Miracle-dz/UniFAR-FTUs huggingface dataset:
Physically Consistent Soft-Sensor Development Using Sequence-to-Sequence Neural Networks

Soft sensors attempt to predict the key quality variables that are infrequently available using the sensor and manipulated variables that are readily available. Since only limited amount of labeled data are available, there is always the concern whether the underlying physics were captured so that the model can be reasonably extrapolated. A sequence-to-sequence model in the form of a nonlinear state-observer/encoder and predictor/decoder was proposed. The observer can be trained using a large amount of unlabeled data, but in a supervised manner in which the process dynamics is tracked. The encoder output and manipulated variables are used to train the quality predictor. The model is applied to the product impurity predictions of an industrial column. Results show that good predictions and excellent consistency in the sign of estimated gains can be achieved even with limited amount of data. These findings indicated that the proposed sequence-to-sequence data-driven approach is able to capture the underlying physics of the process.
Our Data-Driven Future: Promise, Perils, and Prognoses

In this chapter, we discuss both the great advantages and potential harms ushered in by this era of data collection, as well as ways to mitigate the harms while maintaining the benefits. Specifically, we propose and discuss classes of potential solutions: methods for collecting less data overall, transparency of code and models, federated learning, and identity management tools, among others. Some of these solutions can be implemented now, others require a longer horizon, but all can begin through the advocacy of marketing research. We also discuss possible ways to improve on the benefits of data collection – by developing methods to assist individuals pursue their long-term goals while advocating for privacy in such pursuits.
Bibliometric Analysis of Affective Computing Researches during 1999~2018

ABSTRACT Affective computing focuses on technologies and theories that advance understanding of human affect, considering emotion and cognition in the design of related technologies to fulfill human needs, which gains substantial attention of researchers all over the world. To provide an insight into affective computing researches, this paper utilizes the method of bibliometric analysis to obtain information with respect to when and where the researches were performed by whom and how the mainstream contents evolved over the years. BibExcel and CiteSpace were employed to conduct the performance analysis and co-citation network analysis, including the analysis of the performance of countries, journals, institutes, authors and research hotspots. A total number of 1,625 documents published from 1999 ~ 2018 were screened to conduct quantitative analysis, which were retrieved in the Web of Science database with defined search terms. This paper can enable researchers to gain wider and deeper insight into affective computing researches in the last decades through bibliometric analysis, thereby facilitating relevant researchers having a general understanding of aggregate performance in the affective computing field and finding research directions in the future.
AEKF-based Method of SOC Estimation for Batteries under the Active Short-time External Short Circuit

State of charge (SOC) estimation is a key function of battery management system. This study examines the possibility of estimating the battery SOC under a certain short time External short circuit (ESC) process. Active short time ESC is sometimes useful in special applications that may not cause serious outcomes. The following contents are introduced in this papery. Firstly, ESC of cell experiment is designed under different SOC and temperatures. Then, genetic algorithm (GA) is used to identify model parameter, and SOC is estimated based adaptive extended Kalman filter (AEKF). It is illustrated that the presented SOC estimation can be well used in active ESC process with error less than 0.2%.
Multi Objects Detection and Tracking System for Smart Home using Wireless Sensor Network

The current developed event detection and tracking systems have several limitations that make them not suitable or efficient to be utilized in smart homes. These limitations include dealing with direction changes and varying speeds of an evolving events, node fault tolerance, energy consumption, missing of target, tracking precision, dynamicity of the target, and prediction accuracy. The situation become more difficult in term of multi-targets. Efficient tracking after the detection of an event based on robust data control and management would definitely help in making reliable distributed control decisions, therefore a reliable system is highly needed. In this paper we proposed a system for monitoring and detecting of continuous moving objects according to certain requirements and specifications that are related to smart home concept. The system can be detecting and tracking multi objects in the monitoring area that are moving randomly with varying speed by using ordinary sensors. The proposed system able to collect and analysis the data during their development in time and space. In this paper have been used a dynamic sensing model for accurate observation and delectability of the event. The simulation results of the proposed system show high detection and tracking accuracy for detection and tracking multi continues moving objects. The proposed system shows high tracking accuracy in term of single reach to 97 % and multi-targets (up to 5) reach to more than 94 %.
Convergence Rate of Fourier Neural Networks

