Miracle-dz/UniFAR-FTUs huggingface dataset:
ROBUST REGRESSION ANALYSIS UNDER A SPECIAL COMPOUND SYMMETRY STRUCTURE

Most real data sets from many sources such as medical sciences, quality engineering, environmental, econometrics etc. are correlated in nature. The present article aims to derive the necessary regression analysis techniques for a correlated data set with a special form of compound symmetry error structure with two sets of observations such that the first set contains only the first observation, and the other set contains the remaining (N – 1) observations, where N is the total number of observations. A constant correlation (� 1 ) is assumed between the first and anyone of the remaining observation, and for the second set, a constant correlation (�) is assumed between any two observations within themselves. The variance is assumed constant for all the observations. Correlation structural form is known, but the parameters involved in it are always unknown. In the article, we have derived a robust estimating method for the best linear unbiased estimators (BLUE) of all the regression parameters except the intercept, which is often unimportant. In addition, we have developed a robust testing procedure for any set of linear hypotheses regarding the unknown regression coefficients, and along with a confidence ellipsoid for a set of estimable functions of regression coefficients. Index of fit for the fitted regression equation has also been developed. An example with simulated data illustrates all the developed theories in the article.
Comparison of Antioxidant, Flavonoid and Polyphenol Content of Three Selected Solanaceae Genera from Kigezi, Southwest Uganda

Solanaceae is family of plants widely used in food, sauce and herbal medicine because its members are rich in carbohydrates, proteins, fats, vitamins, mineral salts and other trace and essential nutrients like phenols and flavonoids that retard degenerative diseases and stress. Knowledge on purposeful use of solanaceae in food and medicine in Uganda is low. Aqueous extracts of dry leaves of three selected solanum genera growing in Kabale were compared for their polyphenol and flavonoid contents and antioxidant properties. Total polyphenol (TPC) and total flavonoid (TFC) contents were determined by the Folin-Ciocalteu and aluminium chloride colorimetric methods respectively. Antioxidant properties and radical scavenging were determined using DPPH, hydrogen peroxide, thiocyanate-iron (III) complex and iron (II)/(III)-linoleic acid systems. The polyphenol content of dry leaves of S. anguivi was 1750 ± 0.70, that for S. macrocarpon was 104 ± 0.45 and S. nigrum was 97.80 ± 0.15 GAE/g yet the flavonoid content was 7.40 ± 0.30, 35.00 ± 0.60 and 16.40 ± 0.40 mg/QE/g for S. anguivi, S. macrocarpon and S. nigrum respectively. The DPPH scavenging at IC50 were 7.80 ± 0.25; 45.60 ± 0.30 and 42.90 ± 0.20 respectively yet hydrogen peroxide scavenging at IC50 stood at 6.89 ± 0.15; 27.00 ± 0.35 and 17 . 90 ± 0.20 μg/mL in the respective order for S. anguivi, S. macrocarpon and S. nigrum. The available data suggests the plants are very good food supplements of high nutritive and chemotherapeutic values. However, there is need to perform in vivo and vitro experiments to deduce their efficacy on mammals.
Sharp Asymptotics of Self-training with Linear Classifier

Self-training (ST) is a straightforward and standard approach in semi-supervised learning, successfully applied to many machine learning problems. The performance of ST strongly depends on the supervised learning method used in the refinement step and the nature of the given data; hence, a general performance guarantee from a concise theory may become loose in a concrete setup. However, the theoretical methods that sharply predict how the performance of ST depends on various details for each learning scenario are limited. This study develops a novel theoretical framework for sharply characterizing the generalization abilities of the models trained by ST using the non-rigorous replica method of statistical physics. We consider the ST of the linear model that minimizes the ridge-regularized cross-entropy loss when the data are generated from a two-component Gaussian mixture. Consequently, we show that the generalization performance of ST in each iteration is sharply characterized by a small finite number of variables, which satisfy a set of deterministic self-consistent equations. By numerically solving these self-consistent equations, we find that ST's generalization performance approaches to the supervised learning method with a very simple regularization schedule when the label bias is small and a moderately large number of iterations are used.
Helping young designers design for children: evaluating toys and possible values

