Miracle-dz/UniFAR-FTUs huggingface dataset:
“Where’s the I-O?” Artificial Intelligence and Machine Learning in Talent Management Systems

Artificial intelligence (AI) and machine learning (ML) have seen widespread adoption by organizations seeking to identify and hire high-quality job applicants. Yet the volume, variety, and velocity of professional involvement among I-O psychologists remains relatively limited when it comes to developing and evaluating AI/ML applications for talent assessment and selection. Furthermore, there is a paucity of empirical research that investigates the reliability, validity, and fairness of AI/ML tools in organizational contexts. To stimulate future involvement and research, we share our review and perspective on the current state of AI/ML in talent assessment as well as its benefits and potential pitfalls; and in addressing the issue of fairness, we present experimental evidence regarding the potential for AI/ML to evoke adverse reactions from job applicants during selection procedures. We close by emphasizing increased collaboration among I-O psychologists, computer scientists, legal scholars, and members of other professional disciplines in developing, implementing, and evaluating AI/ML applications in organizational contexts.
Learning to Combat Compounding-Error in Model-Based Reinforcement Learning

Despite its potential to improve sample complexity versus model-free approaches, model-based reinforcement learning can fail catastrophically if the model is inaccurate. An algorithm should ideally be able to trust an imperfect model over a reasonably long planning horizon, and only rely on model-free updates when the model errors get infeasibly large. In this paper, we investigate techniques for choosing the planning horizon on a state-dependent basis, where a state's planning horizon is determined by the maximum cumulative model error around that state. We demonstrate that these state-dependent model errors can be learned with Temporal Difference methods, based on a novel approach of temporally decomposing the cumulative model errors. Experimental results show that the proposed method can successfully adapt the planning horizon to account for state-dependent model accuracy, significantly improving the efficiency of policy learning compared to model-based and model-free baselines.
Zeroth order validation of TKP source extraction and source measurement code

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An Empirical and Conceptual Categorization of Value-based Exploration Methods

This paper categorizes several exploration methods for reinforcement learning according to their underlying heuristics. Using linear Q-learning, we compare representative methods from each category of methods on a set of domains designed to pose different exploratory challenges. We find that the relative performance of each method depends on the specific exploratory challenge posed by the domain. Our results suggest that each exploration heuristic encodes a bias which is appropriate for a subset of environments.
CONSUMER PREFERENCE FOR SWINE OFFALS AND ITS HEALTH IMPLICATIONS IN KUMASI, GHANA

Global meat consumption rate had doubled in most countries over the last five decades. In Ghana, total animal production has also increased over the last decade with consumption of pork and its offals becoming prominent. Therefore this study aims to evaluate factors that influence consumer preference for pig offals and the associated public health risks. One hundred (100) respondents in the Kumasi Metropolis were randomly selected through structured questionnaires and samples of offals randomly collected, sectioned, processed and examined for any histopathological indicators. Data collected were analyzed through the use of descriptive statistical tools. The results showed that edible offal preferential consumption exist within the increasing demand and value supply chain with the liver (32%) and stomach (23%) being the most preferred offal. Factors such as nutritional value, delicacy, availability, cost and level of education are noted to influence the purchasing power and preference of consumers. The consumers are less conscious regarding the risks stemming from the common zoonotic diseases and health concerns. Histological assessment of the most preferred offals (liver) showed no remarkable histopathological changes. Based on this, the preference for pig livers may not be associated with considerate health risk.
Delimited control with multiple prompts in theory and practice

The versatile and expressive capabilities of delimited control and composable continuations have gained it attention in both the theory and practice of functional programming with effects. On the more theoretical side, delimited control may be used as a basis for explaining and understanding a wide variety of other computational effects, like mutable state and exceptions, based on Filinski’s [8, 9] observation that composable continuations can be used to represent any monadic effect. On the more practical side, forms of delimited control have been implemented in real-world programming languages [6, 10, 15], and used in the design of libraries like for creating web servers [10]. However, the design space for adding composable continuations to a programming language is vast, and a number of different definitions of delimited control operators have been proposed [3, 4, 7, 12, 21]. This has, in part, caused the theory and practice of delimited control to diverge somewhat from one another: the operators we most often study in theory are typically not the ones we use in practice. In this 30 minute talk, we will consider some of the fundamental, though subtle, differences in delimited control operators that appear in the literature and in programming languages, and some of the efforts to connect these together. We will also look at a common extension of delimited control that occurs in practice — the ability to delimit multiple different operations by name, much like exception handlers for specific subsets of errors — and how it provides another, novel approach for bridging the gap between the different frameworks.
Biphasic insulin aspart 30/70 (BiAsp 30) in the treatment of type 1 and type 2 diabetes

