Reviewerly/exHarmony huggingface dataset:
Proactive Prioritization of App Issues via Contrastive Learning

Mobile app stores produce a tremendous amount of data in the form of user reviews, which is a huge source of user requirements and sentiments; such reviews allow app developers to proactively address issues in their apps. However, only a small number of reviews capture common issues and sentiments which creates a need for automatically identifying prominent reviews. Unfortunately, most existing work in text ranking and popularity prediction focuses on social contexts where other signals are available, which renders such works ineffective in the context of app reviews. In this work, we propose a new framework, PPrior, that enables proactive prioritization of app issues through identifying prominent reviews (ones predicted to receive a large number of votes in a given time window). Predicting highly-voted reviews is challenging given that, unlike social posts, social network features of users are not available. Moreover, there is an issue of class imbalance, since a large number of user reviews receive little to no votes. PPrior employs a pre-trained T5 model and works in three phases. Phase one adapts the pre-trained T5 model to the user reviews data in a self-supervised fashion. In phase two, we leverage contrastive training to learn a generic and task-independent representation of user reviews. Phase three uses radius neighbors classifier t o m ake t he final predictions. This phase also uses FAISS index for scalability and efficient search. To conduct extensive experiments, we acquired a large dataset of over 2.1 million user reviews from Google Play. Our experimental results demonstrate the effectiveness of the proposed framework when compared against several state-of-the-art approaches. Moreover, the accuracy of PPrior in predicting prominent reviews is comparable to that of experienced app developers.
Ensemble of deep transfer learning models for real-time automatic detection of face mask

The COVID-19 pandemic is causing a global health crisis. Public spaces need to be safeguarded from the adverse effects of this pandemic. Wearing a facemask has become an adequate protection solution many governments adopt. Manual real-time monitoring of face mask wearing for many people is becoming a difficult task. This paper applies three heterogeneous deep transfer learning models, viz., ResNet50, Inception-v3, and VGG-16, to prepare an ensemble classification model for detecting whether a person is wearing a mask. The ensemble classification model is underlined by the concept of the weighted average technique. The proposed framework is based on two phases. An off-line phase that aims to prepare a classification model by following training-testing steps to detect and locate facemasks. Then in the second online phase, it is deployed to detect real-time faces from live videos, which are captured by a web-camera. The prepared model is compared with several state-of-the-art models. The proposed model has achieved the highest classification accuracy of 99.97%, precision of 0.997, recall of 0.997, F1-score of 0.997 and kappa coefficient 0.994. The superiority of the model over state-of-the-art compared methods is well evident from the experimental results.
Experimental measurement of quadrature squeezing in quadripartite entanglement

Multipartite entanglement is one of the most fundamental and important resources for quantum information processing in both discrete variable and continuous variable (CV) regimes. For its applications in the CV regime, such as the realization of quantum teleportation networks and quantum dense coding, the quadrature squeezing of multipartite entanglement plays a significant role. Here, we report the first, to the best of our knowledge, experimental measurement of the quadrature squeezing in the quadripartite entanglement generated by the two-beam pumped cascaded four-wave mixing process in a 85Rb vapor cell. Moreover, we find that the quadrature squeezing is nonexistent in each pair of beams, but exists in the whole quadripartite entanglement. Our results may find potential applications in building a multi-user quantum secret sharing network.
Unsupervised learning reveals interpretable latent representations for translucency perception

Humans constantly assess the appearance of materials to plan actions, such as stepping on icy roads without slipping. Visual inference of materials is important but challenging because a given material can appear dramatically different in various scenes. This problem especially stands out for translucent materials, whose appearance strongly depends on lighting, geometry, and viewpoint. Despite this, humans can still distinguish between different materials, and it remains unsolved how to systematically discover visual features pertinent to material inference from natural images. Here, we develop an unsupervised style-based image generation model to identify perceptually relevant dimensions for translucent material appearances from photographs. We find our model, with its layer-wise latent representation, can synthesize images of diverse and realistic materials. Importantly, without supervision, human-understandable scene attributes, including the object's shape, material, and body color, spontaneously emerge in the model's layer-wise latent space in a scale-specific manner. By embedding an image into the learned latent space, we can manipulate specific layers' latent code to modify the appearance of the object in the image. Specifically, we find that manipulation on the early-layers (coarse spatial scale) transforms the object's shape, while manipulation on the later-layers (fine spatial scale) modifies its body color. The middle-layers of the latent space selectively encode translucency features and manipulation of such layers coherently modifies the translucency appearance, without changing the object's shape or body color. Moreover, we find the middle-layers of the latent space can successfully predict human translucency ratings, suggesting that translucent impressions are established in mid-to-low spatial scale features. This layer-wise latent representation allows us to systematically discover perceptually relevant image features for human translucency perception. Together, our findings reveal that learning the scale-specific statistical structure of natural images might be crucial for humans to efficiently represent material properties across contexts.
Distributed Shor's algorithm