When designing for children, it remains important not only to emphasise elements such as ergonomics and usability but also values, that can be translated into pleasurable user experiences for children. While methods such as interviews are commonly used to better understand our users, interviewing children can be a challenge. Experience as part of a year project on designing toys for children highlighted that children might answer questions in a surprising way, or students might struggle to ask questions that are not suggestive. To overcome some of these difficulties, we developed a Value Matrix, that can help students to explore the various values or incentives children might have while playing. This Value Matrix can subsequently be used to scan existing toys or new concepts and translate these insights into toys that consist of a wider variety of values children like to experience in a game or toy. We evaluated this tool with 118 first year Industrial Product Design students. Our results show that the Value Matrix give students guidance and support while designing.
An introduction and systematic review on machine learning for smart environments/cities: An IoT approach

: Over the last centuries, human activities have had a significant impact on the environment, usually damaging and exploiting land, water bodies, and air all around us. With the advent of the Internet of Things (IoT) and machine learning (ML) in recent decades, developments in smart sensing and actuating technologies have been adopted for the environment. As such, interactions between humans and the environment have become more synergetic and efficient, creating so-called “smart environments”. In recent years, smart environments, such as smart homes, smart farms and smart cities, have matured at an increasing rate. Therefore, keeping track of applications for smart environments has become an important aspect of research. Although several research efforts have targeted reviewing aspects of smart environments, such as technologies, architectures, and security, a gap is identified. Reviews focusing on approaches using a combination of IoT and ML in smart environments/cities are lacking. In this chapter, a systematic review of the combination of IoT and ML in smart environments is presented. Moreover, a summary of approaches to combine IoT and ML in smart environments is provided. The findings achieved in this chapter materialize into recommendations for the implementation of IoT and ML in smart environments. It is expected that the recommendations may be used as a basis for successful implementations of IoT and ML in smart environments.
Enriching Data Lakes with Knowledge Graphs (short paper)

Data lakes are repositories of data stored in natural/raw format. A data lake may include structured data from relational databases, semi-structured data (i.e., JSON, CSV), unstructured data (i.e., text data), or binary data (i.e., images, audio, video). It is usually built on top of cost-efficient infrastructures such as Hadoop, Amazon S3, MongoDB, ElasticSearch, etc. Several organisations rely on big data lakes for crucial tasks such as reporting, visualisation, advanced analytics, machine learning, and business intelligence. A major limitation of this solution is that without descriptive metadata and a mechanism to maintain it, such data tend to be noisy, making their management and analysis complex and time-consuming. Therefore, there is the need to add a semantic layer based on a formal ontology to describe the data and efficient mechanism to represent them as a knowledge graph. In this paper, we present a methodology to add a semantic layer to a data lake and thus obtain a knowledge graph that can support structured queries and advanced data exploration. We describe a practical implementation of a methodology applied to a data lake consisting of text data describing the online marketplace for lodging and tourism activities. We report statistics about the data lake and the resulting knowledge graph.
Validation of educational technology on tuberculosis for adolescents

Objective: To validate an educational technology for health education on tuberculosis among adolescent students. Methods: Methodological study of 49 adolescents from public schools in Belém (PA) aged between 13 and 18 years comprising three steps: technology assessment; analysis of attributes to measure the degree of representativeness and clarity; and review according to adolescents’ understanding. A wordsearch was presented to adolescents, who were asked to follow the reading guidelines, locate the highlighted words and answer the questions using the indicated locations. A Likert-type questionnaire was applied to describe the agreement among adolescents in relation to the attributes: objective, organization, writing style, appearance and relevance of the technology, considering the Content Validity Index with a cutoff point of 0.90. The textual corpus was processed recurso gráfico da nuvem de palavras. Os dados numéricos foram sumarizados pelo Statistic Package for Social Sciences , versão 22.0
A Data-Driven Spatial-Temporal Graph Neural Network for Docked Bike Prediction

