Reviewerly/exHarmony huggingface dataset:
Deep Neural Network Based Chinese Dialect Classification

With the recent advance of neural networks in audio speech recognition (ASR), Deep Neural Network Based ASR has been widely used in multiple application scenarios such as smart homes, intelligent customer service, meeting minutes, and real-time translation. However, due to the ethnic variety of China, the pronunciation for the same word is different, which is a big challenge to the speech recognition system, especially for Short Utterances. This paper analyzes three kinds of commonly used speech feature parameters: spectrogram, MFCC (Mel-scale Frequency Cepstral Coefficients), and Fbank (Filter bank) and builds the dialect classification model based on a deep neural network for a dataset of 10 dialects in China. In detail, we study the geographical features of dialects and propose a multi-task model that uses the area of the dialect as an auxiliary task and builds a hard parameter sharing based multi-task learning model. The results show that the performance of this model can achieve up to 79.96%. Furthermore, as the hard parameter sharing model cannot effectively learn the correlation between sub-tasks, we then propose a sparse parameter sharing based multitask learning model. The model uses joint training to automatically learn the correlation between sub-tasks, prune redundant networks, and share network parameters. The experiment results show that the sparse parameter sharing for the multi-task classification model achieves the best accuracy, with an average of 83.59%.
An Efficient Deep Learning Framework for Face Mask Detection in Complex Scenes

COVID-19 has caused a global health crisis that has infected millions of people across the globe. Currently, the fourth wave of COVID-19 is about to be declared as Omicron. The new variant of COVID-19 has caused an unprecedented increase in cases. According to World Health Organization, safety measures must be adopted in public places to prevent the spread of the virus. One effective safety measure is to wear face masks in crowded places. To create a safe environment, government agencies adopt strict rules to ensure adherence to safety measures. However, it is difficult to manually analyze the crowded scenes and identify people violating the safety measures. This paper proposed an automated approach based on a deep learning framework that automatically analyses the complex scenes and identifies people with face masks or without facemasks. The proposed framework consists of two sequential parts. In the first part, we generate scale aware proposal to cover scale variations, and in the second part, the framework classifies each proposal. We evaluate the performance of the proposed framework on a challenging benchmark data set. We demonstrate that the proposed framework achieves high performance and outperforms other reference methods by a considerable margin from experimental results.
International Students in Undergraduate Electrical and Information Engineering Programs in the USA

This research quantitatively analyzes undergraduate EEIE majors (Electrical Engineering, Computer Engineering, or Computer Science). Metrics used are major at university entrance, major at graduation, six-year graduation rate, and stickiness. We examine the data by origin (domestic or international), sex (female or male), and EEIE discipline. Analyses show that males enroll in EEIE disciplines in greater numbers than females, particularly higher for international students. International students graduate at higher rates in EEIE than domestic students by 14.5%. Both female and male international students have much higher stickiness than female and male domestic students. Graduation rates and stickiness are higher in Electrical Engineering than in Computer Engineering or Computer Science. These insights suggest that international students can be included in U.S. classrooms, enriching their educational experience and that of domestic students, while remaining confident that their experience is likely to result in positive academic outcomes.
AMR Alignment for Morphologically-rich and Pro-drop Languages

Alignment between concepts in an abstract meaning representation (AMR) graph and the words within a sentence is one of the important stages of AMR parsing.Although there exist high performing AMR aligners for English, unfortunately, these are not well suited for many languages where many concepts appear from morpho-semantic elements.For the first time in the literature, this paper presents an AMR aligner tailored for morphologicallyrich and pro-drop languages by experimenting on the Turkish language being a prominent example of this language group.Our aligner focuses on the meaning considering the rich Turkish morphology and aligns AMR concepts that emerge from morphemes using a tree traversal approach without additional resources or rules.We evaluate our aligner over a manually annotated gold data set.Our aligner outperforms the Turkish adaptations of the previously proposed aligners for English and Portuguese by an F1 score of 0.87 and provides a relative error reduction of up to 76%.
Transforming traditional institutions and learning environments: STEM education and modern educational landscape

