Miracle-dz/UniFAR-FTUs huggingface dataset:
A Defeasible Logic Implementation of Ethical Reasoning

We present an implementation of ethical reasoning using de-feasible logic. We evaluate this on two case studies illustrating how changing information can affect the outcome of the ethical reasoning. We discuss the system’s strengths and weaknesses in terms of formal ethical reasoning, concluding that defeasible logic is a promising approach with particular value in the way it separates rules from exceptions but that additional work is needed, particularly in understanding how defeasible reasoning interacts with sequences/plans of action.
Enhanced Method of Object Tracing Using Extended Kalman Filter via Binary Search Algorithm

Day by day demand for object tracing is increasing because of the huge scope in real-time applications. Object tracing is one of the difficult issues in the computer vision and video processing field. Nowadays, object tracing is a common problem in many applications specifically video footage, traffic management, video indexing, machine learning, artificial intelligence, and many other related fields. In this paper, the Enhanced Method of Object Tracing Using Extended Kalman Filter via Binary Search Algorithm is proposed. Initially, the background subtraction method was used for merge sort and binary search algorithm to identify moving objects from the video. Merge sort is to divide the regions and conquer the algorithm that arranges the region in ascending order. After sorting, the binary search algorithm detects the position of noise in sorted frames and then the next step extended the Kalman Filter algorithm used to predict the moving object. The proposed methodology is linear about the valuation of
Question Answering with Self-Matching Attention

Neural networks are state-of-the-art for question answering (QA). The basis of the success of neural architectures for QA, including Bidirectional Attention Flow (BiDAF) [1], is that question-passage similarity provides a strong signal to finding answer in the passage. Through our experiments, and without relying on pre-trained language models, we improve the baseline BiDAF model, to perform well on Stanford Question Answering Dataset (SQuAD version 2). In particular, we show how Self-Matching Attention (SMA), first used in R-Net [2], can mitigate information loss in the context-query attention mechanism, and provide considerable improvement to the baseline. In addition, we show how to integrate convolution output of character embedding with word embeddings. We analyze contributions of these techniques in the final model.
Coattention, QANet, and Data Augmentation for Question Answering

Our project aims to explore the effect of different Coattention techniques on the SQuAD question answering dataset, including the Dynamic Coattention Network [1] and the QANet [2]. Our Coattention network with dynamic decoder improved upon the baseline model by a small margin. Our final QANet implementation improved over the baseline model and achieved an F1 score of 69 on the dev set and 66.48 on the test set. We also employ several data augmentation techniques from the Easy Data Augmentation [3]
Insertion Elements on Lactococcal Proteinase Plasmids Insertion Elements on Lactococcal Proteinase Plasmids

DNA segments of 809 and 808 nucleotides, with 18-base-pair terminal inverted repeats, are present on the proteinase plasmids pWV05 from Lactococcus lactis subsp. cremoris Wg2 and pSK111 from L. lactis subsp. cremoris SK1l, respectively. These DNA segments are highly similar: 77% identical nucleotides and both contain an open reading frame that can encode a protein of 226 amino acids. Furthermore, both DNA segments are located downstream of the proteinase maturation gene prtM, but they differ individually in their orientation with respect to the prtM gene. On the basis of the striking similarity between ISS1, an 808-base-pair insertion sequence (IS) from L. lactis subsp. lactis ML3 lactose plasmid pSK08, and the DNA segments of pWV05 and pSK111, we propose that these DNA segments comprise IS elements. The IS elements from strains Wg2 and SK1l were named ISSIW and ISSIN, respectively. On pWV05, ISSIW is flanked on one side by only part of a second IS element, indicating that pWV05 evolved as a deletion derivative of a precursor plasmid that carried at least two IS elements.
Radio on Fiber Technologies and Their Application toward Universal Platform for Heterogeneous Wireless Services