Docked bike systems have been widely deployed in many cities around the world. To the service provider, predicting the demand and supply of bikes at any station is crucial to offering the best service quality. The docked bike prediction problem is highly challenging because of the complicated joint spatial-temporal (ST) dependency as bikes are picked up and dropped off, the so-called “flows”, between stations. Prior works often considered the spatial and temporal dependencies separately using sequential network models, and based on locality assumptions. Without sufficiently capturing the joint spatial and temporal features, these approaches are not optimal for attaining the best prediction accuracy. We propose STGNN-DJD, a novel data-driven Spatial-Temporal Graph Neural Network to solve the bike demand and supply prediction problem by unifiedly embedding the Dynamic and Joint ST Dependency in two novel ST graphs. Given station locations and historical rental data on bike flow over the past time slots 0 to $t-1$, we seek to predict online the bike demand and supply at any station at time $t$. To extract joint spatial-temporal dependency, STGNN-DJD employs a graph generator to construct, at the beginning of time $t$, two graphs that embed the flow relationships between stations at various time slots (flow-convoluted graph) and dynamic demand-supply pattern correlation between stations (pattern correlation graph), respectively. Given the two spatial-temporal graphs, STGNN-DJD subsequently employs a graph neural network with novel flow-based and attention-based aggregators to generate embedding of each station for docked bike prediction. We have conducted extensive experiments on two large bike-sharing datasets. Our re-sults confirm the effectiveness of STGNN-DJD as compared with other state-of-the-art approaches, with significant improvement on RMSE and MAE (by 20%-50%). We also provide a case study on dynamic dependencies between stations and demonstrate that the locality assumption does not always hold for a docked bike system.
RESIN-11: Schema-guided Event Prediction for 11 Newsworthy Scenarios

We introduce RESIN-11, a new schema-guided event extraction&prediction framework that can be applied to a large variety of newsworthy scenarios. The framework consists of two parts: (1) an open-domain end-to-end multimedia multilingual information extraction system with weak-supervision and zero-shot learningbased techniques. (2) schema matching and schema-guided event prediction based on our curated schema library. We build a demo website based on our dockerized system and schema library publicly available for installation (https://github.com/RESIN-KAIROS/RESIN-11). We also include a video demonstrating the system.
The impact of Brexit on the Equity Market

This paper presents results from an empirical study based on evidence from the British stock market, that tests whether the Brexit caused FTSE100 firms to outperform a European and global benchmark. Through the extensive use of several different statistical tools, models, and a unique dataset of cross-sectional and panel data, the impact of Brexit on the British Equity Market will be captured. We find that change in the exchange rate is a driver of short-term abnormal returns, which is a subject with little academic coverage. Also, well-established theories on stock price determinants, such as growth and size, are confirmed by the results. It shows substantial abnormal returns in times of events, but re-integration in a long-run equilibrium.
Acute kidney injury in critically ill patients on positive pressure mechanical ventilation

Objective: To assess the effect of using mechanical ventilation with positive end-expiratory pressure (PEEP) on the renal function of patients admitted to the Intensive Care Unit (ICU). Methods: This is a quantitative retrospective cohort study developed in the ICU of a public hospital in Brasília, Distrito Federal. The sample consisted of 52 medical records of patients admitted to the ICU from November 2016 to December 2018. Data collection was performed through a questionnaire with demographic, clinical and laboratory data. Patients were allocated in two groups: (1) PEEP ≤ 5 cmH2O, (2) PEEP > 5 cmH2O and < 10 cmH2O, and (3) PEEP ≥ 10 cmH2O. Results: The mean age of patients was 59 years and 50% of them were over 63 years. It was found that 63.16% of patients who were on mechanical ventilation with positive end-expiratory pressure ≥ 10 cmH2O evolved in stage 1 (less severe acute kidney injury (AKI)) and 21.5% in stage 2 (moderate gravity). Even so, a small percentage (5.8%) of patients died. Patients who were unsuccessful in weaning from mechanical ventilation had a 10.24-fold chance of developing AKI. Conclusion: mechanical ventilation use can cause damage to the renal function of patients hospitalized in the intensive care unit and that those with greater need to offer PEEP evolved with different severities and persistence of AKI. e persistência da LRA.
Chapter 15: Game Learning Analytics

