Miracle-dz/UniFAR-FTUs huggingface dataset:
An improved backcalculation method to predict flexible pavement layers moduli and bonding condition between wearing course and base course.

The aim of this research project is to develop an improved backcalculation procedure, for the determination of flexible pavement properties from the Falling Weight Deflectometer (FWD) test results. The conventional backcalculation methods estimate the pavement layer moduli assuming full adhesion exists between layers in the analysis process. The method developed in this research can predict the interface condition between the wearing and the base courses in addition to the layer moduli, which can be considered an improvement to the existing procedures. A two stage database procedure has been used to predict the above parameters and to facilitate the determination of the deflection insensitive parameters. The need for this improvement arises from the large number of debonding failures which have been reported in the literature between the wearing and base courses, and the theoretical studies which identified the significance of including the interface bonding condition in the analysis process. The validation of the improved method has been carried out firstly by comparing the backcalculated results for ninety theoretical pavements with their hypothetical values, and secondly by comparing the improved procedure results with other well known programs such as WESDEF and MODULUS. Full scale pavement testing using the FWD has been performed and the backcalculated results compared with measured values for the pavement materials. Indirect tensile tests for resilient modulus of bituminous materials were carried out on cores extracted for the pavements, whereas Dynamic Cone Penetrometer (DCP) tests were conducted for the unbound materials. The Backcalculated and the physically measured results correlated well, validating the improved procedure.
Can SpRY recognize any PAM in human cells?

The application of clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated proteins (Cas) can be limited due to a lack of compatible protospacer adjacent motif (PAM) sequences in the DNA regions of interest. Recently, SpRY, a variant of Streptococcus pyogenes Cas9 (SpCas9), was reported, which nearly completely fulfils the PAM requirement. Meanwhile, PAMs for SpRY have not been well addressed. In our previous study, we developed the PAM Definition by Observable Sequence Excision (PAM-DOSE) and green fluorescent protein (GFP)-reporter systems to study PAMs in human cells. Herein, we endeavored to identify the PAMs of SpRY with these two methods. The results indicated that 5′-NRN-3′, 5′-NTA-3′, and 5′-NCK-3′ could be considered as canonical PAMs. 5′-NCA-3′ and 5′-NTK-3′ may serve as non-priority PAMs. At the same time, PAM of 5′-NYC-3′ is not recommended for human cells. These findings provide further insights into the application of SpRY for human genome editing.
Design & parametric analysis of band reject ultra wideband (UWB) antenna using step impedance resonator

Abstract The design and analysis of step impedance resonator (SIR) based, compact and band-notch UWB antenna, to minimize the potential interference between wide-band (UWB) and narrow-band (WLAN), has been presented in this paper. The notched band covers the 5.15 GHz to 5.85 GHz WLAN range. The frequency band has been obtained by embedding a SIR near the radiating patch of the antenna. The empirical relationship between the SIR design and band-notch characteristics has also been included in the paper. The UWB patch antenna structure is feed by micro-strip line. The parametric-analysis by varying the parameters the SIR as well as the substrate has been performed, to observe the optimal performance the proposed antenna. The design and functional simulation of the proposed antenna structure is performed by using HFSS-v14. A close agreement between simulated and experimental results for the proposed design has been observed and presented in this paper. The design and results provide ample justification for compatibility and application of the structure in UWB communication over the entire frequency range.
Bird streamer initiated breakdowns under HVDC conditions.

This dissertation describes the role played by bird streamers in transmission line faults under HYDC conditions. The research was initiated due to the lack of knowledge of these faults under HYDC conditions. An explanation as to what bird streamers faults are, the role they play in the breakdown of air-gaps and a means of preventing bird streamer caused faults from occurring is made Experimental work has been carried out in order to gain an understanding of these faults under HYDC conditions. The overall aim of the experiments was to find a horizontal distance (protected zone) on an HYDC tower top, in order to prevent birds from perching around the centre of the tower. This will lead to a reduction of bird streamer caused faults. A brass rod was used as the artificial streamer in the experiments, under both negative and positive polarity (voltages in the range 0 to 220 kV DC were applied). Later in the experimental phase, a string soaked in a saline solution was used as a more realistic simulation of a bird streamer. QuickFieldTM, an FEM package, was used to simulate the electric field in the region of the live conductor fitting and the tip of the bird streamer, to assess the degree of distortion of the electric field caused by the introduction of the bird streamer. These simulations served as means of verifying the laboratory experiments. This dissertation has proposed a relationship between the breakdown voltage and the protected zone length, based on the air-gap breakdown voltages for both I-String and V-String insulator configurations in the air-gap range 0 to 350 mm. These curves can be used in the design of transmission lines, as a means of reducing bird streamer faults.
Recovering Through Service: Meaning Making and Resiliency to Trauma

