Miracle-dz/UniFAR-FTUs huggingface dataset:
Green Salamander Estimated Abundance and Environmental Associations in South Carolina

Abstract Green Salamanders, Aneides aeneus, are habitat specialists found in narrow crevices of rock outcrops and under flaky bark of trees. The species has a high conservation priority throughout its range and has been negatively affected by habitat loss, climate change, disease, and overcollection. In portions of the Blue Ridge Escarpment population, many historical locations for this species have not been visited since the 1980s or earlier. Across three counties in South Carolina, we conducted visual encounter surveys of known rock outcrops that were accessible, and used binoculars to conduct arboreal surveys in the adjacent forest. We detected Green Salamanders at 30 of the 61 sites surveyed (49.2%). We collected a variety of habitat variables and compared a suite of N-mixture models using an Akaike information criterion framework. Detection probability was positively influenced by time of day. A model of abundance that included aspect, habitat size, and elevation had the most support. Specifically, Green Salamanders were more abundant at larger sites at lower elevations with south-facing slopes. Knowledge of factors that influence population abundance will help guide future efforts to protect the species in the southern portion of its range.
Religiosity and Emotional Intelligence among University Students

Emotional intelligence is a recent concept that has a clear impact on the lives of humans; it influences ones thoughts, relationships and emotions. This study attempts to justify religiosity on emotional intelligence and to examine the impact of religion and gender on emotional intelligence. One hundred university students were selected by stratified random sampling technique from Ranchi University. The stratification was based on religion (Hindu & Muslim ad gender (Male &female. The emotional Quotient test developed by Dalip Singh and Chadha was administered. The result revealed that Hindu was more emotionally intelligent than their counterpart Muslim students.There is no significant difference between male and female students on emotional intelligence.
STRATEGIC RESPONSES BY KENYA MILK PROCESSORS TO ENVIRONMENTAL CHALLENGES By VICTOR MYVENDVVA CHRISTOPHER

Environment is key to the success o f any organization, as every organization exists to serve the environment. Any level of turbulence in the environment therefore calls for a strategic response for both survival and growth. The effects of the environmental turbulence have not spared the dairy' industry. The environment has not being static but rather characterized by dynamism, the sector therefore like any other sectors face certain challenges and these calls for the management to develop strategic responses to these environmental changes. There is need for alignment and realignment of the organizations processes i.e. organizations must match their capabilities and strategies to the changing environment. The prime task of strategic activities is thinking through the overall mission of business, which leads to setting up o f objectives. Strategic management must address the dynamics of market systems and behavior of the business environment. The objective o f the research was to identify environmental challenges facing the milk processors and the strategic responses the firms adapt to the environmental forces. The design of the research was a survey research design. This was because it collects data from a broad number of member’s population that facilitates comparisons. The researcher sampled the 25 major milk processors firms in Kenya and managed to get 16 completed questionnaires representing a 64% response rate. Data was collected using semi-structured questionnaires and was analyzed using descriptive statistics. The data was coded and entered into a spreadsheet and analyzed using SPSS (Statistical Package for Social Sciences) and presented using bar graphs, percentages and frequency tables. The environmental factors that affect firms operations include political economical social technological ecological factors. All these factors affect the firms in varying degrees. Firms have however developed strategies to counter react to the impact of the environmental forces. The strategies adapted include, restructuring, efficient marketing, information technology, culture changes etc. All the firms agreed that it is beneficial for all of them to adopt timely strategic response to these environmental forces. The firms however were faced by challenges when responding to the environmental forces. These included high cost of maintaining quality services, cost of maintaining skilled labor, increased competition, government policies and regulations, inflation rates etc.
Climate change: Climate engineering through stratospheric aerosol injection

In this progress report on climate change, I examine the growing literature dealing with the proposal to engineer global climate through the deliberate injection of aerosols into the stratosphere. This is just one of a wide range of technology proposals to geoengineer the climate, but one in particular which has gained the attention of Earth System science researchers and which is attracting wider public debate. I review the current status of this technology by exploring a number of different dimensions of the proposal: its history and philosophical and ethical implications; how it is framed in public discourse and perceived by citizens; its economic, political and governance characteristics; and how the proposed technology is being researched through numerical modelling and field experimentation. Unlike many other geoengineering interventions, stratospheric aerosol injection has no additional societal co-benefits: its sole raison d’etre would be to offset planetary heating caused by rising concentrations of greenhouse gases. The deployment of such a technology would have profound implications for the view humans have of themselves in relation to the non-human world.
Interpreting Open-Domain Modifiers: Decomposition of Wikipedia Categories into Disambiguated Property-Value Pairs

