Miracle-dz/UniFAR-FTUs huggingface dataset:
An ubiquitous smart guitar system for collaborative musical practice

ABSTRACT Ubiquitous music (UbiMus) proposes to study how social interaction with mobile and distributed technologies can converge to form novel creativity support tools and music artistic practices. A recent field overlapping with UbiMus is the Internet of Musical Things, which refers to ecosystems of interconnected embedded computers (Musical Things) enabling users to produce, interact with or experience musical content. Musical Things embed electronics, sensors, data forwarding and processing software into physical or virtual objects. Smart musical instruments (SMIs) are an emerging class of Musical Things provided with capabilities of capturing and receiving data supporting instrumental musical practice. Due to their portability and self-containdeness, SMIs enable novel ubiquitous interactions between performers of acoustic and digital musical instruments. After a review of current trends in SMI research, we propose an ubiquitous smart guitar system which uses the guitar as a hub for collaborative music making. We then present a survey conducted with 18 performers to assess the usability, creativity support and engagement with the system. Results show a positive emotional engagement with the system which overall was found easy to use and novel. We also discuss several barriers to creative interaction related to the size of the user interface, creative agency and personalisation.
A hierarchical approach to measure service performance in the resort hotel’s service encounters / Yun Yang and Andrew Chan

Although a number of researchers have proposed models to better investigate service performance in the hospitality industry, many of them are still along the Oliver’s (1981) expectation-disconfirmation theory. To more effectively and directly measure service performance, the performance-based measurement of applying multilevel and multidimensional theory has been highly recommended in recent years. In view of the characteristics of service performance in the resort hotel, a hierarchical model that combined the concept of service encounters and Rust & Oliver’s (1994) three service dimensions is initially proposed in this study according to literature-based insights. Combining the results from in-depth interviews and reviews of the literature on the measurement scales of hospitality service performance, this study then identifies sub-dimensions and underlying attributes in the conceptual model. From perceiving service performance in the resort hotel, obvious distinctions and emphases that are different from those of the traditional or commercial hotel are also found in this study
Modeling and Control of a Tungsten–Bulb Heated Incubator: Teaching Controls Theory in a General Engineering Program

Dr. Jianchu (Jason) Yao is an Associate Professor with the Department of Engineering at East Carolina University (ECU), Greenville, North Carolina, USA. He received a B.S. and M.S. degrees in electrical engineering from Shaanxi University of Science and Technology, China, in 1992 and 1995, respectively, and the Ph. D. degree in electrical engineering from Kansas State University in 2005. He served as a controls engineer in China from 1995 to 2000. His current research interests include wearable medical devices, telehealthcare, bioinstrumentation, biosignal processing, and control systems. His educational research interests are laboratory/project-driven learning and integration of research into undergraduate education. Dr. Yao is a member of the American Society of Engineering Education and a senior member of Institute of Electrical and Electronics Engineers (IEEE).
Analysis of the Proximate Composition, Anti-Nutrients and Mineral Content of Maerua Crassifolia Leaves

This study was carried out to determine the proximate composition, anti-nutrient and the mineral content of Maerua crassifolia leaves. Proximate analysis was performed using standard AOAC methods, mineral contents were determined using atomic absorption spectrometry while the anti-nutrients were also determined using standard methods. The results revealed that the leaves had a moisture content of 62.00 %, an ash content of 12.17± 0.47 g/100 g and a crude protein content of 15.17 ± 0.02 g/100 g. The value of the minerals ranged between 75.33 ± 0.05 mg/kg for potassium and 2.88 mg/kg for magnesium. For the heavy metals, zinc had the highest value of 0.22 ± 0.02 μg/mg. The sodium: potassium ratio was calculated to be 0.92 ±0.00. All the anti-nutrients studied were below the lethal doses recommended. The results obtained in this study indicate that this leaf will be a good source of nourishment is properly utilized.
Application of soy protein isolate in the fining of red wine

Soy protein isolate was evaluated as a potential fining agent as an alternative to the predominant protein commercial fining agents (ovalbumin, porcine gelatin, and pea protein isolate). Two red wines (cv. ‘Merlot’ and cv. ‘Lambrusco Maestri’) were finned, bottled, and analyzed for phenolic content, color, turbidity, and sensory profile. Independent of the protein used, fining promoted a reduction in a majority of the phenolic compound, a slight (but significant) reduction of chromatic properties, and a decrease in the turbidity of the wines. A decrease in astringency, persistence, bitterness, and wine body, as well as an increase in brightness, clarity, and acidity, was also observed in all treatments. The application of soy protein isolates yielded similar results to those obtained with other commercial fining agents, both in the physicochemical and the sensory measures, which favors its usage as an alternative to the traditional fining agents of animal-protein origin.
Apriori Based: Mining Positive and Negative Frequent Sequential Patterns

