Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of Electron Beam Irradiation on the Developmental Stages of Callosobruchus maculatus (Coleoptera: Bruchidae) and Bactrocera zonata (Diptera: Tephritidae)

Abstract An eco-friendly electron beam irradiation (EBI) technology was used to assess the effect of EBI on the different development stages of Callosobruchus maculatus (F.) and Bactrocera zonata (Saunders). No adults emerged after 3-d-old eggs of C. maculatus were irradiated with 304.8 Gy of EBI, and no adults emerged from the F1 generation of larvae and pupae irradiated with 103.6 Gy. The adult stage of C. maculatus was the most tolerant of EBI, with a high dose (414.3 Gy) irradiation of the adult stage resulting in complete prevention of the F1 generation adult emergence. Large-scale tests confirmed that 414.3 Gy was an effective dose for the phytosanitary and security treatment for C. maculatus. An EBI dose of 414.3 Gy prevented B. zonata egg hatching. EBI doses of 304.8, 414.3, and 653.5 Gy prevented the development of first, second, and third larval instars to pupation, respectively. Using the criterion of adult emergence from the F1 generation, targeting the third larval stage, considered the most tolerant stage to EBI, a dose of 304.8 Gy was effective for quarantine and security treatment of B. zonata.
BCS-like action and Lagrangian from the gradient expansion of the determinant of Fermi fields in QCD type, non-Abelian gauge theories with chiral anomalies

An effective field theory of BCS quark pairs is derived from an ordinary QCD type path integral with SU(3) non-Abelian gauge fields. We consider the BCS quark pairs as constituents of nuclei and as the remaining degrees of freedom in a coset decomposition SO(M,M)/U(M)xU(M) of a corresponding total self-energy. The underlying dimension 'M=24' is determined by the product of '2' isospin degrees of freedom, by the 4x4 Dirac gamma matrices with factor '4' and the '3' colour degrees of freedom. Finally, we can compare the derived actions of BCS quark pairs with the ordinary Skyrme Lagrangian and attain the astonishing result that our derived effective actions of BCS quark pairs are more closely related to the Skyrme-Faddeev field theory with the nontrivial Hopf mapping.
Preferences of Lygus pratensis (Hemiptera: Miridae) for Four Potential Trap Crop Plants

Abstract The mirid, Lygus pratensis (L.) (Hemiptera: Miridae), is a major pest of cotton (Gossypium spp.) in Xinjiang Uyghur Autonomous Region, China. In this study, we aimed to reveal the preferences of L. pratensis toward four potential trap crop plants for use in controlling this pest. The population densities of L. pratensis on five host plants were investigated by visual observation, and the behavioral responses of L. pratensis to the five host plants were determined by Y-type olfactory assays and laboratory selectivity assays. The results showed significant differences in host preferences in the field. The average numbers of adults on the five host plants are in the following order, from high to low: Lepidium latifolium (L.), Kochia prostrata (L.) Schrad, Chenopodium glaucum (L.), Medicago sativa (L.), and Gossypium spp. Laboratory selectivity assays found that K. prostrata had the highest number of adults at 5 min, but L. latifolium had the highest number of adults from 1.5 to 4.5 h. The Y-tube olfactometer assays found no significant evidence for selection of L. pratensis between the volatiles of M. sativa and pure air, but did for the other four plants. In conclusion, the blooming stage of L. latifolium was the most attractive to L. pratensis in the field and is, thus, a potential trap crop plant for L. pratensis.
FinRead: A Transfer Learning Based Tool to Assess Readability of Definitions of Financial Terms