To realize user-centric and ∞exible wireless services, the networks for heterogeneous wireless require high radio frequency utilization, dead zone reduction, tra-c dispersion in urban area, and universal service capabilities in low population area. Radio on Fiber technologies are able to transparently transmit various types of radio services, and realize an efiective universal network platform for wireless communications and broadcastings. This paper describes Software Deflnable Radio Networks proposed to realize a universal network for heterogeneous wireless services, and proposes a RoF distributed antenna architecture with MIMO capability. Ubiquitous network should ofier users the environment to access any communication service at any time, any place, and any situations. The demand of users for various multimedia services will be increasing more and more; therefore full IP connectivity will be required to accommodate the variety of contents. However, such diversiflcation appears not only in application services but also in air interfaces of wireless access methods. The heterogeneous wireless networks also strongly depend on users' difierent demands for applications, quality, latency, and moreover users' situations such as indoor, outdoor, and fast/slow mobility. Therefore, the platform for heterogeneous wireless network becomes a key issue to realize ubiquitous networks. In current wireless networks, various operators independently overlaid their own radio base sta- tions and networks. This leads redundant equipments and investments on infrastructures, and prevents the quick start of a new wireless service. From the viewpoint of the improvement in the radio frequency utilization, microcellular or picocellular architecture is much efiective, however, the implementation of difierent types of base stations and networks provided by difierent opera- tors prevent them from adopting microcellular due to its large investment. Therefore, the future ubiquitous wireless networks will need a cross frequency platform, which can be commonly used by various types of wireless services under the multi air interferences coexistence. RoF technologies are available to realize a universal platform for transparent forwarding various types of wireless services under the multi air interferences coexistence in a ubiquitous network environment. To realize future user centric ubiquitous wireless service networks, software deflnable radio network (SDRN) has been proposed (1), that can be conflgured with the combination of RoF entrance networks, software deflnable radio gateway, wireless service over IP, and radio agents. The flrst part of this paper describes the concept of SDRN, and the second part proposes a new type of distributed antenna architecture by use of RoF, and shows some experimental results for the efiect in MIMO (Multiple Input Multiple Output) capability improvement.
the Society for Computation in Linguistics

Inferential reasoning is an essential feature of argumentation. Therefore, a method for mining discourse for inferential structures would be of value for argument analysis and assessment. The logic of relational propositions is a procedure for rendering texts as expressions in propositional logic directly from their rhetorical structures. From rhetorical structures, relational propositions are defined, and from these propositions, logical expressions are then generated. There are, however, unsettled issues associated with Rhetorical Structure Theory (RST), some of which are problematic for inference mining. This paper takes a deep dive into some of these issues, with the aim of elucidating the problems and providing guidance for how they may be resolved.
Automated Essay Scoring with Hint of grade level

Automated essay scoring (AES) is a hot topic involving not only NLP but also education, linguistics and other cross-disciplinary research. One of the most fundamental and long-existing barrier is that in AES there is no such a universally data set that can cover different essay prompts, and annotated information about essay writers’ language proficiency level (i.e., L2 learners) or sophistication background (i.e., grade level). Researchers have found noticeable writing quality improvement for certain prompts than other, which may affected by writers’ grade level in part a function of (a) the types of prompts for which essays were written at each grade level, (b) instructional emphases at the different grades, or (c) differential developmental contribution to writing [1]. Therefore, the raised question is, is it possible to classify collected essays into different grade level subset even lack of annotated sophistication information? and further I will verify if this strategy could help get more relevant automatic scores to the human raters’.
Bounded Adversarial Attack on Deep Content Features

We propose a novel adversarial attack targeting content features in some deep layer, that is, individual neurons in the layer. A naive method that enforces a fixed value/percentage bound for neuron activation values can hardly work and generates very noisy samples. The reason is that the level of perceptual variation entailed by a fixed value bound is non-uniform across neurons and even for the same neuron. We hence propose a novel distribution quantile bound for activation values and a polynomial barrier loss function. Given a benign input, a fixed quantile bound is translated to many value bounds, one for each neuron, based on the distributions of the neuron's activations and the current activation value on the given input. These individualized bounds enable fine-grained regulation, allowing content feature mutations with bounded perceptional variations. Our evaluation on ImageNet and five different model architectures demonstrates that our attack is effective. Compared to seven other latest adversarial attacks in both the pixel space and the feature space, our attack can achieve the state-of-the-art trade-off between attack success rate and imperceptibility. 11Code and Samples are available on Github [37].
Deep Reinforcement Learning for a Multi-Objective Online Order Batching Problem

On-time delivery and low service costs are two important performance metrics in warehousing operations. This paper proposes a Deep Reinforcement Learning (DRL) based approach to solve the online Order Batching and Sequence Problem (OBSP) to optimize these two objectives. To learn how to balance the trade-off between two objectives, we introduce a Bayesian optimization framework to shape the reward function of the DRL agent, such that the influences of learning to these objectives are adjusted to different environments. We compare our approach with several heuristics using problem instances of real-world size where thousands of orders arrive dynamically per hour. We show the Proximal Policy Optimization (PPO) algorithm with Bayesian optimization outperforms the heuristics in all tested scenarios on both objectives. In addition, it finds different weights for the components in the reward function in different scenarios, indicating its capability of learning how to set the importance of two objectives under different environments. We also provide policy analysis on the learned DRL agent, where a decision tree is used to infer decision rules to enable the interpretability of the DRL approach.
Improving Trope Detection with Crowdsourced Examples