The paper investigates a convergence rate for 2-layer feedforward Fourier Neural Network (FNN). Such networks are motivated by the approximation properties of wellknown Fourier series. Several implementations of FNNs were proposed since 1990’s: by Gallant and White; A. Silvescu; Tan, Zuo and Cai; Liu. The main focus of this research is Silvescu’s FNN, because such activation function does not fit into the category of networks, where the linearly transformed input is exposed to activation. The latter ones were extensively described by Hornik in 1989. In regard to non-trivial Silvescu’s FNN, its convergence rate is proven to be of order O(1/n). The paper continues investigating classes of functions approximated by Silvescu FNN, which appeared to be from Schwartz space and space of positive definite functions. Thesis Supervisor: Zhenisbek Assylbekov Title: Assistant Professor
Corpus-Based Techniques for Sentiment Lexicon Generation: A Review

: State-of-the-art sentiment analysis systems rely on a sentiment lexicon, which is the most essential feature that drives their performance. This resource is indispensable for, and greatly contributes to, sentiment analysis tasks. This is evident in the emergence of a large volume of research devoted to the development of automated sentiment lexicon generation algorithms. The task of tagging subjective words with a semantic orientation comprises two core approaches: dictionary-based and corpus-based. The former involves making use of an online dictionary to tag words, while the latter relies on co-occurrence statistics or syntactic patterns embedded in text corpora. The end result is a linguistic resource comprising a priori information about words, across the semantic dimension of sentiment. This paper provides a survey on the most prominent research works that utilize corpus-based techniques for sentiment lexicon generation. We also conduct a comparative analysis on the performance of state-of-the-art algorithms proposed for this task, and shed light on the current progress and challenges in this area.
Power System Optimization: Large-scale Complex Systems Approaches

An original look from a microeconomic perspective for power system optimization and its application to electricity markets • Presents a new and systematic viewpoint for power system optimization inspired by microeconomics and game theory • A timely and important advanced reference with the fast growth of smart grids • Professor Chen is a pioneer of applying experimental economics to the electricity market trading mechanism, and this work brings together the latest research • A companion website is available Edit
Gillespie-Lindenmayer systems for stochastic simulation of morphogenesis

Lindenmayer systems (L-systems) provide a useful framework for modelling the development of multicellular structures and organisms. The parametric extension of L-systems allows for incorporating molecular-level processes into the models. Until now, the dynamics of these processes has been expressed using differential equations, implying continuously valued concentrations of the substances involved. This assumption is not satisfied, however, when the numbers of molecules are small. A further extension that accounts for the stochastic effects arising in this case is thus needed. We integrate L-systems and the Gillespie’s Stochastic Simulation Algorithm to simulate stochastic processes in fixed and developing linear structures. We illustrate the resulting formalism with stochastic implementations of diffusion-decay, reaction-diffusion and auxin-transport-driven morphogenetic processes. Our method and software can be used to simulate molecular and higher-level spatially explicit stochastic processes in static and developing structures, and study their behaviour in the presence of stochastic perturbations.
Weighted support vector machine algorithm for efficient classification and prediction of binary response data

This paper proposes a weighted Support Vector Machine (w-SVM) method for efficient class prediction in binary response data sets. The proposed method was obtained by introducing weights which utilizes the point biserial correlation between each of the predictors and the dichotomized response variable into the standard SVM algorithm to maximize the classification accuracy. The optimal value of the proposed w-SVM cost and each of the kernels parameters were determined by grid search in a 10-fold cross validation resampling method. Monte-Carlo Cross Validation method was employed to examine the predictive power of the proposed method by partitioning the data into train and test samples using different sampling splitting ratios. Application of the proposed method on the simulated data sets yielded high prediction accuracy on the test sample. Results from other performance indices further gave credence to the efficiency of the proposed method. The performance of the proposed method was compared with three of the state-of-the art machine learning methods including the standard SVM and the result showed the superiority of this method over others. Finally, the results generally show that the modified algorithm with Radial Basis Function (RBF) Kernel perform excellently and achieved the best predictive performance than any of the existing classifiers considered.
Resistance Training for Judo: Functional Strength Training Concepts and Principles

POSSESSING THE NECESSARY COMPLEX SKILLS AND TACTICAL EXCELLENCE REQUIRED FOR SUCCESS IN JUDO COMPETITION REQUIRES THE ATHLETE BE ABLE TO MOVE WELL AND EFFICIENTLY AND BE ABSENT OF POSTURAL DISTORTIONS, FAULTY MOVEMENT PATTERNS, AND MUSCULAR IMBALANCES. RESISTANCE TRAINING PROGRAMS FOR THE JUDO ATHLETE SHOULD INCLUDE OBJECTIVES THAT INCREASE HIS/HER ABILITIES TO MOVE EFFICIENTLY, DEVELOP STRENGTH, SPEED, AND EXPLOSIVENESS AND PROMOTE RESISTANCE TO INJURY. INCORPORATING A FUNCTIONAL APPROACH TO A STRENGTH AND CONDITIONING PROGRAM, ONE THAT INCORPORATES RESISTANCE TRAINING TECHNIQUES THAT PROPERLY TRAIN THE ATHLETE'S SITUATIONAL NEEDS AND BODY FUNCTION, WILL FURTHER ENHANCE THESE ABILITIES.
Predicting Social Image Popularity Dynamics at Time Zero

