Reviewerly/exHarmony huggingface dataset:
Above and below verbal roots: A case study of English adverbs of results

This study investigates English resultative adverbs focusing on their syntactic peculiarities. Based on the observation that resultative adverbs can appear in certain activity verb phrases, we argue for an elaborate verbal structure. In particular, we propose that two distinct syntactic heads need to be postulated for resultative adverbial modification (Res(ult)) and for encoding the aspectual information of the verb (v). The proposal correctly predicts the distributional contrasts between resultative and manner adverbs, the patterns of adverbs ambivalent in denoting manner and result meanings, and the possibilities of modification of verb and adjective derivatives. The analysis of resultative adverbs advanced in this study is shown to newly account for measure adverbs. In doing so, we demonstrate that certain roots can head-adjoin either to Res or v with the corollary that the resulting verb can either denote a result or manner. Finally, an implication of this study is discussed regarding the relationship between result roots and the Res head.
Brief Cognizance into Tampered Image Detection

Abstract: This paper is a testament to the research carried out to detect tampered images using deep learning and machine learning techniques, including the implementation of various standard and hybrid convolution neural networks, thus creating a comparative study for the neural networks. Tampered images can be found on various social media networks and sites thus creating a false impression in the minds of people consuming the information from them. Hence, to filter out these fake images the following research work and implementation are carried out.
Mixed-Reality-in-the-Loop Simulation based on a Digital Twin as a Service concept for Learning Factories

This paper presents the Mixed-Reality-in-the-Loop Simulation (MRiLS) for training in mechanical engineering in combination with virtual and hybrid Learning Factories. MRiLS combines the methods of X-in-the-Loop Simulation (XiLS) used in the mechatronic development process with visualization and interaction methods of Mixed Reality (MR), thus enabling the comprehensive integration of humans and their behavior into the simulation loop. In addition to the egocentric visualization of the virtual content, MRiLS also requires intuitive multimodal human-model interaction and multiuser collaboration.For a location-independent deployment of the MRiLS, the Digital Twin as a Service (DTaaS) concept is presented. Besides to the flexible coupling of industrial control technology with the MR devices, this approach enables the device-independent provision of Digital Twins as well as the use of different virtuality and interaction modalities in a multiuser environment. The basis for the use of the virtuality and interaction modalities with a shared Digital Twin is an abstraction layer between the device technology and the behavioral and interaction model of the Digital Twin. The viability of the methods presented is demonstrated by a selected Digital Twin of a Smart Factory test environment.
Research on English-Chinese Translation Quality Evaluation Agorithm Based on Cross-Language Pre-Training Mode

To overcome the shortcomings of the quality evaluation of translation in the low-resource corpus, a cross-sentence pre-training model is proposed for English-Chinese translation. First of all, we provide an embedding technology to automatically adjust the position of words by using attention thought for reference. Then, the cross-layer language pre-training model is introduced into the reading efficiency test to solve the information sparsity caused by the low resource conditions of English. By regressing the sentence vector, the mechanical evaluation of translation quality is completed. The test results show that this model significantly improves the effectiveness of the evaluation of the quality of English-Chinese translation. Compared with the CEstmodel, the Pearson correlation coefficient of this algorithm has increased by 0.35, and the Spielbman correlation coefficient has increased by 0.15.
Research on Intelligent Classification And Grading Algorithm of English Composition Based on Support Vector Machine

We study the language characteristics that change the writing quality of language users to enhance the accuracy of the existing automatic evaluation system for writing using integrated learning technology. The FCE test sample in Cambridge is used, and the object is filtered by vector regression and random forest algorithm to establish and evaluate the automatic scoring mode. Compared with the existing technology, the accuracy of the evaluation using the integrated method is improved. This method effectively evaluates the writing efficiency of English learners and is used to develop a writing self-help evaluation system for large-scale computer tests and online autonomous learning systems.
Design and Implementation of The Big-Data-Based English Digital Teaching Platform

The big-data-based English digital teaching platform is the product of the development of modern education and teaching reform. Its main purpose is to apply Internet technology to teaching and education and achieve the new mode of “Internet plus Education”. The big-data-based English digital teaching platform is designed for teachers to construct online English courses and carry out English teaching with an innovative teaching model and teaching content for students to learn anytime and anywhere. Then, the English learning effect is improved, and the goals of personalized and autonomous learning are fulfilled. The big-data-based English digital teaching platform makes up for the deficits of classroom teaching and improves the effect of students' English study.
Development and Application of Spoken French Corpus Based on AI Speech Recognition