Games are a pervasive cultural phenomenon with intriguing connections to learning, and the use of learning analytics can inform our understanding of learning in the context of games. In this chapter we identify four principles that are fundamental to both compelling gameplay and meaningful learning – agency , engagement , growth , and social connection . Agency in learners helps them grow and feel safe to fail, persist, and feel ownership of their learning. Engagement, both as great interest and active involvement, is essential to learning, and digital games can be very engaging. Growth involves increases in ability that are developed through effort, perseverance, trying alternative strategies, and seeking help from others. Social connection with other players both within and outside of games facilitates learning. We propose that these four principles serve as an entry point for understanding and conducting game learning analytics work. For each principle we provide examples of evidence-based approaches to the design, measurement, and analysis of learning in game-based contexts to guide thinking and work in the nascent ﬁeld of game learning analytics. This chapter is intended to be useful not only to game learning analytics practitioners but also to people working in LA-adjacent domains, such as game design, classroom learning, data security, and educational policy. We suggest that designers, practitioners, educators, and learners could all beneﬁt from the translation of academic GLA work into a form that is useful to this broader constituency.
Feasibility of Random-Forest Approach for Prediction of Ground Settlements Induced by the Construction of a Shield-Driven Tunnel

AbstractGround settlements above a tunnel as a result of tunnel construction can be predicted with the help of input variables that have direct physical signiﬁcance. Several empirical and artificial intelligence methods for estimating ground settlements have been established by researchers. However, these methods have some limitations because the large number of influential factors involved makes tunnel–ground interaction complicated. In this work, a random forest (RF) was developed and employed to predict ground settlements above tunnels. To achieve this goal, tunnel geometry, geological properties, and construction parameters were investigated as input variables to utilize in the RF modeling, resulting in the maximum surface settlement value (Smax) and trough width (i) as the ground surface settlement index. To demonstrate the applicability of the RF model, two data sets associated with different features, which were obtained from a detailed investigation of different tunnel projects published in litera.
Comparative Efficacy of Rotator Interval Versus Posterior Capsule Approach Intraarticular Corticosteroid Injections for Primary Frozen Shoulder: A Single-blind, Randomized Trial.

BACKGROUND Intraarticular (IA) corticosteroid injection is commonly performed in patients with primary frozen shoulder (PFS). However, the best administration site remains controversial. OBJECTIVES To compare the efficacy of rotator interval (RI) vs posterior capsule (PC) approach for ultrasound-guided corticosteroid injections into the glenohumeral joint of patients with PFS. STUDY DESIGN A randomized, exploratory, prospective study. SETTING A single fellowship training institution in Daegu, Republic of Korea. METHODS This study was approved by the Institutional Review Board (2019-04-047-001). Ninety patients with PFS were randomly assigned to either RI approach (RI group, n = 43) or PC approach (PC group, n = 45) for ultrasound-guided IA corticosteroid injection. Fluoroscopic images to assess the accuracy of the injection were obtained immediately after injection by a shoulder specialist. Visual Analog Scale for pain, the American Shoulder and Elbow Surgeons score, the subjective shoulder value, and range of motion (ROM) were used to assess clinical outcomes for all patients at the time of presentation, and at 3, 6, and 12 weeks after injection. RESULTS The accuracy of injection was 76.7% (33/43) and 93.3% (42/45) in the RI and PC groups, respectively; the between-group difference was statistically significant (P = .028). Significant improvements were observed in both groups in terms of all clinical scores and ROMs throughout follow-up until 12 weeks after the injection (all P < .001). At 12 weeks, better improvements in forward flexion and abduction (P = .049 and .044) were observed in the RI group than in the PC group. No adverse effect related to injection was observed in either group. LIMITATIONS This study had no control group receiving placebo injections and limited follow-up time. CONCLUSIONS Both groups showed significant pain reduction and functional improvement until 12 weeks after injection. Although no significant differences were observed in pain and functional scores between the 2 groups, the RI group showed better improvement of ROM than the PC group. These results indicate that the RI and anterior structures are a major site in the pathogenesis and treatment target of PFS.
The research on algorithms for optoelectronic tracking servo control systems

