Miracle-dz/UniFAR-FTUs huggingface dataset:
Joint-Label Learning by Dual Augmentation for Time Series Classification

Recently, deep neural networks (DNNs) have achieved excellent performance on time series classification. However, DNNs require large amounts of labeled data for supervised training. Although data augmentation can alleviate this problem, the standard approach assigns the same label to all augmented samples from the same source. This leads to the expansion of the data distribution such that the classification boundaries may be even harder to determine. In this paper, we propose Joint-label learning by Dual Augmentation (JobDA), which can enrich the training samples without expanding the distribution of the original data. Instead, we apply simple transformations to the time series and give these modified time series new labels, so that the model has to distinguish between these and the original data, as well as separating the original classes. This approach sharpens the boundaries around the original time series, and results in superior classification performance. We use Time Series Warping for our transformations: We shrink and stretch different regions of the original time series, like a fun-house mirror. Experiments conducted on extensive time-series datasets show that JobDA can improve the model performance on small datasets. Moreover, we verify that JobDA has better generalization ability compared with conventional data augmentation, and the visualization analysis further demonstrates that JobDA can learn more compact clusters.
A Hybrid Stochastic Gradient Hamiltonian Monte Carlo Method

Recent theoretical analyses reveal that existing Stochastic Gradient Markov Chain Monte Carlo (SG-MCMC) methods need large mini-batches of samples (exponentially dependent on the dimension) to reduce the mean square error of gradient estimates and ensure non-asymptotic convergence guarantees when the target distribution has a nonconvex potential function. In this paper, we propose a novel SG-MCMC algorithm, called Hybrid Stochastic Gradient Hamiltonian Monte Carlo (HSG-HMC) method, which needs merely one sample per iteration and possesses a simple structure with only one hyperparameter. Such improvement leverages a hybrid stochastic gradient estimator that exploits historical stochastic gradient information to control the mean square error. Theoretical analyses show that our method obtains the best-known overall sample complexity to achieve epsilon-accuracy in terms of the 2-Wasserstein distance for sampling from distributions with nonconvex potential functions. Empirical studies on both simulated and real-world datasets demonstrate the advantage of our method.
Resilient Multi-Agent Reinforcement Learning with Adversarial Value Decomposition

We focus on resilience in cooperative multi-agent systems, where agents can change their behavior due to udpates or failures of hardware and software components. Current state-of-the-art approaches to cooperative multi-agent reinforcement learning (MARL) have either focused on idealized settings without any changes or on very specialized scenarios, where the number of changing agents is fixed, e.g., in extreme cases with only one productive agent. Therefore, we propose Resilient Adversarial value Decomposition with Antagonist-Ratios (RADAR). RADAR offers a value decomposition scheme to train competing teams of varying size for improved resilience against arbitrary agent changes. We evaluate RADAR in two cooperative multi-agent domains and show that RADAR achieves better worst case performance w.r. t. arbitrary agent changes than state-of-the-art MARL.
Recent Nutritional Advances of Neutral Detergent Fiber in Dairy Cow

Recent studies on nutritional regulation of neutral-detergent fiber(NDF) and physically effective neutral-detergent fiber(peNDF) on dairy cow are reviewed in the paper.The regulation of NDF on rumen fermentation of cow is by means of effect on food intake,chew and rumination and regulating organic acid,pH and microbial biomass;the regulation of peNDF is on the base of effect on food intake and digestion.Benefiting contents of both NDF and peNDF are key factors to maintain health and production performance of cow.
Determination of the Triterpenes Constituents in Centella asiatica L.by HPLC

OBJECTIVE:To establish a method which can determine the four major constituents simultaneously.METHODS:Using C 18 reversed phase column,mobile phase as acetonitrile water with gradient elution,detection wavelength at 205 nm.RESULTS:The average recovery of assiaticoside,madecassoside,madecassic acid and asiatic acid is 97.62%(RSD=1.95%,n=5),98.96%(RSD=2.01%,n=5),101.89%(RSD=1.54%,n=5) and 101.28%(RSD=1.38,n=5),respectively.CONCLUSION:This method is simple and accurate for control quality of origin medicinal materials of Contella asiatica and its preparation.
DETERMINANTS OF FEMALE ENTREPRENEURSHIP SUCCESS IN THE AGRICULTURAL SECTOR: AN EXAMINATION OF SMES IN SOUTH AFRICA

