Reviewerly/exHarmony huggingface dataset:
Analysis of Topic and Sentiment Trends in Customer Reviews Before and After Covid-19 Pandemic

The Covid-19 pandemic has impacted many people’s lives. Many researches have studied the impact of the pandemic on customer opinion change regarding services, yet there are still few researches regarding the change towards products. As a product category that experienced a significant increase in sales since the pandemic began, headphones have become a suitable product category to analyze the change. To analyze the change, this paper aims to discover the topics that customers discuss in their reviews. Latent Dirichlet Allocation (LDA) is selected as the topic modeling method to obtain the topics (i.e., aspects of a product) that are discussed in the customer reviews. In the case study, six topics that are discussed by customers are discovered, i.e., Durability Issues, Usage Contexts, Noise Cancellation, Features, Quality, and Customer Service. The monthly proportion of sentences that discuss a topic provides the topic trend. Among those six topics, the discussion about the Usage Contexts topic has increased since the beginning of the pandemic, while the other topics do not show a clear trend related to the pandemic. SentiWordNet is selected as the sentiment analysis method to capture the positive and negative sentiment towards the topics. Among the six topics, the Durability Issues and Noise Cancellation topics showed an improved sentiment after the pandemic began, while the sentiment for Usage Contexts, Features, and Quality topics worsened. Future research may be suggested to explain the worsening trend for those topics, especially the Usage Contexts topic that gained significant negativity after the pandemic began.
Discovering Unknown Labels for Multi-Label Image Classification

A multi-label learning (MLL) method can simul-taneously process the instances with multiple labels, and many well-known methods have been proposed to solve various MLL-related problems. The existing MLL methods are mainly applied under the assumption of a fixed label set, i.e., the class labels are all observed for the training data. However, in many real-world applications, there may be some unknown labels outside of this set, especially for large-scale and complex datasets. In this paper, a multi-label classification model based on deep learning is proposed to discover the unknown labels for multi-label image classification. It can simultaneously predict known and unknown labels for unseen images. Besides, an attention mechanism is introduced into the model, where the attention maps of unknown labels can be used to observe the corresponding objects of an image and to get the semantic information of these unknown labels.
AdaVITS: Tiny VITS for Low Computing Resource Speaker Adaptation

Speaker adaptation in text-to-speech synthesis (TTS) is to fine-tune a pre-trained TTS model to adapt to new target speakers with limited data. While much effort has been conducted towards this task, seldom work has been performed for low computational resource scenarios due to the challenges raised by the requirement of the lightweight model and less computational complexity. In this paper, a tiny VITS-based [1] TTS model, named AdaVITS, for low computing resource speaker adaptation is proposed. To effectively reduce the parameters and computational complexity of VITS, an inverse short-time Fourier transform (iSTFT)-based wave construction decoder is proposed to replace the upsampling-based decoder which is resource-consuming in the original VITS. Besides, NanoFlow is introduced to share the density estimate across flow blocks to reduce the parameters of the prior encoder. Furthermore, to reduce the computational complexity of the textual encoder, scaled-dot attention is replaced with linear attention. To deal with the instability caused by the simplified model, we use phonetic posteriorgram (PPG) as a frame-level linguistic feature for supervising the model process from phoneme to spectrum. Experiments show that AdaVITS can generate stable and natural speech in speaker adaptation with 8. 97M model parameters and 0.72 GFlops computational complexity. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
The X-Lance Speaker Diarization System for the Conversational Short-phrase Speaker Diarization Challenge 2022

This paper describes X-Lance Speaker Diarization System submitted to the Conversational Short-phrase Speaker Diarization Challenge. The system outputs the ensemble results of the four modules: self-attentive-based VAD, uniform segmentation, ECAPA-TDNN-based embedding extractor, and spectral clustering. We evaluated our system on the Conversational Short-phrase Speaker Diarization (CSSD) dataset, which is based on MagicData-RAMC and contains plenty of conversational short-phrase segments. Besides being different from other diarization challenges, the challenge proposes a metric called Conversational Diarization Error Rate (CDER), which focuses on evaluating short segments. In this paper, we will analyze this metric and conduct extensive experiments. Finally, our system achieves CDER of 13.2% and 8.0% in the CSSD_dev and unseen CSSD_eval set, respectively.
Incorporating Accuracy and Diversity in a News Recommender System