Simplified definitions of complex terms help learners to understand any content better. Comprehending readability is critical for the simplification of these contents. In most cases, the standard formula based readability measures do not hold good for measuring the complexity of definitions of financial terms. Furthermore, some of them works only for corpora of longer length which have at least 30 sentences. In this paper, we present a tool for evaluating readability of definitions of financial terms. It consists of a Light GBM based classification layer over sentence embeddings (Reimers et al., 2019) of FinBERT (Araci, 2019). It is trained on glossaries of several financial textbooks and definitions of various financial terms which are available on the web. The extensive evaluation shows that it outperforms the standard benchmarks by achieving a AU-ROC score of 0.993 on the validation set.
Comparative Analysis of Melodia and Time-Domain Adaptive Filtering based Model for Melody Extraction from Polyphonic Music

Among the many applications of Music Information Retrieval (MIR), melody extraction is one of the most essential. It has risen to the top of the list of current research challenges in the field of MIR applications. We now need new means of defining, indexing, finding, and interacting with musical information, given the tremendous amount of music available at our fingertips. This article looked at some of the approaches that open the door to a broad variety of applications, such as automatically predicting the pitch sequence of a melody straight from the audio signal of a polyphonic music recording, commonly known as melody extraction. It is pretty easy for humans to identify the pitch of a melody, but doing so on an automated basis is very difficult and time-consuming. In this article, a comparison is made between the performance of the currently available melody extraction approach that is state-of-the-art Melodia and the technique based on time-domain adaptive filtering for melody extraction in terms of evaluation metrics introduced in MIREX 2005. Motivating by the same, this paper focuses on the discussion of datasets and state-of-the-art approaches for the extraction of the main melody from music signals. Additionally, a summary of the evaluation matrices based on which methodologies have been examined on various datasets is also present in this paper.
Algebraic graph theory in the analysis of frequency assignment problems

Frequency Assignment Problems (FAPs) arise when transmitters need to be allocated frequencies with the aim of minimizing interference, whilst maintaining an efficient use of the radio spectrum. In this thesis FAPs are seen as generalised graph colouring problems, where transmitters are represented by vertices, and their interactions by weighted edges. Solving FAPs often relies on known structural properties to facilitate algorithms. When no structural information is available explicitly, obtaining it from numerical data is difficult. This lack of structural information is a key underlying motivation for the research work in this thesis. If there are TV transmitters to be assigned, we assume as given an N x N "influence matrix" W with entries Wij representing influence between transmitters i and j. From this matrix we derive the Laplacian matrix L = D—W, where D is a diagonal matrix whose entries da are the sum of all influences working in transmitter i. The focus of this thesis is the study of mathematical properties of the matrix L. We generalise certain properties of the Laplacian eigenvalues and eigenvectors that hold for simple graphs. We also observe and discuss changes in the shape of the Laplacian eigenvalue spectrum due to modifications of a FAP. We include a number of computational experiments and generated simulated examples of FAPs for which we explicitly calculate eigenvalues and eigenvectors in order to test the developed theoretical results. We find that the Laplacians prove useful in identifying certain types of problems, providing structured approach to reducing the original FAP to smaller size subproblems, hence assisting existing heuristic algorithms for solving frequency assignments. In that sense we conclude that analysis of the Laplacians is a useful tool for better understanding of FAPs.
Sdutta at ComMA@ICON: A CNN-LSTM Model for Hate Detection

In today’s world, online activity and social media are facing an upsurge of cases of aggression, gender-biased comments and communal hate. In this shared task, we used a CNN-LSTM hybrid method to detect aggression, misogynistic and communally charged content in social media texts. First, we employ text cleaning and convert the text into word embeddings. Next we proceed to our CNN-LSTM based model to predict the nature of the text. Our model achieves 0.288, 0.279, 0.294 and 0.335 Overall Micro F1 Scores in multilingual, Meitei, Bengali and Hindi datasets, respectively, on the 3 prediction labels.
Leveraging Expectation Maximization for Identifying Claims in Low Resource Indian Languages