The inevitable changes in the educational sphere today are largely due to the fact that new resources are constantly appearing that feed the idea of continuity of professional engineering and technological education. Today, the ideologists of the educational sphere are talking more and more about the duality of training engineers of a new formation, about combining their theoretical training with the cultivation of the ability to solve production problems practically. Transdisciplinarity sets the most important vector for the development of technological education. The article discusses the question of how these sources are interconnected and how dependent they are on each other. The circumstances of the last decades have drastically affected the situation in education. Integration, transnationalization, information openness, commercialization and digitalization of all aspects of life have not bypassed the sphere of education, development, and training of an engineer. The events of the past year related to the pandemic have caused the active development of online learning. But, despite the gigantic changes, technical education has been and remains a problematic area. The purpose of this work is to identify problems and show the possibilities of the STEM approach. The research author uses such methods as: analysis of theoretical ideas about engineering economics, technological education, transdisciplinarity and continuity of education for the future engineer; analysis of pedagogical and research experience in the field of technological education; a local expert survey conducted in November 2021 in an online format — to identify the problems and opportunities of today's popular STEM education; content analysis of answers to open-ended questions offered to respondents working in STEM technology. The results showed that the technical educational space requires reflection of a special responsibility. The authors believe that the strategies, values, meanings and patterns of behavior of the engineering education subjects are waiting for a new understanding and analysis.
Contrastive Visual Semantic Pretraining Magnifies the Semantics of Natural Language Representations

We examine the effects of contrastive visual semantic pretraining by comparing the geometry and semantic properties of contextualized English language representations formed by GPT-2 and CLIP, a zero-shot multimodal image classifier which adapts the GPT-2 architecture to encode image captions.We find that contrastive visual semantic pretraining significantly mitigates the anisotropy found in contextualized word embeddings from GPT-2, such that the intra-layer self-similarity (mean pairwise cosine similarity) of CLIP word embeddings is under .25 in all layers, compared to greater than .95 in the top layer of GPT-2.CLIP word embeddings outperform GPT-2 on wordlevel semantic intrinsic evaluation tasks, and achieve a new corpus-based state of the art for the RG65 evaluation, at .88.CLIP also forms fine-grained semantic representations of sentences, and obtains Spearman's ρ = .73on the SemEval-2017 Semantic Textual Similarity Benchmark with no fine-tuning, compared to no greater than ρ = .45 in any layer of GPT-2.Finally, intra-layer self-similarity of CLIP sentence embeddings decreases as the layer index increases, finishing at .25 in the top layer, while the self-similarity of GPT-2 sentence embeddings formed using the EOS token increases layer-over-layer and never falls below .97.Our results indicate that high anisotropy is not an inevitable consequence of contextualization, and that visual semantic pretraining is beneficial not only for ordering visual representations, but also for encoding useful semantic representations of language, both on the word level and the sentence level.
NASformer: Neural Architecture Search for Vision Transformer

Vision transformer has shown strong representation power by modeling the long-range dependencies. However, the self-attention mechanism in transformer has a quadratic complexity to the sequence length, limiting the generalization to dense prediction downstream tasks. Besides, it is challenging to design the architecture for vision transformer manually. To alleviate these two issues, we propose an efficient and parameter-free self-attention mechanism, named dilated window, which limits the self-attention to non-overlapping windows but still retains the ability to refer to global features. The dilated window scheme relies only on changes to data layout (reshapes and transpositions), which can be implemented with one line code. Furthermore, based on that, we proposed an efficient and effective hierarchical vision transformer architecture called NASformer by using one-shot neural architecture search. The searched architectures are superior to achieve better performances than recent state-of-the-arts, such as ViT, DeiT, and Swin on many vision tasks, including image classification, object detection, and semantic segmentation.
Towards Reproducible Machine Learning Research in Natural Language Processing

Motivation & ObjectivesWhile recent progress in the field of ML has been significant, the reproducibility of these cuttingedge results is often lacking, with many submissions lacking the necessary information in order to ensure subsequent reproducibility (Hutson, 2018).Despite proposals such as the Reproducibility Checklist (Pineau et al., 2020) and reproducibility criteria at several major conferences (NAACL, 2021; Dodge, 2020a;Beygelzimer et al., 2021), the reflex for carrying out research with reproducibility in mind is lacking in the broader ML community.We propose this tutorial as a gentle introduction to ensuring reproducible research in ML, with a specific emphasis on computational linguistics and NLP. Target Audience and PrerequisitesThis tutorial targets senior researchers in academic institutions who want to include reproducibility initiatives in their coursework, and well as junior researchers who are interested in participating in reproducibility initiatives.The only prerequisite for this tutorial is a basic understanding of the scientific method.
The HW-TSC’s Simultaneous Speech Translation System for IWSLT 2022 Evaluation

