Miracle-dz/UniFAR-FTUs huggingface dataset:
Open-Closed-Loop PD Iterative Learning Control Corrected With the Angular Relationship of Output Vectors for a Flexible Manipulator

A new iterative learning control (ILC) approach combined with an open-closed-loop PD scheme is presented for a flexible manipulator with a repeatable motion task in the case that only the endpoint pose of the flexible link is measurable. This approach takes advantage of the fact that the ILC performance is independent of the model used, thereby overcoming the drawback of the heavy reliance of PD controllers on the modeling accuracy. The open-closed-loop PD controller is mainly used to simultaneously reduce the effects of the modeling error and disturbances to enhance the controller’s robustness. Meanwhile, an angular correction term is introduced by using the angular relationship of the system outputs to reward or penalize the ILC law. Specifically, when the current output tends toward the expected trajectory, the ILC law is enhanced accordingly; otherwise, it is penalized. The convergence conditions for the proposed approach are obtained through theoretical analysis, and experiments using a real flexible manipulator are presented. The results show that the proposed ILC scheme can overcome the impact of the endpoint error caused by link flexibility and has a good control effect.
Toward the quality prognostic of an aircraft engine workpiece in Inconel Alloy 625: case study and proposed system architecture

Manufacturing companies are under a constant pressure due to multiple factors: new competition, disruptive innovations, cost reduction request, etc. To survive, they must strive to innovate and adapt their business model to improve their productivity. Recent developments based on the concept of Industry 4.0 such as big data, new communication protocols and artificial intelligence provide several new avenues to explore. In the specific context of machining, we are working toward the development of a system capable of making the prognostic of the quality (in terms of dimensional conformance) of a workpiece in real time while it is being manufactured. The goal of this paper is to showcase a prototype of the data acquisition aspect of this system and a case study presenting our first results. This case study has been conducted at our industrial partner facility (Quebec, Canada) and is based on the manufacturing of an aircraft component made from Inconel alloy 625 (AMS5666). The proposed prototype is a data acquisition system installed on a 5 axis CNC machines (GROB model G352) used to acquire and to contextualize the vibration signal obtained from the CNC machine sensor. The contextualization of the data is a key component for future work regarding the development of a prognostic system based on supervised machine learning algorithms. In the end, this paper depicts the system architecture as well as its interactions between the multiple systems and software already in place at our industrial partner. This paper also shows preliminary results describing the relationship between the workpiece quality (in terms of respect toward the dimensional requirements) and the extracted features from the sensors signals. We conclude that it is now possible to do the diagnostic of a cutting operation. Additionally, with the same information we show that it is possible to quickly do the general diagnostic of the health state of the machine. Future work regarding this project will include data acquisition from a wider range of products (i.e. different shapes, materials, processes, etc.) and the development of a machine learning based prognostic model.
Emerging Solutions to the Standby Power Problem

Author(s): Gerber, Daniel; Meier, Alan; Hosbach, Robert; Liou, Richard | Abstract: Despite technical advances in efficiency, devices in standby continue to consume a significant amount of energy. Finding practical, cost-effective reductions is difficult. While the power consumption per unit has fallen, the number of units continuously drawing power continues to grow. This work investigates a family of technologies that offers a means of greatly reducing standby consumption in many types of electrical products. The underlying principle involves producing a wake-up signal just before service is needed. The wake-up signal activates a footer switch, which connects the device to the power supply. This work studies and prototypes several methods of generating the wake-up signal. The first generates a wake-up signal from harvested light energy and is applicable to remote-controlled devices with a line of sight activation, such as set-top boxes, ceiling fans, and motorized curtains. The second method uses an RF-based wake-up signal to activate a wake-up radio and is applicable to any wireless products. No single technology will address all standby power situations; however, these emerging solutions appear to have broad applicability to save standby energy in miscellaneous loads.
Machine Learning Approach for Text Summarization

With the abundance of interminable text documents, providing summaries can help in retrieval of relevant information very quickly. The technique is to extract those sentences from the document that contain important information. This paper presents the results of our research on extractive summarization with a method based on Support Vector Machines (SVMs). The SVMs are trained using DUC-2002 dataset and the importance of sentences is judged on the basis of salient features. To evaluate the performance of our system, comparisons are conducted with two existing methods. ROUGE scores are used to compare the system generated summaries with the human generated summaries, and the experimental results show that our system's performance achieved high metrics
Improving diagnosis of some brain disease by analysing chaotic indices of EEG signals

