Reviewerly/exHarmony huggingface dataset:
The Effect of Implementing Android Based Information System on Waste Management

Waste transaction management at the Srayan Makarya waste bank is still done manually and to increase the time efficiency of transaction data reporting, researchers design and build an android-based application and test it comparatively in terms of time to see if it can improve the time efficiency of the transaction data reporting process. Researchers also evaluate the application in terms of efficiency, effectiveness and usability of the business processes that occur in the organization. And the results of the comparative test found that there was a significant time difference from the previous 8.8928 minutes to 4.2197 in the transaction data reporting process. And the results of the benefit test also obtained an efficiency value of 93.5%, effectiveness of 96.3% and usability of 92.6%.
THE ROLE OF AFFECTIVE AND COGNITIVE RESPONSE IN MEDIATING THE EFFECT OF NEW PRODUCT DESIGN ON THE BEHAVIORAL BRAND LOYALTY OF LE MINERALE DRINKING WATER

This study aims to determine the effect of Le Minerale's new drinking water product design on consumer response and consumer loyalty. This study includes causal research, namely research that finds and describes a causal relationship between the Le Minerale retailer population. The data collection technique used the observational method by distributing questionnaires. Types and sources of data using primary data, validity and reliability are used to test the questionnaire. After the data is collected, the model test and hypothesis test are carried out. SPSS was used for data analysis. The results of this study, the new product design variable has a significant positive effect on cognitive response. New product design has a significant positive effect on affective response. Cognitive response and affective response have a positive and significant effect on behavioral brand loyalty. New product design has an effect on behavioral brand loyalty through cognitive response. New product design affects behavioral brand loyalty through affective response.
Blind Deblurring of Hyperspectral Document Images

Most computer vision and machine learning-based approaches for historical document analysis are tailored to grayscale or RGB images and thus, mostly exploit their spatial information. Multispectral (MS) and hyperspectral (HS) images contain, next to the spatial information, a much richer spectral information than RGB images (usually spreading beyond the visible spectral range) that can facilitate more effective feature extraction, more accurate classification and recognition, and thus, improved analysis. Although utilization of a rich spectral information can improve historical document analysis tremendously, there are still some potential limitations of HS imagery such as camera induced noise and blur that require a carefully designed preprocessing step. Here, we propose a novel blind HS image deblurring method tailored to document images. We exploit a low-rank property of HS images (i.e., by projecting a HS image to a lower dimensional subspace) and utilize a text tailor image prior to performing a PSF estimation and deblurring of subspace components. The preliminary results show that the proposed approach gives good results over all spectral bands, removing successfully image artefacts introduced by blur and noise and significantly increasing the number of bands that can be used in further analysis.
A Meaning Apart from Its Indistinguishable Words

ABSTRACT What difference did Hawthorne make in nineteenth-century US fiction? An unanswerable question, yet one that we can begin to address in a surprisingly literal way by using word embeddings to analyze the large corpora of nineteenth-century US fiction now available. To specify this question, how does a corpus of nineteenth-century US fiction including Hawthorne differ from one wherein his work is experimentally excluded? Using this approach, I show how Hawthorne’s work changed the vector semantics of US fiction. Readers of Hawthorne will be pleased if unsurprised to find his characteristic ambivalence reaffirmed by quantitative methods. More novel is the new evidence this method provides for measuring the ways in which Hawthorne distinctively differs from his peers. Because the model reveals which words Hawthorne embedded most similarly and most dissimilarly from his contemporaries, we can take these quantitative and qualitative differences in word usage as starting-points from which to address the question that began this abstract. The article concludes with a reading of Hawthorne’s distinctive use of the word “likewise,” focusing on how it advances the satire of “The Procession of Life.”
Using Human Intelligence to Test the Impact of Popular Preprocessing Steps and Feature Extraction in the Analysis of Human Language

More than half a century has passed since Chomsky's theory of language acquisition, Green and colleagues' first natural language processor Baseball, and the Brown Corpus creation.Throughout the early decades, many believed that once computers became powerful enough, the development of A.I. systems that could understand and interact with humans using our natural languages would quickly follow.Since then, Moore's Law has basically held; computer storage and performance has kept pace with our imaginations.And yet, 60 years later, even with these dramatic advances in computer technology, we still face major challenges in using computers to understand human language.The authors suggest that these same exponential increases in computational power have led current efforts to rely too much on techniques designed to exploit raw computational power and, in so doing, efforts have been diverted from advancing and applying the theoretical study of language to the task.In support of this view, the authors provide empirical evidence exposing the limitations of techniques -such as n-gram extraction -used to pre-process language.In addition, the authors conducted an analysis comparing three leading natural-language processing question-answering systems to human performance, and found that human subjects far outperformed all question answering-systems tested.The authors conclude by advocating for efforts to discover new approaches that use computational power to support linguistic and cognitive approaches to natural language understanding, as opposed to current techniques founded on patterns of word frequency.
Four-Channel Parallel Broadband Quantum Entropy Source for True Random Number Generation at 100 Gbps