The photoelectric servo control system based on PC controllers is mainly used to control the speed and position of the load. This paper analyzed the mathematical modeling and the system identification of the servo system. In the aspect of the control algorithm, the IP regulator, the fuzzy PID, the Active Disturbance Rejection Control (ADRC) and the adaptive algorithms were compared and analyzed. The PI-P control algorithm was proposed in this paper, which not only has the advantages of the PI regulator that can be quickly saturated, but also overcomes the shortcomings of the IP regulator. The control system has a good starting performance and the anti-load ability in a wide range. Experimental results show that the system has good performance under the guarantee of the PI-P control algorithm.
NIOSOMES: A PROMISING NANOCARRIER APPROACH FOR DRUG DELIVERY

Niosome is a novel, vesicular drug delivery system meant to deliver drug in a controlled manner to increase its bioavailability, therapeutic efficacy and duration of action thereby minimizing side-effects. Niosomes are structurally similar to liposomes. However, the bilayer of niosomes is made of Non-ionic surfactants rather than phospholipids. Niosomes offer several advantages over conventional drug therapy, but niosomes also have some serious challenges. This paper overviews the types of niosomes, its formulation, effect of individual components on niosomes, various methods of preparation, evaluation and its applications. Finally, patents on niosomal formulations are also discussed.
Ultra-low Voltage Embedded Memories – Design Aspects and a Biomedical Use-case

As the Internet of Things (IoT) era emerges the need for ultra-low power (ULP) devices is becoming more eminent. A research-proven approach to achieve ULP consumption is to aggressively lower the supply voltage (VDD) below or in the vicinity of the transistor threshold voltage (Vth) and operate the transistors in the subthreshold (sub-Vth) region. Operating below or near the threshold voltage, i.e., near threshold (near-Vth), incurs exponentially degrade performance, however, if tolerable, leads to a more energy efficient operation. In this doctoral thesis memory design for near-Vth and sub-Vth operation is explored using standard-cell based memories (SCMs) where the storage element, which accounts for ∼2/3 of total memory area, is replaced by a full-custom designed alternative. The designed memories are used in a biomedical circuit for atrial fibrillation detection.Paper I presents an ULP synthesizable memory using commercial standard-cells complemented with a low-leakage full-custom developed D-Latch with integrated 3-state output buffers as read-logic.Paper II presents two ULP synthesizable memories which use a full-custom developed dual-Vth D-Latch, where PMOS transistors are implemented using a lower-Vth than NMOS transistors. The read-logic is implemented using complementary metal oxide semiconductors (CMOS) multiplexers in one of the memories and a mixture of 3-state buffers and CMOS multiplexers in the second memory.Paper III presents a synthesizable memory using an area-optimized full-custom pass-latch where the pass transistors are implemented using a lower-Vth than the remaining transistors.Paper IV explores the use of a general purpose (GP) process option [instead of low power (LP)] to achieve a higher maximum frequency at ultra-low voltage (ULV) and presents a coherent summary of the different trade-offs for SCMs, i.e., area, leakage power, access speed, access energy and retention voltage.Paper V presents a wide operating range synthesizable memory designed in a 28 nm fully-depleted silicon-on-insulator (FD-SOI) process that takes advantage of the body bias capabilities to compensate for a slow corner using forward body bias (FBB). Paper VI is a case study of an atrial fibrillation (AF) detector designed for sub-Vth operation combined with an ULP memory using standard-cells. The detector is aimed to be operated together with a pacemaker on a single battery charge for 10 years.Paper VII presents energy savings by using clock- and power-gating of the AF detector presented in Paper VI. (Less)