There are certain challenges in news recommender systems that arise due to changing users’ preferences over dynamically generated news articles. It is important to expose users to a variety of information. Diversity is required in a news recommender system not only so that users do not get bored of reading similar news but because so that they do not get trapped in information bubbles. We propose a deep neural network based on a two-tower architecture that learns news representation through a news item tower and users’ representations through a query tower. To learn diversity, we introduce a category loss function that aligns items’ representation of uneven news categories. Experimental results on two news datasets reveal that our proposed architecture is more effective compared to the state-of-the-art methods and achieves a balance between accuracy and diversity.
A polyhedral study of lifted multicuts

Fundamental to many applications in data analysis are the decompositions of a graph, i.e. partitions of the node set into component-inducing subsets. One way of encoding decompositions is by multicuts, the subsets of those edges that straddle distinct components. Recently, a lifting of multicuts from a graph G=(V,E) to an augmented graph Ĝ=(V,E∪F) has been proposed in the field of image analysis, with the goal of obtaining a more expressive characterization of graph decompositions in which it is made explicit also for pairs F⊆V2∖E of non-neighboring nodes whether these are in the same or distinct components. In this work, we study in detail the polytope in RE∪F whose vertices are precisely the characteristic vectors of multicuts of Ĝ lifted from G, connecting it, in particular, to the rich body of prior work on the clique partitioning and multilinear polytope.
IoT Applications in Business and Marketing During the Coronavirus Pandemic

The internet of things (IoT) is a widely used technology in day-to-day life and in many business fields. the applications of IoT are used in many businesses and in marketing as main part of business activities, but during the coronavirus pandemic, IoT was intensively used, as a practical solution to many challenges. In this paper, the use of IoT is discussed generally in business such as in supply chain and logistics, human resources management, and in organizational performance. The paper will also tackle the use of IoT in both marketing and business activities and finally discussing some of the limitations and challenges businesses face when implementing IoT.
Predictive and Prescriptive Analytics Tools, How to Add Value to Knowledge-Based Economy: Dubai Case Study

Innovation and technology continue to be the driving force of growing economies. The need for constant change and improvement is driven by competing economies fueled by a more educated workforce and backed by proper governance structures. Dubai which has earned immense wealth in the petroleum industry is now venturing into these industries. Smart Dubai was open in 2000 as a smart city solution as an innovative initiative, large scale project for collaboration of private and public sector. These initiatives of Dubai Pulse which involves all stakeholders and entrepreneurs in accessing and sharing of data. It is having been touted for its analytical services. It will be instrumental to the future of uncovering Dubai from the veil of oil money. Dubai banks on technology and innovation particularly predictive and prescriptive analysis. The research study was conducted with the goals of analyzing the usage of both predictive and perspective analysis in the knowledge-based economy. The study also hopes to understand the value created through the use of predictive and perspective analysis in Smart Dubai and DEWA. The study raises conclusions on why the smart city project should be considered a success. Furthermore, there are areas that have been attributed to this success. The information was primarily collected through an interview with employees who heads the data analytics department at Smart Dubai and employees in the middle management level at DEWA. The feedback from the interviews were elaborate and thorough to the research being gathered. The interview was divided into three concerning knowledge-based economy, use of analytical tools and Dubai Pulse Initiative. The use of the interview arises from the fact that the subject of study needed to be informed by firsthand knowledge. However, this primary source was complemented by other secondary sources. The data from Smart Dubai and DEWA were analyzed to determine the opportunities that the use of the analytical tools will have along with the challenges and benchmarking with global successful cases. Then the recommendations part provides similar governments with the roadmap of how to implement the analytical tools and overcome the challenges.
Machine Learning Price Prediction During and Before COVID-19 and Consumer Buying Behavior

In the unprecedented situation of COVID-19, the global economy has turned upside down. This has led to sudden and unprecedented pressures on products demand and price forecasting. The study utilized regression techniques to predict product prices during and before COVID-19 using multiple influencing factors such as increase of COVID-19 positive cases on daily basis, number of deaths on a particular day, and government restrictions level. The data was gathered from worldometers website and combined with local store on sales based on the date. The results were eye opening as the product sold in the months of Mach, April, May and June 2020 were different than last year. This means the customers buying habits were totally altered due to many reasons such as job loss, wages reduction due to remote working, or promotions. Moreover, these products prices were directly proportional to increase of new COVID-19 cases, rise of daily deaths and government restriction levels imposed during the pandemic. The study uses machine learning data mining algorithms such as Logistic regression (LR), Decision Tree, Random Forest and K-Nearest Neighbor. Decision Tree and Random Forest works best in the pandemic situation to predict product price as compared to Logistic Regression and KNN. However, different outcomes were recorded when comparing the sales during pandemic and before pandemic.
Secure CoAP Application Layer Protocol for the Internet of Things Using Hermitian Curves