Sequential pattern mining is aims to discover interesting sequential patterns in a sequence database and it is one of the essential data mining tasks widely used in various application fields. Positive and negative sequential Patterns mining is an aim to find more interesting sequential patterns, considering the different minimum support of each data item in a sequence database. Generally, the generation order of data elements is considered to find sequential patterns. Advancements in Statistics, Machine Learning, Artificial Intelligence, Pattern Recognition and Computation capabilities have evolved the present day's data mining applications and these applications have enriched the various fields of human life including business, education, medical, scientific etc.
Symmetry Degree Measurement and its Applications to Anomaly Detection

Anomaly detection is an important technique used to identify patterns of unusual network behavior and keep the network under control. Today, network attacks are increasing in terms of both their number and sophistication. To avoid causing significant traffic patterns and being detected by existing techniques, many new attacks tend to involve gradual adjustment of behaviors, which always generate incomplete sessions due to their running mechanisms. Accordingly, in this work, we employ the behavior symmetry degree to profile the anomalies and further identify unusual behaviors. We first proposed a symmetry degree to identify the incomplete sessions generated by unusual behaviors; we then employ a sketch to calculate the symmetry degree of internal hosts to improve the identification efficiency for online applications. To reduce the memory cost and probability of collision, we divide the IP addresses into four segments that can be used as keys of the hash functions in the sketch. Moreover, to further improve detection accuracy, a threshold selection method is proposed for dynamic traffic pattern analysis. The hash functions in the sketch are then designed using Chinese remainder theory, which can analytically trace the IP addresses associated with the anomalies. We tested the proposed techniques based on traffic data collected from the northwest center of CERNET (China Education and Research Network); the results show that the proposed methods can effectively detect anomalies in large-scale networks.
Development Of Highly Active Virginiamycin-producing Strain And Improvement Of Its Productivity Using Synthetic Adsorbing Resins

Virginiamycin, an antibiotic produced by some Streptomyces species, is widely used in veterinary and bioethanol production. It represents a natural mix of two different macrocyclic components, among which M1 and S1 factors are the main acting components. M1 and S1 act synergistically when present in the optimum ratio of 60-75% of M1 and 25-40% of S1. Due to a large number of genes involved into the virginiamycin biosynthesis, the development of overproducing strains able to synthesize M1 and S1 at a synergistic ratio with the total productivity exceeding 3-4 g/L still remains a relevant problem. Using a multi-step random UV mutagenesis of the Streptomyces sp. strain DSM40559, a highly active strain S 15-30 was obtained, which virginiamycin titer on a basic medium significantly increased that of the parental strain (2.6 and 0.35 g/L, respectively), and the M1:S1 ratio remained synergistic (72:28). Various sources of carbon, nitrogen, and macroelements were evaluated for medium improvement, and several different types of synthetic macroporous resins were tested to provide the highest virginiamycin titer in culture broth of the developed strain. The resulting improved fermentation medium supplemented with 20 g/L of Amberlite XAD-16 resin increased strain productivity up to 5.03 ± 0.12 g/L with the simultaneous maintenance of the M1:S1 ratio within the synergistic range (72:28) and highly selective level of adsorption of the antibiotic from culture broth (92-95%). The variability of the M1:S1 ratio in the total antibiotic titer depending on various medium composition and resin type was first demonstrated. The obtained strain is promising for the industrial use due to its high productivity and the optimal M1:S1 ratio. Results of the study will be used for the further selection of overproducing strains and the scaling up of the virginiamycin production. The obtained data can be interesting for other researchers working in the field of production of macrolide antibiotics.
A 2.5- to 3.5-Gb/s adaptive FIR equalizer with continuous-time wide-bandwidth delay line in 0.25-μm CMOS

This paper presents an adaptive finite impulse response (FIR) equalizer with continuous-time wide-bandwidth delay line in CMOS 0.25-pm process for 2.5-Gb/s to 3.5-Gb/s data communications. To achieve wide bandwidth, fractionally spaced structure is used and an inverter with active-inductor load design is proposed as the delay cell of the tap delay line. Close loop adaptation of the fractionally spaced FIR equalizer is demonstrated using a low-power and area-efficient pulse extraction method as on-chip error detector. Measurement results show that the proposed adaptive equalizer achieves over 75% horizontal eye opening when the channel loss at the half-data-rate frequency varies from 4 dB to 21 dB at 2.5-Gb/s data rate. At 3.5-Gb/s data rate, the equalizer achieves 68% horizontal eye opening when the channel loss is about 9.3 dB at the half-data-rate frequency. The adaptive equalizer including the FIR filter and the error detector occupies 0.095 mm 2 die area and dissipates 95 mW at 2.5-Gb/s data rate from 2.5-V voltage supply.
Human Activity Recognition : Preliminary Results for Dataset Portability using FMCW Radar