This paper presents our work in the participation of IWSLT 2022 simultaneous speech translation evaluation.For the track of textto-text (T2T), we participate in three language pairs and build wait-k based simultaneous MT (SimulMT) model for the task.The model was pretrained on WMT21 news corpora, and was further improved with in-domain fine-tuning and self-training.For the speech-to-text (S2T) track, we designed both cascade and end-to-end form in three language pairs.The cascade system is composed of a chunking-based streaming ASR model and the SimulMT model used in the T2T track.The end-to-end system is a simultaneous speech translation (SimulST) model based on wait-k strategy, which is directly trained on a synthetic corpus produced by translating all texts of ASR corpora into specific target language with an offline MT model.It also contains a heuristic sentence breaking strategy, preventing it from finishing the translation before the the end of the speech.We evaluate our systems on the MUST-C tst-COMMON dataset and show that the end-to-end system is competitive to the cascade one.Meanwhile, we also demonstrate that the SimulMT model can be efficiently optimized by these approaches, resulting in the improvements of 1-2 BLEU points.
Toward a New Process for Candidate Key-Phrases Extraction

Key-phrases in a document are the terms that allow us to have or know an idea of its content without having to read it. They can be used in many Natural Language Processing (NLP) applications, such as text summarization, machine translation, and text classification. These phrases are selected from a set of terms in the document called candidate key-phrases. Thus, any flaws that may arise during the selection of candidate phrases may affect the automatic key-phrase extraction (AKE). Despite the importance of identifying candidate key-phrases in the AKE process, we found a very limited number of researchers interested in identifying their features in the document. In this paper, we will present the features that will allow the identification of candidate key-phrases, based on the study and analysis of the features of 60,000 key-phrases manually selected from five different datasets. To improve the performance of AKE approaches.
Development of an Information Research Platform for Data-Driven Agriculture

Comprehensive research data are acknowledged as a necessity for research acceleration. Research institutes and universities are engaged in developing research data management systems. The National Agriculture and Food Research Organization of Japan (NARO) developed NARO-linked databases (Narolin DBs) in addition to a supercomputer. In the Narolin DB various research data on agriculture are cataloged using common metadata. The relationship between complicated data in natural science is described in RDF, property graph, or RDB format to facilitate the application of statistical analysis and machine learning. Our system is unique in that it is connected to a data catalog, a private cloud database, a supercomputer for data analysis, and a data/service portal for business applications, such as a data pipeline. Through the development of agricultural information research platforms, NARO will accelerate data-driven agricultural research at various stages in the agricultural supply chain, ranging from genome analysis to plant breeding, cultivation, food processing, and food distribution.
An Improved Animal Migration Optimization Approach for Extreme Learning Machine Tuning

Extreme learning machine, a relatively recent learning algorithm that can be applied to single hidden layer feed-forward neural networks, has recently gained much attention from researchers world-wide. By virtue of its characteristics, extreme learning machine can be considered as exceptionally swift learning method with superior generalization capabilities and less required supervising by humans than other techniques. However, there are still open challenges in this domain and one of the biggest open tasks is that the capacity of extreme learning machine at large extent depends on the assigned weights and biases for the hidden layer, which represents NP-hard real-parameter optimization problem. To tackle this issue, in this research a modified variant of the animal migration optimization metaheuristics is applied for optimizing extreme learning machine hidden layer weights and biases. Suggested algorithm was tested on 7 well-recognized classification benchmarking datasets and compared with the basic animal migration optimization metaheuristics and other techniques developed thus far. According to experimental findings, proposed approach obtains improved generalization performance than the other methods.
A Novel Global Set-Membership Filtering Approach for Localization of Automatic Guided Vehicles