: The pharmacological advantages of the rapid-acting analog, insulin aspart, over human insulin have contributed to the widespread prescription of the premix, biphasic insulin aspart 30/70 (BIAsp 30), in type 1 (T1DM) and type 2 diabetes (T2DM). This article reviews the available literature on the pharmacology, efficacy and safety of BIAsp 30 in T1DM and T2DM from an online search of the PubMed database. Following injection, BIAsp 30 reaches higher plasma insulin levels more quickly than human premix or basal insulin, giving effective reduction of postprandial hyperglycemia. In T1DM patients, randomized controlled trials (RCTs) have shown that HbA 1c reduction is similar, but postprandial glycemic control is better, with BIAsp 30 than with human insulin regimens. In T2DM patients, lowering of HbA 1c and postprandial hyperglycemia with BIAsp 30 compare favorably with optimized oral antidiabetes drug treatment, insulin glargine, and, in obese patients, human premix. An increase in minor hypoglycemia with BIAsp 30 relative to basal insulin has been reported in T2DM patients, but major and nocturnal hypoglycemia rates are generally low. Findings from RCTs in T2DM patients are supported by large observational studies. In summary, BIAsp 30 once to three times daily represents a simple and effective tool for the modern management of diabetes.
icnetic diversity of Aloe species in Kenya and the efficacy of Aloe secundiflora , Aloe lateritia and Aloe turkanesis on Fusarium oxysporum and Pythium ultimum 1

Aloe species are common in Kenya and have many health benefits which include promotion of wound healing, antifungal activity, hypoglycemic or anti-diabetic effects, anti­ inflammatory and anticancer properties. However, little has been done to study the genetic variability of Aloe species in Kenya. Fusarium oxysporum and Pythium ultimum are economically important plant pathogens with a wide host range causing several diseases including root rots and damping off. The objectives of this study were to genetically characterize Aloe species found in Kenya and assess the efficacy of their extracts on Fusarium oxysporum and Pythium ultimum. Thirty one morphologically diverse Aloe species were sampled from ex-situ and in-situ collections. Morphological characterization was done using leaf color, presence or absence of variegation and color of spines. DNA extraction was carried out using cetyl trimethyl ammonium bromide (CTAB) method. Sequence related amplified polymorphism (SRAP) markers were used to determine genetic diversity of the Aloe species Forty eight primer pairs were screened to select those that had the highest number of polymorphisms. Eight primer pairs, which were polymorphic, were selected to genotype the Aloe species. Crude extracts of the leafs were made by extracting with dichloromethane: methanol in the ratio of 1:1 and made into concentrations of 100mg/100|il, 50mg/100pl, 25mg/100pl, 12.5mg/100|il and 6.25mg/l OOpI using sterile distilled water. Similar concentrations were done with the antifungal drug triazole as the positive control. Assessment of the efficacy of different concentrations of the Aloe species leaf extracts against Fusarium oxysporum and Pythium ultimum was carried out using the agar disc diffusion method. Paper discs impregnated with different concentrations of the Aloe extracts were placed in the petri plates and zones of inhibition measured in millimeters for 11 days. SRAP primers produced 893 genetic loci with percentage polymorphic loci of 56.8%. Nei genetic distance for the populations ranged from 0.094 to 0.362 with 9% and 91% variation among xiii populations and within individuals, respectively. Organic crude extracts of A. Sccundijlora showed the highest activity (Mean inhibition = 16.01 mm) against the test fungi compared to A. later ilia (Mean inhibition = 13.09 mm) and A. turkanensis (Mean inhibition = 11.04 mm). The crude extracts showed higher activity against F. oxysporum than P. ultimum; but there was no activity for all the crude extracts at 6.25mg/ml. None of the crude extracts showed higher activity than the commercially used fungicide triazole. The findings of this study could form basis for better conservation of the genus which has potential to control fungi of agricultural importance alongside other control measures.
FPGA-BASED PULSED-RF PHASE AND AMPLITUDE DETECTOR AT SLRI

In this paper, the prototype of phase and amplitude detector for pulsed-RF measurement is described. The hardware is designed in VHDL and implemented using Field Programmable Gate Array (FPGA) for digital processing. The main phase and amplitude detection algorithm is implemented using state machine in the MicroBlaze soft processor. The detector system is designed to measure the phase and amplitude of a 5-microsecond wide 2,856 MHz pulsed-RF at a repetition rate of 0.5 Hz. The front-end hardware for the pulsed-RF signal acquisition is also described with the interface to the FPGAbased controller part. Initial test results of the prototype are presented.
Social workers’ experience working with families with children facing death