Abstract Trauma is a present issue on college campuses, yet there is a lack of research on how best to promote positive outcomes for college students who have experienced trauma. With college being an important time for developmental growth, it is vital to encourage more research that investigates the effect of trauma on students as well as ways that student affairs professionals may help students exposed to trauma be successful in college. The dominant paradigm in most existing research positions trauma survivors within a deficit framework. Deficit frameworks situate survivors in a position where scholars focus primarily on barriers to success as opposed to the competencies students arrived with and experiences on campus that can increase positive outcomes. This approach has a substantial impact on practice within higher education and student services. This research fills the gap in the literature by using frameworks that focus on student trauma survivors’ sense of worth, engagement with others, and development of life goals. In addition, this research adds to student affairs practice by shifting the focus from risks posed to student success toward a deeper understanding of resiliency and the support of trauma survivors through critical community engagement. Focusing on a case study of students’ experience within a community engagement summer program that sought to foster the development of critical consciousness, findings from this research illustrate that the meaning-making process inherent in critical community engagement aids college students’ resiliency to trauma by focusing on the meaning making of trauma and recovery through service that develops students’ critical consciousness. This evidence that critical community engagement can increase trauma survivors’ recovery and resiliency creates an imperative for student affairs professionals to better understand the role trauma may play in college students’ development.
Preliminary study to isolate petroleum degrading bacteria from cow dung to degrade kerosene a derivative of petroleum

Pollution due to petroleum is a constant global issue mainly due to the extensive use of petroleum and its by-products that severely leads to tremendous global environmental pollution. This pollution further projects harmful effects to the ecosystem and the natural balance of it. Various method and technologies are being adopted to curb this problem including bioremediation. Various bioremediation techniques are being developed including bioremediation of pollutants through the use of cow dung. The use of animal manure such as cow dung to biodegrade pollutants at a wide scale are still in development. This study is undertaken to show the potential of the bacteria isolated from cow dung to degrade kerosene, a derivative of petroleum. Two bacteria were successfully isolated from the cow dung. The isolated petroleum degrading bacteria from the cow dung are added into a sample containing kerosene as the sole carbon source together with the nutrient medium. The degrading ability of the bacteria was determined using two methods which are using the optical density (OD) data obtained through spectrophotometer and colorimetric technique in which dichlorophenolindophenol (DCPIP) a color indicator is used. The increasing OD value indicates the ability of the bacteria to grow in the medium containing kerosene thus refl ecting biodegradation. Another method is made through the addition of DCPIP into the prepared sample. Biodegradation of kerosene by the bacteria is determined by the changes occurred in the color concentration of DCPIP, which is blue that turned colorless once biodegradation occurred. Based on the result obtained it is observed that the bacteria isolated from cow dung are capable of degrading kerosene. This study serves as a preliminary research to show that isolates from cow dung are capable of degrading hydrocarbon, and thus further exploration into this idea will be able to produce a new environmentally safe bioremediation method to curb environmental pollution caused by petroleum pollution.
Incremental Intent Detection for Medical Domain with Contrast Replay Networks

Conventional approaches to medical intent detection require fixed pre-defined intent categories. However, due to the incessant emergence of new medical intents in the real world, such requirement is not practical. Considering that it is computationally expensive to store and re-train the whole data every time new data and intents come in, we propose to incrementally learn emerged intents while avoiding catastrophically forgetting old intents. We first formulate incremental learning for medical intent detection. Then, we employ a memory-based method to handle incremental learning. We further propose to enhance the method with contrast replay networks, which use multilevel distillation and contrast objective to address training data imbalance and medical rare words respectively. Experiments show that the proposed method outperforms the state-of-the-art model by 5.7% and 9.1% of accuracy on two benchmarks respectively.
G_i2P_i Rule-based, index-preserving grapheme-to-phoneme transformations

This paper describes the motivation and implementation details for a rule-based, index-preserving grapheme-to-phoneme engine ‘G_i2P_i' implemented in pure Python and released under the open source MIT license. The engine and interface have been designed to prioritize the developer experience of potential contributors without requiring a high level of programming knowledge. ‘G_i2P_i' already provides mappings for 30 (mostly Indigenous) languages, and the package is accompanied by a web-based interactive development environment, a RESTful API, and extensive documentation to encourage the addition of more mappings in the future. We also present three downstream applications of ‘G_i2P_i' and show results of a preliminary evaluation.
Learning Bias-reduced Word Embeddings Using Dictionary Definitions

