Reviewerly/exHarmony huggingface dataset:
Fulfillment-Time-Aware Personalized Ranking for On-Demand Food Recommendation

On-demand food delivery (OFD) platforms have greatly impacted the food service industry, where OFD recommendation systems play a central role in enhancing user experience and raising revenues. OFD recommendation, compared with existing online e-commerce recommendation systems, needs to put more emphasis on fulfillment time related variables, because the order fulfillment cycle time (OFCT) which refers to the time elapsed between a user placing a food order and receiving the food significantly influences a user's choice from the recommended items. In this paper, we investigate the OFCT related information and propose a Fulfillment-Time-Aware Personalized Ranking (FTAPR) method for recommendation. FTAPR mainly consists of three components. First, Transformers are used to estimate OFCT based on a large amount of user order sequences. Then, the predicted OFCT and other OFCT related features are fused and encoded by a deep & cross network to learn fulfillment time related feature representation. At the last step, the time bias representation from the deep & cross network is integrated into the ranking system to deliver final search results. Extensive offline and online experiments on real-world datasets collected from one of China's largest OFD platforms Ele.me show the superiority of our model, e.g., an online A/B testing shows that FTAPR brings 1.3% and 2.5% gains in CTR and CVR compared with baselines.
Punctured Large Distance Codes, and Many Reed-Solomon Codes, Achieve List-Decoding Capacity.

We prove the existence of Reed-Solomon codes of any desired rate $R \in (0,1)$ that are combinatorially list-decodable up to a radius approaching $1-R$, which is the information-theoretic limit. This is established by starting with the full-length $[q,k]_q$ Reed-Solomon code over a field $\mathbb F_q$ that is polynomially larger than the desired dimension $k$, and it by including $k/R$ randomly chosen codeword positions. Our puncturing result is more general and applies to any code with large minimum distance: we show that a random rate $R$ puncturing of an $\mathbb F_q$-linear mother code whose relative distance is close enough to $1-1/q$ is list-decodable up to a radius approaching the $q$-ary list-decoding capacity bound $h_q^{-1}(1-R)$. In fact, for large $q$, or under a stronger assumption of low-bias of the mother-code, we prove that the threshold rate for list-decodability with a specific list-size (and more generally, any local property) of the random puncturing approaches that of fully random linear codes. Thus, all current (and future) list-decodability bounds shown for random linear codes extend automatically to random puncturings of any low-bias (or large alphabet) code. This can be viewed as a general derandomization result applicable to random linear codes. To obtain our conclusion about Reed-Solomon codes, we establish some hashing properties of field trace maps that allow us to reduce the list-decodability of RS codes to its associated trace (dual-BCH) code, and then apply our puncturing theorem to the latter. Our approach implies, essentially for free, optimal rate list-recoverability of punctured RS codes as well.
Frustum FusionNet: Amodal 3D Object Detection with Multi-Modal Feature Fusion

Three-dimensional (3D) object detection plays an important role in computer vision and intelligent transportation systems. The location and direction of obstacles in a road scene can be specified to provide navigation for unmanned vehicles. In this paper, we propose a novel network architecture called Frustum FusionNet (F-FusionNet), which can effectively extract and concatenate features from frustum point clouds and RGB images to generate amodal 3D object detection results. To simultaneously detect objects of different sizes, our method divides each frustum point cloud into continuous segments. Our MSE-Net module fully extracts and fuses segment-wise local features of different scales by utilizing a multi-scale sliding window and segment-wise adaptive learning fusion algorithm. Moreover, the image features are aggregated to refine the 3D object detection using F-FusionNet. For different objects, our robust method has exactly the same network architecture and parameters for practicability. Our method is evaluated on a road scene from the KITTI dataset. Extensive experiments and comparisons were conducted on the KITTI benchmark, which demonstrates the effectiveness of the proposed method.
Detection of mobile network abnormality using deep learning models on massive network measurement data

In recent years, with the continuous growth of mobile network users, the traditional methods based on expert experiences and threshold analysis find it hard to meet the need of network maintenance. These methods expose a lot of weakness, such as subjectivity, differences, and inconsistency. This paper proposes a deep learning method to make use of massive mobile data and replace the expert system. Network measurement data and pseudo labels are leveraged to train the supervised learning model, which assists in the feature selection for the deep learning model. Through extensive experiments and analysis, our method is proved to be effective in abnormality detection and achieves better performance than the traditional expert system.
Learning Sparse Binary Code for Maximum Inner Product Search