The Agricultural sector in South Africa is amongst the main contributors to job-creation South Africa. Since the post-apartheid era, South Africa has seen a severe transformation in this sector, of which previously disadvantaged farmers (especially women) are now on the forefront in terms of new developments, and SME business expansion. However, the efforts of the government to sustain SMEs are yet to alleviate the challenges agricultural SMEs are facing. This article looks at the determinants of business successes for female owned SMEs. The trustworthiness and internal consistency examination of the instrument was done, with test re-test reliability method and Cronbach Alpha index. They both generated an R-value of 0.70 and 0.875 respectively. Multiple Regression technique was used in estimating the coefficients’ of impact on success. Findings showed that certain characteristics of the entrepreneur, including Entrepreneurship Skill, Financial Resources, Capital Start-up Amount and Customer and Market Access have a significant impact on Female Entrepreneurship Success. We recommend a major overhaul of government interventions designed to enhance the skills of female entrepreneurs as well as broadening their customer and market access base. This has become imperative for addressing the challenges of business success for female entrepreneurs.
AFRL-AFOSR-UK-TR-2018-0041 Relationship between trust and entrainment in speech

We have designed experimental scenarios and collected data from Slovak and Argentinian Spanish testing the effect of manipulating acoustic-prosodic features of synthesized voices, to either entrain or disentrain with respect to the human user's voice, on trust of users toward the avatars with these synthesized voices. The results of these experiments and our continued exploration of human-human entrainment in spoken dialogue corpora provide a rather complex and multidimensional picture of prosodic entrainment and its potential for calibrating trust in human-machine spoken interactions. The key results include: 1) not only entrainment but also disentrainment in terms of acoustic-prosodic features might facilitate trust of humans towards computer avatars, 2) different prosodic features affect the trust-entrainment relationship differently, 3) most of our results were consistent for both Slovak and Argentinian Spanish, which points to complex, yet systematic and cross-culturally valid, nature of the trust-entrainment relationship, and 4) the observations from human-human dialogues are generally consistent with human-machine experiments and offer also novel factors, such as biological sex and task-role, that affect speech entrainment and the entrainment-trust relationship.
Biological Evaluation and Theoretical Study of Bi-dentate Ligand for Amoxicillin Derivative with Some Metal Ions

في هذا البحث تم تحضير معقدات قواعد شف للمركب ميثيل 6- (2- (ثنائي فينيل ميثيلين) أمينو) -2- (4-هيدروكسي فينيل) أسيتاميدو) -2،2-ثنائي ميثيل -5-أوكسو -1-ثيا-4-أزاباي سايكلو(3،2،0)هبتانس -3-كاربوكسيلت مع ايونات كوبلت ،نيكل ،نحاس والزنك ثنائية التكافؤ. تم تشخيص المعقدات المحضرة بطرق مختلفة منها طيف الاشعة تحت الحمراء (FT-IR)وطيف الاشعة فوق البنفسجية-المرئية (Uv-vis) ،الحساسية المغناطيسية ،التحليل الذري للعناصر (C.H.N.S) ،الامتصاص الذري والتوصيلية المولارية. الواضح من الدراسة الطيفية ان المعقدات تمتلك تركيب احادي النوية وان الليكاند المحضر متناسق مع الايونات الفلزية بشكل ثنائي السن مع شكل ثماني السطوح باستثناء معقد الكوبلت الثنائي فانة يمتلك شكل رباعي السطوح. استخدام برنامج Hyper Chem-8.0. 7لتوقع الشكل الهندسي للمركبات جنبا إلى جنب مع حرارة التكوين (°ΔH f) ، طاقة الربط (ΔEb) ، عزم ثنائي القطب (µ) ، ترددات FT-IR في الحالة الغازية . تم فحص هذه المركبات ونشاطهاالحيوي كمضادات للجراثيم والفطريات.
The Unanswerable Gap: An Exploration of Approaches for Question Answering on SQuAD 2.0