Identification of the checkable claims is one of the important prior tasks while dealing with infinite amount of data streaming from social web and the task becomes a compulsory one when we analyze them on behalf of a multilingual country like India that contains more than 1 billion people. In the present work, we describe our system which is made for detecting check-worthy claim sentences in resource scarce Indian languages (e.g., Bengali and Hindi). Firstly, we collected sentences from various sources in Bengali and Hindi and vectorized them with several NLP features. We labeled a small portion of them for check-worthy claims manually. However, in order to label rest amount of data in a semi-supervised fashion, we employed the Expectation Maximization (EM) algorithm tuned with the Multivariate Gaussian Mixture Model (GMM) to assign weakly labels. The optimal number of Gaussians in this algorithm is traced by using Logistic Regression. Furthermore, we used different ratios of manually labeled data and weakly labeled data to train our various machine learning models. We tabulated and plotted the performances of the models along with the stepwise decrement in proportion of manually labeled data. The experimental results were at par with our theoretical understanding, and we conclude that the weakly labeling of check-worthy claim sentences in low resource languages with EM algorithm has true potential.
Prediction of Video Game Development Problems Based on Postmortems using Different Word Embedding Techniques

The interactive entertainment industry is being actively involved with the development, marketing and sale of video games in the past decade. The increasing interest in video games has led to an increase in video game development techniques and methods. It has emerged as an immensely large sector, and now it has grown to be larger than the movie and music industry combined. The postmortem of a game outlines and analyzes the game’s history, team goals, what went right, and what went wrong with the game. Despite its significance, there is little understanding related to the challenges encountered by the programmers. Postmortems are not properly maintained and are informally written, leading to a lack of trustworthiness. In this study, we perform a systematic analysis on different problems faced in the video game development. The need for automation and ML techniques arises because it could help game developers easily identify the exact problem from the description, and hence be able to easily find a solution. This work could also help developers in identifying frequent mistakes that could be avoided, and will provide researchers a beginning point to further consider game development in context of software engineering.
Image2tweet: Datasets in Hindi and English for Generating Tweets from Images

Image Captioning as a task that has seen major updates over time. In recent methods, visual-linguistic grounding of the image-text pair is leveraged. This includes either generating the textual description of the objects and entities present within the image in constrained manner, or generating detailed description of these entities as a paragraph. But there is still a long way to go towards being able to generate text that is not only semantically richer, but also contains real world knowledge in it. This is the motivation behind exploring image2tweet generation through the lens of existing image-captioning approaches. At the same time, there is little research in image captioning in Indian languages like Hindi. In this paper, we release Hindi and English datasets for the task of tweet generation given an image. The aim is to generate a specialized text like a tweet, that is not a direct result of visual-linguistic grounding that is usually leveraged in similar tasks, but conveys a message that factors-in not only the visual content of the image, but also additional real world contextual information associated with the event described within the image as closely as possible. Further, We provide baseline DL models on our data and invite researchers to build more sophisticated systems for the problem.
Classifying Emotional Utterances by Employing Multi-modal Speech Emotion Recognition

Deep learning methods are being applied to several speech processing problems in recent years. In the present work, we have explored different deep learning models for speech emotion recognition. We have employed normal deep feedforward neural network (FFNN) and convolutional neural network (CNN) to classify audio files according to their emotional content. Comparative study indicates that CNN model outperforms FFNN in case of emotions as well as gender classification. It was observed that the sole audio based models can capture the emotions up to a certain limit. Thus, we attempted a multi-modal framework by combining the benefits of the audio and text features and employed them into a recurrent encoder. Finally, the audio and text encoders are merged to provide the desired impact on various datasets. In addition, a database consists of emotional utterances of several words has also been developed as a part of this work. It contains same word in different emotional utterances. Though the size of the database is not that large but this database is ideally supposed to contain all the English words that exist in an English dictionary.
Towards Personality-Aware Chatbots