This paper presents some preliminary results to develop a generalized system for human activity recognition (HAR) and detecting fall events using micro-Doppler signatures exploiting frequency modulated continuous wave (FMCW) radar. The core idea of this work is to demonstrate the portability and applicability of radar datasets for HAR, independent of geometrical environments and subjects involved. The experimental campaign involved different volunteers at four different geometrical locations. Two different machine learning algorithms such as support vector machine (SVM) and k-nearest neighbour (KNN), and one deep learning classifier namely GoogleNet are used to classify various human activities. The transfer learning method leveraging AlexNet algorithm is used to extract features from spectrograms to train and test the SVM and KNN classifiers. Four different scenarios are presented where datasets from three locations are combined to train and validate the classifiers, and test it on the remaining (leave-one-out) one. It is observed that the GoogleNet algorithm provides a consistent test accuracy between 68.5% to 81% for four different locations.
Cálculos ab initio para investigação de propriedades eletrônicas e espectroscópicas de complexos de epiisopiloturina com Cu e Zn

Understanding the properties of metal complexes is fundamental goal in the development of drugs. However, both from the experimental point of view and theoretical modeling, many difficulties in the simulation and evaluatio of the changes that the metal causes upon complexation still remains. This work presents ab initio computational simulations of Epiisopiloturin complexes with Cu and Zn. We make structural optimizations, simulations of vibrational spectroscopy, NMR and EPR, as well as reactivity studies. We obtain an optimized geometry that corresponds to that of the crystallized form. In addition, the spectroscopic results confirm that the complexes remain intact in solution. Finally, the reactivity studies confirm that the copper complex increases the reactivity of the molecule.
From "Formative Evaluation" to "Assessment for Learning"

Since the concept of "formative evaluation" was brought up,its meaning has been changing,and the term used shifted as well.In its historical evolution,the term used shifted from "formative evaluation" to "formative assessment",and then "assessment for learning". During the process,the connotation has also developed,that is,the assessment has expanded from a focus on the effects of curriculum development to that on the students' learning quality.As for student learning quality,the emphasis is shifted from diagnosing students' learning outcome periodically to collecting information of learning process frequently to obtain students' learning feedback and adjust instruction accordingly,and then to the integration of different assessment methods with the core value of facilitating learning and integrating them into the instruction process continuously to promote students' self-directed learning.
3 Dimensional Manipulation Operation in Virtual and Voice Integrated Environment (3D M. O. V. V. I. E)

As the technology developed over by time people wants to get along with it. One such technology is the virtual and voice integrated environment, there has been several research going on with these technology, it is so far in the development stage. In this paper creating this technology by using a 3D frame which can rotate about 360 degree and monitor every movement for the virtual control environment and the XML coding overlay upon the windows speech recognition for the voice integrated environment by combining these two with the Internet of Things We are taking this technology to the next level by creating a complete and total control over any IOT enabled devices and the PC. By combining all this technology into a single unit it can serve wide range of applications such a starting from taking a full control over PC to automating a day to day electronic devices. There is no limit to the number of possible combinations so the application will be limitless. In this paper the work is on a complete virtual and voice environment with the cutting edge virtual and voice platform which are in experimental stage. This paper have also devised a prototype with virtual capacitive environment that works on the basic concept of capacitive effect yet the prototype environment was a highly successful model that transformed basic capacitance into a highly accurate virtual control environment.
Determination of Free Doxycycline Concentrations in the Plasma and Milk of Sheep and in the Plasma of Rabbits by using the HPLC Method

Abstract A fast and sensitive HPLC method was validated in order to analyze doxycycline in plasma and milk of sheep and in plasma of rabbits. The samples were processed with trifluoroacetic acid (TFA). After the centrifugation step, a supernatant containing extracted doxycycline and internal standard oxytetracycline was injected into the HPLC system with PDA detection. The method showed linearity in the range of 0.125 - 2.5 µg/mL for ovine plasma, 0.125 – 5.0 µg/mL for ovine milk, and 0.125 – 1 µg/mL for rabbit plasma. The inter-assay precision varied between 5.69 – 13.55 %. Values for intra-assay precision were between 0.62 – 8.67 %. Accuracy was higher than 90% in all of the tested concentrations in the three types of biological matrices. The mean extraction recovery was higher than 90 % for all matrices. In order to handle only with free drug concentrations, microfiltration of standard solutions with low (0.25mg/mL), medium (0.5mg/mL) and high (1.0mg/mL) concentration was performed. A percentage for correction of the quantified doxycycline was calculated. The most significant adjustments should be made at the low concentrations. The correction for rabbit plasma is 24.63±5.03%, for ovine plasma is 20.10±8.01% and for milk–16.68±0.04 %. This method can be used for routine determination of doxycycline concentrations for pharmacokinetic studies and further dosage adjustment.
Multiple video texture mapping and fusion based on OpenGL shading language