In this project, we implemented models that were trained and evaluated using the Stanford Question Answering Dataset (SQuAD) 2.0. For a majority of our models, we incorporated character-level embeddings to strengthen the system’s understanding of the semantics and syntax of each context and question. Our implementations were based on two main architectures: the baseline Bidirectional Attention Flow (BiDAF) model and the Dynamic Coattention Network (DCN), which we implemented in full. We found that the baseline BiDAF model with character-level embeddings performed the best and received an EM/F1 score of 61.771/65.089 on the test set.
Quantitative Proteomics Analysis to Study the Protective Effects of Nerve Growth Factor on Hippocampal Mitochondria in VD Rats

Background Vascular dementia (VD) is a kind of clinical syndrome characterized with the impairment cognitive function caused by cerebrovascular disease. Genetics, biochemical, and morphological analyses of cell and animal models, reveal that mitochondria could have roles in this neurodegeneration. Methods We used Sprague-Dawley rats to establish VD model, and used the proteomics method based on relative quantification (iTRAQ) to identify the differentially expressed proteins in hippocampus mitochondria. Results A total of 33 differentially expressed proteins were identified between the VD rats and the VD rats treated with nerve growth factor groups. And five differentially expressed proteins (Rgs14, Slc7a14, Ppm1l, Kcnj10 and Syngr1) were identified after completing the sham-operate control, VD rats and VD rats treated with nerve growth factor groups, then successfully confirmed by western blot. Bioinformatics analysis suggested that the mitochondrial molecular mechanism of VD and the protective effect of nerve growth factor on mitochondrial function of VD rats may be due to different molecular mechanisms. Conclusion We estimated that mitochondrial dysfunction may be the onset of VD and key role in the pathological process of VD. This study not only has a deeper understanding of the mitochondrial molecular mechanism of VD, but also is helpful for the screening of drug targets.
Memorandum – Academic Literature on Potential Benefits for Portfolio Efficiency of Private Equity Investments

This memorandum is intended to facilitate consideration by the Subcommittee of certain issues relating to private equity investments, namely whether private equity, as an asset class, provides incremental portfolio efficiency benefits vis-à-vis other assets classes, such as public equity and debt.2 The memorandum proceeds in two steps. First, it surveys the literature on the risk-adjusted returns of private equity investments relative to various public market benchmarks. Second, it collects the main findings in the literature as to whether expanding an investor's opportunity set to include private equity as an asset class may provide portfolio efficiency benefits by improving the risk-return tradeoff of an investor’s portfolio. This memorandum is based on a review of academic literature and DERA has not undertaken an independent analysis of the risk-adjusted returns or potential portfolio efficiency benefits of private equity investments.
Survey on Vision Based Banknote Recognition Systems using Machine Learning and Deep Learning Methods

- Visually impaired people faced a problem in identifying and recognizing the different types of banknote due to some reasons. This problem draws researchers’ attention to introduce an automated banknote recognition system that can be divided into a vision-based system and sensor-based system. As Sensor based system requires many hardwares and it get costly, so Vision based systems are more convenient, the main aim of this study is to have deeper analysis on the effect of region and orientation on the performance of Machine Learning and Deep Learning respectively in recognizing banknotes. The different region and orientations of banknotes is captured by using handphone camera in a controlled environment. Feature extraction of the RGB values called RB, RG, and GB from banknote image with different region were used to the machine learning classification algorithms. Banknote image with different orientation was directly feed to AlexNet, a pre-trained model of Convolutional Neural Network, the most popular image processing structure of Deep Learning Neural Network. It also can be concluded that AlexNet can only perform great in testing new data if only the data had previously been trained with similar orientation. The performance of AlexNet model varies according to orientation of banknote. Deep Learning approaches can give more powerful, accurate and error-free system.
THE EFFECTS OF LOCALLY-GENERATED REVENUE (LGR) AND REVENUE SHARING FUNDS (RSF) ON REGIONAL MINIMUM WAGES TO PROMOTE HUMAN DEVELOPMENT INDEX (HDI) IN BANTEN PROVINCE