We demonstrate a spatially-multiplexed quantum entropy source with a low-loss 1×8 Silica PLC splitter. Four-channel quantum randomness with 4-GHz/ch bandwidth, based on vacuum fluctuations, is generated, and random bits at 100 Gbps are extracted offline.
The Application of Cloud Computing Service and Voicematic Identification Intelligence in the Supply Chain during the COVID-19 Pandemic

After the outbreak of the COVID-19 pandemic, cloud computing and voice recognition services have provided a more critical role in the enterprise supply chain management process. Speech emotion recognition technology can identify the content and emotion of the speaker according to the content and tone of speech. Analyzing the attitude and tone behind a statement through language and high-level information acquisition is very difficult. Cloud computing technology is becoming more widely used each day and is the cornerstone of enterprise information development. The anti-globalization caused by COVID-19 has made enterprises pay increasing attention to supply chain management. Determining how to optimize and integrate the supply chain has become an urgent problem for enterprises. Based on the work and research completed in this field, this paper first analyzes the algorithm and model used in speech recognition and then tests the system. To improve the utilization rate of enterprise resources and enterprise industrial benefits, this paper puts forward the optimization scheme of the enterprise supply chain from the perspective infrastructure construction to product development to backstage management and procurement.
Face-Based CNN on Triangular Mesh with Arbitrary Connectivity

Applying convolutional neural networks (CNNs) to triangular meshes has always been a challenging task. Because of the complex structure of the meshes, most of the existing methods apply CNNs indirectly to them, and require complex preprocessing or transformation of the meshes. In this paper, we propose a novel face-based CNN, which can be directly applied to triangular meshes with arbitrary connectivity by defining face convolution and pooling. The proposed approach takes each face of the meshes as the basic element, similar to CNNs with pixels of 2D images. First, the intrinsic features of the faces are used as the input features of the network. Second, a sort convolution operation with adjustable convolution kernel sizes is constructed to extract the face features. Third, we design an approximately uniform pooling operation by learnable face collapse, which can be applied to the meshes with arbitrary connectivity, and we directly use its inverse operation as unpooling. Extensive experiments show that the proposed approach is comparable to, or can even outperform, state-of-the-art methods in mesh classification and mesh segmentation.
Special Issue: Deep Learning and Neuro-Evolution Methods in Biomedicine and Bioinformatics

Emerging technologies in biomedicine and bioinformatics are generating an increasing amount of complex and heterogeneous data [...]
Fidelity-Restorable Universal Linear Optics and Neuromorphic Photonics

Universal linear operators realized in photonics are expected to drive the next wave of ultra-high throughput neuromorphic computing. We present integrated photonic solutions for implementing unitary and arbitrary complex matrix employed in matrix multiplication operations.
Quantum Interference between Integrated and Independently Controlled Quantum Dots

We realize a quantum photonic circuit enabling the resonant excitation of multiple quantum emitters. Using independent electrical control, we demonstrate quantum interference between two embedded quantum dots in a single device. Our result represents a crucial step towards on-chip multi-photon generation for quantum information applications.
Opportunities and challenges for deep physical neural networks based on nonlinear optical pulse propagation

We have shown that nonlinear pulse propagation can be designed to directly, all-optically implement machine learning calculations. Building from proof-of-concept experiments, we analyze opportunities for ultrafast, ultra-efficient nonlinear optical neuromorphic systems and smart sensors.
Ultrafast Switching in Integrated Photonics using Antimony

In this work, we demonstrate the use of Antimony as a reversible memory element in integrated photonics. We show antimony can be used as a multilevel memory with an ultrafast speed of operations.
Large Scale Frequency-Encoded RF-Photonic Neural Networks

Photoelectric multiplication between RF-encoded optical frequency combs allows single-shot matrix-vector multiplication and nonlinear activation, leading to high throughput and ultra-low latency. This architecture allows for an arbitrary number of layers to be cascaded in analog.
MODERN ENGLISH INTERNET DISCOURSE