Shor's algorithm is one of the most important quantum algorithm proposed by Peter Shor [Proceedings of the 35th Annual Symposium on Foundations of Computer Science, 1994, pp. 124--134]. Shor's algorithm can factor a large integer with certain probability and costs polynomial time in the length of the input integer. The key step of Shor's algorithm is the order-finding algorithm, the quantum part of which is to estimate $s/r$, where $r$ is the ``order" and $s$ is some natural number that less than $r$. {{Shor's algorithm requires lots of qubits and a deep circuit depth, which is unaffordable for current physical devices.}} In this paper, to reduce the number of qubits required and circuit depth, we propose a quantum-classical hybrid distributed order-finding algorithm for Shor's algorithm, which combines the advantages of both quantum processing and classical processing. {{ In our distributed order-finding algorithm, we use two quantum computers with the ability of quantum teleportation separately to estimate partial bits of $s/r$.}} The measuring results will be processed through a classical algorithm to ensure the accuracy of the results. Compared with the traditional Shor's algorithm that uses multiple control qubits, our algorithm reduces nearly $L/2$ qubits for factoring an $L$-bit integer and reduces the circuit depth of each computer.
Professional Development of Rural Teachers Based on Digital Literacy

This research focuses on the professional development of teachers in rural areas based on digital literacy. It aims to measure the existing digital literacy and competence of rural teachers in Indonesia, Russia, and the Middle East and to assess the extent of digital literacy training provided to rural teachers for their professional development in these regions. In pursuit of this objective, a quantitative research method was incorporated into the study. The survey was conducted specifically for the research, and SPSS was used for statistical analysis. The report reflected the challenges that digitalization faces in rural areas and the differences in the provision of digital literacy training in Indonesia, Russia, and the Middle East. Various limitations are identified as hampering the integration of digital literacy in underdeveloped areas, such as the lack of proper digital infrastructure. However, the situation was found to be somewhat better in the case of Russia, with the teachers reporting the provision of adequate hardware and infrastructure, which were lacking in the cases of Indonesia and the Middle East. The same pattern was found in the case of the provision of digital literacy training opportunities in Indonesia and the Middle East, which lag behind Russia. All in all, it is imperative to develop introductory courses for using the internet and their general application for teachers in rural areas. Doi: 10.28991/ESJ-2022-06-06-019 Full Text: PDF
Maximally nonlinear functions over finite fields

Abstract An n -place function over a field <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:msub> <m:mrow> <m:mi mathvariant="bold">F</m:mi> </m:mrow> <m:mi>q</m:mi> </m:msub> </m:math> $ \mathbf {F}_q $ with q elements is called maximally nonlinear if it has the largest nonlinearity among all q -valued n -place functions. We show that, for even n =2, a function is maximally nonlinear if and only if its nonlinearity is <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:msup> <m:mi>q</m:mi> <m:mrow> <m:mi>n</m:mi> <m:mo>−</m:mo> <m:mn>1</m:mn> </m:mrow> </m:msup> <m:mo stretchy="false">(</m:mo> <m:mi>q</m:mi> <m:mo>−</m:mo> <m:mn>1</m:mn> <m:mo stretchy="false">)</m:mo> <m:mo>−</m:mo> <m:msup> <m:mi>q</m:mi> <m:mrow> <m:mfrac> <m:mi>n</m:mi> <m:mn>2</m:mn> </m:mfrac> <m:mo>−</m:mo> <m:mn>1</m:mn> </m:mrow> </m:msup> </m:math> $ q^{n-1}(q - 1) - q^{\frac n2-1} $ ; for n =1, the corresponding criterion for maximal nonlinearity is q − 2. For <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mi>q</m:mi> <m:mo>&gt;</m:mo> <m:mn>2</m:mn> </m:math> $ q \gt 2 $ and even n =2, we describe the set of all maximally nonlinear quadratic functions and find its cardinality. In this case, all maximally nonlinear quadratic functions are quadratic bent functions and their number is smaller than the halved number of the bent functions.
Sentiment analysis and causal learning of COVID-19 tweets prior to the rollout of vaccines

