Miracle-dz/UniFAR-FTUs huggingface dataset:
On Uses of Van der Waerden Test: A Graphical Approach

Although several nonparametric tests are available for testing population identical distributions or equal means in multiple groups problem, the Van der Waerden test has asymptotically the same efficiency as the classical one-way analysis of variance test under normality assumption where it depends on inverse normal score. In this study the Waerden test statistic is used to create a form that assists in deriving adjusted p values chart in terms of Bonferroni and Benjamini-Hochberg methods. The sampling distribution of the proposed form is derived as a gamma distribution that utilized to acquire initial p values. The proposed method will provide well recognition and deep knowledge where the changes occur. Simulation study is executed to study the characteristic of the proposed method in terms of size and power of the test and compared with original Waerden test and one-way analysis of variance test. The results are of great practical values since the proposed method stretches visible shape to decision maker to gives complete picture and deep insight where the differences happen, keep size and power of the test almost similar to original Waerden test and gives a glimpse of various information that completes the pairwise comparisons. To show the benefits of the proposed method, two applications are presented.
EAG: Extract and Generate Multi-way Aligned Corpus for Complete Multi-lingual Neural Machine Translation

Complete Multi-lingual Neural Machine Translation (C-MNMT) achieves superior performance against the conventional MNMT by constructing multi-way aligned corpus, i.e., aligning bilingual training examples from different language pairs when either their source or target sides are identical. However, since exactly identical sentences from different language pairs are scarce, the power of the multi-way aligned corpus is limited by its scale. To handle this problem, this paper proposes “Extract and Generate” (EAG), a two-step approach to construct large-scale and high-quality multi-way aligned corpus from bilingual data. Specifically, we first extract candidate aligned examples by pairing the bilingual examples from different language pairs with highly similar source or target sentences; and then generate the final aligned examples from the candidates with a well-trained generation model. With this two-step pipeline, EAG can construct a large-scale and multi-way aligned corpus whose diversity is almost identical to the original bilingual corpus. Experiments on two publicly available datasets i.e., WMT-5 and OPUS-100, show that the proposed method achieves significant improvements over strong baselines, with +1.1 and +1.4 BLEU points improvements on the two datasets respectively.
Neural mechanisms underlying the temporal organization of naturalistic animal behavior

Naturalistic animal behavior exhibits a strikingly complex organization in the temporal domain, with variability arising from at least three sources: hierarchical, contextual, and stochastic. What neural mechanisms and computational principles underlie such intricate temporal features? In this review, we provide a critical assessment of the existing behavioral and neurophysiological evidence for these sources of temporal variability in naturalistic behavior. Recent research converges on an emergent mechanistic theory of temporal variability based on attractor neural networks and metastable dynamics, arising via coordinated interactions between mesoscopic neural circuits. We highlight the crucial role played by structural heterogeneities as well as noise from mesoscopic feedback loops in regulating flexible behavior. We assess the shortcomings and missing links in the current theoretical and experimental literature and propose new directions of investigation to fill these gaps.
GraspARL: Dynamic Grasping via Adversarial Reinforcement Learning

Grasping moving objects, such as goods on a belt or living animals, is an important but challenging task in robotics. Conventional approaches rely on a set of manually defined object motion patterns for training, resulting in poor generalization to unseen object trajectories. In this work, we introduce an adversarial reinforcement learning framework for dynamic grasping, namely GraspARL. To be specific. we formulate the dynamic grasping problem as a 'move-and-grasp' game, where the robot is to pick up the object on the mover and the adversarial mover is to find a path to escape it. Hence, the two agents play a min-max game and are trained by reinforcement learning. In this way, the mover can auto-generate diverse moving trajectories while training. And the robot trained with the adversarial trajectories can generalize to various motion patterns. Empirical results on the simulator and real-world scenario demonstrate the effectiveness of each and good generalization of our method.
A Study on the Prediction of Road Pavement Deterioration according to the Road Quality and ESAL 1

