of using special tea implements in a performance comprising a series of occupational procedures from making the tea and hosting guests, to putting things away in a special place. Findings showed how engaging in tea ceremony enabled the sharing of common emotions, a sense of ongoing personal development, concentration, and a feeling of presence. The women's stories showed how the values espoused in the tea room, which is a special place in Japanese culture, gave meaning to ongoing participation in this tradition. These findings reveal that specific places have the potential to influence presence during occupational engagement and encourage the suspension of usual routines, learning new skills, and connecting with others who share common goals.
SSTD

Streaming spatio-textual data that contains geolocations and textual contents, e.g., geo-tagged tweets, is becoming increasingly available. Users can register continuous queries to receive up-to-date results continuously, or pose snapshot queries to receive results instantly. The large scale of spatio-textual data streams and huge amounts of queries pose great challenges to the current location-based services, and call for more efficient data management systems. In this paper, we present SSTD (Streaming Spatio-Textual Data), a distributed in-memory system supporting both continuous and snapshot queries with spatial, textual, and temporal constraints over data streams. Compared with existing distributed data stream management systems, SSTD has at least three novelty: (1) It supports more types of queries over streamed spatio-textual data; (2)SSTD adopts a novel workload partitioning method termed QT (Quad-Text) tree, that utilizes the joint distribution of queries and spatio-textual data to reduce query latency and enhance system throughput. (3) To achieve load balance and robustness, we develop three new workload adjustment methods for SSTD to fit the changes in the distributions of data or queries. Extensive experiments on real-life datasets demonstrate the superior performance of SSTD.
Pre-filtering Mobile Malware with Heuristic Techniques

With huge amounts of new Android applications released every day, in dozens of different marketplaces, Android malware unfor- tunately have no difficulty to sneak in and silently spread, and put a high pressure on antivirus teams. To try and spot them more easily, we built an infrastructure, named SherlockDroid, whose goal is to filter out the mass of applications and only keep those which are the most likely to be malicious for future inspection by anti-virus teams. SherlockDroid is made of marketplace crawlers, code-level property extractors and a data mining software which decides whether the sample looks malicious or not. This data mining part is named Alligator, and is the main focus of the paper. Alligator classifies samples using clustering techniques. It first relies on a learning phase that determines the intermediate scores to apply to clustering algorithms of Alligator. Second, an operational phase classifies new samples using previously selected algorithms and scores. Alligator has been trained over an extensive set of both genuine Android applications and known malware. Then, it was tested for proactiveness, over new and more recent applications. The results are very encouraging and demonstrate the efficiency of this first heuristics engine for efficiently pre-filtering Android malware.
Semantic Visual Decomposition Modelling for Improving Object Detection in Complex Scene Images

We propose a systematic method for constructing a compositional model for recognising object instances in images of real life subjects. The model is trained on a set of visual examples of contained in a given image, in order to capture the visual characteristics of the contained objects, and to derive spatial relationships between the internal key sub-components of each object instance. The recognition method focuses on extracting visual similarities at the component level in three feature spaces: histogram of boundary distribution, intensity histogram, and histogram of oriented gradient (HOG). Principle Component Analysis (PCA) is used for the component selection and feature weighting. The proposed recognition method is not only capable of improving the accuracy of popular object detection algorithms, but also offers a systematic way of generating detection models.