Maximum inner product search (MIPS), combined with the hashing method, has become a standard solution to similarity search problems. It often achieves an order of magnitude speedup over nearest neighbor search (NNS) under similar settings. Motivated by the work and achievements along this line, in this paper, we developed a sparse binary hashing method for MIPS to preserve the pairwise similarities with the support of two asymmetric hash functions. We proposed a simple and efficient algorithm that learns two hash functions for the query database and the search database respectively. We conducted experiments to evaluate the proposed method, relying on image retrieval tasks on four benchmark datasets. The empirical results clearly demonstrated the algorithm's promising potential on practical applications in terms of search accuracy and scalability.
Business Benefits of Instagram Scraping: Questionable Uses of Data

Nowadays, the use of social networks has become, for many people, a daily task. The publications and interactions that take place on this type of digital platforms generate a huge amount of data very valuable to companies. In fact, due to the results of different analyses of this information, marketers have turned Instagram into one of the most used media for marketing campaigns nowadays. However, the tasks of obtaining and storing this data sometimes involve complications and restrictions that make it difficult or impossible to analyze the information generated on social networks. For this reason, information retrieval techniques such as web scraping are used. This paper proposes a system for data mining in Instagram and presents a real scenario of obtaining and exploiting this information in the business environment. The main objective of the paper is to analyze the value that the use of scraping in social networks brings to the company and to consider the ethical implications of it.
Theoretical Substantiation for the Model of Civic Identity Formation in the Process of Foreign Language Teaching at a Cadet School

The study aims to model the process of cadets’ civic identity formation within the framework of the “Foreign Language” student course. The paper considers features of cadets’ self-identification through mastering the processes of their own activities, acquiring social and civic functions in the school environment. The specificity of pedagogical work on pupils’ civic identity formation as an educational result, based, when it comes to a military institution, on creating the “image” of a future member of the military as a citizen and patriot was determined. The author identified and described the structural components of the proposed model, the testing of which is planned to be carried out at the Tyumen Presidential Cadet School in order to improve and adjust it for its further transfer to general practice and adaptation to other types of schools. Scientific novelty of the study lies in the fact that the author proposes and substantiates a system of pedagogical mechanisms for cadets’ civic identity formation in the socio-cultural environment of a cadet school within the framework of the “Foreign Language” student course. As a result, the model for cadets’ civic identity formation has been developed, its structure has been described.
Timing-Based Browsing Privacy Vulnerabilities Via Site Isolation

Chromium’s site isolation ensures that different sites are rendered by different processes, which is a vision that academic researchers set forth over a decade ago. The journey from academic prototypes to the commercial availability represents a holistic rethinking about the security architecture for modern browsers. In this paper, we emphasize that the timing issues under site isolation need a thorough study. Specifically, we show that site isolation enables a realistic timing attack, which allows the attacker to identify which websites in a given target-sites set are loaded into the browser, as well as the website the user is currently interacting with. Through these vulnerabilities, the user’s site-visit behavior is leaked to the attacker. Our evaluation using Alexa Top 3000 websites gives very high vulnerability percentages – 99%, 99% and 95% for our three key metrics of vulnerabilities. Moreover, the attack is very robust without any special assumption, so will be effective if deployed in the field. The main challenge revealed by our work is the tension between the scarcity of processes and the obligation to isolate cross-site frames in different processes. We are working with the Google Chrome team and Microsoft Edge team to propose and evaluate mitigation options.
Cyberbullying in e-Learning platform: a text mining analytics

The adoption and use of Learning Management System (LMS) in Higher Education Institutions (HEIs) have become inevitable for teaching and learning process. It facilitates communications between teachers and students, as well as among students, regardless of physical barriers and time. However, there is also a dark side to the online environment, given more and more adolescents admit they have been the victims or bystanders of cyberbullying. In order to ensure that the online learning environment is safe for everyone, it is critical to assess the extent of the conversation carried out through the platform so as to ensure that no such misconduct is carried out via LMS. This paper also aimed to propose a practical solution to combat cyberbullying in an educational platform, and shed some lights on the potential applications of text mining techniques in educational research.
A Comparative Study: Performance and Optimization of Artificial Neural Networks in Blood Cell Datasets

Abstract Artificial Neural Networks (ANN) generally referred to as Neural Networks are the statistical model, It is mainly focused on the biological neuron for the information or signal processing. A neural network is a dynamic system consisting of a community of interconnected neurons that offers a very exciting solution to complex problem solving and other applications that can play a crucial role in today’s technology. In this paper we are using various process like pattern recognition, prediction and optimization of blood cells is usually undertaken to help clinicians determine a wide variety of haematics. Pathologies identify dengue, anemia, diabetes and other related diseases.
Estimation of global visibility for low-quality fringe pattern using Fourier-polar transform