This study intends to analyze the pavement life data on the target ar- ea in consideration of load size and VI grade for determining the influence level. Here, VI grade is the road surface status investigation which indicates the defect status on the pavement surface, and the level of defect on the surface is divided into 7 grades, and these grades are divided into 3 grades again. For the case analysis, the road maintenance zone conducted by Daejeon Regional Construc- tion Management Administration in 2011 was analyzed. For the classification according to the load, Low level for MESAL 0.0-0.10, Medium level for 0.11- 0.30, and High level for over 0.31 are selected, and VI grades are classified as follows. (A level: 1(Good), B level: 2~3(Normal), C level: 4~7(Bad)) (Korea Institute of Construction Technology, 2010). The analysis result shows that the reliability of road pavement decreases according to the road quality and accu- mulated ESAL, the reliability of road decreases as the accumulated ESAL is higher even if the road quality is same. And also, the reliability of road decreas- es as the level drops even if the ESAL is same.
Abuse and Fraud Detection in Streaming Services Using Heuristic-Aware Machine Learning

This work presents a fraud and abuse detection framework for streaming services by modeling user streaming behavior. The goal is to discover anomalous and suspicious incidents and scale the investigation efforts by creating models that characterize the user behavior. We study the use of semi-supervised as well as supervised approaches for anomaly detection. In the semi-supervised approach, by leveraging only a set of authenticated anomaly-free data samples, we show the use of one-class classification algorithms as well as autoencoder deep neural networks for anomaly detection. In the supervised anomaly detection task, we present a so-called heuristic-aware data labeling strategy for creating labeled data samples. We carry out binary classification as well as multi-class multi-label classification tasks for not only detecting the anomalous samples but also identifying the underlying anomaly behavior(s) associated with each one. Finally, using a systematic feature importance study we provide insights into the underlying set of features that characterize different streaming fraud categories. To the best of our knowledge, this is the first paper to use machine learning methods for fraud and abuse detection in real-world scale streaming services.
User-Centered Design (VIII): A New Framework of Intelligent Sociotechnical Systems and Prospects for Future Human Factors Research

Traditional sociotechnical systems (STS) theory has been widely used, but there are many new characteristics in the STS environment as we enter the intelligence era, resulting in the limitations of traditional STS. Based on the"user-centered design"philosophy, this paper proposes a new framework of intelligent sociotechnical systems (iSTS) and outlines the new characteristics of iSTS as well as its implications for the development of intelligent systems. Future research of iSTS requires interdisciplinary collaboration, including human factors engineering, this paper finally proposes suggestions from two aspects of human factors engineering methodology and approaches.
Deep Visual Navigation under Partial Observability

How can a robot navigate successfully in rich and diverse environments, indoors or outdoors, along office corridors or trails on the grassland, on the flat ground or the staircase? To this end, this work aims to address three challenges: (i) complex visual observations, (ii) partial observability of local visual sensing, and (iii) multimodal robot behaviors conditioned on both the local environment and the global navigation objective. We propose to train a neural network (NN) controller for local navigation via imitation learning. To tackle complex visual observations, we extract multi-scale spatial representations through CNNs. To tackle partial observability, we aggregate multi-scale spatial information over time and encode it in LSTMs. To learn multimodal behaviors, we use a separate memory module for each behavior mode. Importantly, we integrate the multiple neural network modules into a unified controller that achieves robust performance for visual navigation in complex, partially observable environments. We implemented the controller on the quadrupedal Spot robot and evaluated it on three challenging tasks: adversarial pedestrian avoidance, blind-spot obstacle avoidance, and elevator riding. The experiments show that the proposed NN architecture significantly improves navigation performance.
Iterative Saliency Enhancement using Superpixel Similarity