The scientific article highlights the approaches to interpreting the notion of discourse. First, it considers the three most important approaches to defining this concept: formal, functional, and situational. Next, the article outlines constitutive, genre-stylistic, substantive, and formal-structural discourse categories. Finally, it analyses distinctive features of discourse, among which contextuality, personality, procedurality, theology, and the closed structure of discourse are distinguished, provided that textual information is available. Moreover, the article outlines the peculiarities of Internet discourse formation and highlights the approaches to its definition. Finally, it analyzes distinctive features of Internet discourse. It highlights its globality, the efficiency of information transfer, unlimited amount of information and possibility of its constant updating, accessibility, anonymity, visual decontextuality, interactivity, indirectness, emotionality, hypertextuality, hybridity, and equality, etc. The article proves that Internet discourse borrows the features of oral and written speech and acquires some distinctive features. In particular, Internet discourse is characterized by the lack of synchronous feedback, making it difficult to understand the perception of information by the recipient. In addition, this type of discourse is characterized by a relatively slow pace of Internet communication, complications or lack of communicative role change, lack of prosody and paralingual means, and the impossibility of live spontaneous speech as a reaction to what is heard. The article highlights the prospects for further research. It stresses the significance of studying the genre and stylistic features of Internet discourse and its structural and communicative-pragmatic features.
Instructional strategies that foster reading skills of learners with intellectual disability: A scoping review

Effective learning and classroom interaction depend on learners’ reading skills. In essence, reading is the cornerstone of academic success. Thus, functional literacy enhances learners' abilities irrespective of their intellectual abilities. Learners with intellectual disability will have an improved ability to understand and apply the information learnt to real-life situations. This scoping review aimed to examine instructional strategies adopted to foster the reading skills of learners with intellectual disabilities. A database search of relevant studies revealed 522 academic papers. Only six articles met the inclusion criteria for an in-depth analysis of studies on the use of instructional strategies to improve reading skills among learners with intellectual disabilities. The study included in this review used six strategies to improve reading skills. Furthermore, all strategies except phonics-based instruction effectively enhanced reading skills. Based on these findings, we recommend that workshops be organized for professionals providing services to learners with intellectual disabilities on the advantages of applying appropriate instructional strategies to develop reading skills among learners with intellectual disability. Moreover, larger sample sizes are required in future studies to draw generalizable conclusions.&#x0D; Key words: fostering; learners; instructional strategies; intellectual disability; reading skills
Criminal Regulations of China’s Cybercrime and Its International Cooperation

This speech is delivered at the opening ceremony of the 10th Session of the International Forum of Crime and Criminal Law in the Global Era (IFCCLGE). The speaker commenced by memorizing Professor He Bingsong, the initiator and founder of the IFCCLGE, and expressed his pleasure and gratitude to meet the experts and scholars present at the Forum. He then stated his opinions on criminal regulations on cybercrime in China and the International Cooperation as well.
Image Captioning using Neural Networks

Authors have given a thorough review of all previous work on deep image captioning models. Authors describe multiple kinds of attention mechanisms used in deep learning models for image captioning. We want to find the most effective types of algorithms in deep models for image captioning by improving an organization results from various vigilant deep models for image captioning.
Self-Supervised Augmentation and Generation for Multi-lingual Text Advertisements at Bing

Multi-lingual text advertisement generation is a critical task for international companies, such as Microsoft. Due to the lack of training data, scaling out text advertisements generation to low-resource languages is a grand challenge in the real industry setting. Although some methods transfer knowledge from rich-resource languages to low-resource languages through a pre-trained multi-lingual language model, they fail in balancing the transferability from the source language and the smooth expression in target languages. In this paper, we propose a unified Self-Supervised Augmentation and Generation (SAG) architecture to handle the multi-lingual text advertisements generation task in a real production scenario. To alleviate the problem of data scarcity, we employ multiple data augmentation strategies to synthesize training data in target languages. Moreover, a self-supervised adaptive filtering structure is developed to alleviate the impact of the noise in the augmented data. The new state-of-the-art results on a well-known benchmark verify the effectiveness and generalizability of our proposed framework, and deployment in Microsoft Bing demonstrates the superior performance of our method.
FemNet: Distilling Responsible Frames from Ultrasound Cineclips of Breast Cancer Screening Using Feature Entropy Empowered Deep Learning