Pre-trained word embeddings, such as GloVe, have shown undesirable gender, racial, and religious biases. To address this problem, we propose DD-GloVe, a train-time debiasing algorithm to learn word embeddings by leveraging \underline{d}ictionary \underline{d}efinitions. We introduce dictionary-guided loss functions that encourage word embeddings to be similar to their relatively neutral dictionary definition representations. Existing debiasing algorithms typically need a pre-compiled list of seed words to represent the bias direction, along which biased information gets removed. Producing this list involves subjective decisions and it might be difficult to obtain for some types of biases. We automate the process of finding seed words: our algorithm starts from a single pair of initial seed words and automatically finds more words whose definitions display similar attributes traits. We demonstrate the effectiveness of our approach with benchmark evaluations and empirical analyses. Our code is available at https://github.com/haozhe-an/DD-GloVe.
Simple and Effective Knowledge-Driven Query Expansion for QA-Based Product Attribute Extraction

A key challenge in attribute value extraction (AVE) from e-commerce sites is how to handle a large number of attributes for diverse products. Although this challenge is partially addressed by a question answering (QA) approach which finds a value in product data for a given query (attribute), it does not work effectively for rare and ambiguous queries. We thus propose simple knowledge-driven query expansion based on possible answers (values) of a query (attribute) for QA-based AVE. We retrieve values of a query (attribute) from the training data to expand the query. We train a model with two tricks, knowledge dropout and knowledge token mixing, which mimic the imperfection of the value knowledge in testing. Experimental results on our cleaned version of AliExpress dataset show that our method improves the performance of AVE (+6.08 macro F1), especially for rare and ambiguous attributes (+7.82 and +6.86 macro F1, respectively).
Semantic Composition with PSHRG for Derivation Tree Reconstruction from Graph-Based Meaning Representations

We introduce a data-driven approach to generating derivation trees from meaning representation graphs with probabilistic synchronous hyperedge replacement grammar (PSHRG). SHRG has been used to produce meaning representation graphs from texts and syntax trees, but little is known about its viability on the reverse. In particular, we experiment on Dependency Minimal Recursion Semantics (DMRS) and adapt PSHRG as a formalism that approximates the semantic composition of DMRS graphs and simultaneously recovers the derivations that license the DMRS graphs. Consistent results are obtained as evaluated on a collection of annotated corpora. This work reveals the ability of PSHRG in formalizing a syntax–semantics interface, modelling compositional graph-to-tree translations, and channelling explainability to surface realization.
UMUTeam@TamilNLP-ACL2022: Abusive Detection in Tamil using Linguistic Features and Transformers

Social media has become a dangerous place as bullies take advantage of the anonymity the Internet provides to target and intimidate vulnerable individuals and groups. In the past few years, the research community has focused on developing automatic classification tools for detecting hate-speech, its variants, and other types of abusive behaviour. However, these methods are still at an early stage in low-resource languages. With the aim of reducing this barrier, the TamilNLP shared task has proposed a multi-classification challenge for Tamil written in Tamil script and code-mixed to detect abusive comments and hope-speech. Our participation consists of a knowledge integration strategy that combines sentence embeddings from BERT, RoBERTa, FastText and a subset of language-independent linguistic features. We achieved our best result in code-mixed, reaching 3rd position with a macro-average f1-score of 35%.
OneAligner: Zero-shot Cross-lingual Transfer with One Rich-Resource Language Pair for Low-Resource Sentence Retrieval

Aligning parallel sentences in multilingual corpora is essential to curating data for downstream applications such as Machine Translation. In this work, we present OneAligner, an alignment model specially designed for sentence retrieval tasks. This model is able to train on only one language pair and transfers, in a cross-lingual fashion, to low-resource language pairs with negligible degradation in performance. When trained with all language pairs of a large-scale parallel multilingual corpus (OPUS-100), this model achieves the state-of-the-art result on the Tateoba dataset, outperforming an equally-sized previous model by 8.0 points in accuracy while using less than 0.6% of their parallel data. When finetuned on a single rich-resource language pair, be it English-centered or not, our model is able to match the performance of the ones finetuned on all language pairs under the same data budget with less than 2.0 points decrease in accuracy. Furthermore, with the same setup, scaling up the number of rich-resource language pairs monotonically improves the performance, reaching a minimum of 0.4 points discrepancy in accuracy, making it less mandatory to collect any low-resource parallel data. Finally, we conclude through empirical results and analyses that the performance of the sentence alignment task depends mostly on the monolingual and parallel data size, up to a certain size threshold, rather than on what language pairs are used for training or evaluation.
Auxiliary tasks to boost Biaffine Semantic Dependency Parsing

