Reviewerly/exHarmony huggingface dataset:
Improving Document-Level Sentiment Analysis with User and Product Context

Past work that improves document-level sentiment analysis by encoding user and product information has been limited to considering only the text of the current review.We investigate incorporating additional review text available at the time of sentiment prediction that may prove meaningful for guiding prediction.Firstly, we incorporate all available historical review text belonging to the author of the review in question.Secondly, we investigate the inclusion of historical reviews associated with the current product (written by other users).We achieve this by explicitly storing representations of reviews written by the same user and about the same product and force the model to memorize all reviews for one particular user and product.Additionally, we drop the hierarchical architecture used in previous work to enable words in the text to directly attend to each other.Experiment results on IMDB, Yelp 2013 and Yelp 2014 datasets show improvement to state-of-the-art of more than 2 percentage points in the best case.
Modeling Local Contexts for Joint Dialogue Act Recognition and Sentiment Classification with Bi-channel Dynamic Convolutions

In this paper, we target improving the joint dialogue act recognition (DAR) and sentiment classification (SC) tasks by fully modeling the local contexts of utterances.First, we employ the dynamic convolution network (DCN) as the utterance encoder to capture the dialogue contexts.Further, we propose a novel context-aware dynamic convolution network (CDCN) to better leverage the local contexts when dynamically generating kernels.We extended our frameworks into bi-channel version (i.e., BDCN and BCDCN) under multi-task learning to achieve the joint DAR and SC.Two channels can learn their own feature representations for DAR and SC, respectively, but with latent interaction.Besides, we suggest enhancing the tasks by employing the DiaBERT language model.Our frameworks 1 obtain state-of-the-art performances against all baselines on two benchmark datasets, demonstrating the importance of modeling the local contexts.
Building a Libyan Dialect Lexicon-Based Sentiment Analysis System Using Semantic Orientation of Adjective-Adverb Combinations

Twitter is a social media network website, where its users can post their opinions and sentiments about issues, objects, services, places or people in short text messages called tweets.The sentiment information which is extracted from analyzing tweets is very useful in various aspects such as business, governments and so on.Although Arabic dialects social media sentiment analysis has attracted several studies, yet there has been almost no work on the Libyan dialect sentiment analysis.In this research, an adjective priority scoring algorithm which calculates the sentiment orientation of adjective-adverb combinations is used to build a fine-grained sentiment analysis system for classifying Libyan dialect tweets into seven categories.Therefore, we exploit a freely available Libyan dialect twitter corpus, which contains 5000 sentences or tweets to carry out our work, the tweets in the corpus were equally divided into two data sets (study and test).Adjectives and adverbs in the study data set were manually collected to construct sentiment dictionaries or lexicons.Consequently, approximately 108 adjectives were stored in a adjectives dictionary, the polarities or semantic orientation scores of these adjectives were manually assigned by two annotators in the range of [+2,-2].Likewise, each adverb of degree was scored in the range from 0 to 1 and stored them in a separate dictionary which totally contains 27 adverbs.Our system yields an F-score of 82.19% on the test data set.
Enhanced Cycle Generative Adversarial Network for Generating Face Images of Untrained Races and Ages for Age Estimation

The datasets used in recent age estimation studies largely consist of two races (i.e., Asians or Westerners), and despite the large amount of data available, the problems regarding age-class imbalances still arise, owing to different age distributions.This causes overfitting in training process, reducing the generality of the age estimation.This problem typically occurs in homogeneous datasets, e.g., using the same Asian database for training and testing or using a database with the same age range for training and testing.Consequently, the problems arise in heterogeneous datasets, e.g., using an Asian database in training and a Westerner database in testing or using databases of different age ranges in training and testing, and the accuracy inevitably degrades when heterogeneous datasets are used for training and testing.To solve these problems, we proposes an enhanced cycle generative adversarial network (CycleGAN)-based heterogeneous race and age image transformation technique, which can transform the images of one race and age range to those of different race and age range.The encoder and decoder of the generator in the proposed enhanced CycleGAN include residual connections, thereby preventing information loss as much as possible as the layer deepens.In addition, the generator of the enhanced CycleGAN uses identity loss and age loss functions between the generator-produced image and a multi-channel input image obtained through 3D one-hot encoding.Through this, the training is directed to increasing the similarity not only between the images but also between the age class labels.And the enhanced CycleGAN uses a second discriminator in addition to the existing discriminator, thereby addressing a problem in which training is not properly performed when the discriminator converges too fast relative to the generator in a conventional CycleGAN.Experiments with three open databases demonstrated that our method outperforms state-of-the-art methods for facial image transformation and age estimation.
AF-EMS Detector: Improve the Multi-Scale Detection Performance of the Anchor-Free Detector