This paper proposes an open-domain method for automatically annotating modifier constituents (20th-century’) within Wikipedia categories (20th-century male writers) with properties (date of birth). The annotations offer a semantically-anchored understanding of the role of the constituents in defining the underlying meaning of the categories. In experiments over an evaluation set of Wikipedia categories, the proposed method annotates constituent modifiers as semantically-anchored properties, rather than as mere strings in a previous method. It does so at a better trade-off between precision and recall.
Connecting the Dots: Event Graph Schema Induction with Path Language Modeling

Event schemas can guide our understanding and ability to make predictions with respect to what might happen next. We propose a new Event Graph Schema, where two event types are connected through multiple paths involving entities that fill important roles in a coherent story. We then introduce Path Language Model, an auto-regressive language model trained on event-event paths, and select salient and coherent paths to probabilistically construct these graph schemas. We design two evaluation metrics, instance coverage and instance coherence, to evaluate the quality of graph schema induction, by checking when coherent event instances are covered by the schema graph. Intrinsic evaluations show that our approach is highly effective at inducing salient and coherent schemas. Extrinsic evaluations show the induced schema repository provides significant improvement to downstream end-to-end Information Extraction over a state-of-the-art joint neural extraction model, when used as additional global features to unfold instance graphs.
Constrained Fact Verification for FEVER

Fact-verification systems are well explored in the NLP literature with growing attention owing to shared tasks like FEVER. Though the task requires reasoning on extracted evidence to verify a claim’s factuality, there is little work on understanding the reasoning process. In this work, we propose a new methodology for fact-verification, specifically FEVER, that enforces a closed-world reliance on extracted evidence. We present an extensive evaluation of state-of-the-art verification models under these constraints.
IIRC: A Dataset of Incomplete Information Reading Comprehension Questions

Humans often have to read multiple documents to address their information needs. However, most existing reading comprehension (RC) tasks only focus on questions for which the contexts provide all the information required to answer them, thus not evaluating a system's performance at identifying a potential lack of sufficient information and locating sources for that information. To fill this gap, we present a dataset, IIRC, with more than 13K questions over paragraphs from English Wikipedia that provide only partial information to answer them, with the missing information occurring in one or more linked documents. The questions were written by crowd workers who did not have access to any of the linked documents, leading to questions that have little lexical overlap with the contexts where the answers appear. This process also gave many questions without answers, and those that require discrete reasoning, increasing the difficulty of the task. We follow recent modeling work on various reading comprehension datasets to construct a baseline model for this dataset, finding that it achieves 31.1% F1 on this task, while estimated human performance is 88.4%. The dataset, code for the baseline system, and a leaderboard can be found at this https URL.
Debiasing Knowledge Graph Embeddings

It has been shown that knowledge graph embeddings encode potentially harmful social biases, such as the information that women are more likely to be nurses, and men more likely to be bankers. As graph embeddings begin to be used more widely in NLP pipelines, there is a need to develop training methods which remove such biases. Previous approaches to this problem both significantly increase the training time, by a factor of eight or more, and decrease the accuracy of the model substantially. We present a novel approach, in which all embeddings are trained to be neutral to sensitive attributes such as gender by default using an adversarial loss. We then add sensitive attributes back on in whitelisted cases. Training time only marginally increases over a baseline model, and the debiased embeddings perform almost as accurately in the triple prediction task as their non-debiased counterparts.
How to Make Neural Natural Language Generation as Reliable as Templates in Task-Oriented Dialogue

Neural Natural Language Generation (NLG) systems are well known for their unreliability. To overcome this issue, we propose a data augmentation approach which allows us to restrict the output of a network and guarantee reliability. While this restriction means generation will be less diverse than if randomly sampled, we include experiments that demonstrate the tendency of existing neural generation approaches to produce dull and repetitive text, and we argue that reliability is more important than diversity for this task. The system trained using this approach scored 100% in semantic accuracy on the E2E NLG Challenge dataset, the same as a template system.
Multi-Stage Pre-training for Automated Chinese Essay Scoring