The biaffine parser of (CITATION) was successfully extended to semantic dependency parsing (SDP) (CITATION). Its performance on graphs is surprisingly high given that, without the constraint of producing a tree, all arcs for a given sentence are predicted independently from each other (modulo a shared representation of tokens). To circumvent such an independence of decision, while retaining the O(n^2) complexity and highly parallelizable architecture, we propose to use simple auxiliary tasks that introduce some form of interdependence between arcs. Experiments on the three English acyclic datasets of SemEval-2015 task 18 (CITATION), and on French deep syntactic cyclic graphs (CITATION) show modest but systematic performance gains on a near-state-of-the-art baseline using transformer-based contextualized representations. This provides a simple and robust method to boost SDP performance.
That Slepen Al the Nyght with Open Ye! Cross-era Sequence Segmentation with Switch-memory

The evolution of language follows the rule of gradual change. Grammar, vocabulary, and lexical semantic shifts take place over time, resulting in a diachronic linguistic gap. As such, a considerable amount of texts are written in languages of different eras, which creates obstacles for natural language processing tasks, such as word segmentation and machine translation. Although the Chinese language has a long history, previous Chinese natural language processing research has primarily focused on tasks within a specific era. Therefore, we propose a cross-era learning framework for Chinese word segmentation (CWS), CROSSWISE, which uses the Switch-memory (SM) module to incorporate era-specific linguistic knowledge. Experiments on four corpora from different eras show that the performance of each corpus significantly improves. Further analyses also demonstrate that the SM can effectively integrate the knowledge of the eras into the neural network.
Can Emotion Carriers Explain Automatic Sentiment Prediction? A Study on Personal Narratives

Deep Neural Networks (DNN) models have achieved acceptable performance in sentiment prediction of written text. However, the output of these machine learning (ML) models cannot be natively interpreted. In this paper, we study how the sentiment polarity predictions by DNNs can be explained and compare them to humans’ explanations. We crowdsource a corpus of Personal Narratives and ask human judges to annotate them with polarity and select the corresponding token chunks - the Emotion Carriers (EC) - that convey narrators’ emotions in the text. The interpretations of ML neural models are carried out through Integrated Gradients method and we compare them with human annotators’ interpretations. The results of our comparative analysis indicate that while the ML model mostly focuses on the explicit appearance of emotions-laden words (e.g. happy, frustrated), the human annotator predominantly focuses the attention on the manifestation of emotions through ECs that denote events, persons, and objects which activate narrator’s emotional state.
Learning Adaptive Segmentation Policy for End-to-End Simultaneous Translation

End-to-end simultaneous speech-to-text translation aims to directly perform translation from streaming source speech to target text with high translation quality and low latency. A typical simultaneous translation (ST) system consists of a speech translation model and a policy module, which determines when to wait and when to translate. Thus the policy is crucial to balance translation quality and latency. Conventional methods usually adopt fixed policies, e.g. segmenting the source speech with a fixed length and generating translation. However, this method ignores contextual information and suffers from low translation quality. This paper proposes an adaptive segmentation policy for end-to-end ST. Inspired by human interpreters, the policy learns to segment the source streaming speech into meaningful units by considering both acoustic features and translation history, maintaining consistency between the segmentation and translation. Experimental results on English-German and Chinese-English show that our method achieves a good accuracy-latency trade-off over recently proposed state-of-the-art methods.
Towards Comprehensive Patent Approval Predictions:Beyond Traditional Document Classification

Predicting the approval chance of a patent application is a challenging problem involving multiple facets. The most crucial facet is arguably the novelty — 35 U.S. Code § 102 rejects more recent applications that have very similar prior arts. Such novelty evaluations differ the patent approval prediction from conventional document classification — Successful patent applications may share similar writing patterns; however, too-similar newer applications would receive the opposite label, thus confusing standard document classifiers (e.g., BERT). To address this issue, we propose a novel framework that unifies the document classifier with handcrafted features, particularly time-dependent novelty scores. Specifically, we formulate the novelty scores by comparing each application with millions of prior arts using a hybrid of efficient filters and a neural bi-encoder. Moreover, we impose a new regularization term into the classification objective to enforce the monotonic change of approval prediction w.r. t. novelty scores. From extensive experiments on a large-scale USPTO dataset, we find that standard BERT fine-tuning can partially learn the correct relationship between novelty and approvals from inconsistent data. However, our time-dependent novelty features offer a boost on top of it. Also, our monotonic regularization, while shrinking the search space, can drive the optimizer to better local optima, yielding a further small performance gain.
XLM-EMO: Multilingual Emotion Prediction in Social Media Text

Detecting emotion in text allows social and computational scientists to study how people behave and react to online events. However, developing these tools for different languages requires data that is not always available. This paper collects the available emotion detection datasets across 19 languages. We train a multilingual emotion prediction model for social media data, XLM-EMO. The model shows competitive performance in a zero-shot setting, suggesting it is helpful in the context of low-resource languages. We release our model to the community so that interested researchers can directly use it.