Tropes are cultural narrative conventions that shape our expectations of stories. They thoughtfully encompass our understanding of the world and the representation of our culture. The use and comprehension of tropes require advanced human language skills such as causal, motivational, or pragmatic inferences. Detecting them is still an open and challenging research question that might be key to build ‘intelligent’ machines. In this regard, we propose to leverage the knowledge gathered by thousands of passionate people about tropes on tvtropes.org . Our contribution is twofold. First, we provide a dataset of specific examples for each trope extracted from the tropes wiki. From this scraped data, we propose the original task of the classification of tropes’ examples. It offers the main advantages of being more workable and interpretable due to the concision and relevance of the examples annotated by the community of tropers. Second, we present attempts to tackle this new challenge based on a word and sentence-level analysis and show the potential applications of such models on the more general task of detecting tropes in larger texts such as synopses. The proposed models outperform the state-of-the-art network when trained on individual tropes by making use of different levels of cues (word, sentence, or full context) based on the nature of the trope studied. This confirms the need to adapt the detection method or to build a multi-level comprehension model.
Metabolomics reveal the mechanism for anti-renal fibrosis effects of an n-butanol extract from Amygdalus mongolica

Abstract To reveal the mechanism of anti-renal fibrosis effects of an n-butanol extract from Amygdalus mongolica, renal fibrosis was induced with unilateral ureteral obstruction (UUO) and then treated with an n-butanol extract (BUT) from Amygdalus mongolica (Rosaceae). Sixty male Sprague-Dawley rats were randomly divided into the sham-operated, renal fibrosis (RF) model, benazepril hydrochloride-treated model (1.5 mg kg−1) and BUT-treated (1.75, 1.5 and 1.25 g kg−1) groups and the respective drugs were administered intragastrically for 21 days. Related biochemical indices in rat serum were determined and histopathological morphology observed. Serum metabolomics was assessed with HPLC-Q-TOF-MS. The BUT reduced levels of blood urea nitrogen, serum creatinine and albumin and lowered the content of malondialdehyde and hydroxyproline in tissues. The activity of superoxide dismutase in tissues was increased and an improvement in the severity of RF was observed. Sixteen possible biomarkers were identified by metabolomic analysis and six key metabolic pathways, including the TCA cycle and tyrosine metabolism, were analyzed. After treatment with the extract, 8, 12 and 9 possible biomarkers could be detected in the high-, medium- and low-dose groups, respectively. Key biomarkers of RF, identified using metabolomics, were most affected by the medium dose. A. mongolica BUT extract displays a protective effect on RF in rats and should be investigated as a candidate drug for the treatment of the disease.
SMU Data Science Review SMU Data Science

Memory stability analysis traditionally relied heavily on circuit simulation-based approaches that run Monte Carlo (MC) analysis over various manufacturing and use condition parameters. This paper researches application of Machine Learning approaches for memory element failure analysis which could mimic simulation-like accuracy and minimize the need for engineers to rely heavily on simulators for their validations. Both regressor and classifier algorithms are benchmarked for accuracy and recall scores. A high recall score implies fewer escapes of fails to field and is the metric of choice for comparing algorithm. The paper identifies that recall score in excess of 0.97 can be achieved through stack ensemble and logistic regression-based approaches. The high recall score suggests machine learning based approaches can be used for memory failure rate assessments.
Comparing Approaches to Question-Answering on SQuAD 2.0

In this project, we qualitatively and quantitatively compared two different approaches to the question-answering task on the SQuAD 2.0 dataset: 1) BiDAF: a bi-directional attention flow approach with recurrent neural networks (RNNs), and 2) QANet: a non-recurrent Transformer-based approach that relies on position encoding, convolutions, and self-attention. Our goal was to systematically evaluate claims made by the authors of the QANet paper that their proposed model is both faster to train and evaluate as well as exhibits better performance than traditional recurrence-based architectures. We were able to significantly improve the original BiDAF baseline by adding character-level embeddings to the input layer as well as introducing an MLP "fusion function" as a post-processing step to the attention flow layer, achieving 60.33 EM and 64.19 F1 on the dev set. Furthermore, we trained multiple configurations of QANet by varying the number of stacked encoder blocks and self-attention heads to evaluate the performance/model size trade off. Our best-performing QANet model had 8 attention heads and 7 model encoder blocks with layer dropout, achieving 62.181 EM and 65.694 F1 on the test set.
A Novel model for diagnosing high-risk pregnancies mothers using Bayesian belief network algorithm and particle optimization

people high-risk pregnancies, people with medium-risk and low-risk pregnancies. the data include six independent variables. A combination of Bayesian belief network algorithms and particle optimization was used to predict pregnancy risk. Results: For validation, the data model was divided into two sets of training and testing based on the method of 30-70. Then the proposed model was designed by training data. Then the model for training and testing data was evaluated in terms of accuracy parameters 99.18 and 98.32% accuracy were obtained, respectively. It has also performed between 0.5 and 8% better than similar work in the past. a new model for designing Bayesian belief network was presented and it was found that this model can be useful for predicting maternal pregnancy risk.
Improved QANet on SQuAD 2.0