Spoken authentic corpus is of great practical significance for the dynamic research of language and teaching and acquisition of spoken French. Recently, the establishment of the spoken French corpus and empirical research based on spoken authentic corpus has become the direction of language research. Thus, we introduce the establishment and application of AI speech recognition technology in a small French spoken corpus in detail as a platform to conduct an empirical study on the phenomenon of code-switching in French classroom teachers' discourse. In the research, we find that the corpus is objective and effective in revealing the types, motivations, and functions of code-switching in French classroom teachers' discourse. The corpus also provides a reference for future research on the teaching of spoken French based on the corpus.
Quantitative Expression of Elderly Multi-Modal Emotions with Spoken Dialogue Agent and Edge AI

In a super-aging society, the problem of aging of the physical and cognitive functions of older adults receiving home care is becoming more serious. In the previous study, we developed a spoken dialogue agent system that allowed older adults to communicate with machines by voice. However, it was difficult to visualize the thoughts extracted from the spoken dialog of the elderly in detail. Therefore, this study was carried out to extract the emotional characteristics of the elderly and propose a method to visualize them quantitatively. The proposed method uses AI technology to extract and express emotional features from multiple perspectives while protecting the privacy of the elderly. In this way, the method objectively reflects the personality and preferences of the elderly and leads to the prevention of dementia.
Research on Financial Data Analysis and Early Warning Model of Vocational Colleges Based on Particle Swarm Optimization

A financial early warning model of vocational schools is proposed in this study based on the least squares support vector machine. In the model, the relevant data is provided by particle swarm optimization. By designing appropriate verification performance indicators and using the particle swarm optimization method, the correlation value of the vector machine was optimized for minimizing the multiplication, and through the data of listed companies, the financial analysis of vocational colleges was verified. The simulation results showed that the simulation accuracy of the method was satisfactory, and the method was also reasonable and efficient.
Linguistic Refoulement

Speakers of Indigenous languages represent between ten and forty-five percent of new arrivals to the U.S.-Mexico border and over four percent of all cases pending before U.S. immigration courts. The government deports many of them without ever providing interpreters who speak their languages.This Article considers the relationship between language and access to asylum, employing as a case study Indigenous-language speakers seeking protection in the United States. It makes two principal contributions to the study of international human rights law—one theoretical and one empirical.First, it introduces linguistic refoulement as a theoretical tool for understanding the intersection of language and refugee law. The principle of non-refoulement undergirds international and U.S. refugee law, mandating that governments refrain from returning people to situations where they will face particular harms. When a government expels an asylum seeker on account of the language they speak, the expelling country commits linguistic refoulement. This Article argues that governments contravene critical protections for asylum seekers when they fail to recognize and mitigate the multiple ways in which language functions as a nexus of refoulement. The Article identifies five mechanisms of linguistic refoulement that lead to the disproportionate return of Indigenous-language speakers: neglect, erasure, impatience, subordination, and isolation.Second, this Article applies the linguistic-refoulement framework to offer empirical evidence that the U.S. immigration system undermines non-refoulement for speakers of Indigenous languages. Analyzing the results of a mixed-method study, the Article demonstrates that the U.S. neglects the language needs of asylum seekers who speak K’iche’, Mam, Mixteco, and the hundreds of other Indigenous languages spoken in the Western Hemisphere. As a result, the U.S. disproportionately returns Indigenous-language speakers to situations of persecution and torture.Only by understanding the varied manifestations of linguistic refoulement can governments and advocates meaningfully protect asylum seekers from it. The Article concludes by proposing policy changes that the United States government should enact to deter linguistic refoulement, including creating a special immigrant visa for interpreters and abolishing immigration detention for linguistically vulnerable populations.
Optimal Path Planning for Mobile Robots in Complex Environments Based on the Gray Wolf Algorithm and Self-Powered Sensors