This study aims to analyze the effects of Revenue Sharing Funds (RSF) and LocallyGenerated Revenue (LGR) on the Human Development Index (HDI) through Regional Minimum Wages (RMW) in Banten province. The data used is panel data, namely a combination of time series data and cross-section data. Time series data are taken from 2014 to 2019 while crosssection data are taken from all districts/cities in Banten Province. The total data obtained are 48. The data analysis technique used the Structural Equation Model (SEM) with Partial Least Square (PLS). The results show that LGR has positive effects on RMW and HDI, while RSF has negative effects on RMW and HDI. RMW has positive effects on HDI.
Compendia: reducing virtual-memory costs via selective densification

Virtual-to-physical memory translation is becoming an increasingly dominant cost in workload execution; as data sizes scale, up to four memory accesses are required per translation, and 24 in virtualised systems. However, the radix trees in use today to hold these translations have many favorable properties, including cacheability, ability to fit in conventional 4 KiB page frames, and a sparse representation. They are therefore unlikely to be replaced in the near future. In this paper we argue that these structures are actually too sparse for modern workloads, so many of the overheads are unnecessary. Instead, where appropriate, we expand groups of 4 KiB layers, each able to translate 9 bits of address space, into a single 2 MiB layer, able to translate 18 bits in a single memory access. These fit in the standard huge-page allocations used by most conventional operating systems and architectures. With minor extensions to the page-table-walker structures to support these, and aid in their cacheability, we can reduce memory accesses per walk by 27%, or 56% for virtualised systems, without significant memory overhead.
Rapid, accurate and dye-free quantification and quality control of nucleic acids – in a single measurement

Characterization of nucleic acid and protein samples is a quality control measure that should be adopted by every molecular and biochemistry lab. It encompasses quantification and detection of impurities present in nucleic acid samples, ranging from proteins to salts and organic solvents. The presence of co-purified substances (like polyphenolic metabolites, polysaccharides, hemoglobin or chlorophyll), carry-over from the isolation buffers, such as chaotropic salts or phenol, as well as turbidity effects caused by cell debris or residual magnetic beads can have a profound effect on the sensitivity and efficiency of downstream assays. Accurate and timely assessment of sample yield and purity can help you make well-informed decisions regarding whether a sample is suitable for downstream processing, or whether further refinement is necessary in order to ascertain the reliability of the resulting data [1, 2].
Treasury Single Account Compliance: Accounting Practices for Effectiveness and Sustainability in Institutions

This study considered treasury single account compliance as an accounting practice for effective and sustainability in institutions. The general objective is on how treasury single account could plug leakages within the system, while the specific objective would be on how treasury single account could dovetail into a transparent reportage in a system. Legitimacy theory and public finance management theory adopted for the treasury single account, therefore foresees that government will implement policies to guarantee the society and that agencies are complying by disclosing all transactions in treasury single account. This study was based on the data from both primary source and empirical works of previous researchers. The questionnaires were administered to the bursary staff of The Polytechnic in Ibadan. The questions were on the five-point Likert type questions, with a choice of strongly agree to strongly disagree. The data gathered were analyzed using chi-square statistical packages for social sciences. Data were gathered from the questionnaires analyzed using chi-square in SPSS, and findings revealed that treasury single account compliance have a significant effect in the effective accounting practices and sustainability in institution. This paper concluded that loss of revenue has a multitude of collision on effects, including the fact that lower revenue means that less money will be available to provide facilities for sustainability. Therefore, this paper recommends that, the effectiveness of treasury single account compliance (TSAC) should be broadminded for the sustainability in institutions. There should be seal for both transparency and accountability to block the leakages within the system. The permissible laws should be appraised and adapted where mandatory training should be offered to appropriate bursary staff of The Polytechnic Ibadan to guarantee effective operation.
Show Me What You're Looking For Visualizing Abstracted Transformer Attention for Enhancing Their Local Interpretability on Time Series Data