This work addresses the task of forecasting the popularity achieved by images shared through social media over time. This task is known as “Popularity Dynamic Prediction”. The work is motivated by the fact that the popularity of social images, which is usually estimated at a precise instant of the post lifecycle, could be affected by the period of the post (i.e., how old is the post). To this aim, we exploited a recently released dataset for popularity dynamic prediction that includes about 20.000 images uploaded on Flickr and their sequences of engagement scores (i.e., number of views, number of comments and number of favorites) with a daily granularity. To build such a dataset, each image and its accompanying meta-data and statistics are downloaded within a few hours from the image posting on the social platform. Then, an automatic procedure collected the daily engagement scores of each observed picture for 30 days. The paper presents an approach in which the engagement score is formulated as a composition of two information associated to the evolution over time (shape) and the order of magnitude (scale) of the sequence. The two properties are inferred individually, then the two results are combined to predict the popularity dynamics over <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> days. This paper presents exhaustive experiments on the addressed task, evaluating a large number of experimental settings for the predictions of popularity sequences with different length <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">$n = 10$ </tex-math></inline-formula>, 20 or 30). In all settings, the prediction performed by the proposed method can be computed before the image is posted (i.e., at time zero).
How do I use the

The diaphragm is a barrier method of birth control which is inserted in a woman's vaginal canal to prevent pregnancy. It is a dome-shaped rubber cup with a flexible rim and is designed to be used with a spermicidal cream or jelly and must be inserted into the vagina before intercourse. It fits snugly under the bottom portion of the cervix and behind the pubic bone creating a barrier between sperm and the cervix.
Vitamin D – beyond skeletal activities

Witamina D jest znanym od lat czynnikiem niezbędnym dla kości oraz prawidłowego funkcjonowania gospodarki mineralnej. Udział jej niedoborów w patogenezie schorzeń z pogranicza endokrynologii, ginekologii i reumatologii, tj. krzywicy, osteomalacji oraz osteoporozy, jest dobrze udokumentowany. Głównym źródłem witaminy D u ludzi jest synteza skórna. Pomimo braku wspólnie przyjętej definicji optymalnego stężenia witaminy D w surowicy liczne opublikowane prace donoszą o rozpowszechnieniu hipowitaminozy D na całym świecie. Polscy eksperci zalecają wdrożenie populacyjnej strategii uzupełniania cholekalcyferolu u osób dorosłych. Szczególnie ważna w tym zakresie jest grupa kobiet w wieku okołomenopauzalnym – udowodniono dla niej skuteczność suplementacji witaminą D w profilaktyce złamań osteoporotycznych zarówno kręgowych, jak i pozakręgowych. Pośrednio prawidłowe zaopatrzenie w witaminę D wpływa na ryzyko upadków, które także stanowią czynnik złamania osteoporotycznego. Coraz więcej jest doniesień mówiących o licznych, pozaustrojowych działaniach tej rozpuszczalnej w tłuszczach witaminy, m.in. : w modulowaniu odpowiedzi immunologicznej, hamowaniu nowotworzenia, wpływie na wysokość ciśnienia tętniczego i gospodarkę węglowodanową. Hipowitaminozę D łączy się także ze zwiększonym ryzykiem rozwoju chorób sercowo-naczyniowych oraz śmiertelności z tych przyczyn. Molekularne podłoże tych zależności jest ciągle dyskutowane. Brakuje jednoznacznych dowodów co do wpływu suplementacji cholekalcyferolu na rozwój omawianych procesów, jednak dane z badań przekrojowych i obserwacyjnych wskazują na związek hipowitaminozy D z rozlicznymi patologiami. Słowa kluczowe: witamina D, cholekalcyferol, osteoporoza, nadciśnienie tętnicze, choroby autoimmunologiczne, choroby sercowo-naczyniowe, cukrzyca.
A reply

To the Editor: We note the comments made by Brothers et al., and as highlighted, the incorrectly quoted p-value was indeed referring to the significantly increased rates of severe abacavir hypersensitivity detected in the Zodiac Study (1). We would like to point out that despite the authors’ reservations regarding conclusions drawn from the James et al. Zodiac data, the current US prescribing information for abacavir states ‘once-daily dosing of ZIAGEN was associated with more severe hypersensitivity reactions’ based on this study (2). The Brothers et al. analysis of four randomised controlled trials of once-daily abacavir (CAN 30021, CAL 30001, ESS 30009 and COL 40263) demonstrated suspected and severe hypersensitivity rates of 8 and 4%, respectively (3).
Role of Novel Sequencing Approaches in General Health Development