To establish the wide-field augmented virtual environment(AVE)by fusing multiple real-time videos with 3Dmodels,and to eliminate the errors of reverse projections,texture distortions and obvious video seams,the reverse projection culling algorithm and the depth test algorithm were applied to the fragment shader base on the OpenGL shading language(GLSL). Then the horizontal weighted algorithm and the diagonal weighted algorithm based on the GLSL were implemented and applied to fuse the overlap area of two adjacent videos.The results show that the proposed algorithms can fuse multiple videos with 3Dmodels efficiently,which runs a three-dimensional scene containing 2million triangular facets and 6real-time videos at more than 40 fps on a computer with 1GB graphics card memory.
The Fundamentals of Typography

Demonstrating the power and variety of typography from hand-drawn to kinetic, this fully updated new edition of The Fundamentals of Typography covers the principles of using type across a range of media. Starting with a comprehensive introduction to the history of typography, the authors provide detailed explanations and inspirational examples of type usage from leading practitioners from around the world. With expanded practice exercises and four new case studies, this book gives students everything they need to know to use type effectively and creatively.
Heterogeneous Silicon III-V Mode-Locked Lasers

Author(s): Davenport, Michael Loehrlein | Advisor(s): Bowers, John E | Abstract: Mode-locked lasers are useful for a variety of applications, such as sensing, telecommunication, and surgical instruments. This work focuses on integrated-circuit mode-locked lasers: those that combine multiple optical and electronic functions and are manufactured together on a single chip. While this allows production at high volume and lower cost, the true potential of integration is to open applications for mode-locked laser diodes where solid state lasers cannot fit, either due to size and power consumption constraints, or where small optical or electrical paths are needed for high bandwidth. Unfortunately, most high power and highly stable mode-locked laser diode demonstrations in scientific literature are based on the Fabry-Perot resonator design, with cleaved mirrors, and are unsuitable for use in integrated circuits because of the difficulty of producing integrated Fabry-Perot cavities.We use silicon photonics and heterogeneous integration with III-V gain material to produce the most powerful and lowest noise fully integrated mode-locked laser diode in the 20 GHz frequency range. If low noise and high peak power are required, it is arguably the best performing fully integrated mode-locked laser ever demonstrated. We present the design methodology and experimental pathway to realize a fully integrated mode-locked laser diode. The construction of the device, beginning with the selection of an integration platform, and proceeding through the fabrication process to final optimization, is presented in detail. The dependence of mode-locked laser performance on a wide variety of design parameters is presented.Applications for integrated circuit mode-locked lasers are also discussed, as well as proposed methods for using integration to improve mode-locking performance to beyond the current state of the art.
Predicting student performance with data from an interactive learning system

Nowadays Interactive Learning Systems have been developed to provide students with new forms of practicing concepts. In this work we propose to predict if the student fails or succeeds in the introductory mathematics course based on the information collected by an interactive learning platform. The predicting models are based on binary support vector machines (SVM). As some of the collected data sets are unbalanced the study was conducted with suitable strategies to train this binary classiﬁer
A Fully Convolutional Neural Network for Recognition of Diabetic Retinopathy in Fundus Images

Diabetic retinopathy is one of the complexities of diabetics and a major cause of vision loss worldwide which come into sight due to prolonged diabetes. For the automatic detection of diabetic retinopathy through fundus images several technical approaches have been proposed. The visual information processing by convolutional neural network makes itself more suitable due to its spatial arrangement of units. Convolutional neural networks are at their peak of development and best results can be gained by proper use of the technique. The local connectivity, parameter sharing and pooling of hidden units are advantageous for various predictions. Objective of this paper is to design a model for classification of diabetic retinopathy. A fully convolutional neural network model is developed to classify the diseased and healthy fundus images. Here, proposed neural network consists of six convolutional layers along with rectified linear unit activations and max pooling layers. The absence of fully connected layer reduces the computational complexity of the model and trains faster as compared to traditional convolutional neural network models. The validation of the proposed model is accomplished by training it with a publicly available High-Resolution Fundus image database. The model is also compared with various existing state-of-the-art methods which show competitive result as compared to these models. A behavioural study of different parameters of the network model is represented. The intelligence of our model lies in its ability to re-tune weight to overcome outliers encountered in future. The proposed model works well with satisfactory performance.