Saliency Object Detection (SOD) has several applications in image analysis. The methods have evolved from image-intrinsic to object-inspired (deep-learning-based) models. When a model fail, however, there is no alternative to enhance its saliency map. We fill this gap by introducing a hybrid approach, named \textit{Iterative Saliency Enhancement over Superpixel Similarity} (ISESS), that iteratively generates enhanced saliency maps by executing two operations alternately: object-based superpixel segmentation and superpixel-based saliency estimation -- cycling operations never exploited. ISESS estimates seeds for superpixel delineation from a given saliency map and defines superpixel queries in the foreground and background. A new saliency map results from color similarities between queries and superpixels at each iteration. The process repeats and, after a given number of iterations, the generated saliency maps are combined into one by cellular automata. Finally, the resulting map is merged with the initial one by the maximum bewteen their average values per superpixel. We demonstrate that our hybrid model can consistently outperform three state-of-the-art deep-learning-based methods on five image datasets.
Computing Policies That Account For The Effects Of Human Agent Uncertainty During Execution In Markov Decision Processes

When humans are given a policy to execute, there can be policy execution errors and deviations in policy if there is uncertainty in identifying a state. This can happen due to the human agent's cognitive limitations and/or perceptual errors. So an algorithm that computes a policy for a human to execute ought to consider these effects in its computations. An optimal Markov Decision Process (MDP) policy that is poorly executed (because of a human agent) maybe much worse than another policy that is suboptimal in the MDP, but considers the human-agent's execution behavior. In this paper we consider two problems that arise from state uncertainty; these are erroneous state-inference, and extra-sensing actions that a person might take as a result of their uncertainty. We present a framework to model the human agent's behavior with respect to state uncertainty, and can be used to compute MDP policies that accounts for these problems. This is followed by a hill climbing algorithm to search for good policies given our model of the human agent. We also present a branch and bound algorithm which can find the optimal policy for such problems. We show experimental results in a Gridworld domain, and warehouse-worker domain. Finally, we present human-subject studies that support our human model assumptions.
Reconstructing Nonlinear Dynamical Systems from Multi-Modal Time Series

Empirically observed time series in physics, biology, or medicine, are commonly generated by some underlying dynamical system (DS) which is the target of scientific interest. There is an increasing interest to harvest machine learning methods to reconstruct this latent DS in a data-driven, unsupervised way. In many areas of science it is common to sample time series observations from many data modalities simultaneously, e.g. electrophysiological and behavioral time series in a typical neuroscience experiment. However, current machine learning tools for reconstructing DSs usually focus on just one data modality. Here we propose a general framework for multi-modal data integration for the purpose of nonlinear DS reconstruction and the analysis of cross-modal relations. This framework is based on dynamically interpretable recurrent neural networks as general approximators of nonlinear DSs, coupled to sets of modality-specific decoder models from the class of generalized linear models. Both an expectation-maximization and a variational inference algorithm for model training are advanced and compared. We show on nonlinear DS benchmarks that our algorithms can efficiently compensate for too noisy or missing information in one data channel by exploiting other channels, and demonstrate on experimental neuroscience data how the algorithm learns to link different data domains to the underlying dynamics.
Design methodology for a switching-mode RF CMOS power amplifier with an output transformer

In this study, we propose a design methodology for a switching-mode RF CMOS power amplifier with an output transformer. For a given supply voltage, output power, and target efficiency, the initial values of the transistor size, output inductance, and capacitance can be sequentially determined during the design of the power amplifier. The breakdown voltage of the power transistor is considered in the design methodology. To prove the feasibility of the proposed design methodology, we provide the design example of a 2.4-GHz switching-mode CMOS power amplifier with 180-nm RF CMOS technology. From the measured results, the feasibility of the proposed design methodology is proved.
EXPERIMENTAL STUDY ON THE EFFECT OF POUNDING BETWEEN THE ADJACENT GIRDERS ON THE FALLING GIRDERS