In April 2003, the Human Genome Project (HGP) was completed after 13 years of continuous genome analysis using Sanger sequencing (1). More than 20 institutions, companies, and laboratories were involved in HGP. Although this is commonly acknowledged as a historical improvement in human genetic studies, there are still hundreds of genomic gaps left which must be resolved. Furthermore, the project costed nearly $3 billion (roughly $5 billion considering inflation)! Nowadays, however, such enterprise can be finished only in one day, at a cost a little more than a grant per sample, thanks to next generation sequencing (NGS) platforms. NGS (e.g. Roche, Illumina, SOLiD) and the further novel technology of next-next generation sequencing (e.g. Pac-Bio SMRT) have obviously fueled genetic studies, especially molecular cancer studies, at dramatically lower costs (2). This significantly contributes to the human health improvement and facilitates finding some new treatment modalities for the old diseases (e.g. cancers). The sequencing platforms and related computational biology analyses have even created a novel terminology called dry labs. Dry labs are laboratories where computational/applied mathematical analyses are carried out using computer-generated models to simulate biological processes in silico. Today, projects such as 1000 Genomes Project and 100 000 Genomes Project are programmed to help us better understand human genome and various deficiencies originating from it. Moreover, several other projects such as Human Connectome Project, Human Cytome Project, Human Microbiome Project, Human Proteome Project, and Human Variome Project are carried out to address various factors involving human health/disease balance. One of the main genetic study areas that can mostly be benefited from is microbiome. Although the vital role of microbiota in eukaryotes has been known for a long time, recent metagenomic studies have amazingly revealed that this role is not limited to what we previously assumed. Generally, human body is the host of approximately 100 trillion microbial cells (3). This number is nearly 10 times greater than the number of human somatic cells and even stars within the Milky Way Galaxy! More than 10 000 microbial species have already been identified in human body. Since nearly 95% of the human microbiota reside in gastrointestinal tract, it is suggested that more than 90% of all diseases are somehow associated to the oral microbiota population. Examples include a wide variety of diseases from simple constipation, obesity, and irritable bowel syndrome to metabolic and mental disorders, autoimmune deficiencies, and cancers. However, this is not limited to those in gastrointestinal tract, as other microbiota such as those in respiratory tract or urogenital system are linked to local or general diseases. Thus, metagenomic studies conducted on the basis of novel sequencing approaches on microflora of human body seem to be a master key for the currently closed medical doors.
An Embedded Visualization Method through Temporal Bibliographic Analysis

Visual analytics play an important role in understanding complex datasets, e.g. the bibliographic data set. Word clouds, which use a compact visual description of words, have been leveraged widely to provide the content overview of a set of documents, especially the hot topic trends. In this paper, we proposed a visualization method that embedding the temporal patterns along the word to visually illustrate the hot topic evolution.
StandUp: Engaging Professionals to Coach Design Projects

: To investigate how we might expand learning environments to include professionals to coach students enacting disciplinary practices, we created StandUp, a socially-shared regulation of learning (SSRL) system. We implemented StandUp in an undergraduate design program with 3 student teams and 5 volunteer professionals. We captured 12 online coaching interactions that significantly changed project trajectories. This suggests SSRL designs can encourage online coaching that influences project trajectories.
Human embryonic stem cell-derived mesenchymal stem cells as a therapy for spinal cord injury

Traumatic injury to the spinal cord interrupts ascending and descending pathways leading to severe functional deficits of sensory motor and autonomic function which depend on the level and severity of the injury. There are currently no effective therapies for treating such injuries and the adult central nervous system has very limited capacity for repair so that recovery is very limited and functional deficits are usually permanent. Cell transplantation is a potential therapy for spinal cord injury and a range of cell types are being investigated as candidates. Mesenchymal stem cells (MSCs) obtained from bone marrow are one cell type quite extensively studied. When transplanted into animal models of spinal cord injury these cells are reported to affect various aspects of repair and in some cases to improve functional outcome according to behavioural measures. However, the use of these cells has several limitations including the need for an invasive harvesting procedure, variability in cell quality and slow expansion in culture. This project therefore had two main aims: Firstly to investigate whether MSC-like cells closely equivalent to bone marrow derived MSCs could be reliably and consistently differentiated from human embryonic stem cells (hESCs) in order to provide an “off the shelf” cellular therapy product for spinal cord injury and secondly, to transplant such cells into animal models of spinal cord injury in order to, determine whether hESC-derived MSCs replicate or improve on the repair mechanisms reported for bone marrow MSCs.