The task of question answering is trivial for humans, but it has long been a known problem to solve for NLP models. Neural networks like Bidirectional Attention Flow for Machine Comprehension (BiDAF) and Bidirectional Encoder Represen-tations from Transformers (BERT) have been dominating the leaderboard when tested on the Stanford Question Answering Dataset (SQuAD) 2.0, differentiated from SQuAD 1.0 due to a larger corpus of unanswerable questions. We invalidated our initial hypothesis that an extended BiDAF model with a Transformer encoder would improve upon baseline BiDAF results. However, we propose that with certain alterations, QANet can perform better than the baseline BiDAF model when evaluated on SQuAD 2.0. Through many iterations and experiments, we find that an improved QANet with data augmentation and learned positional encodings outperforms the BiDAF baseline.
A Mixture Of Experts for Out-Of-Domain Expertise

Out of Domain question answering is still an open problem for NLP systems. In this project, I explore the use of a Mixture-of-Experts (MoE) model in order to better tackle out of domain questions, as explained in the paper by Jacobs et al [1]. The MoE model is trained on 3 in-domain datasets, finetuned and validated on a small subset of out of domain datasets, and finally tested on out of domain datasets. I have used Cantor Pairing as a means of feeding the start and end locations of the answer span into the gating mechanism of the Mixture of Experts model [1]. I have also used a custom loss function based on the mixture of Gaussians approach described in the Jacobs et al paper
EXPLORING DATA ANALYSIS METHODS TO FIND CORRELATIONS BETWEEN PHYSIOLOGICAL DATA AND FLOW

In learning situations, achieving Flow, the state of ideal experience of an activity, can greatly support the learning efficacy. Utilizing this connection by detecting and measuring Flow during the learning activity could potentially help to improve the learning rate in Serious Games. In a prior study aimed towards developing a tool to link Flow to physiological measurements, no meaningful correlations were found. However, this does not rule out the existence of such a correlation and different analysis methods might deliver results that are more favorable. In this work in progress paper, the previously collected data is revisited and an approach is outlined to explore the use of a multitude of machine learning methods, based on multiple physiological measurements, to detect Flow and to investigate, whether it provides adequate tools to gain further insight into the link between physiological data and Flow states.
Finite element analysis of eddy current losses in steel laminations of inverter-fed electrical machines

OF DOCTORAL DISSERTATION AALTO UNIVERSITY SCHOOL OF SCIENCE AND TECHNOLOGY P.O. BOX 11000, FI-00076 AALTO http://www.aalto. fi Author Jenni Pippuri Name of the dissertation Finite element analysis of eddy current losses in steel laminations of inverter-fed electrical machines Manuscript submitted 21.6. 2010 Manuscript revised 1.10. 2010 Date of the defense 5.11. 2010 Monograph Article dissertation (summary + original articles) Faculty Faculty of Electronics, Communications and Automation Department Department of Electrical Engineering Field of research Electromechanics Opponent(s) Professor João Pedro A. Bastos Supervisor Professor Antero Arkkio Instructor Docent Anouar Belahcen Abstract This thesis investigates the eddy currents and regarding losses in electrical steel sheets. For the purpose, two methods of numerical analysis of electromagnetic fields are developed. Both of those are based on the twodimensional (2-D) finite element computation of the fields. Since the eddy currents of the sheets are, by definition, excluded from the 2-D formulation they need to be included separately. Within this work, the goal is achieved by modeling the eddy currents via a one-dimensional (1-D) penetration equation in association with the 2-D analysis. Formulations for both, general time variation and sinusoidal time variation are shown. The coupling of the 2-D and 1-D models is investigated from several perspectives. For representing the 1-D eddy current solution within the time-discretized analysis two approaches are considered. Numerous simulations carried out reveal that inappropriate coupling might not affect the losses to be obtained but other electromagnetic quantities such as input power or power factor. A proper coupling method that accords with the computation results is proposed. The combining of those two field formulations is discussed in the case of the timeharmonic approach as well, for which the concept of complex reluctivity is employed. The second objective of the work is to study in detail the losses in rotating electrical machines fed by a frequency converter. The higher harmonics associated with the frequency-converter supply are known to increase to losses in comparison with a sinusoidal voltage supply. According to the simulations, (i) the eddy current losses in the rotor sheets are the most sensitive to the distorted input voltage waveforms and (ii) coincidently can be the most reduced by choosing the switching frequency of the frequency converter suitably. The measurements carried out on the test machine and comparative studies of those and the simulation results verify the methods proposed.