本研究は, 兵庫県南部地震で実際に被災した橋梁をモデルに縮小した2径間の橋梁模型を製作し, 2次元振動台を用いて振動させたときの橋桁や橋脚の動的応答から桁間衝突による落橋のメカニズムを調べたものである. まず, 落橋防止連結板を取り付けていない場合について, 桁の衝突による落橋の有無を観察し, 桁間衝突が落橋におよぼす影響について検討した. その結果, 橋桁の落下には橋脚を中心とした設計振動単位の共振現象が強く影響することが確認できた. さらに, 落橋防止連結板に緩衝材を挿入し, それによって衝撃的な外力を吸収できるかどうか検討し, 緩衝材として望ましい材料特性について検討した.
Effect of Inlet Skew on Heat/Mass Transfer From a Simulated Turbine Blade

Heat (mass) transfer experiments are conducted to study the effect of an inlet skew on a simulated gas-turbine blade placed in a linear cascade. The inlet skew simulates the relative motion between rotor and stator endwalls in a single turbine stage. The transverse motion of a belt, placed parallel to and upstream of the turbine cascade, generates the inlet skew. With the freestream velocity constant at approximately 16 m/sec, which results in a Reynolds number (based on the blade chord length of 0.184 m) of 1.8 × 105 , a parametric study was conducted for three belt-to-freestream velocity ratios. The distribution of the Sherwood number on the suction surface of the blade shows that the inlet skew intensifies the generation of the horseshoe vortex close to the endwall region. This is associated with the development of a stronger passage vortex for a higher velocity ratio, which causes an earlier transition to turbulence. Corresponding higher mass transfer coefficients are measured between the mid-height of the blade and the endwall, at a mid-chord downstream location. However, a negligible variation in transport properties is measured above the two-dimensional region of the blade at the higher velocity ratios. In contrast, the inlet skew has a negligible effect on the distribution of the Sherwood number on the entire pressure surface of the blade. This is mainly because the skew is directed along the passage vortex, which is from the pressure surface of the airfoil to the suction surface of the adjacent airfoil. © 2011 ASME
Prediction for NCAA championship

In recent years, predicting sports results has become increasingly popular. Basketball is one of the most popular sports in the world, especially in USA, the NCAA game attract lot of fans around over 1000 colleges. In this paper, a new machine learning framework for predicting NCAA game results is proposed in order to find the features that affects NBA game results, and obtain the best model to predict the result of NCAA game. We wanted to determine whether the machine learning approach was applicable to predicting the result of NCAA game and what were the important factors that affected the result of the game. We proposed to use different machine learning models to predict the winner. Specifically, different classification models such as decision tree, random forest, LDA, QDA, SVM, Naïve bayes were used to predict the result. Then for each model, 10-fold cross validation was used to check the mean accuracies of each model and obtain the highest accuracy. By comparing the accuracies and performances of each model, we can obtain the best model for prediction and know which features affect the result of NCAA regular season and tourney season. For regular season, the LDA, QDA, random forest and SVM models show good performance and the accuracy is about 99%. For tourney season, LDA and SVM should be good models to do the prediction and the accuracy is about 90%. In general, LDA, random forest and SVM could be the convinced model in this case to predict the winner of NCAA basketball game, and there are some possibilities for improvement. Pruning the tree, different type of kernel of SVM such as linear, radial basis function, polynomial, and more classification models will be used in the future to predict the winner of NCAA games.
Responsible Big Data Analytics for E-Business Services

This paper examines responsible big data analytics for e-business services and looks at how to use responsible big data analytics to obtain responsible e-business services. It addresses why responsibility matters to big data analytics and e-business services. It reviews big data analytics and looks at Google Analytics as a data processing flow-oriented analytics, and presents a data processing flow approach to big data analytics. This paper discusses responsible big data and big data analytics based on two case studies and examines big data analytics services as an e-business service and proposes two strategies on how to apply responsible big data analytics to obtain responsible e-business services. The proposed approach in this paper might facilitate the research and development of big data, business analytics, responsible big data analytics, e-business, e-services, and e-society.
Smart Distribution Systems