Internet of Things (IoT) is a modern technology that allows devices to sense status and take suitable reactions without human interference; this leads machines to interact directly with another machine, called machine to machine (m2m) communication. Many network technologies were involved in making the IoT idea work well. Devices in IoT are connected through the wireless network. Thus, different protocols on different network layers are used in IoT. One of the primary routing protocols used on the application layer is Constrained Application Protocol (CoAP) which is a new web application transfer protocol. Where it is better suited to constrained environments than HTTP for IoT. In this study, Hermitian-based Cryptosystem (HBC) is applied on the CoAP protocol in order to increase the security of IoT. The Hermitian curve is an algebraic geometric curve capable of generating a vast number of points. Simulation results are presented, showing significant performance improvements of AG codes over Hermitian codes against Reed-Solomon (RS) codes and Elliptic Curve Cryptography (ECC). This study proved that HBC outperformed the RS and ECC in terms of several points and key length, which leads to increase security.
Counterdiabatic reverse annealing

We present Counterdiabatic Reverse Annealing, a novel quantum annealing protocol that extends the range of application of reverse annealing to the previously inaccessible short-time domain. This is achieved by exploiting approximate counterdiabatic driving expanded in low-order nested commutators. In this work, we offer a comparative study of the performance of this new technique to that of unassisted reverse annealing in terms of metrics such as the ground-state fidelity and the time to solution. We provide a quantitative measure of the energetic cost of counterdiabatic reverse annealing and show that significant improvements are possible even using local counterdiabatic potentials, paving the way toward the experimental implementation in near-term quantum devices.
Effects of Social Media Marketing on Consumer Perception in Liverpool, UK

The purpose of this research is to analyze the effects of social media marketing on consumer perception for the people in Liverpool, UK. The descriptive research approach is used for this research. The research collected quantitative data and smart PLS analysis is done to analyze those collected data. It is found out from this research that the effect of social media marketing on consumer perception is positive and is increasing day by day because of the increasing use of the internet. The main limitation of this research was that the researcher was only able to work on the people of Liverpool. If the researcher would get more time and more financial support, the research could be done with vast information collected from a bigger sample size from other areas in the UK too. This research will help the companies to understand the power of social media platforms towards increasing consumer perception and it will also show them the ways to properly use the social media platforms to retain current customers and get new customers.
Augmented Reality (AR) Brand Storytelling: The Role of Flow in Attitude Formation and Associative Learning

Organizations and brands have long since used stories to communicate and resonate with their audiences. Nowadays, novel interactive media formats are used to enhance these brand-consumer interactions. Augmented reality (AR) holds the potential to aid brands in having immersive and exploratory interactions with consumers. The aim of this study is to examine the effects of AR in brand storytelling on brand attitude and brand associations, and to explore to what extent (the dimensions of) flow can explain these effects. A single factorial (Type of brand storytelling: AR vs. non-AR) between-subjects field experiment is conducted (N = 83). The results show that AR brand storytelling leads to a higher perceived flow than regular brand storytelling. Furthermore, flow mediates the effects of AR brand storytelling on both brand attitude and brand associations. Notably, the flow dimensions control and attention focus are found to be particularly important for explaining the effect on brand associations.
A prefix and attention map discrimination fusion guided attention for biomedical named entity recognition

The biomedical literature is growing rapidly, and it is increasingly important to extract meaningful information from the vast amount of literature. Biomedical named entity recognition (BioNER) is one of the key and fundamental tasks in biomedical text mining. It also acts as a primitive step for many downstream applications such as relation extraction and knowledge base completion. Therefore, the accurate identification of entities in biomedical literature has certain research value. However, this task is challenging due to the insufficiency of sequence labeling and the lack of large-scale labeled training data and domain knowledge.In this paper, we use a novel word-pair classification method, design a simple attention mechanism and propose a novel architecture to solve the research difficulties of BioNER more efficiently without leveraging any external knowledge. Specifically, we break down the limitations of sequence labeling-based approaches by predicting the relationship between word pairs. Based on this, we enhance the pre-trained model BioBERT, through the proposed prefix and attention map dscrimination fusion guided attention and propose the E-BioBERT. Our proposed attention differentiates the distribution of different heads in different layers in the BioBERT, which enriches the diversity of self-attention. Our model is superior to state-of-the-art compared models on five available datasets: BC4CHEMD, BC2GM, BC5CDR-Disease, BC5CDR-Chem, and NCBI-Disease, achieving F1-score of 92.55%, 85.45%, 87.53%, 94.16% and 90.55%, respectively.Compared with many previous various models, our method does not require additional training datasets, external knowledge, and complex training process. The experimental results on five BioNER benchmark datasets demonstrate that our model is better at mining semantic information, alleviating the problem of label inconsistency, and has higher entity recognition ability. More importantly, we analyze and demonstrate the effectiveness of our proposed attention.
Short Text Classification of Chinese with Label Information Assisting