The human severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2), causing the COVID-19 disease, has continued to spread all over the world. It menacingly affects not only public health and global economics but also mental health and mood. While the impact of the COVID-19 pandemic has been widely studied, relatively fewer discussions about the sentimental reaction of the population have been available. In this article, we scrape COVID-19 related tweets on the microblogging platform, Twitter, and examine the tweets from Feb~24, 2020 to Oct~14, 2020 in four Canadian cities (Toronto, Montreal, Vancouver, and Calgary) and four U.S. cities (New York, Los Angeles, Chicago, and Seattle). Applying the Vader and NRC approaches, we evaluate the sentiment intensity scores and visualize the information over different periods of the pandemic. Sentiment scores for the tweets concerning three anti-epidemic measures, masks, vaccine, and lockdown, are computed for comparisons. The results of four Canadian cities are compared with four cities in the United States. We study the causal relationships between the infected cases, the tweet activities, and the sentiment scores of COVID-19 related tweets, by integrating the echo state network method with convergent cross-mapping. Our analysis shows that public sentiments regarding COVID-19 vary in different time periods and locations. In general, people have a positive mood about COVID-19 and masks, but negative in the topics of vaccine and lockdown. The causal inference shows that the sentiment influences people's activities on Twitter, which is also correlated to the daily number of infections.
LEXpander: Applying colexification networks to automated lexicon expansion

Recent approaches to text analysis from social media and other corpora rely on word lists to detect topics, measure meaning, or to select relevant documents. These lists are often generated by applying computational lexicon expansion methods to small, manually curated sets of seed words. Despite the wide use of this approach, we still lack an exhaustive comparative analysis of the performance of lexicon expansion methods and how they can be improved with additional linguistic data. In this work, we present LEXpander, a method for lexicon expansion that leverages novel data on colexification, i.e., semantic networks connecting words with multiple meanings according to shared senses. We evaluate LEXpander in a benchmark including widely used methods for lexicon expansion based on word embedding models and synonym networks. We find that LEXpander outperforms existing approaches in terms of both precision and the trade-off between precision and recall of generated word lists in a variety of tests. Our benchmark includes several linguistic categories, as words relating to the financial area or to the concept of friendship, and sentiment variables in English and German. We also show that the expanded word lists constitute a high-performing text analysis method in application cases to various English corpora. This way, LEXpander poses a systematic automated solution to expand short lists of words into exhaustive and accurate word lists that can closely approximate word lists generated by experts in psychology and linguistics.
Exploring the Cookieverse: A Multi-Perspective Analysis of Web Cookies

Web cookies have been the subject of many research studies over the last few years. However, most existing research does not consider multiple crucial perspectives that can influence the cookie landscape, such as the client’s location, the impact of cookie banner interaction, and from which operating system a website is being visited. In this paper, we conduct a comprehensive measurement study to analyze the cookie landscape for Tranco top-10k websites from different geographic locations and analyze multiple different perspectives. One important factor which influences cookies is the use of cookie banners. We develop a tool, BannerClick, to automatically detect, accept, and reject cookie banners with an accuracy of 99%, 97%, and 87%, respectively. We find banners to be 56% more prevalent when visiting websites from within the EU region. Moreover, we analyze the effect of banner interaction on different types of cookies (i.e., first-party, third-party, and tracking). For instance, we observe that websites send, on average, $$5.5\times $$ more third-party cookies after clicking “accept”, underlining that it is critical to interact with banners when performing Web measurements. Additionally, we analyze statistical consistency, evaluate the widespread deployment of consent management platforms, compare landing to inner pages, and assess the impact of visiting a website on a desktop compared to a mobile phone. Our study highlights that all of these factors substantially impact the cookie landscape, and thus a multi-perspective approach should be taken when performing Web measurement studies.
3D Deformable Convolution Temporal Reasoning network for action recognition

Modeling and reasoning of the interactions between multiple entities (actors and objects) are beneficial for the action recognition task. In this paper, we propose a 3D Deformable Convolution Temporal Reasoning (DCTR) network to model and reason about the latent relationship dependencies between different entities in videos. The proposed DCTR network consists of a spatial modeling module and a temporal reasoning module. The spatial modeling module uses 3D deformable convolution to capture relationship dependencies between different entities in the same frame, while the temporal reasoning module uses Conv-LSTM to reason about the changes of multiple entity relationship dependencies in the temporal dimension. Experiments on the Moments-in-Time dataset, UCF101 dataset and HMDB51 dataset demonstrate that the proposed method outperforms several state-of-the-art methods.
Distilling Privileged Knowledge for Anomalous Event Detection From Weakly Labeled Videos