This article investigates the localization problem of the automatic guided vehicle (AGV) system. In order to improve the reliability and flexibility of the localization process, a distributed sensor network structure is introduced to realize the localization of the AGV. Moreover, considering the influence of unknown-but-bounded noise and the accuracy requirements of the localization, a novel global set-membership filtering approach is proposed to obtain accurate localization results including a distributed set-membership filtering (DSMF) strategy and a circumscribed rectangle method. First, a DSMF strategy is designed to obtain local state estimation ellipsoids. Sufficient conditions for the existence of the state estimation ellipsoids are derived and a convex optimization process is developed to obtain the optimal local estimation ellipsoids. Then, a circumscribed rectangle method is proposed to fuse all local state estimation ellipsoids and obtain global set-membership filtering results. The proposed fusion method does not have a complicated optimization process, and can obtain more accurate estimation ellipsoids than local estimation results. Performance analysis verifies the effectiveness of the proposed global set-membership filtering approach.
A Study of Vulnerability Identifiers in Code Comments: Source, Purpose, and Severity

Software vulnerability is one of the weaknesses in computer security that challenges developers to rectify. Software maintainers rely on code comments to maintain their source code, including fixing vulnerability issues. To facilitate understanding the security issues in the related code, vulnerability identifiers are commonly included in code comments. However, not all vulnerability-related code comments describe clearly the purposes of the inclusion of the identifiers. Based on this evidence, we investigate the importance of vulnerability identifiers contained in source code comments, which is the novelty of this paper. We performed a study of 1,491 code comments that refer to vulnerability identifiers to define their categories. We then applied a mixed-method approach to classifying the types of the related repository and code, the rationale of identifier references, and the severity level of vulnerabilities in the code. The results indicate that vulnerability identifiers in code comments are useful to notify security issues for the related source code, and our study widens up chances for future work to further investigate these problems.
Cross-Language Transfer Learning-based Lhasa-Tibetan Speech Recognition

As one of Chinese minority languages, Tibetan speech recognition technology was not researched upon as extensively as Chinese and English were until recently. This, along with the relatively small Tibetan corpus, has resulted in an unsatisfying performance of Tibetan speech recognition based on an end-to-end model. This paper aims to achieve an accurate Tibetan speech recognition using a small amount of Tibetan training data. We demonstrate effective methods of Tibetan end-to-end speech recognition via cross-language transfer learning from three aspects: modeling unit selection, transfer learning method, and source language selection. Experimental results show that the Chinese-Tibetan multi-language learning method using multi-language character set as the modeling unit yields the best performance on Tibetan Character Error Rate (CER) at 27.3%, which is reduced by 26.1% compared to the language-specific model. And our method also achieves the 2.2% higher accuracy using less amount of data compared with the method using Tibetan multi-dialect transfer learning under the same model structure and data set.
A Novel Voice Recognition System with Artificial Intelligence

An AI personal assistant is a software that takes verbal commands and execute task assigned by the user. It is done using Python using libraries like pyttsx3, sapi5 and many more. The proposed voice assistant can also open YouTube, Gmail, Google Chrome and system applications. It can also show the current time, take a picture, check Wikipedia for data gathering, forecast the weather of multiple cities, get the daily news headline, search for the songs to play and much more. Aim is to create an assistant which answer voice and save user’s time.
Detecting Word-Level Adversarial Text Attacks via SHapley Additive exPlanations

State-of-the-art machine learning models are prone to adversarial attacks: Maliciously crafted inputs to fool the model into making a wrong prediction, often with high confidence.While defense strategies have been extensively explored in the computer vision domain, research in natural language processing still lacks techniques to make models resilient to adversarial text inputs.We adapt a technique from computer vision to detect word-level attacks targeting text classifiers.This method relies on training an adversarial detector leveraging Shapley additive explanations and outperforms the current state-of-the-art on two benchmarks.Furthermore, we prove the detector requires only a low amount of training samples and, in some cases, generalizes to different datasets without needing to retrain.
Selecting Mobile Services in Cloud and Edge Environment by Moth-Flame Optimization Algorithm

Mobile edge computing is playing an increasingly important role in the rise of mobile internet technology. Services deployed on edge servers nearby mobile users would provide computing capabilities with low latency and high scalability. Usually, a single service is challenging to meet a complex user request, which asks for composing services. With the increasing number of services in the cloud and edge computing environment and the user mobility, selecting appropriate services to meet the complex mobile user's requests becomes a crucial problem. This paper proposes a modified moth-flame optimization algorithm using overall QoS for service selection. Specifically, the overall QoS of services is calculated by combining the subjective and objective QoS with the ordinal relationship and coefficient of variation, and the moth-flame optimization algorithm is improved by adding the differential evolution algorithm. The experimental results show that the proposed approach outperforms some other services selection approaches.