While Transformers have shown their advantages consideringtheir learning performance, their lack of explainabilityand interpretability is still a major problem.This specifically relates to the processing of time series,as a specific form of complex data. In this paper,we propose an approach for visualizing abstracted informationin order to enable computational sensemakingand local interpretability on the respective Transformermodel. Our results demonstrate the efficacy ofthe proposed abstraction method and visualization, utilizingboth synthetic and real world data for evaluation.
GPs perspective on the usability of the Dutch Primary Care Practice Report: a qualitative interpretative approach

Background Audit and feedback informs healthcare providers and may affect professional practice and patient outcomes. The Primary Care Practice Report (PCPR) is a web based personalized feedback instrument for general practitioners (GPs) in the Netherlands, based on claims data. Its yearly uptake is limited. In order to improve the use and usability of the report this study aims to identify key criteria that GPs deem important for audit and feedback.Methods A qualitative interpretative approach was used. We interviewed 12 GPs about their use of the practice report. These interviews followed the Three-Step Test-Interview method. Thematic content analysis was used to investigate the perception of the GP on the report and their perspective on the usability. The interviews resulted in critical items for audit and feedback, on which all tables and graphs in the report were systematically assessed.Results From the interviews with GPs recurring criteria emerged that were identified as decisive for the effectiveness of performance feedback: content, reliability, validity and usability. The 34 tables and graphs of the PCPR were assessed using factual characteristics. Content analysis shows that the PCPR has a strong focus on costs. Assessment on validity indicates that casemix correction is always performed if relevant, but explanatory notes hardly clarify which (sub)population is measured (3/34), therefore GP’s in general have difficulty interpreting the results. Assessment on usability shows that, although benchmark figures are almost always presented based on national references, the formulation of goals or any specific attention for an action perspective is lacking because the perceived comparability with the patient group of the GP is limited.Conclusions The current PCPR does not meet key criteria for effective audit and feedback, as defined by GPs. It has a strong focus on costs instead of clinical behavior and is poorly understood when it comes to the specific population the data reflect. The results are in line with theoretical perspectives on learning and improvement of professionals. Improvement of the PCPR requires information on aspects of clinical behavior that are recognizable for GPs and they actually can influence. First steps are made to improve the method of case-mix correction in the benchmarking.
Department Informatik

—It is now well-known that, for various reasons, Android has become the leading OS for smartphones with more than 50% of worldwide market share within only a few years. This fast growth rate also has an evil side. Android brought backdoors and trojans to the yet spared Linux world with growth rates of over 3000% and more than 13000 malicious applications. These malicious apps are only seldom obfuscated and very basic in their functionality. In this technical report, we give a short overview of the existing malware families and their main functionality. As an example, we present the results of reverse engineering two paradigmatic malware samples of the Bmaster and FakeRegSMS families. These samples were chosen because they try to implement the ﬁrst very simple approaches of obfuscation and behavior hiding. We conclude with discussing the following questions:
Influence of lactic acid bacteria–fermented Helianthus tuberosus L. and Lupinus luteus on quality of milk products

ABSTRACT In order to improve the nutritional value and quality of milk products, Helianthus tuberosus L. (HT) and Lupinus luteus (LL) fermented with lactic acid bacteria (LAB) (Pediococcus pentosaceus KTU05-9, P. acidilactici KTU05-7 and Lactobacillus sakei KTU05-6) were used, and the quality parameters (physicochemical, microbiological and sensory) of new fermented milk products were evaluated for the first time ever. Chemical analysis of fermented milk products showed a positive effect of the edible plants. The safety profile and nutritional value were higher in milk-LL products compared with fermented milk-HT products. Fermented milk-HT products were more acceptable because of higher taste pleasure, lower smoothness and external taste compared to fermented milk-LL products. The results clearly indicated that HT rich in inulin and LL rich in proteins can be used with selected LAB to improve the nutritional value and increase the assortment of fermented milk products.
SOTA Attention Mechanism and Activation Functions on XLNet

In this project, we re-implemented a XLNet model in PyTorch from scratch, and experimented with SOTA activation functions including GELU and Mish, as well as Attention on Attention (AoA) mechanism. We analyzed the effect of the above techniques on our XLNet model evaluated on its pretraining behavior and then further implemented such techniques on the original XLNet model. We found beneﬁcial effect of Mish on XLNet