As a common language form in oral communication, short text is hard to be used in the applications such as intent understanding, text classification and so on due to its limited content and information, as well as irregular expression and missing components. To increase the availability of short texts in real applications, we propose a Label Information Assisting-based Model (LIAM) for Chinese short text classification. In the model, we jointly use sentence-level features and word-level features to reduce text information loss. And the sentence-level features are fused with relevant label information by the Label Information Extending and Fusion (LIEF) module while the word-level features are also enhanced with assistance of relevant label information. By utilizing the text-related information from labels as extended information, the model enriches and enhances the features of short text, benefiting classification. To verify the correctness and effectiveness of the proposed method, we conduct extensive experiments on four Chinese datasets and six sub-datasets with different models. The experimental results show that LIAM presented can effectively enrich information for text and much improve the performance of short text classification. It performs much better than other methods do. What is more, the less the training set, the greater the advantages of the model.
Assessing the Early Bird Heuristic (for Predicting Project Quality)

Before researchers rush to reason across all available data or try complex methods, perhaps it is prudent to first check for simpler alternatives. Specifically, if the historical data has the most information in some small region, then perhaps a model learned from that region would suffice for the rest of the project. To support this claim, we offer a case study with 240 projects, where we find that the information in those projects “clumps” towards the earliest parts of the project. A quality prediction model learned from just the first 150 commits works as well, or better than state-of-the-art alternatives. Using just this “early bird” data, we can build models very quickly and very early in the project life cycle. Moreover, using this early bird method, we have shown that a simple model (with just a few features) generalizes to hundreds of projects. Based on this experience, we doubt that prior work on generalizing quality models may have needlessly complicated an inherently simple process. Further, prior work that focused on later-life cycle data needs to be revisited, since their conclusions were drawn from relatively uninformative regions. Replication note: All our data and scripts are available here: https://github.com/snaraya7/early-bird.
Towards Happy Housework: Scenario-Based Experience Design for a Household Cleaning Robotic System

INTRODUCTION: In the interwoven trend of the experience economy and advanced information technology, user experience becomes the substantial value of an interactive system. As one of the early innovations of a smart home, the current design of household cleaning robots is still driven by technology with a focus on pragmatic quality rather than the experiential value of a robotic system.OBJECTIVES: This paper aims to uplift the design vision of a cleaning robot from an automatic household appliance towards a meaningful robotic system engaging users in happy housework.METHODS: Theoretically, experience design and scenario-based design methods were combined into a specific design framework for domestic cleaning robotic systems. Based on the user study and technology trend analysis, we first set three experience goals (immersion, trust, and inspiration) to drive the design process, then chose 3D point cloud and AI recognition as backup technologies and afterwards extracted three main design scenarios (scanning and mapping, intelligent cleaning, and live control).RESULTS: The design features multi-view switching, a combination of animation rendering and real scene, fixed-point cleaning, map management, lens control and flexible remote, and shooting modes are proposed. Seventy-one participants evaluated the concept with online AttrakDiff questionnaires. The results indicate the targeted experience is fulfilled in the design concept.CONCLUSION: By integrating experience design and scenario-based design methods with technology trend analysis, designers can envision experiential scenarios of meaningful life and potentially expand the design opportunity space of interactive systems.
Synthetic aperture radar image despeckling neural network based on maximum a posteriori probability estimation

Automatic despeckling from synthetic aperture radar (SAR) imagery plays a significant role in many urban applications. Recently, owing to the impressive performance of deep learning, various SAR despeckling methods based on the convolutional neural network (CNN) have been proposed for optical SAR images. However, existing CNN-based despeckling methods and spatial-domain-based methods exhibit certain limitations. The introduction of prior knowledge is not considered in the designing of classical denoising and certain CNN-based methods, thus leading to the loss of edge information and artefacts in the results. To address this issue, an a priori-based deep neural network SAR image despeckling method (SAR-PBDNN) was proposed. In this approach, the Bayesian formula is modelled to calculate the objective function suitable for the despeckling problem and is optimized using the alternating direction method of multipliers algorithm and deep learning method. The introduction of prior information effectively improved the denoising effect of SAR images. The SAR-PBDNN was evaluated using both simulated and real SAR images. The comparisons demonstrate the effectiveness of the proposed method. We conclude that the SAR-PBDNN has the potential to automatically despeckle SAR images with an accuracy that renders it a useful tool for practical application scenarios.