As a precursor step for computer vision algorithms, object detection plays an important role in various practical application scenarios. With the objects to be detected becoming more complex, the problem of multi-scale object detection has attracted more and more attention, especially in the field of remote sensing detection. Early convolutional neural network detection algorithms are mostly based on artificially preset anchor-boxes to divide different regions in the image, and then obtain the prior position of the target. However, the anchor box is difficult to set reasonably and will cause a large amount of computational redundancy, which affects the generality of the detection model obtained under fixed parameters. In the past two years, anchor-free detection algorithm has achieved remarkable development in the field of detection on natural image. However, there is no sufficient research on how to deal with multi-scale detection more effectively in anchor-free framework and use these detectors on remote sensing images. In this paper, we propose a specific-attention Feature Pyramid Network (FPN) module, which is able to generate a feature pyramid, basing on the characteristics of objects with various sizes. In addition, this pyramid suits multi-scale object detection better. Besides, a scale-aware detection head is proposed which contains a multi-receptive feature fusion module and a size-based feature compensation module. The new anchor-free detector can obtain a more effective multi-scale feature expression. Experiments on challenging datasets show that our approach performs favorably against other methods in terms of the multi-scale object detection performance.
Protocol for millionaires' problem in malicious models

Secure multiparty computation is a focus of the international cryptographic community. The millionaires problem is the most important problem in secure multiparty computation and is a building block for constructing other secure multiparty computation protocols. Several solutions are available to solve this problem, but except for protocols based on garbled circuits, the existing solutions based on public key cryptosystems are only secure in semihonest models. No solution based on a public key cryptosystem is secure against malicious adversaries. This state restricts the resolution of many secure multiparty computation problems in malicious scenarios. A solution that is secure in malicious models is highly applicable in practical application scenarios and is generally appealing. Therefore, the study of the solution to the millionaires problem in a malicious model is of great theoretical and practical significance. In this work, we propose a multiparty computation protocol for the millionaires problem that is secure in a semihonest model. The proposed protocol is simple and easily understandable. We analyze the possible malicious behaviors in this protocol and use zero-knowledge proof and cut-and-choose techniques to resist possible malicious behaviors and thereby convert the protocol into one that is secure in the malicious model. We prove that the proposed protocol is secure in the malicious model by using the well-accepted ideal-real paradigm. Theoretical efficiency analysis shows that the efficiency of our protocol is at least six times that of existing protocols.
Cryptoassets as an emerging class of digital assets in the financial accounting

Introduction. Currently, there is no single, unified framework for the classification of cryptoassets. Consequently, there is no generally applied definition of neither cryptoassets, nor digital tokens, due to the variety of features and bespoke nature of the transactions in practice. The objective of this paper is to define the essence of cryptoassets in the financial accounting, identify attributes for its taxonomy and provide a multipurpose overview of cryptoassets market environment. Methods. In a comprehensive overview of cryptoassets market environment, the authors have used statistical monitoring, as well as dynamic, comparative and structural analysis. The selected sample includes daily data of cryptoassets market capitalization. Data were gathered from multiple sources at various time points during February 2016 - July 2020. Results. According to the conducted research, the countries with the largest number of registered cryptoassets exchanges are: the UK, Hong Kong, Singapore and the US, however, about 17% of exchanges still stay with unknown countries of registration. It is expected that the number of such exchanges will reduce soon, due to the adoption of certain legislative frameworks regarding cryptoassets. The authors define the essence of such terms as: cryptoassets, cryptocurrency, digital tokens and propose a cryptoassets taxonomy, based on the token’s functionality and characteristics. Four main types of cryptoassets have been identified and defined, namely: cryptocurrency (payment tokens), security tokens, utility tokens, asset-backed tokens and hybrid (or mixed) tokens. The authors suggest possible financial accounting treatment for each type of the cryptoassets. Conclusions. The cryptoassets market capitalization reacted to the factors such as global financial fluctuations due to macroeconomic factors and the COVID-19 pandemic as well as increasing digital asset regulations in early 2020. Cryptoassets remain largely a self-regulated industry and they still have no legal definition. The authors define cryptoassets as transferable digital assets recorded with a distributed ledger technology, which prohibits their copying or duplication. Due to the plethora amount of types of cryptoassets, a case-specific review should be required to determine the corresponding financial accounting treatment. The methodology of cryptoassets accounting treatment require further research.
Design, education, and the online tech-pandemic