This paper proposes a pre-training based automated Chinese essay scoring method. The method involves three components: weakly supervised pre-training, supervised cross- prompt fine-tuning and supervised target- prompt fine-tuning. An essay scorer is first pre- trained on a large essay dataset covering diverse topics and with coarse ratings, i.e., good and poor, which are used as a kind of weak supervision. The pre-trained essay scorer would be further fine-tuned on previously rated es- says from existing prompts, which have the same score range with the target prompt and provide extra supervision. At last, the scorer is fine-tuned on the target-prompt training data. The evaluation on four prompts shows that this method can improve a state-of-the-art neural essay scorer in terms of effectiveness and domain adaptation ability, while in-depth analysis also reveals its limitations.
Coding Textual Inputs Boosts the Accuracy of Neural Networks

Natural Language Processing (NLP) tasks are usually performed word by word on textual inputs. We can use arbitrary symbols to represent the linguistic meaning of a word and use these symbols as inputs. As “alternatives” to a text representation, we introduce Soundex, MetaPhone, NYSIIS, logogram to NLP, and develop fixed-output-length coding and its extension using Huffman coding. Each of those codings combines different character/digital sequences and constructs a new vocabulary based on codewords. We find that the integration of those codewords with text provides more reliable inputs to Neural-Network-based NLP systems through redundancy than text-alone inputs. Experiments demonstrate that our approach outperforms the state-of-the-art models on the application of machine translation, language modeling, and part-of-speech tagging. The source code is available at https://github.com/abdulrafae/coding_nmt.
TED-CDB: A Large-Scale Chinese Discourse Relation Dataset on TED Talks

As different genres are known to differ in their communicative properties and as previously, for Chinese, discourse relations have only been annotated over news text, we have created the TED-CDB dataset. TED-CDB comprises a large set of TED talks in Chinese that have been manually annotated according to the goals and principles of Penn Discourse Treebank, but adapted to features that are not present in English. It serves as a unique Chinese corpus of spoken discourse. Benchmark experiments show that TED-CDB poses a challenge for state-of-the-art discourse relation classifiers, whose F1 performance on 4-way classification is 60%. This is a dramatic drop of 35% from performance on the news text in the Chinese Discourse Treebank. Transfer learning experiments have been carried out with the TED-CDB for both same-language cross-domain transfer and same-domain cross-language transfer. Both demonstrate that the TED-CDB can improve the performance of systems being developed for languages other than Chinese and would be helpful for insufficient or unbalanced data in other corpora. The dataset and our Chinese annotation guidelines will be made freely available.
Domain Adaptation of Thai Word Segmentation Models Using Stacked Ensemble

Like many Natural Language Processing tasks, Thai word segmentation is domain-dependent. Researchers have been relying on transfer learning to adapt an existing model to a new domain. However, this approach is inapplicable to cases where we can interact with only input and output layers of the models, also known as "black boxes''. We propose a filter-and-refine solution based on the stacked-ensemble learning paradigm to address this black-box limitation. We conducted extensive experimental studies comparing our method against state-of-the-art models and transfer learning. Experimental results show that our proposed solution is an effective domain adaptation method and has a similar performance as the transfer learning method.
What Time Is It? Temporal Analysis of Novels

Recognizing the flow of time in a story is a crucial aspect of understanding it. Prior work related to time has primarily focused on identifying temporal expressions or relative sequencing of events, but here we propose computationally annotating each line of a book with wall clock times, even in the absence of explicit time-descriptive phrases. To do so, we construct a data set of hourly time phrases from 52,183 fictional books. We then construct a time-of-day classification model that achieves an average error of 2.27 hours. Furthermore, we show that by analyzing a book in whole using dynamic programming of breakpoints, we can roughly partition a book into segments that each correspond to a particular time-of-day. This approach improves upon baselines by over two hours. Finally, we apply our model to a corpus of literature categorized by different periods in history, to show interesting trends of hourly activity throughout the past. Among several observations we find that the fraction of events taking place past 10 P.M jumps past 1880 - coincident with the advent of the electric light bulb and city lights.
Affective Event Classification with Discourse-enhanced Self-training

Prior research has recognized the need to associate affective polarities with events and has produced several techniques and lexical resources for identifying affective events. Our research introduces new classification models to assign affective polarity to event phrases. First, we present a BERT-based model for affective event classification and show that the classifier achieves substantially better performance than a large affective event knowledge base. Second, we present a discourse-enhanced self-training method that iteratively improves the classifier with unlabeled data. The key idea is to exploit event phrases that occur with a coreferent sentiment expression. The discourse-enhanced self-training algorithm iteratively labels new event phrases based on both the classifier’s predictions and the polarities of the event’s coreferent sentiment expressions. Our results show that discourse-enhanced self-training further improves both recall and precision for affective event classification.
A Centering Approach for Discourse Structure-aware Coherence Modeling

