Reviewerly/exHarmony huggingface dataset:
Let’s Get Ready for Work – Employability Skills Development in an IS Capstone Project

Aim/Purpose. This study aims to explore undergraduate IS students’ employability skills development while performing their final capstone project during their academic studies. Background. The importance of soft skills in the Information Systems industry is not an arguable fact and has been broadly discussed both in the industry and the academic literature. The ability of professionals to collaborate, communicate, manage time, negotiate, solve problems, make decisions, and self-learning, called employability skills, are essential skills needed in today’s industry. The development of these skills during undergraduate studies is essential for graduate students’ readiness for work. Methodology. A mixed methods approach was employed using exploratory research design, including qualitative and quantitative approaches. First, a qualitative analysis of 156 reflections was performed, resulting in the conceptual framework of facets. Then, a quantitative analysis of the data was performed to examine the facets and the differences between the stages of the capstone project. Contribution. This study contributes to both academy and industry. The former may use this study’s findings to upgrade academy courses and capstone projects in order to raise students’ readiness for the industry. The later may learn the approaches the academy use and give appropriate feedback. Findings. The results showed that students’ reflections on their motivation, knowledge, and skills (teamwork, time management, and inter-communication) demonstrate the importance of those facets in the process they underwent, especially since the reflections collected were unstructured. Recommendations for Practitioners. Information Systems undergraduate academic programs should develop structured capstone projects to provide the students with a better platform to learn and experience the employability skills the industry requires. Recommendations for Researchers . The study presents a conceptual model based on students’ reflections on their experience performing a capstone project. The impact and influence of each of the model’s components should be further researched and measured. Impact on Society. Moreover, it is very important to prepare the students for employability by including a “real-life” capstone project at the end of their undergraduate studies to prepare them to be valuable in the workforce, even at their start point as juniors. Future Research. To evaluate the process of employability skills development, a further study can examine students’ perceptions prior to the capstone, during, and after to assess the progress and changes. To generalize the results, it would be valuable to investigate whether the findings of this study are consistent across different institutions and disciplines.
mask-Net: Learning Context Aware Invariant Features using Adversarial Forgetting (Student Abstract)

Training a robust system, e.g.,Speech to Text (STT), requires large datasets. Variability present in the dataset such as unwanted nuisances and biases are the reason for the need of large datasets to learn general representations. In this work, we propose a novel approach to induce invariance using adversarial forgetting (AF). Our initial experiments on learning invariant features such as accent on the STT task achieve better generalizations in terms of word error rate (WER) compared to the traditional models. We observe an absolute improvement of 2.2% and 1.3% on out-of-distribution and in-distribution test sets, respectively.
False Positive Intent Detection Framework for Chatbot Annotation

For chatbots answering thousands of user queries daily, it requires huge annotation efforts or explicit signals from users to identify incorrect chatbot predictions. Identification of such False Positives is key to improving chatbot accuracy and is a challenging problem due to the high cost and limited explicit signals from users. In this paper, we present a framework for automatically detecting False Positive intents in an employee chatbot through implicit feedback by capturing specific user behavior using techniques such as detection of repeated queries and leveraging on active learning sampling strategies to find cases where the chatbot might have provided an incorrect response. Using this approach within the bank, annotators can prioritize their efforts and detect False Positive intent approximately three times better than manual screening of random chatbot dialogues. This framework can be reused across different chatbot applications.
Word Embedding in Nepali Language using Word2Vec

Word embedding is a technique for understanding the relationship among words by mapping words to numbers. Several kinds of research have been carried out in this field in different languages such as English, Hindi, Bengali etc. but very few works are available in the Nepali language domain. In this work, the word embedding technique using Word2Vec is implemented for Nepali news data. The methodology involved in this work includes Dataset preparation and Word2Vec modelling. Gensim package is used for implementing the Word2Vec model and its output shows the similarity between Nepali words. The work mainly focuses on developing word embedding on Nepali words generated by scraping the health section of Nepali news portals and has shown promising results.
E-LIBRARY MANAGEMENT: A STUDENTS SERVICES ENHANCEMENT TOOL TO THEIR BEHAVIOR AND STUDY HABITS