Chatbots are increasingly used to automate operational processes in customer service. However, most chatbots lack adaptation towards their users which may results in an unsatisfactory experience. Since knowing and meeting personal preferences is a key factor for enhancing usability in conversational agents, in this study we analyze an adaptive conversational agent that can automatically adjust according to a user’s personality type carefully excerpted from the Myers-Briggs type indicators. An experiment including 300 crowd workers examined how typifications like extroversion/introversion and thinking/feeling can be assessed and designed for a conversational agent in a job recommender domain. Our results validate the proposed design choices, and experiments on a user-matched personality typification, following the so-called law of attraction rule, show a significant positive influence on a range of selected usability criteria such as overall satisfaction, naturalness, promoter score, trust and appropriateness of the conversation.
How vulnerable are you? A Novel Computational Psycholinguistic Analysis for Phishing Influence Detection

This document contains our work and progress regarding phishing detection by searching for proper influential sentences. Currently, the world is becoming smart, as a result most of the transactions and posting offers happen online. So, human beings have become the most vulnerable to security breach or hacking through phishing attacks, or being persuaded through influential texts in social media sites. We have analyzed influential and non-influential sentences and populated our dataset with those. We have proposed a computational model for implementing Cialdini and we got state of the art accuracy with our model. Our approach is language independent and domain independent and it is applicable to any problem where persuation detection is important. Our dataset and proposed computational psycholinguistic approach will motivate researchers to work more in the area of persuasion detection.
Improve Sinhala Speech Recognition Through e2e LF-MMI Model

Automatic speech recognition (ASR) has experienced several paradigm shifts over the years from template-based approaches and statistical modeling to the popular GMM-HMM approach and then to deep learning hybrid model DNN-HMM. The latest shift is to end-to-end (e2e) DNN architecture. We present a study to build an e2e ASR system using state-of-the-art deep learning models to verify the applicability of e2e ASR models for the highly inflected and yet low-resource Sinhala language. We experimented on e2e Lattice-Free Maximum Mutual Information (e2e LF-MMI) model with the baseline statistical models with 40 hours of training data to evaluate. We used the same corpus for creating language models and lexicon in our previous study, which resulted in the best accuracy for the Sinhala language. We were able to achieve a Word-error-rate (WER) of 28.55% for Sinhala, only slightly worse than the existing best hybrid model. Our model, however, is more context-independent and faster for Sinhala speech recognition and so more suitable for general purpose speech-to-text translation.
Building a Linguistic Resource : A Word Frequency List for Sinhala

A word frequency list is a list of unique words in a language along with their frequency count. It is generally sorted by frequency. Such a list is essential for many NLP tasks, including building language models, POS taggers, spelling checkers, word separation guides, etc., in addition to assisting language learners. Such lists are available for many languages, but a large-scale word list is still not available for Sinhala. We have developed a comprehensive list of words, together with their frequency and part-of-speech (POS), from a large textbase. Unlike many other such lists, our list includes a large number of low-frequency words (many of which are erroneous), which enables the analysis of such words, including the frequencies of errors. In addition to the main list, we have also prepared a list of linguistically verified words. The word frequency list and the verified word list are the largest collections of words lists that are available for the Sinhala language.
DIFFUSIVE SHOCK ACCELERATION IN TEST-PARTICLE REGIME

We examine the test-particle solution for diffusive shock acceleration, based on simple models for thermal leakage injection and Alfvénic drift. The critical injection rate, ξc, above which the cosmic-ray (CR) pressure becomes dynamically significant depends mainly on the sonic shock Mach number, M, and preshock gas temperature, T1. In the hot-phase interstellar medium (ISM) and intracluster medium, ξc ≲ 10−3 for shocks with M ≲ 5, while ξc ≈ 10−4(T1/106 K)1/2 for shocks with M ≳ 10. For T1 = 106 K, for example, the test-particle solution would be valid if the injection momentum pinj >3.8pth (where pth is thermal momentum). This leads to a postshock CR pressure less than 10% of the shock ram pressure. If the Alfvén speed is comparable to the sound speed in the preshock flow, as in the hot-phase ISM, the power-law slope of CR spectrum can be significantly softer than the canonical test-particle slope. Then, the CR spectrum at the shock can be approximated by the revised test-particle power law with an exponential cutoff at the highest accelerated momentum, pmax(t). An analytic form of the exponential cutoff is also suggested.
Ethical HIV research with transgender and non‐binary communities in the United States

