Miracle-dz/UniFAR-FTUs huggingface dataset:
On the ability of chromatographic mass balance to predict solute diffusivity in drug delivery systems

Correspondence Filippo Rossi, Department of Chemistry, Materials and Chemical Engineering “Giulio Natta”, Politecnico di Milano, via Mancinelli 7, 20131 Milan, Italy. Email: filippo.rossi@polimi.it Abstract The ability to predict the drug diffusion coefficient within hydrogel-based drug delivery devices has a pivotal role in the design of these materials. In the last years, many mathematical models have been developed, but they often rely on fitted parameters with a consequent limitation in terms of prediction. Indeed, they are mainly centered on the pure Fickian diffusion together with degradation and swelling contributions. However, especially with a drug concentration typical of pharmacological treatments, several other mechanisms such as drug–polymer and drug–drug interactions cannot be neglected. In this work, we checked the ability of a simple mathematical model to estimate diffusion coefficients of drugs loaded within hydrogel considered as a chromatographic stationary phase. Mathematical modeling satisfactorily matched with different sets of literature data proving that our assumptions are able to describe the key phenomena governing the device's behavior.
Design of K-means clustering algorithm in PGAS based Mapreduce framework

Clustering large volumes of data is a complicated and time-consuming task. The main goal of clustering task is to explore a given dataset and find appropriate set of cluster centers that have maximum within-cluster and minimum inter-cluster similarity.In our work we present parallel K-means clustering algorithm based on Partitioned Global Address Space implementation of Mapreduce model. The main idea of our approach is to exploit data locality of partitioned global address space model to assign threads to different cluster centers.
Visual Tracking using Learning Histogram of Oriented Gradients by SVM on Mobile Robot

The intelligence of a mobile robot is highly dependent on its vision. The main objective of an intelligent mobile robot is in its ability to the online image processing, object detection, and especially visual tracking which is a complex task in stochastic environments. Tracking algorithms suffer from sequence challenges such as illumination variation, occlusion, and background clutter, so an accurate tracker should employ the appropriate visual features to identify target. In this paper, we propose using the histogram of oriented gradient (HOG), as an important descriptor. The descriptor simulates the performance of the complex cells in the primary visual cortex (V1) and it has low sensitivity to the illumination changes. In the proposed method, firstly, an object model is generated by training the HOG of multi first frames via an SVM classifier. Then, in order to track a new frame, the HOG descriptors are extracted from the surrounding areas of the target in the previous frame and convolved with the object model. Finally, the location with the highest score is defined as the target. The experimental results demonstrate the proposed method has significant performance compare to the state-of-the-art methods. Furthermore, we apply our algorithm to the mobile robot built by the robotics team to ensure its performance in a real environment.
Making Data Tangible for Data-driven Innovations in a Business Model Context

As digital transformation has occurred over the last decade, organizations have been compelled to seek new business models. As a consequence of this development, the impact of data on business models has become a focus of interest in research as well as in practice. Based on typical characteristics of data-driven business models (DDBMs), this paper develops 19 design principles for their visual representation. The design principles were derived from semi-structured interviews with experts in the field of DDBMs and were clustered into the Business Model Canvas (BMC). The contribution of this paper is threefold. First, the developed design principles deepen the knowledge base on DDBMs. Second, other business model representations can be assessed against these design principles and new or aligned representations can be developed. Third, the design principles can be used by practitioners to develop a DDBM.
Discovering Semantics from Visual Information

Visual information has been immensely used in various domains such as web, education, health, and digital libraries, due to the advancements of computing technologies. Meanwhile, users realize that it has been more and more difficult to find desired visual content such as images. Though traditional content-based retrieval (CBR) systems allow users to access visual information through query-by-example with low level visual features (e.g. color, shape, and texture), the semantic gap is widely recognized as a hurdle for practical adoption of CBR systems. Wealthy visual information (e.g. user generated visual content) enables us to derive new knowledge at a large scale, which will significantly facilitate visual information management. Besides semantic concept detection, semantic relationship among concepts can also be explored in visual domain, other than traditional textual domain. Therefore, this chapter aims to provide an overview of the state-of-the-arts on discovering semantics in visual domain from two aspects, semantic concept detection and knowledge discovery from visual information at semantic level. For the first aspect, various aspects of visual information annotation are discussed, including content representation, machine learning based annotation methodologies, and widely used datasets. For the second aspect, a novel data driven based approach is introduced to discover semantic relevance among concepts in visual domain. Future research topics are also outlined. Zhiyong Wang University of Sydney, Australia
Deep Reinforcement Learning for Process Control: A Primer for Beginners