This paper investigated the effect of Electroencephalogram signals on diagnosis of the possible brain dysfunctions. It is the most common non-offensive way to analyse brain healthiness on the basis of chaotic theory. Normal person's EEG signals are different from the people who suffer from epilepsy, schizophrenia, post-traumatic stress disorder and Alzheimer's diseases in many respects, namely amplitude, frequency, statistical features and in general dynamic behaviour. The findings indicate EEG signals are non-linear and chaotic. EEG signal was considered as chaotic time series in the research. Diagnosis of diseases was conducted using analysis of chaotic parameters. They include Lyapunov exponent and correlation dimension. For this end, appropriate algorithms were rendered to extract necessity parameters for reconstructing phase space and calculated chaotic indices with relative consideration. The findings showed patients are recognised from healthy persons. It was also possible to distinguish two types of epilepsy, namely grand mal and temporal lobe. The results showed acceptable way of predicating epilepsy. Visual diagnosis of disorders by EEG is a big challenge for neurologist as complexity of the brain dysfunctions. This research can be useful for physicians to diagnose and predict diseases.
Pheniqs: Fast and flexible quality-aware sequence demultiplexing

Motivation Output from high throughput sequencing instruments often exceeds what is necessary to assay a single sample. To better utilize this capacity, multiple samples are independently tagged with a unique “barcode” sequence and are then pooled, or “multiplexed”, and sequenced together. Classifying, or “demultiplexing”, the reads involves decoding the barcode sequence. Although instruments estimate the probability of incorrectly calling each nucleobase, available demultiplexers do not consult those estimates or report classification error probabilities. Results We present Pheniqs, a fast and flexible sequence demultiplexer and quality analyzer. In addition to providing an efficient implementation of the widespread minimum distance decoder, Pheniqs introduces a novel Phred-adjusted maximum likelihood decoder that consults base calling quality scores and estimates the probability of a barcode decoding error. Setting an upper bound on the permissible error provides an intuitive way to control demultiplexing confidence and directly influence precision and recall. Pheniqs supports FASTQ and multiple Sequence Alignment/Map formats and uses auxiliary SAM tags to report both library classification and demultiplexing error probability. Evaluation on both real and semi-synthetic data indicates that Pheniqs is faster than existing demultiplexers, substantially when demultiplexing longer reads, and achieves greater accuracy by correctly reflecting quality measurements. Availability and Implementation Implemented in multithreaded C++ and available under the terms of the AGPL-3.0 license agreement at http://github.com/biosails/pheniqs. Manual and examples are available at http://biosails.github. io/pheniqs.
A Picture is Worth a Thousand Words: Measuring Investor Sentiment by Combining Machine Learning and Photos from News

By applying machine learning to accurately and cost effectively classify photos based on sentiment, we introduce a daily market-level investor sentiment index (Photo Pessimism) from a large sample of news photos. Between 1926 and 2018, Photo Pessimism predicts market return reversal and increase in trading volume. The predictability pattern is concentrated among stocks with high limits to arbitrage and during high uncertainty periods. Photo Pessimism enhances pessimism embedded in text, but subsumes it during days with traumas and influential photos. Photo Pessimism from business news has over six times stronger predictive power than Photo Pessimism from general news.
Salience in Usage-Based SLA

Psychological research uses the term salience to refer to the property of a stimulus to stand out from the rest. Salient items or features are attended, are more likely to be perceived, and are more likely to enter into subsequent cognitive processing and learning. Salience can be independently determined by physics and the environment, and by our knowledge of the world. It is useful to think of three aspects of salience, one relating to psychophysics, the other two to what we have learned:
Visualization of Biomedical Data

The rapid increase in volume and complexity of biomedical data requires changes in research, communication, and clinical practices. This includes learning how to effectively integrate automated analysis with high–data density visualizations that clearly express complex phenomena. In this review, we summarize key principles and resources from data visualization research that help address this difficult challenge. We then survey how visualization is being used in a selection of emerging biomedical research areas, including three-dimensional genomics, single-cell RNA sequencing (RNA-seq), the protein structure universe, phosphoproteomics, augmented reality–assisted surgery, and metagenomics. While specific research areas need highly tailored visualizations, there are common challenges that can be addressed with general methods and strategies. Also common, however, are poor visualization practices. We outline ongoing initiatives aimed at improving visualization practices in biomedical research via better tools, peer-to-peer learning, and interdisciplinary collaboration with computer scientists, science communicators, and graphic designers. These changes are revolutionizing how we see and think about our data.
Impact of corpus domain for sentiment classification: An evaluation study using supervised machine learning techniques