Because transgender individuals experience disproportionately high rates of HIV infection, this population is an increasing focus of epidemiological and implementation science research to combat the epidemic. However, study participants, providers and other advocates have become increasingly concerned about research practices that may alienate, objectify, exploit or even re‐traumatize the communities they are designed to benefit. This commentary explores the common pitfalls of HIV research with transgender communities and provides a potential framework for ethical, community‐engaged research practice.
THE 2011 ERUPTION OF THE RECURRENT NOVA T PYXIDIS: THE DISCOVERY, THE PRE-ERUPTION RISE, THE PRE-ERUPTION ORBITAL PERIOD, AND THE REASON FOR THE LONG DELAY

We report the discovery by M. Linnolt on JD 2,455,665.7931 (UT 2011 April 14.29) of the sixth eruption of the recurrent nova T Pyxidis. This discovery was made just as the initial fast rise was starting, so with fast notification and response by observers worldwide, the entire initial rise was covered (the first for any nova), and with high time resolution in three filters. The speed of the rise peaked at 9 mag day−1, while the light curve is well fit over only the first two days by a model with a uniformly expanding sphere. We also report the discovery by R. Stubbings of a pre-eruption rise starting 18 days before the eruption, peaking 1.1 mag brighter than its long-time average, and then fading back toward quiescence 4 days before the eruption. This unique and mysterious behavior is only the fourth known (with V1500 Cyg, V533 Her, and T CrB) anticipatory rise closely spaced before a nova eruption. We present 19 timings of photometric minima from 1986 to 2011 February, where the orbital period is fast increasing with yr. From 2008 to 2011, T Pyx had a small change in this rate of increase, so that the orbital period at the time of eruption was 0.07622950 ± 0.00000008 days. This strong and steady increase of the orbital period can only come from mass transfer, for which we calculate a rate of (1.7–3.5) × 10−7 M☉ yr−1. We report 6116 magnitudes between 1890 and 2011, for an average B = 15.59 ± 0.01 from 1967 to 2011, which allows for an eruption in 2011 if the blue flux is nearly proportional to the accretion rate. The ultraviolet–optical–infrared spectral energy distribution is well fit by a power law with fν∝ν1. 0, although the narrow ultraviolet region has a tilt with a fit of fν∝ν1/3. We prove that most of the T Pyx light is not coming from a disk, or any superposition of blackbodies, but rather is coming from some nonthermal source. We confirm the extinction measure from IUE with E(B − V) = 0.25 ± 0.02 mag.
Risks of misinterpretation in the evaluation of Distant Supervision for Relation Extraction

: Distant Supervision is frequently used for addressing Relation Extraction. The evaluation of Distant Supervision in Relation Extraction has been attempted through Precision-Recall curves and/or calculation of Precision at N elements. However, such evaluation is challenging because the labeling of the instances results from an automatic process that can introduce noise into the labels. Consequently, the labels are not necessarily correct, affecting the learning process and the interpretation of the evaluation results. Therefore, this research aims to show that the performance of the methods measured with the mentioned evaluation strategies varies significantly if the correct labels are used during the evaluation. Besides, based on the preceding, the current interpretation of the results of these measures is questioned. To this end, we manually labeled a subset of a well-known data set and evaluated the performance of 6 traditional Distant Supervision approaches. We demonstrate quantitative differences in the evaluation scores when considering manually versus automatically labeled subsets. Consequently, the ranking of performance among distant supervision methods is different with both labeled.