Weakly supervised video anomaly detection (WS-VAD) aims to identify the snippets involving anomalous events in long untrimmed videos, with solely video-level binary labels. A typical paradigm among the existing WS-VAD methods is to employ multiple modalities as inputs, e.g., RGB, optical flow, and audio, as they can provide sufficient discriminative clues that are robust to the diverse, complicated real-world scenes. However, such a pipeline has high reliance on the availability of multiple modalities and is computationally expensive and storage demanding in processing long sequences, which limits its use in some applications. To address this dilemma, we propose a privileged knowledge distillation (KD) framework dedicated to the WS-VAD task, which can maintain the benefits of exploiting additional modalities, while avoiding the need for using multimodal data in the inference phase. We argue that the performance of the privileged KD framework mainly depends on two factors: 1) the effectiveness of the multimodal teacher network and 2) the completeness of the useful information transfer. To obtain a reliable teacher network, we propose a cross-modal interactive learning strategy and an anomaly normal discrimination loss, which target learning task-specific cross-modal features and encourage the separability of anomalous and normal representations, respectively. Furthermore, we design both representation-and logits-level distillation loss functions, which force the unimodal student network to distill abundant privileged knowledge from the well-trained multimodal teacher network, in a snippet-to-video fashion. Extensive experimental results on three public benchmarks demonstrate that the proposed privileged KD framework can train a lightweight yet effective detector, for localizing anomaly events under the supervision of video-level annotations.
Single-shot 3D measurement of highly reflective objects with deep learning

Three-dimensional (3D) measurement methods based on fringe projection profilometry (FPP) have been widely applied in industrial manufacturing. Most FPP methods adopt phase-shifting techniques and require multiple fringe images, thus having limited application in dynamic scenes. Moreover, industrial parts often have highly reflective areas leading to overexposure. In this work, a single-shot high dynamic range 3D measurement method combining FPP with deep learning is proposed. The proposed deep learning model includes two convolutional neural networks: exposure selection network (ExSNet) and fringe analysis network (FrANet). The ExSNet utilizes self-attention mechanism for enhancement of highly reflective areas leading to overexposure problem to achieve high dynamic range in single-shot 3D measurement. The FrANet consists of three modules to predict wrapped phase maps and absolute phase maps. A training strategy directly opting for best measurement accuracy is proposed. Experiments on a FPP system showed that the proposed method predicted accurate optimal exposure time under single-shot condition. A pair of moving standard spheres with overexposure was measured for quantitative evaluation. The proposed method reconstructed standard spheres over a large range of exposure level, where prediction errors for diameter were 73 µm (left) and 64 µm (right) and prediction error for center distance was 49 µm. Ablation study and comparison with other high dynamic range methods were also conducted.
On the Way to the Digital Transformation of Library and Information Activities

The article presents the review of the monograph “Transformation of library and information activities under the influence of the digital environment” (2021), prepared by the team of authors of the Kemerovo State Institute of Culture and the V.D. Fedorov State Scientific Library of Kuzbass. The review reveals the content of the monograph — the trends in the development of library and information activities in the context of digitalization; transformation of local history library services in the electronic environment; user service by means of multimedia; promotion of reading of young people in the conditions of digitalization; virtualization of methodological support in libraries. The chapters of the publication, based on the results of serious research by the authors, show the trends of transformation of library and information activities. The content of the monograph is based on the analysis of existing experience, the disclosure of the characteristic features of the problem, its solution and prospects. The book has an extensive reference apparatus. It is recommended to make the monograph more accessible to users in the digital environment.
Identification of key breast features using a neural network: applications of machine learning in the clinical setting of Plastic Surgery

Background: In plastic surgery, evaluation of breast symmetry is an important aspect of clinical practice. Computer programs have been developed for this purpose, but most of them require operator input. Artificial intelligence has been introduced into many aspects of medicine. In plastic surgery, automated neural networks for breast evaluation could improve quality of care. In this work, the authors evaluate the identification of breast features with an ad hoc trained neural network. Methods: An ad hoc convolutional neural network was developed on the YOLOV3 platform to detect key features of the breast that are commonly used in plastic surgery for symmetry evaluation. The program was trained with 200 frontal photographs of patients who underwent breast surgery and was tested on 47 frontal images of patients who underwent breast reconstruction after breast cancer surgery. Results: The program was able to detect key features in 97.74% of cases (boundaries of the breast in 94 of 94 cases, the nipple-areola complex in 94 of 94 cases, and the suprasternal notch in 41 of 47 cases). Mean time of detection was 0.52 seconds. Conclusions: The ad hoc neural network was successful in localizing key breast features, with a total detection rate of 97.74%. Neural networks and machine learning have the potential to improve the evaluation of breast symmetry in plastic surgery by automated and quick detection of features used by surgeons in practice. More studies and development are needed to further knowledge in this area.