This study was conducted to determine the relationship between the features of E-Library Management System to the behavior and study habits of the students of Lumban National High School.Specifically, it sought answers to the following questions: What is the level of Features of E-Library with regards to: Functionality, Usability, Reliability, Maintainability and Efficiency.What is the level of students' behavior in terms of: Persistence, Curiosity and, Creativity.What is the status of study habits relative to: Set study goals.Does the features of E-Library have a significant relationship to the behavior of the respondents.Does the feature of E-Library have a significant relationship to the study habits of the respondents?The descriptive method of research was used in conducting this study.The study used a questionnaire as the research instrument.200 students each from the abovementioned school were selected purposively.It implies that there is significant relationship between the features of E-Library management system and the behavior of the respondents.While there is no significant relationship between the study habits of the respondents who assessed the E-Library Management system.
Twenty First Century’s Challenges, Changing Trends and Urdu Novel: A Critical Analysis

The recent pandemic has not only formally announced the entry of man into the 21st century but also indicate the upcoming challenges for humanbeing. The individual of this Century suffers from duality/binary opposition where he is in an isolation/alone but not in the previous sense as he is at the same time sitting in middle of the whole world. This Mob, not his family, but the whole world has gathered at his fingertips, Man has begun to travel for centuries in minutes and seconds.Youval Noh Hariri a Nobel Prize winner philosopher of new era says man has been hacked by his own discovered biotechnology and information technology. This InfoTech or biotech data has snatched man s free will. This algorithm now replace the ancient times place of philosophers, thinkers, saints, sages, writers and leads/compels man to choices of corporate world. Now the corporate world better known mans choices by this algorithm but the man himself notter. The Challenges of self identity is the most important question of this century. The trend of predicting the future is growing day by day. Now a strange situation is knocking on human beings doors. Nature is disappearing. It is impossible to escape from artificial intelligence. Every second changing of technology results in rapid change of life and social taboos.&#x0D; &#x0D;
How does AI help in Rural Development in Healthcare Domain: A Short Survey

Abstract: Artificial intelligence (AI) has the potential to revo- lutionize healthcare delivery in rural areas by addressing various challenges such as scarcity of healthcare resources, lack of health- care professionals, and inadequate healthcare infrastructure. In this paper, we explore the role of AI in rural development in the healthcare domain, specifically focusing on how it can be leveraged to improve healthcare outcomes in rural areas. weoutline the following main aspects of Artificial intelligence (AI) in rural development based on our findings from the literature review on Artificial Intelligence and various techniques used in the healthcare domain such as Robotics Process Automation (RPA), Machine Learning (ML), Natural Language Processing (NLP), Deep Learning(DL) including a recent breakthrough in AI technology. Identify the challenges and legal barriers in rural areas.
MoEC: Mixture of Expert Clusters

Sparsely Mixture of Experts (MoE) has received great interest due to its promising scaling capability with affordable computational overhead. MoE models convert dense layers into sparse experts, and utilize a gated routing network to make experts conditionally activated. However, as the number of experts grows, MoE with outrageous parameters suffers from overfitting and sparse data allocation. Such problems are especially severe on tasks with limited data, thus hindering the progress towards improving performance by scaling up. We verify that there exists a performance upper bound of scaling up sparse MoE. In this work, we propose Mixture of Expert Clusters — a general approach to enable expert layers to learn more diverse and appropriate knowledge by imposing variance-based constraints on the routing stage. Given this, we could further propose a cluster-level expert dropout strategy specifically designed for the expert cluster structure. Our experiments reveal that MoEC could improve performance on machine translation and natural language understanding tasks. MoEC plays a positive role in mitigating overfitting and sparse data allocation problems, thus fully releasing the potential of large-scale sparse models.
Which Shortcut Solution Do Question Answering Models Prefer to Learn?

Question answering (QA) models for reading comprehension tend to exploit spurious correlations in training sets and thus learn shortcut solutions rather than the solutions intended by QA datasets. QA models that have learned shortcut solutions can achieve human-level performance in shortcut examples where shortcuts are valid, but these same behaviors degrade generalization potential on anti-shortcut examples where shortcuts are invalid. Various methods have been proposed to mitigate this problem, but they do not fully take the characteristics of shortcuts themselves into account. We assume that the learnability of shortcuts, i.e., how easy it is to learn a shortcut, is useful to mitigate the problem. Thus, we first examine the learnability of the representative shortcuts on extractive and multiple-choice QA datasets. Behavioral tests using biased training sets reveal that shortcuts that exploit answer positions and word-label correlations are preferentially learned for extractive and multiple-choice QA, respectively. We find that the more learnable a shortcut is, the flatter and deeper the loss landscape is around the shortcut solution in the parameter space. We also find that the availability of the preferred shortcuts tends to make the task easier to perform from an information-theoretic viewpoint. Lastly, we experimentally show that the learnability of shortcuts can be utilized to construct an effective QA training set; the more learnable a shortcut is, the smaller the proportion of anti-shortcut examples required to achieve comparable performance on shortcut and anti-shortcut examples. We claim that the learnability of shortcuts should be considered when designing mitigation methods.
Large-State Reinforcement Learning for Hyper-Heuristics