The increasing importance of system reliability and resilience is changing the way distribution systems are planned and operated. To achieve a distribution system self-healing against power outages, emerging technologies and devices, such as remote-controlled switches (RCSs) and smart meters, are being deployed. The higher level of automation is transforming traditional distribution systems into the smart distribution systems (SDSs) of the future. The availability of data and remote control capability in SDSs provides distribution operators with an opportunity to optimize system operation and control. In this paper, the development of SDSs and resulting benefits of enhanced system capabilities are discussed. A comprehensive survey is conducted on the state-of-the-art applications of RCSs and smart meters in SDSs. Specifically, a new method, called Temporal Causal Diagram (TCD), is used to incorporate outage notifications from smart meters for enhanced outage management. To fully utilize the fast operation of RCSs, the spanning tree search algorithm is used to develop service restoration strategies. Optimal placement of RCSs and the resulting enhancement of system reliability are discussed. Distribution system resilience with respect to extreme events is presented. Test cases are used to demonstrate the benefit of SDSs. Active management of distributed generators (DGs) is introduced. Future research in a smart distribution environment is proposed.
Judges and Academics, and the Endless Road to Unattainable Perfection

This article is a lightly revised version of the Lionel Cohen Lecture 2021 given by the author in October 2021. It looks at the relationship between legal academics and judges. The title of the article derives from one of the judgments of the great Lord Goff. The article has three main parts. First, it explores the increased influence of legal academics on judges in England and Wales towards the end of the twentieth century. Secondly, it examines how academic work can assist judges and makes a plea for recognition of the importance of ‘practical legal scholarship’. Finally, drawing on the author's own recent experience, the article examines how being a judge of the United Kingdom Supreme Court differs from being an academic.
Entomopathogenic nematodes associated with organic honeybush (Cyclopia spp.) cultivation in South Africa

Abstract Entomopathogenic nematodes (EPNs) of the genera Steinernema and Heterorhabditis have demonstrated great potential as useful bio-control agents in the management of certain important soil-inhabiting insect pests of agricultural crops. In a survey of nematodes associated with organic honeybush cultivation, soil samples were obtained from nine organic honeybush plots, which are located in the Bredasdorp area of the Western Cape province of South Africa. The EPNs were isolated from soil by baiting with larvae of Galleria mellonella (greater wax moth) and identified by the amplification of the internal transcribed spacer region using the primer set TW81 and AB28. EPNs were abundant in the honeybush orchards, accounting for about 50% of the sampled fields and five EPN species were identified including Heterorhabditis bacteriophora, Heterorhabditis safricana, Steinernema khoisanae,. Steinernema nguyeni, Oscheius sp. and an unknown EPN, therefore suggesting a substantial diversity of EPNs in the sampled fields. A 100% mortality of infected G. mellonella larvae was recorded within 48 h of exposure to the nematodes. However, although these EPNs have been previously reported in South Africa, it is the first time they are found in such diversity on a conservative tillage management system in organic honeybush cultivation.
Comparison of vertical jump performance of male hockey and football players

The present study was conducted on 30 male players (fifteen male hockey players; age: 16.80 ± 1.52 years & fifteen male football players; age: 16.13 ± 0.83 years) comprising of inmates of Sports Training Centre, scheme of Sports Authority of India and players training under the guidance of Punjab State coaches in Patiala (India). The experimental protocol developed by Bosco et al., (1983) and Mcguigan et al., (2006) were used to measure the vertical jump performance of male hockey and football players. Test of significance of the differences was applied and data was judged at 0.01 and 0.05 level of significance. The analysis of data shows that the male hockey players performed better in vertical jump test parameters like the squat jump flight time, squat jump height, counter movement jump height, counter movement flight time, Eccentric Utilization Ratio (EUR), Elasticity Index (EI) than the male football players. But male football players performed better in continuous vertical jump test (60sec) parameters like Peak Power (0-15sec), Peak Power (45-60sec) and Mean Power (0-60sec), as compared to male hockey players.