Breast Cancer is the most common cancer in the world and the single leading cause of cancer mortality in women. Heavy workload and shortage of ultrasound specialists impede the penetration of breast cancer screening. To reduce the burden on sonographers and empower physicians, we propose a novel framework FemNet by integrating deep learning architecture with a newly designed technique of feature entropy minimization (FEM). FemNet is capable of auto-selecting responsible frames from ultrasound cineclips of screening and classifying breast nodules with only the few selected frames. A combination of 13702 images and 2141 cineclips from breast ultrasound exams are used to train and test the robustness of the proposed framework. In a five-fold cross-validation on cineclips set, utilizing FemNet-selected responsible frames as inputs resulted in a ROC statistically superior to using physician-selected responsible frames (0.916 ± 0.008 vs. 0.906 ± 0.019, p=0.012) . In the comparison of FemNet and physicians, FemNet could bring 7.14% improvement in specificity under Physician-BI-RADS’s (physician’s diagnosis based on BI-RADS) sensitivity level and 4.27% improvement in sensitivity under Physician-BI-RADS’s specificity level. We demonstrated that FemNet can potentially ‘handpick’ a few frames from a lengthy cineclip for breast nodule assessment, much like physicians do.Funding Information: This project was supported by Commission of Science and Technology of Shenzhen(GJHZ20200731095401004)&(JCYJ20190806151807192).Declaration of Interests: We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled.Ethics Approval Statement: Ethical approval is obtained from the Institutional Review Board of the Shenzhen People’s Hospital. The approval included the collection of data on implied consent. We only used retrospective data and the patients were not actively involved in the study. The requirement of written informed consent was waived by the Institutional Review Board.
Wireless Body Sensor Networks for Sign Language Recognition with Real-time Data Analysis

To improve the communications between the deaf and the hearers using hand-held devices, we propose a lightweight approach to quickly identify the word in American Sign Language (ASL). We acquire inertial data and muscular activity during hands movements. Then we aggregate the received data to reduce the required processing complexity and memory usage in a portable device. Afterward, we feed extracted features from aggregated data into the Support Vector Machine (SVM) to identify the associated word. Our experimental results showed that our data aggregation approach was able to enhance the recognition accuracy of the associated word when comparing the performance of SVM and Decision Tree (DT) classifiers with and without data aggregation. We conduct a performance analysis and we showed that our proposed approach is faster and able to achieve better recognition accuracy (92%) when compared with existing work.
Using Blockchain to Share Data Securely in the IntelliSense Environment

With the development of the Internet of everything, all kinds of IntelliSense-based applications are blooming. Since the perception data are always sensitive and different IntelliSense system often needs to exchange or share perception data with others in many cases, the access control mechanism is a prerequisite for sharing data across domains. Considering the IntelliSense systems are both independent and cooperative, we use a simplified consortium blockchain to construct a distributed access control framework for sharing data securely among different domains, in which all operations on the perception data are treated as transactions and the consensus/agreement is reached only by the related parties. And four transactions are designed to perform the access control.
Blockchain With Real and Sharing Economy

This paper studies the impact of blockchain on the real economy and sharing economy. Considering that the topic is relatively cutting-edge and the availability of relevant data is not high, this paper adopts a theoretical comb to show the internal logic of the impact of blockchain on the real economy. Firstly, this paper briefly combs the relationship between the real economy and blockchain, and then analyzes the positive impact of blockchain on the real economy. Finally, from the sharing economy, we will talk about how to serve the real economy with the idea of block chain. The research in this paper provides a good reference for how to learn the blockchain and its impacts.
A Quantitative Study on the Efficiency of Different EB-5 Investment Amounts from 2014-2021

This research paper assesses the efficiency of the EB-5 Investment Program before and after November 21, 2019. This research paper explicitly uses data from Regional Centers by comparing the program's cost per job and the number of investors over the past seven years. The EB-5 program has undergone many changes in the past five years, including increasing the minimum investment amount from $500,000 to $900,000. This specific edit to the program has reduced the number of investors per project and increased the cost per job. The research paper introduces the EB-5 program to a large audience, given that this program is limited to a population whose net worth is over a million dollars at least. The research paper explains the steps to achieving an EB-5 visa, introduces various regional centers, and collects data from their websites. As a result, this research paper concluded that the increase in the EB-5 investment amount had reduced the efficiency of projects because an increased amount leads to a smaller pool of investors, which in turn reduces the number of jobs per investor.