Hyper-heuristics are a domain-independent problem solving approach where the main task is to select effective chains of problem-specific low-level heuristics on the fly for an unseen instance. This task can be seen as a reinforcement learning problem, however, the information available to the hyper-heuristic is very limited, usually leading to very limited state representations. In this work, for the first time we use the trajectory of solution changes for a larger set of features for reinforcement learning in the novel hyper-heuristic LAST-RL (Large-State Reinforcement Learning). Further, we introduce a probability distribution for the exploration case in our epsilon-greedy policy that is based on the idea of Iterated Local Search to increase the chance to sample good chains of low-level heuristics. The benefit of the collaboration of our novel components is shown on the academic benchmark of the Cross Domain Heuristic Challenge 2011 consisting of six different problem domains. Our approach can provide state-of-the-art results on this benchmark where it outperforms recent hyper-heuristics based on reinforcement learning, and also demonstrates high performance on a benchmark of complex real-life personnel scheduling domains.
A Domain-Transfer Meta Task Design Paradigm for Few-Shot Slot Tagging

Few-shot slot tagging is an important task in dialogue systems and attracts much attention of researchers. Most previous few-shot slot tagging methods utilize meta-learning procedure for training and strive to construct a large number of different meta tasks to simulate the testing situation of insufficient data. However, there is a widespread phenomenon of overlap slot between two domains in slot tagging. Traditional meta tasks ignore this special phenomenon and cannot simulate such realistic few-shot slot tagging scenarios. It violates the basic principle of meta-learning which the meta task is consistent with the real testing task, leading to historical information forgetting problem. In this paper, we introduce a novel domain-transfer meta task design paradigm to tackle this problem. We distribute a basic domain to each target domain based on the coincidence degree of slot labels between these two domains. Unlike classic meta tasks which only rely on small samples of target domain, our meta tasks aim to correctly infer the class of target domain query samples based on both abundant data in basic domain and scarce data in target domain. To accomplish our meta task, we propose a Task Adaptation Network to effectively transfer the historical information from the basic domain to the target domain. We carry out sufficient experiments on the benchmark slot tagging dataset SNIPS and the name entity recognition dataset NER. Results demonstrate that our proposed model outperforms previous methods and achieves the state-of-the-art performance.
Permutation scheme with an upper limitation of ints for its fixed elements

Problems of enumerative combinatorics are solved for the proposed scheme, i. e. the number of its outcomes is determined, their direct listing is constructed, the numbering problem is solved, the probability distribution of the scheme outcomes is found, and a procedure for their modeling is proposed.
Don’t Be So Sure! Boosting ASR Decoding via Confidence Relaxation

Automatic Speech Recognition (ASR) systems frequently use a search-based decoding strategy aiming to find the best attainable transcript by considering multiple candidates. One prominent speech recognition decoding heuristic is beam search, which seeks the transcript with the greatest likelihood computed using the predicted distribution. While showing substantial performance gains in various tasks, beam search loses some of its effectiveness when the predicted probabilities are highly confident, i.e., the predicted distribution is massed for a single or very few classes. We show that recently proposed Self-Supervised Learning (SSL)-based ASR models tend to yield exceptionally confident predictions that may hamper beam search from truly considering a diverse set of candidates. We perform a layer analysis to reveal and visualize how predictions evolve, and propose a decoding procedure that improves the performance of fine-tuned ASR models. Our proposed approach does not require further training beyond the original fine-tuning, nor additional model parameters. In fact, we find that our proposed method requires significantly less inference computation than current approaches. We propose aggregating the top M layers, potentially leveraging useful information encoded in intermediate layers, and relaxing model confidence. We demonstrate the effectiveness of our approach by conducting an empirical study on varying amounts of labeled resources and different model sizes, showing consistent improvements in particular when applied to low-resource scenarios.
Learning to Know Myself: A Coarse-to-Fine Persona-Aware Training Framework for Personalized Dialogue Generation