Amidst the COVID-19, the use of technology in the learning environment was no longer a matter of choice. Forced by circumstance, educators had to adapt in order to see the academic year through. While for some, already used to an online modality, it was business as always, for others was the start of a journey through unfamiliar territory. This study inserts itself in such context. It presents and discusses results gathered through an online questionnaire about the perceptions and personal experiences of design educators in Higher Education (HE) caught in this move from in-class face-to-face onto online teaching. Objectively, it portrays how this shift impacted their ability to teach, the compromises made or alternatives sought, and views towards a more technologically enabled future in HE. From a more extensive reliance on Learning Management Systems (LMS), changes in the learning environment, and perspectives of near-future uses of Virtual Reality (VR) in distance education, this study covers uses of technology but also the identification of pain points influencing the overall experience, as well as positive perceptions and significant changes made to the learning environment.
Research and Implementation of Scheduling Strategy in Kubernetes for Computer Science Laboratory in Universities

How to design efficient scheduling strategy for different environments is a hot topic in cloud computing. In the private cloud of computer science labs in universities, there are several kinds of tasks with different resource requirements, constraints, and lifecycles such as IT infrastructure tasks, course design tasks submitted by undergraduate students, deep learning tasks and and so forth. Taking the actual needs of our laboratory as an instance, these tasks are analyzed, and scheduled respectively by different scheduling strategies. The Batch Scheduler is designed to process tasks in rush time to improve system throughput. Dynamic scheduling algorithm is proposed to tackle long-term lifecycle tasks such as deep learning tasks which are hungry for GPU resources and have dynamically changing priorities. Experiments show that the scheduling strategies proposed in this paper improve resource utilization and efficiency.
Blind Digital Watermarking Framework Based on DTCWT and NSCT for Telemedicine Application

In this work, a blind watermarking framework for medical images based on Dual-Tree Complex Wavelet Transform (DTCWT) and Non-subsampled Contourlet Transform (NSCT) is proposed. The core idea of this technique is to embed the watermark in the appropriate NSCT sub-band obtained by decomposing the cover image low coefficients purchased from the DTCWT standing on a quantization embedding function, the extraction phase is done without the requirement of the original cover image, what makes it a fully blind process. As clarity and integrity of the retrieved watermark are mandatory, a series of tests were exerted to affirm the robustness of the proposed scheme. The effectiveness of the watermarking is validated by using the peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM) through experiments. Simulation results demonstrate the invisibility of the proposed method and its strong robustness against various attacks, including additive noise, image filtering, JPEG compression, amplitude scaling, rotation attack, and combinational attack. Furthermore, the method in hands outperformance within the quantitative comparisons with other techniques in the literature in terms of rapid execution time, and quality extraction of hidden information, and appropriateness to be integrated for secure exchange in the healthcare sector.
Purification complexity without purifications

A bstract We generalize the Fubini-Study method for pure-state complexity to generic quantum states by taking Bures metric or quantum Fisher information metric (QFIM) on the space of density matrices as the complexity measure. Due to Uhlmann’s theorem, we show that the mixed-state complexity exactly equals the purification complexity measured by the Fubini-Study metric for purified states but without explicitly applying any purification. We also find the purification complexity is non-increasing under any trace-preserving quantum operations. We also study the mixed Gaussian states as an example to explicitly illustrate our conclusions for purification complexity.
Classification of the Disposition of Patients Hospitalized with COVID-19: Reading Discharge Summaries Using Natural Language Processing

Background Medical notes are a rich source of patient data; however, the nature of unstructured text has largely precluded the use of these data for large retrospective analyses. Transforming clinical text into structured data can enable large-scale research studies with electronic health records (EHR) data. Natural language processing (NLP) can be used for text information retrieval, reducing the need for labor-intensive chart review. Here we present an application of NLP to large-scale analysis of medical records at 2 large hospitals for patients hospitalized with COVID-19. Objective Our study goal was to develop an NLP pipeline to classify the discharge disposition (home, inpatient rehabilitation, skilled nursing inpatient facility [SNIF], and death) of patients hospitalized with COVID-19 based on hospital discharge summary notes. Methods Text mining and feature engineering were applied to unstructured text from hospital discharge summaries. The study included patients with COVID-19 discharged from 2 hospitals in the Boston, Massachusetts area (Massachusetts General Hospital and Brigham and Women’s Hospital) between March 10, 2020, and June 30, 2020. The data were divided into a training set (70%) and hold-out test set (30%). Discharge summaries were represented as bags-of-words consisting of single words (unigrams), bigrams, and trigrams. The number of features was reduced during training by excluding n-grams that occurred in fewer than 10% of discharge summaries, and further reduced using least absolute shrinkage and selection operator (LASSO) regularization while training a multiclass logistic regression model. Model performance was evaluated using the hold-out test set. Results The study cohort included 1737 adult patients (median age 61 [SD 18] years; 55% men; 45% White and 16% Black; 14% nonsurvivors and 61% discharged home). The model selected 179 from a vocabulary of 1056 engineered features, consisting of combinations of unigrams, bigrams, and trigrams. The top features contributing most to the classification by the model (for each outcome) were the following: “appointments specialty,” “home health,” and “home care” (home); “intubate” and “ARDS” (inpatient rehabilitation); “service” (SNIF); “brief assessment” and “covid” (death). The model achieved a micro-average area under the receiver operating characteristic curve value of 0.98 (95% CI 0.97-0.98) and average precision of 0.81 (95% CI 0.75-0.84) in the testing set for prediction of discharge disposition. Conclusions A supervised learning–based NLP approach is able to classify the discharge disposition of patients hospitalized with COVID-19. This approach has the potential to accelerate and increase the scale of research on patients’ discharge disposition that is possible with EHR data.
TSQA: Tabular Scenario Based Question Answering