Thanks to the development of the internet, a large community now has the possibility to communicate and express its opinions and preferences through multiple media such as blogs, forums, social networks and e-commerce sites. Today, it becomes clearer that opinions published on the web are a very valuable source for decision-making, so a rapidly growing field of research called “sentiment analysis” is born to address the problem of automatically determining the polarity (Positive, negative, neutral,…) of textual opinions. People expressing themselves in a particular domain often use specific domain language expressions, thus, building a classifier, which performs well in different domains is a challenging problem. The purpose of this paper is to evaluate the impact of domain for sentiment classification when using machine learning techniques. In our study three popular machine learning techniques: Support Vector Machines (SVM), Naive Bayes and K nearest neighbors(KNN) were applied on datasets collected from different domains. Experimental results show that Support Vector Machines outperforms other classifiers in all domains, since it achieved at least 74.75% accuracy with a standard deviation of 4,08.
High-carbohydrate–low-glycaemic index dietary advice improves glucose disposition index in subjects with impaired glucose tolerance

Controversy exists about the optimal amount and source of dietary carbohydrate for managing insulin resistance. Therefore, we compared the effects on insulin sensitivity (S I ), pancreatic responsivity (AIR glu ) and glucose disposition index (DI=SI×AIRglu) of dietary advice aimed at reducing the amount or altering the source of dietary carbohydrate in subjects with impaired glucose tolerance (IGT). Subjects were randomized to high-carbohydrate–high-glycaemic index (GI) (high-GI, n 11), high-carbohydrate–low-GI (low-GI, n 13), or low-carbohydrate–high-monounsaturated fat (MUFA, n 11) dietary advice, with S I , AIR glu and DI measured using a frequently sampled, intravenous glucose tolerance test before and after 4 months treatment. Carbohydrate and fat intakes and diet GI, respectively, were: high-GI, 53 %, 28 %, 83; low-GI, 55 %, 25 %, 76; MUFA, 47 %, 35 %, 82. Weight changes on each diet differed significantly from each other: high-GI, -0·49 (SEM 0·29) KG; LOW-GI, -0·19 (sem 0·40) kg; MUFA +0·27 (sem 0·45) kg. Blood lipids did not change, but glycated haemoglobin increased significantly on MUFA, 0·02 (sem 0·11) %, relative to low-GI, -0·19 (sem 0·08) %, and high-GI, -0·13 (sem 0·14) %. Diastolic blood pressure fell by 8 mmHg on low-GI relative to MUFA ( P =0·038). Although S I and AIR glu did not change significantly, DI, a measure of the ability of β-cells to overcome insulin resistance by increasing insulin secretion, increased on low-GI by >50 % ( P =0·02). After adjusting for baseline values, the increase in DI on low-GI, 0·17 (sem 0·07), was significantly greater than those on MUFA, -0·09 (sem 0·08) and high-GI, -0·03 (sem 0·02) ( P =0·019). Thus, the long-term effects of altering the source of dietary carbohydrate differ from those of altering the amount. High-carbohydrate–low-GI dietary advice improved β -cell function in subjects with IGT, and may, therefore, be useful in the management of IGT.
Systems Modeling Usage in Project Management among Junior and Senior Business System Developers

Unified Modeling Language (UML) is accepted as the standard for modeling software designs and is commonly used in software development Thus this paper investigated the practice of Junior and Senior Business System Developers in the use of UML and other Diagrams. Part of the study is the UML Systems modeling usage for Junior Developers by investigating the documentation used in development. This study used several methods in gathering data, interview, and questionnaire from junior and senior developers was used to come up with a result. It also investigates the possible usage of UML Diagrams in Business field which will be a basis for a future proposed content curriculum for both Business Administration and Information Technology. The result of the study shows that junior developers are not using UML Systems modeling, while senior developers increase as the experience increase in development. Most of the respondents used a diagram that is outside in the UML family. The study also shows that UML helps the managers and business people to build models and diagram that helps them to put things into perspective. It proposed that UML Diagrams should include in business Subject for both programs, the business and information technology program should unite to increase the UML usage.
Foot strike posture and lower-limb dynamics during sidestepping among elite female athletes: implications for ACL injury risk

The purpose of this study was to compare the lower-limb dynamics between fore-foot (FF) and rear-foot (RF) strike patterns during unplanned sidestepping. Three-dimensional (3D) motion capture data were collected from 16 elite female hockey players. Ankle, knee, and hip: angle at initial foot contact (IC), range of motion (ROM), peak moment, and negative peak net joint power during weight acceptance phase were compared between athletes using natural RF and FF strike techniques. Results showed ankle and hip angle at IC, ankle ROM, peak ankle and knee extension moments, peak knee valgus moments, and ankle and knee negative peak net power between RF and FF strike patterns were significantly different (a < 0.05). These findings show foot strike technique during unplanned sidestepping can effect athlete lower-limb dynamics, where RF strike athletes may be at higher risk of ACL injury.
The role of cytochrome P450 in antiretroviral drug interactions