The term “mobile robot” or “intelligent mobile robot” refers to an autonomous system with specific sensory skills that can move independently in an environment that contains obstacles and can perform certain activities and belongs to the category of intelligent robots. The use of mobile robots combines expertise from many different academic fields, including strongly integrated techniques in biology, anthropology, computer science, control theory, power transmission, measurement, and sensing. At present, there are already cases of using mobile robots to perform various simple tasks, but due to the low efficiency of optimal path planning, the scope of application cannot be further expanded. Therefore, based on the improved gray wolf algorithm (IGWA) and self-powered sensor technology, this article optimized the optimal path planning method and energy driving method of the mobile robot, so that the mobile robot can be applied in complex environments. The experiment compared the improved path planning algorithm in this article with the traditional path planning algorithm. The results show that the optimal path planning using the IGWA can search the random tree algorithm, the improved bee colony algorithm, and the multiobjective based on edge distribution estimation faster. Compared with the optimization algorithm, the optimization effect was increased by 14.84%. It is beneficial to the optimal path planning of mobile robots in complex environments.
Mutually Unbiased Measurements, Hadamard Matrices, and Superdense Coding

Mutually unbiased bases (MUBs) are highly symmetric bases on complex Hilbert spaces, and the corresponding rank-1 projective measurements are ubiquitous in quantum information theory. In this work, we study a recently introduced generalisation of MUBs called mutually unbiased measurements (MUMs). These measurements inherit the essential property of complementarity from MUBs, but the Hilbert space dimension is no longer required to match the number of outcomes. This operational complementarity property renders MUMs highly useful for device-independent quantum information processing. It has been shown that MUMs are strictly more general than MUBs. In this work we provide a complete proof of the characterisation of MUMs that are direct sums of MUBs. We then construct new examples of MUMs that are not direct sums of MUBs. A crucial technical tool for this construction is a correspondence with quaternionic Hadamard matrices, which allows us to map known examples of such matrices to MUMs that are not direct sums of MUBs. Furthermore, we show that—in stark contrast with MUBs—the number of MUMs for a fixed outcome number is unbounded. Next, we focus on the use of MUMs in quantum communication. We demonstrate how any pair of MUMs with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> outcomes defines a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> -dimensional superdense coding protocol. Using MUMs that are not direct sums of MUBs, we disprove a recent conjecture due to Nayak and Yuen on the rigidity of superdense coding, for infinitely many dimensions. The superdense coding protocols arising in the refutation reveal how shared entanglement may be used in a manner heretofore unknown.
Super-Resolution Degradation Model: Converting High-Resolution Datasets to Optical Zoom Datasets

Despite remarkable progress in single-image super-resolution based on neural networks, the results are not ideal when applied to real-world images, because the real-world degradation process is unknown and complex. The emergence of some optical zoom datasets shows that neural networks still achieve good results on real-world images as long as the low-resolution images used for training have similar features and distributions with the real-world images. However, obtaining such optical zoom datasets is complicated and the datasets are only applicable to specific cameras and shooting conditions. By studying the optical zoom datasets, we propose a super-resolution image degradation model consisting of blurring, frequency domain processing, adding noise and downsampling. Specifically, blurring uses a blur kernel with a wave-like shape inferred from the point spread function, which produces the artifacts like real-world images. Frequency domain processing simulates the frequency domain aliasing of real-world images, such as jagged edges and background stripes. Experiments demonstrate that the new degradation model achieves visual effects comparable to optical zoom datasets. Existing high-resolution datasets can be converted to “optical zoom datasets” by the degradation model, where the synthetic low-resolution images have real-world image features, thereby extending super-resolution methods to real-world images.
Liberator: A Data Reuse Framework for Out-of-Memory Graph Computing on GPUs

Graph analytics are widely used including recommender systems, scientific computing, and data mining. Meanwhile, GPU has become the major accelerator for such applications. However, the graph size increases rapidly and often exceeds the GPU memory, incurring severe performance degradation due to frequent data transfers between the main memory and GPUs. To relieve this problem, we focus on the utilization of data in GPUs by taking advantage of the data reuse across iterations. In our studies, we deeply analyze the memory access patterns of graph applications at different granularities. We have found that the memory footprint is accessed with a roughly sequential scan without a hotspot, which infers an extremely long reuse distance. Based on our observation, we propose a novel framework, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Liberator</i> , to exploit the data reuse within GPU memory. In <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Liberator</i> , GPU memory is reserved for the data potentially accessed across iterations to avoid excessive data transfer between the main memory and GPUs. For the data not existing in GPU memory, a Merged and Aligned memory access manner is employed to improve the transmission efficiency. We also further optimize the framework by parallel processing of data in GPU memory and data in the main memory. We have implemented a prototype of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Liberator</i> framework and conducted a series of experiments on performance evaluation. The experimental results show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Liberator</i> can significantly reduce the data transfer overhead, which achieves an average of 2.7x speedup over a state-of-the-art approach.
A Robust Approach to Identify Banglish Words using Bangla Scripts