This study addressed the question: “What meaning do social workers give to their experience of working with families with children facing death? ” Its purpose was to explore the meaning social workers attach to their unique professional role in this particular area of practice. Four social work participants from a children’s hospital were recruited, chosen because of their experience in working directly with this population. Criteria for inclusion were a minimum education level of a Master’s Degree in Social Work and at least two years of experience working in the hospital setting. A qualitative descriptive approach, drawing on phenomenology, was utilized. Each participant was individually interviewed for one hour using a semi-structured format. A phenomenological approach to data analysis was used. After careful review, four areas of meaning-making emerged: what brought participants to this work; meaning-making within the function of the role; connection and companionship; and, challenges faced within the role. These results add to the relatively small base of knowledge regarding the experience of social workers who work in pediatric end-of-life care in a healthcare setting.
A Hybrid Ensemble Method for Accurate Breast Cancer Tumor Classification using State-of-the-Art Classification Learning Algorithms

Breast cancer is the most common cause of death for women worldwide. Thus, the ability of artificial intelligence systems to predict and classify breast cancer is very important. In this paper, a hybrid ensemble method classification mechanism is proposed based on a majority voting mechanism. First, the performance of different state-of-the-art machine learning classification algorithms for the Wisconsin Breast Cancer Dataset (WBCD) were evaluated. The three best classifiers were then selected based on their F3 score. F3 score is used to emphasize the importance of false negatives (recall) in breast cancer classification. Then, these three classifiers, simple logistic Regression learning, stochastic gradient descent learning and multilayer perceptron network, are used for ensemble classification using a voting mechanism. We also evaluated the performance of hard and soft voting mechanism. For hard voting, majority-based voting mechanism was used and for soft voting we used average of probabilities, product of probabilities, maximum of probabilities and minimum of probabilities-based voting methods. The hard voting (majority-based voting) mechanism shows better performance with 99.42% as compared to the state-of-the-art algorithm for WBCD.
Model-based Biomarker Detection and Systematic Analysis in Translational Science

Model-based Biomarker Detection and Systematic Analysis in Translational Science. (May 2012) Youting Sun, B.S., Tsinghua University Co–Chairs of Advisory Committee: Dr. Edward R. Dougherty Dr. Ulisses Braga-Neto This dissertation is concerned with the application of mathematical modeling and statistical signal processing into the rapidly expanding fields of proteomics and genomics. The research is guided by a translational goal which drives the problem formalization and experimental design, and leads to optimization, prediction and control of the underlying system. The dissertation is comprised of three interconnected subjects. In the first part of the dissertation, two Bayesian peptide detection algorithms are proposed to optimize the feature extraction step, which is the most fundamental step in mass spectrometry-based proteomics. The algorithms are designed to tackle data processing challenges that are not satisfactorily addressed by existing methods. In contrast to most existing methods, the proposed algorithms perform deisotoping and deconvolution of mass spectra simultaneously, which enables better identification of weak peptide signals. Unlike greedy template-matching algorithms, the proposed methods have the capability to handle complex spectra where features overlap. The proposed methods achieve better sensitivity and accuracy compared to many popular software packages such as msInspect. In the second part of the dissertation, we consider modeling and assessing the entire mass spectrometry-based proteomic data analysis pipeline. Different modules
Postgraduate writing e-communities: de-marginalising remote participants

This paper considers how building e-communities can support and socialise postgraduate learners of Writing in an online Master of Arts taught from Melbourne, Australia. Although participants may be geographically remote, active establishment and maintenance of online communities of practice can help to break down the feeling of marginalisation that online and distance learning participants report (Caplan 2003). This study uses pedagogical theories of building and maintaining e-communities born of Lave and Wenger’s social constructivist thinking about communities of practice (Lave and Wenger 1992; Wenger 1998) to introduce a pilot study of e-community in first year subjects in Swinburne University’s Master of Arts in Writing. The pilot study draws on the insights of the university’s tutors, experienced in teaching writing online, and their perceptions of postgraduate Writing students’ needs to belong. In a first year core paper in the degree, learners work closely with a critical friend or in a small group to develop their Writing in the context of a selected project. This participation requires them to engage in virtual collaborations and learning e-communities. This paper shows how such an approach can de-marginalise geographically remote writers by providing insider support, harnessing common goals, encouraging shared discourse and promoting membership.
Balancing Capacitors Voltages and Currents Control of a Hybrid Active Power Filter Comprising 3-Level NPC Inverter Based on PI-Predictive Control System

A model based predictive control system of a three-level four-wire NPC inverter employed in a hybrid active power filter (HAPF) is proposed in this paper. The inverter and two parallel passive filters (PPFs) tuned in 5 and 7 orders of harmonic currents form the HAPF. The PPFs are designed to compensate the source reactive currents, and to filter fifth and seventh orders of the source currents harmonic components for decreasing of the NPC inverter rated power. Also, this paper presents a regulation and balancing control system of the capacitors voltages of the 3-level NPC inverter. The proposed control system is simulated by Matlab/Simulink software. The presented control system is assessed under balanced and imbalanced loads. In order to validate effectiveness of the proposed control system the simulation results are prepared. Keywords—Hybrid active power system, NPC-inverter, predictive control, capacitor voltage balancing
Establishment of Acceptance Criteria for Tomography Inspection of Aluminium Alloy Castings