As millions of patients with HIV/AIDS are put on treatment with the highly active antiretroviral therapy (HAART), drug interactions have become a major concern for healthcare providers. The use of HAART as a combination of 3 – 4 drugs creates potential for antiretroviral (ARV) drug interactions, and this is complicated by the addition of other drugs for treatment of other ailments such as comorbid chronic conditions and/or opportunistic infections. It has been observed that most ARV drug interactions involve drugs that interact with CYP enzymes. Specifically, protease inhibitors (PIs) and non-nucleoside reverse transcriptase inhibitors (NNRTIs) are the most implicated in ARV drug interactions and are metabolised by CYP isoenzymes. Because PIs and NNRTIs can also inhibit and induce some of the CYP isoenzymes, they often interfere with the metabolism of several drugs eliminated by CYP isoenzymes, and the converse is true. The drug groups most implicated in CYP-mediated interactions with ARV drugs include: rifamycins; statins; antibiotics; antifungals; antiulcer drugs; contraceptives; immunosuppressant drugs; drugs for erectile dysfunction; drugs of abuse; drugs for treatment of addiction; benzodiazepines; anticonvulsants; psychotropic agents; herbal products; antiarrhythmias; antimalarials; anticoagulants; and antiasthma drugs. Unfortunately, this information is published in different resources where it may not be accessible to many, and is also liable to misinterpretation if read in isolation. Here, this information has been pooled and discussed with a hope that it will enable appropriate use in patients with HIV/AIDS. The review was confined to CYP-associated ARV drug interactions to emphasise that prevention of ARV drug interactions requires thorough knowledge of CYP function and regulation by healthcare providers.
Data Mining Techniques: Modern Approaches to Application in Credit Scoring

Importance This article examines the current state of research in machine learning and data mining, which computational methods get combined with conventional lending models such as scoring, for instance. Objectives The article aims to classify the modern methods of credit scoring and describe models for comparing the effectiveness of the various methods of credit scoring. Methods To perform the tasks, we have studied relevant scientific publications on the article subject presented in Google Scholar. Results The article presents a classification of modern data mining techniques used in credit scoring. Conclusions and Relevance Credit scoring models using machine learning procedures and hybrid models using combined methods can provide the required level of efficiency in the modern environment.
Supportive frameworks that increase mathematical knowledge and confidence in students enrolled in bridging mathematics courses.

Internationally, universities are changing the focus of their undergraduate degrees, increasing enrolments and broadening participation. As a result, non-traditional students who would once have been excluded from university studies are now being accepted. As expected, these changes are not without consequences, and one is left asking “How do we adequately prepare non-traditional students academically and increase their confidence enabling them to cope with undergraduate study? ” Preparatory mathematics courses that follow adult learning principles can both provide content knowledge and increase confidence. A study conducted by CQUniversity Australia (CQU) examining students’ mathematical confidence prior to and after completing at least one Transition Mathematics course, found students reported a reduction in their fear of mathematics. Courses with supportive frameworks, containing formative assessment and prompt feedback, are essential for ensuring students are engaged and understand the course content. Course scaffolding and support also increase student confidence in mathematics which in turn benefits academic progression.
Convolutional neural network with transfer learning for rice type classification

Presently, rice type is identified manually by humans, which is time consuming and error prone. Therefore, there is a need to do this by machine which makes it faster with greater accuracy. This paper proposes a deep learning based method for classification of rice types. We propose two methods to classify the rice types. In the first method, we train a deep convolutional neural network (CNN) using the given segmented rice images. In the second method, we train a combination of a pretrained VGG16 network and the proposed method, while using transfer learning in which the weights of a pretrained network are used to achieve better accuracy. Our approach can also be used for classification of rice grain as broken or fine. We train a 5-class model for classifying rice types using 4000 training images and another 2- class model for the classification of broken and normal rice using 1600 training images. We observe that despite having distinct rice images, our architecture, pretrained on ImageNet data boosts classification accuracy significantly.
Prevention of Hearing Loss by Alteration of the Systemic Immune System

Although congenital sensorineural hearing loss (SHL) in the bilateral cochleae mainly results from genetic abnormalities, chronic SHL progressing in later life is often influenced by systemic immune disturbances, including autoimmunity, chronic inflammation, and immunosenescence. We have investigated the relationship between the inner ear and systemic immunity and reviewed the possibilities to prevent SHL, including autoimmune SHL and age-related SHL. We also demonstrated two lymphocyte populations, interleukin 1 receptor type II (IL-1R2)-positive T cells (T1R2) and naturally occurring regulatory T cells (nTregs) in CD4+ T cells, which increase with aging, suppress host immune function and promote organ degeneration. Alterations in systemic immunity by fewer microbial antigen challenges in the living environment, elimination of immune suppressive lymphocytes, or immune rejuvenation with a reconstituted thymus may contribute not only to renew the cochlear function in SHL, but also to extend the healthy life of functional organs in a vigorous and youthful body, one of humanity’s greatest dreams.