English word written in Bangla in the Bangla script is mentioned as “Banglish” word. Frequently use of Banglish words is degrading the standard of Bangla writing. There are many types of researches have been done on Bangla Natural Language Processing but detection of Banglish words from Bangla script still no remarkable works have been done. In this research paper, we have proposed a robust system and presented how to resolve the issue of identifying Banglish words. Moreover, our designed system could also be used to change these Banglish words into standard Bangla words using NER-BERT model. After cleaning the raw input data, the system parses it into individual sentences with unique ID then the sentences are split into several words. After getting each word, the system adds tags EN for English words, BN for Bangla words and UN for others. For training the system, two corpora are used. One corpus is used for word label tagging and another is used for dictionary. After identifying the Banglish words, the system changes automatically these into standard Bangla words based on the trained dictionary. The Dataset contains 700 sentences with various Banglish words. Our proposed system achieves overall accuracy of 80% for Banlish words identification and transformation into Standard Bangla words.
A Multiclass Approach to Identify Misogynistic Bangla Text from Social Media

Misogyny is defined as hostility, dominance, harassment, intimidation, and violence against women. Social media networks are rapidly developing. As a result, misogyny is becoming increasingly fashionable. This paper presents a solution to the problem of automatically classifying misogynistic Bengali messages on social media platforms. Several procedures have been taken to preprocess the data. Tf-Idf and Word Embedding with BERT are implemented so that computers can read text. Several machine learning-based models including Random Forest, SVM Polynomial kernel, SVM Signal kernel, and Adaptive Boosting have been tested to build a multi-class analyzer that identifies Misogynistic text in Bengali on social media. The models have been evaluated with the performance measures such as accuracy, precision, recall, and F1 scores. With 62.61 percent accuracy, the SVM sigmoid kernel outperforms all other models. As per our knowledge, no earlier research has been conducted on the classification of Bengali misogynistic text; consequently, the current research is the most comprehensive to date.
Curvelet Transformed NIG Image-based Breast Tumor Classification Using Deep Learning

Notwithstanding ultrasound (US) B-Mode images, automatic diagnosis of benign and malignant breast cancers is still an unexplored area of study. In this study, a deep convolutional neural network architecture based on parametric imaging is proposed to categorize and identify breast tumors from breast ultrasound images. Here, an appropriate model for explaining the ultrasound image statistics within the Curvelet Transform domain is the Normal Inverse Gaussian (NIG) distribution. To categorize parametric images produced by locally calculated parameter values of the NIG distributions in various Curvelet sub-bands, the proposed convolutional neural network is used. 100 benign fibroadenomas and 100 malignant cases from a publicly accessible dataset of 780 breast US images are undertaken. The proposed technique yields 95.5%, 96.91%, 94.17%, 94%, and 97% accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), respectively. Additionally, it is shown that the Proposed Method's accuracy exceeds a number of recently published findings.
Automatic 3D Animated Bangla Sign Language Gestures Generation from Bangla Text and Voice

Every human being has a fundamental need for communication since it allows them to learn, express their emotions, and share ideas. However, sign language users are unable to hear or speak. They learn a variety of hand signals known as Sign Language (SL), which they use to communicate with themselves and others. Non-sign language users, on the other hand, have a difficult time understanding sign language. We have proposed a real-time bidirectional 3D Bangla Sign Language (BdSL) translator that is more dynamic and real-time than the traditional system for the simplest way to communicate. To facilitate effective communication between Sign and Unsign people, we have introduced an Automatic Animated BdSL Gesture Generation from Bangla Text and Voice. The recommended system takes input as Bangla Voice or Bangla Text and parses the output texts of corresponding inputs based on Signing Gesture Markup Language (SigML). Creating a bridge between Sign People and Normal People is the main purpose of this paper. A 3D animated Bangla sign gesture is generated dynamically based on the trained SigML. In this paper, the model is trained only for 13 Bangla Numeral gestures (0-9, Thousand, Lakh, and Crore) but the system can generate any 3D Animated Bangla Numeral gestures dynamically. 100 different lengths of Bangla Composite Numbers are being tested for the evaluation of the system. The system achieves an average interpretation accuracy of 97.80% for voice and 100% for Bangla Text input of Bangla Composite Numerals with a computation cost of 6.5 milliseconds per gesture.