Advanced model-based controllers are well established in process industries. However, such controllers require regular maintenance to maintain acceptable performance. It is a common practice to monitor controller performance continuously and to initiate a remedial model re-identification procedure in the event of performance degradation. Such procedures are typically complicated and resource-intensive, and they often cause costly interruptions to normal operations. In this paper, we exploit recent developments in reinforcement learning and deep learning to develop a novel adaptive, model-free controller for general discrete-time processes. The DRL controller we propose is a data-based controller that learns the control policy in real time by merely interacting with the process. The effectiveness and benefits of the DRL controller are demonstrated through many simulations.
A Review on Object Detection Based on Deep Convolutional Neural Networks for Autonomous Driving

Vehicle and pedestrian detection is significant in autonomous driving. It provides information for path planning, lane selection, pedestrian and vehicle tracking, pedestrian behavior prediction, etc. In recent years, the state-of-the-art object detection algorithms have been emerged on the base of deep convolutional neural networks, which can get higher accuracy and efficiency detection results than traditional vision detection algorithms. In this paper, we first introduce and summarize some state-of-the-date object detection algorithms based of deep convolutional neural networks and the improvement ideas of these algorithms. Their frameworks are extracted. Then, we choose several different algorithms and analyze their running results on challenging datasets, Pascal VOC and KITTI. Next, we analyze the current detection challenges as well as their solutions. Finally, we provide insights into use in autonomous driving, such as vehicle and pedestrian detection and driving control.
Beyond Matrix Completion of the traditional Recommender System

: The main goal of recommender systems is to predict unknown ratings of items for users. This can be seen as the task to complete the user-item matrix. Method such as matrix factorization can solve this task and have been successfully applied in various domains. However, for some scenarios these general approaches work not as well. So there is a need of some mechanism which can use user-item information and user ratings instead of classical two dimensional matrix recommender systems. In this paper, we have incorporated user-item background information using fuzzy logic in such a way that it improves the performance of traditional recommender systems.
Development of Beta (β) Carotene Enriched Drinking Yoghurt by Incorporating Carrot (Daucus carota) Pulp and Orange (Citrus sinensis) Juice

Beta (β) carotene is a natural antioxidant which is omnipresent in yellow, green vegetables and fruits. The main objective of this study was to develop a β carotene enriched drinking yoghurt by incorporating carrot pulp and orange juice. The mixing ratio of carrot pulp, orange juice and yoghurt base was selected by analyzing the data gathered from a preliminary sensory trial with respect to appearance, colour, taste, mouth feel, and overall acceptability of drinking yoghurt samples. The product was developed according to the selected best formula and was subjected to analysis of chemical composition and shelf life evaluation. The sample containing 10% carrot pulp, 10% orange juice and 80% yoghurt base was selected as the best formula. Coliform, yeast and mold were absent after 35 days. The β-carotene content, antioxidant capacity and pH of the developed product were 0.495±0.001 mg/100 g, 55.245±0.008% and 4.416±0.005 respectively. Collectively these results suggest that, the developed drinking yoghurt has 35 days of shelf life with high nutritional value including pro-vitamin A which is a natural antioxidant. Keywords: β-carotene, vitamin A, natural antioxidant, drinking yoghurt, carrot pulp
Analysis of mass spectrometric data: peak picking and map alignment