Previous neural coherence models have focused on identifying semantic relations between adjacent sentences. However, they do not have the means to exploit structural information. In this work, we propose a coherence model which takes discourse structural information into account without relying on human annotations. We approximate a linguistic theory of coherence, Centering theory, which we use to track the changes of focus between discourse segments. Our model first identifies the focus of each sentence, recognized with regards to the context, and constructs the structural relationship for discourse segments by tracking the changes of the focus. The model then incorporates this structural information into a structure-aware transformer. We evaluate our model on two tasks, automated essay scoring and assessing writing quality. Our results demonstrate that our model, built on top of a pretrained language model, achieves state-of-the-art performance on both tasks. We next statistically examine the identified trees of texts assigned to different quality scores. Finally, we investigate what our model learns in terms of theoretical claims.
CLIRMatrix: A Massively Large Collection of Bilingual and Multilingual Datasets for Cross-Lingual Information Retrieval

We present CLIRMatrix, a massively large collection of bilingual and multilingual datasets for Cross-Lingual Information Retrieval extracted automatically from Wikipedia. CLIRMatrix comprises (1) BI-139, a bilingual dataset of queries in one language matched with relevant documents in another language for 139x138=19,182 language pairs, and (2) MULTI-8, a multilingual dataset of queries and documents jointly aligned in 8 different languages. In total, we mined 49 million unique queries and 34 billion (query, document, label) triplets, making it the largest and most comprehensive CLIR dataset to date. This collection is intended to support research in end-to-end neural information retrieval and is publicly available at [url]. We provide baseline neural model results on BI-139, and evaluate MULTI-8 in both single-language retrieval and mix-language retrieval settings.
Towards Enhancing Faithfulness for Neural Machine Translation

Neural machine translation (NMT) has achieved great success due to the ability to generate high-quality sentences. Compared with human translations, one of the drawbacks of current NMT is that translations are not usually faithful to the input, e.g., omitting information or generating unrelated fragments, which inevitably decreases the overall quality, especially for human readers. In this paper, we propose a novel training strategy with a multi-task learning paradigm to build a faithfulness enhanced NMT model (named FEnmt). During the NMT training process, we sample a subset from the training set and translate them to get fragments that have been mistranslated. Afterward, the proposed multi-task learning paradigm is employed on both encoder and decoder to guide NMT to correctly translate these fragments. Both automatic and human evaluations verify that our FEnmt could improve translation quality by effectively reducing unfaithful translations.
MovieChats: Chat like Humans in a Closed Domain

Being able to perform in-depth chat with humans in a closed domain is a precondition before an open-domain chatbot can be ever claimed. In this work, we take a close look at the movie domain and present a large-scale high-quality corpus with fine-grained annotations in hope of pushing the limit of movie-domain chatbots. We propose a unified, readily scalable neural approach which reconciles all subtasks like intent prediction and knowledge retrieval. The model is first pretrained on the huge general-domain data, then finetuned on our corpus. We show this simple neural approach trained on high-quality data is able to outperform commercial systems replying on complex rules. On both the static and interactive tests, we find responses generated by our system exhibits remarkably good engagement and sensibleness close to human-written ones. We further analyze the limits of our work and point out potential directions for future work
GeobacillusBacteria: Potential Commercial Applications in Industry, Bioremediation, and Bioenergy Production

The genus Geobacillus is represented by obligately thermophilic bacteria able to grow in the temperature range of 35–75°C. They are modest bacteria isolated from various sources on routine media such as nutrient agar. Originally classified as representatives of Bacillus , the species of Geobacillus were established in 2001 as a new genus. However, sequence similarity between all species indicates that at least some species need to be reclassified at the genus level. In addition to 16S rRNA, housekeeping genes, 16S-23S rRNA gene internal transcribed spacer, and repetitive sequences can be used in classification and identification of thermophilic bacteria. The ability to survive and grow at high temperatures as well as utilization and synthesis of a wide range of compounds makes these bacteria and their products attractive for use in various spheres: food, paper, biotechnology industries, medicine, bioremediation, etc. A broad spectrum of applications arouses increased interest in the study of physiological and biochemical characteristics and triggers emergence of new usage areas for Geobacillus , such as bioenergy. The growing demand for energy leads to the development of alternative technologic options. Geobacillus species demonstrated the ability to generate or enhance productivity of important sources of bioenergy such as ethanol, isobutanol, 2,3-butanediol, biodiesel, and biogas.