Scenario-based question answering (SQA) has attracted an increasing research interest. Compared with the well-studied machine reading comprehension (MRC), SQA is a more challenging task: a scenario may contain not only a textual passage to read but also structured data like tables, i.e., tabular scenario based question answering (TSQA). AI applications of TSQA such as answering multiple-choice questions in high-school exams require synthesizing data in multiple cells and combining tables with texts and domain knowledge to infer answers. To support the study of this task, we construct GeoTSQA. This dataset contains 1k real questions contextualized by tabular scenarios in the geography domain. To solve the task, we extend state-of-the-art MRC methods with TTGen, a novel table-to-text generator. It generates sentences from variously synthesized tabular data and feeds the downstream MRC method with the most useful sentences. Its sentence ranking model fuses the information in the scenario, question, and domain knowledge. Our approach outperforms a variety of strong baseline methods on GeoTSQA.
Research on Customer Satisfaction Based on Multidimensional Analysis

Sentiment analysis has been extensively studied recently for developing methodologies to automatically extract information from online reviews, which is important for manufacturers to improve their products or services.Unfortunately, most of current studies don't take several key factors (e.g., sentiment strength, background of customer) into account.In this study, after building feature, sentiment, and degree vocabulary from online reviews using part-of-speech and word similarity analysis, a reviewfeature sentiment value (R-FSV) matrix is developed and classified by a model combining the long short-term memory and gated recurrent unit ensemble.The R-FSV matrix and online rating associated with the review are analyzed by a multivariate linear regression model to derive customer satisfaction.Since the sentiment values for each feature of the product are calculated with consideration of sentiment strength, more accurate sentiment orientation is obtained for each review, which is used to develop a set of rules to identify customer requirements based on the Kano model.By taking into account of both product perspective (e.g., product upgrade) and customer perspective (e.g., background of customers), a multidimensional analysis model is proposed to further analyze customer requirements.This sheds light on the dynamic and diversity of customer satisfaction, which help manufacturers to gain insight on not only how the customer satisfaction correlates with product improvements, but also how to develop products for particular group of customers.The proposed method is deployed to study the online reviews of mobile phones from one of the main e-commerce companies in China (i.e., JD.com).The results show that our method is capable to identify the change of customer requirements over time and the preferences of different types of users (i.e., iOS or Android).Hence, the proposed method is more effective in extracting information from the online reviews for manufacturer to improve customer satisfaction efficiently.
Solar PV System Performance Ratio Evaluation for Electric Vehicles Charging Stations in Transit Oriented Development (TOD) Areas

Transit Oriented Development (TOD) areas are locations that have limited land area. Solar PV systems are planned to be installed in these areas to support electric vehicles such as e-scooters, electric cars, motorcycles, and buses. However, solar PV systems in general require a large land area. The purpose of this paper is to find out and compare the Performance Ratios (PR) of a solar PV system installed on the rooftop with a floating solar PV system installed on the lake to determine which solar PV system fits better for TOD areas. PR analysis uses two methods, PVSyst software simulation and is validated using mathematical calculations. The result of the PR of floating solar PV is 76.39% using PVSyst simulation and 80.24% using mathematical calculation. Meanwhile, the PR of rooftop solar PV is 82.69% using PVSyst simulation and 73.41% using mathematical calculation. The significant factors that influence PR value are the energy produced by the solar PV system, its losses, and albedo value of the reflector surface for bifacial solar PV. Albedo value has to be maximized in order to obtain a higher performance ratio value. Based on this study, both rooftop and floating PV systems are equally suitable for TOD areas.