We study two fundamental processing steps in mass spectrometric data analys is from a theoretical and practical point of view. For the detection and extraction of mass spectral peaks we developed an e fficient peak picking algorithm that is independent of the underlying machine or ionization me thod, and is able to resolve highly convoluted and asymmetric signals. The method uses the multis cale nature of spectrometric data by first detecting the mass peaks in the wavelet-tr ansformed signal before a given asymmetric peak function is fitted to the raw data. In two optiona l stages, highly overlapping peaks can be separated or all peak parameters can be fur ther improved using techniques from nonlinear optimization. In contrast to currently established techniques, our algorithm is able to separate overlapping peaks of multiply charged peptides in LC -ESI-MS data of low resolution. Furthermore, applied to high-quality MALDI-TOF spectra it yields a high degree of accuracy and precision and compares very favorably with th e algorithms supplied by the vendor of the mass spectrometers. On the high-resolution MALDI spe ctra as well as on the low-resolution LC-MS data set, our algorithm achieves a fast runtime of o nly a few seconds. Another important processing step that can be found in every typical pro tocol for labelfree quantification is the combination of results from multiple LC-MS experiments to improve confidence in the obtained measurements or to compare results from differe nt samples. To do so, a multiple alignment of the LC-MS maps needs to be estimated. The alignment h as to correct for variations in mass and elution time which are present in all mass s pectrometry experiments. For the first time we formally define the multiple LC-MS raw and featu r map alignment problem using our own distance function for LC-MS maps. Fur thermore, we present a solution to this problem. Our novel algorithm aligns LC-MS sample s and matches corresponding ion species across samples. In a first step, it us es an adapted pose clustering approach to efficiently superimpose raw maps as well as feature maps. This is done in a star-wise manner, where the elements of all maps are transformed onto the c ordinate system of a reference map. To detect and combine corresponding featu res in multiple feature maps into a so-called consensus map, we developed an additional step base d on techniques from computational geometry. We show that our alignment approach is fast and reliable as compared to five other alignment approaches. Furthermore, we prove its robustness in the presence of noise and its ability to accurately align samples with only few commo n ion species.
Jointly Image Topic and Emotion Detection using Multi-Modal Hierarchical Latent Dirichlet Allocation

Image topic and emotion analysis is an important component of online image retrieval, which nowadays has become very popular in the widely growing social media community. However, due to the gaps between images and texts, there is very limited work in literature to detect one image’s Topics and Emotions in a unified framework, although topics and emotions are two levels of semantics that often work together to comprehensively describe one image. In this work, a unified model, Joint Topic/Emotion Multi-Modal Hierarchical Latent Dirichlet Allocation (JTE-MMHLDA) model, which extends previous LDA, mmLDA, and JST model to capture topic and emotion information at the same time from heterogeneous data, is proposed. Specifically, a two level graphical structured model is built to realize sharing topics and emotions among the whole document collection. The experimental results on a Flickr dataset indicate that the proposed model efficiently discovers images’ topics and emotions, and significantly outperform the text-only system by 4.4%, vision-only system by 18.1% in topic detection, and outperforms the text-only system by 7.1%, vision-only system by 39.7% in emotion detection.
Developing Visual Studio .NET Macros and Add-Ins

From the Publisher: Whether you simply need to automate a repetitive task or make a full-scale improvement to Visual Studio . NET, Jeff Cogswell will quickly teach you how in this comprehensive book. You'll find all the tools necessary to create customized macros and add-ins using the Macros Integrated Development Environment (IDE) of Visual Studio . NET, and the only print documentation available on how to gain complete control over the IDE's menus. With clear and concise details, Cogswell walks you through macro development, showing you how to automate processes in Visual Studio . NET. He explains how to use Visual Studio . NET to write add-ins for Microsoft Office, and how to integrate Microsoft Office products such as Word, Excel, and Outlook right into Visual Studio . NET. Along the way, he also uncovers the ins and outs of deploying your add-ins and presents expert tips and techniques on how to supercharge Visual Studio . NET.
Towards Safe Artificial General Intelligence

The field of artificial intelligence has recently experienced a number of breakthroughs thanks to progress in deep learning and reinforcement learning. Computer algorithms now outperform humans at Go, Jeopardy, image classification, and lip reading, and are becoming very competent at driving cars and interpreting natural language. The rapid development has led many to conjecture that artificial intelligence with greater-thanhuman ability on a wide range of tasks may not be far. This in turn raises concerns whether we know how to control such systems, in case we were to successfully build them. Indeed, if humanity would find itself in conflict with a system of much greater intelligence than itself, then human society would likely lose. One way to make sure we avoid such a conflict is to ensure that any future AI system with potentially greater-thanhuman-intelligence has goals that are aligned with the goals of the rest of humanity. For example, it should not wish to kill humans or steal their resources. The main focus of this thesis will therefore be goal alignment, i.e. how to design artificially intelligent agents with goals coinciding with the goals of their designers. Focus will mainly be directed towards variants of reinforcement learning, as reinforcement learning currently seems to be the most promising path towards powerful artificial intelligence. We identify and categorize goal misalignment problems in reinforcement learning agents as designed today, and give examples of how these agents may cause catastrophes in the future. We also suggest a number of reasonably modest modifications that can be used to avoid or mitigate each identified misalignment problem. Finally, we also study various choices of decision algorithms, and conditions for when a powerful reinforcement learning system will permit us to shut it down. The central conclusion is that while reinforcement learning systems as designed today are inherently unsafe to scale to human levels of intelligence, there are ways to potentially address many of these issues without straying too far from the currently so successful reinforcement learning paradigm. Much work remains in turning the high-level proposals suggested in this thesis into practical algorithms, however. Central claim: There are a number of theoretically valid, partial solutions to the problem of keeping artificial general intelligence both safe and useful.
Speech emotion recognition using emotion perception spectral feature