A critical challenge for open-domain dialogue agents is to generate persona-relevant and consistent responses. Due to the nature of persona sparsity in conversation scenarios, previous persona-based dialogue agents trained with Maximum Likelihood Estimation tend to overlook the given personas and generate responses irrelevant or inconsistent with personas. To address this problem, we propose a two-stage coarse-to-fine persona-aware training framework to improve the persona consistency of a dialogue agent progressively. Specifically, our framework first trains the dialogue agent to answer the constructed persona-aware questions, making it highly sensitive to the personas to generate persona-relevant responses. Then the dialogue agent is further trained with a contrastive learning paradigm by explicitly perceiving the difference between the consistent and the generated inconsistent responses, forcing it to pay more attention to the key persona information to generate consistent responses. By applying our proposed training framework to several representative baseline models, experimental results show significant boosts on both automatic and human evaluation metrics, especially the consistency of generated responses.
Domain-Adapted Dependency Parsing for Cross-Domain Named Entity Recognition

In recent years, many researchers have leveraged structural information from dependency trees to improve Named Entity Recognition (NER). Most of their methods take dependency-tree labels as input features for NER model training. However, such dependency information is not inherently provided in most NER corpora, making the methods with low usability in practice. To effectively exploit the potential of word-dependency knowledge, motivated by the success of Multi-Task Learning on cross-domain NER, we investigate a novel NER learning method incorporating cross-domain Dependency Parsing (DP) as its auxiliary learning task. Then, considering the high consistency of word-dependency relations across domains, we present an unsupervised domain-adapted method to transfer word-dependency knowledge from high-resource domains to low-resource ones. With the help of cross-domain DP to bridge different domains, both useful cross-domain and cross-task knowledge can be learned by our model to considerably benefit cross-domain NER. To make better use of the cross-task knowledge between NER and DP, we unify both tasks in a shared network architecture for joint learning, using Maximum Mean Discrepancy(MMD). Finally, through extensive experiments, we show our proposed method can not only effectively take advantage of word-dependency knowledge, but also significantly outperform other Multi-Task Learning methods on cross-domain NER. Our code is open-source and available at https://github.com/xianghuisun/DADP.
Quantum-Inspired Representation for Long-Tail Senses of Word Sense Disambiguation

Data imbalance, also known as the long-tail distribution of data, is an important challenge for data-driven models. In the Word Sense Disambiguation (WSD) task, the long-tail phenomenon of word sense distribution is more common, making it difficult to effectively represent and identify Long-Tail Senses (LTSs). Therefore exploring representation methods that do not rely heavily on the training sample size is an important way to combat LTSs. Considering that many new states, namely superposition states, can be constructed from several known states in quantum mechanics, superposition states provide the possibility to obtain more accurate representations from inferior representations learned from a small sample size. Inspired by quantum superposition states, a representation method in Hilbert space is proposed to reduce the dependence on large sample sizes and thus combat LTSs. We theoretically prove the correctness of the method, and verify its effectiveness under the standard WSD evaluation framework and obtain state-of-the-art performance. Furthermore, we also test on the constructed LTS and the latest cross-lingual datasets, and achieve promising results.
Zero-Shot Face-Based Voice Conversion: Bottleneck-Free Speech Disentanglement in the Real-World Scenario

Often a face has a voice. Appearance sometimes has a strong relationship with one's voice. In this work, we study how a face can be converted to a voice, which is a face-based voice conversion. Since there is no clean dataset that contains face and speech, voice conversion faces difficult learning and low-quality problems caused by background noise or echo. Too much redundant information for face-to-voice also causes synthesis of a general style of speech. Furthermore, previous work tried to disentangle speech with bottleneck adjustment. However, it is hard to decide on the size of the bottleneck. Therefore, we propose a bottleneck-free strategy for speech disentanglement. To avoid synthesizing the general style of speech, we utilize framewise facial embedding. It applied adversarial learning with a multi-scale discriminator for the model to achieve better quality. In addition, the self-attention module is added to focus on content-related features for in-the-wild data. Quantitative experiments show that our method outperforms previous work.
Learning Compositional Tasks from Language Instructions

The ability to combine learned knowledge and skills to solve novel tasks is a key aspect of generalization in humans that allows us to understand and perform tasks described by novel language utterances. While progress has been made in supervised learning settings, no work has yet studied compositional generalization of a reinforcement learning agent following natural language instructions in an embodied environment. We develop a set of tasks in a photo-realistic simulated kitchen environment that allow us to study the degree to which a behavioral policy captures the systematicity in language by studying its zero-shot generalization performance on held out natural language instructions. We show that our agent which leverages a novel additive action-value decomposition in tandem with attention based subgoal prediction is able to exploit composition in text instructions to generalize to unseen tasks.