In October 2018, the French company CTIF, in collaboration with the laboratory LVA of INSA Lyon, has launched a PhD thesis. The aim of this thesis is to establish a set of acceptance criteria for tomography inspection of aluminium alloy castings. Currently, the agreement on conformity of aluminium castings inspected by 2D X-ray radiography is based on the ASTM reference images. However, when it comes to tomography inspection, such images have not yet been established in 3D. Consequently, tomography inspectors and their casting customers do not have an approved reference on which they could rely to create their own scope statement and evaluate the 3D casting defects and their criticality. Based on the aforementioned motivation, the objective of our thesis is the establishment of such quality standards that will be applicable to the characterization of aluminium castings defects and their types by 3D tomography. The methodological approach proposed to reach our endeavour is as follows: (1) development of imageprocessing algorithms to establish automatic detection of casting defects in the tomographic slices, (2) a 3D reconstruction of the defects, and finally (3) automatic processing and measurement of features from the 3D volume of the defects to classify them and finally determine a 3D quality reference. For the first step, which is currently in making, two classes of regions can be detected on a digital tomographic slice of an aluminium casting after segmentation: regions belonging to the geometric structures of the specimen or tomographic artefacts (false alarms), and those relating to real defects. The distinction between these two classes can be done by using pattern recognition techniques for object detection and classification. By using a defects library, a classifier can be trained to determine whether a segmented indication is a false alarm, or whether it belongs to a real defect.
Fast Video Shot Segmentation and Retrieval Based on Principal Component Space Trace Geometry

Zhu Li, Aggelos K. Katsaggelos, and Bhavan Gandhi Department of Electrical & Computer Engineering, Northwestern University, Evanston, Illinois. Multimedia Communication Research Lab (MCRL), Motorola Labs, Schaumburg, Illinois. Email: {zli, aggk}@ece.northwestern. edu, gandhi@labs.mot. com Abstract— Content-based video retrieval technology holds the key to the efficient management and sharing of video content from different sources, in different scales, across different platforms, and over different communication channels. In this work we present fast video shot segmentation and retrieval algorithms based on the geometry of video sequence traces in the principal component space. Techniques to address scale (spatial and temporal) issues, as well as, noise and other possible distortions, such as frame dropping, are discussed. Experimental results demonstrate the effectiveness of the proposed approach.
December 2019 A Study on Effective Online Data Mining Tools

Data mining is the technique of extraction of predictive facts from massive facts hundreds and it strongly associated with facts technological know-how that entails manipulation and class of facts by making use of statistical and mathematical. Statistics managing and coping with of massive facts units, there are numerous facts mining equipment are to be had which improve data high quality from past to provide. Data mining tools are supplied for preprocessing records, feeding it into a diffusion of mastering schemes, and analyzing the resulting classifiers and their performance, ideas. Nowadays, big portions of records are being accrued. Seeking knowledge from huge statistics is one of the maximum desired attributes of data mining. In the analysis of examining information, some preliminary suspicion about the data is known, but data mining may want to support roughly the records in a more in-depth expertise. Recently manual data analysis has been small, but it creates a bottleneck to analyze large data.
To Analyse Usefulness of Immunohistochemical Analysis of Cell Block Specimens in Minimally Invasive Procedures of Pulmonary Lesions

Many lung cancers present in an advanced stage and only cytology specimens or small biopsies are available for diagnosis. In modern era therapy for lung cancer is based on subtype of cancer. Immunohistochemistry done on cell blocks of pleural samples help in subtyping of lung cancers. In this study,immunohistochemical analysis on plasma thromboplastin cell blocks of cytology samples of pulmonary lesions received as pleural effusion, bronchial wash and guided FNA of lung lesions were included. A total of 100 cases were studied by plasma thromboplastin method. By cell block technique, cytology specimen were made into mini paraffin embedded biopsy materials which are available for immunohistochemistry . All the 14 cases diagnosed to be malignant on cell block by Hematoxylin and Eosin stain were confirmed and subtyped by immunohistochemistry. A panel of immunohistochemistry markers CK7, CK20, PanCK, Synaptophysin, p63,TTF-1,Calretinin and Estrogen receptor were used. This study showed confirmation of malignancy by immunohistochemistry on cell blocks on all cases (100%) of malignant pulmonary lesions. Subtyping of malignancy could also be done in 11 out of 14 cases (78%)