Speech emotion recognition is an important technique for human‐computer interface applications. Due to contain rich information of emotion, the spectral feature is widely used for emotion recognition. However, the recognition performance is limited because of imprecise extracted rule and uncertain size of resolution of spectral feature. To address this issue, motivated by speech coding, we introduced psychoacoustics model, provided a perception spectral subband partition method for obtaining more precise frequency resolution. Moreover, we also provided a new spectral feature on the divided subband frequency signals. The proposed feature includes emotional perception entropy, spectral inclination, and spectral flatness. Then, a Support Vector Machine classifier is used to recognize emotion categories. The experiment results show that the proposed spectral feature is superior to the traditional MFCC feature, and also better than the state‐of‐the‐art Fourier feature and multi‐resolution amplitude feature.
Implementation of Network Coding in XDK Sensors

The data collection has become more important for the industrial world in the last years. The data collected can be used for a large variety of purposes, as detecting fires and intruders, controlling machines’ behavior or simply controlling the environmental regulation of the heating or humidifiers. This work aims to implement and test the first Wireless Sensor Network (WSN) using XDK c © small wireless sensors created by Bosch GmbH, that have been used for gathering environmental data and exploiting the benefits of network coding. We introduce a simple routing protocol that allows the nodes to determine the useful packets, enabling them to collaborate with each other to route their measurements to the sink. Thanks to the network coding implementation, we will get some of the advantages, such as high reliability and ordered delivery, using the UDP protocol over wireless links even when the channel conditions are adverse. The proposed network coding scheme will implemented the finite field GF(2), which does not require high computational capabilities, as it only needs to use the XOR operation for encoding and decoding the packets. Moreover, the use of network coding takes advantage of the broadcast nature of the wireless medium, reduces the packet transmissions, has the potential of increasing the traffic balancing, therefore the nodes’ lifetime, which makes it perfect for the WSN world. Our implementation results will prove that the XDK sensors are capable of using network coding with small fields and small generation sizes. Finally we will present the future lines of work to improve the performance of the test bed, its utility and make its use and future updates more convenient for the users and the developers.
Optimization of Genetic Algorithm Toolbox Based on Matlab

Genetic algorithm is a random search and optimization method based on natural select and genetic mechanism of the living beings.It is successfully used in solving the complex optimization and the industrial engineering problem,and has attracted a lot of attention.GA toolbox is convenient to be used and has the characteristics such as as reliable calculation,high efficiency and visualized graphic results,so it has a broad application prospect.This paper introduces the GA toolbox.Expounds on the Encoding,Initiation,Selection,Xover,Mutation operation and Fitness function.Finally simulation shows the effectiveness and practicability of the GAOT in Matlab.
Multicast Joining Algorithm with QoS Constraints

One of multicast key technologies is to construct a multicast tree by group members'implicit joining or explicit joining.Aiming at explicit joining,this paper presents a multicast joining algorithm with QoS constraints.The algorithm introduces QoS parameters which not only satisfies the application requirement for QoS but also reduces the flooding range of the probe message.Finally,this paper proves that the path the algorithm finds satisfies QoS constraints.
Synthesis and Characterization of Urea–Formaldehyde Eco-Friendly Composite Based On Natural Fibers

Present work explains the synthesis of urea–formaldehyde composites with Allo (Girardinia diversifolia) and cotton (Gossypium arboreum) fibers. Urea–formaldehyde resin was synthesized by condensation polymerization of urea and formaldehyde monomers. Cellulose fibers were extracted by various chemical treatments of natural fibers. Composites were prepared with neat and extracted cellulose from Allo and cotton fibers by solution casting method. Fibers and composites were characterized by Fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM), optical microscopy (OM), thermo-gravimetric analysis (TGA) and micro-indentation tests. Results showed that composite with treated Allo fibers had better morphological, mechanical and thermal properties than untreated Allo and cotton fibers.