When the interferometers with adjustable fringe visibility is used for high precision wavefront measurement, it needs to estimate the visibility of experimental patterns so as to obtain the interference patterns with the highest visibility. A new method combining Fourier-polar transform with directional projection is developed to estimate the global visibility of carrier fringe pattern. This method can also be applied to low-quality fringe patterns such as low contrast and low signal-to-noise ratio (SNR) that often appear in the interferometer testing. Theoretical analysis shows that this method is obviously superior to the direct calculation method. In addition, an illustrative experiment based on the radial shearing interferometer is given. Results generated from this method are compared with the derived values from theoretical model of experimental system, and exemplary agreement between both is demonstrated.
Recommendation System Based on Semantic Web

The rapid development of the Internet has greatly facilitated people’s work and life, but at the same time, the information overload has also made people feel at a loss in the face of mixed data. The search engine and recommendation system created for this can help users filter and filter what they need. Information, but as people’s requirements for personalized services are getting higher and higher, traditional recommendation methods cannot meet more precise needs. Therefore, this article comprehensively explains the new recommendation system that introduces the concept of the Semantic Web, and introduces its origin, implementation, and finally, the successful application of semantic recommendation system in four different fields is cited.
Minimizing Wasserstein-1 Distance by Quantile Regression for GANs Model

In recent years, Generative Adversarial Nets (GANs) as a kind of deep generative model has become a research focus. As a representative work of GANs model, Wasserstein GAN involves earth moving distance which can be applied to un-overlapped probability distributions, and promoted the stability of model training. However, the alternative optimization of the discriminative network in the Wasserstein GAN model makes it impossible to accurately calculate the Wasserstein-1 distance between the real data distribution and generated data distribution. It may result in unstable training sometimes. So we propose minimizing the Wasseerstain-1 distance by Quantile Regression algorithm which works well on minimizing the Wasserstein-1 distance between the real data score distribution and the generated data score distribution. We named our method QR-GAN. QR-GAN involves the information about data distribution instead of distinguishing data between fake and real in original GANs model. Meanwhile, QR-GAN model adds the real data information to the updating of generator network, which makes the parameter updating of generator more effective. In order to verify the effectiveness of our method, we perform experiments on MNIST, CIFAR-10, STL-10, LSUN-Tower and the results demonstrate superiority of our method.
Atlastic Reputation AI: Four Years of Advancing and Applying a SOTA NLP Classifier

We report on the main challenges we had to overcome while bootstrapping, commercializing and scaling our automated sentiment analysis solution in the media intelligence industry. Over the span of four years, our solution evolved from a run-of-the-mill component to a bespoke in-house bred flagship feature that, to our knowledge, trumps those of our industry peers. From all the learnings to date, the most significant one has been shifting our data labeling efforts from a third party to a completely in-house controlled process &#x2014; a move that brought us noticeable improvements in inferencing quality.
Bayesian Independence Test with Mixed-type Variables

A fundamental task in AI is to assess (in)dependence between mixed-type variables (text, image, sound). We propose a Bayesian kernelised correlation test of (in)dependence using a Dirichlet process model. The new measure of (in)dependence allows us to answer some fundamental questions: Based on data, are (mixed-type) variables independent? How likely is dependence/independence to hold? How high is the probability that two mixed-type variables are more than just weakly dependent? We theoretically show the properties of the approach, as well as algorithms for fast computation with it. We empirically demonstrate the effectiveness of the proposed method by analysing its performance and by comparing it with other frequentist and Bayesian approaches on a range of datasets and tasks with mixed-type variables.
Feeding a Gazetteer

We propose a workflow for retrieving place names from a corpus of Hebrew historical newspapers. We show that using an initial curated set of unambiguous toponyms and vector similarity is a productive method to populate a gazetteer with previously unknown variant names of known places, as well as names of places which are not yet included in the Gazetteer. We examine several parameters for enhancing accuracy and suggest a workflow that combines computation with human expertise, and is valuable to spatial history as well as to other domains.
Virtual Guia Fortress: A 3D-printed eXtended Reality Playset

This paper reports a work-in-progress that explores the use of 3D-printing for marker-based Tangible User Interfaces for use with smartphone-based eXtended Reality experiences. We have explored several ways to 3D-print matrix markers that are re-configurable so that the underlying values can change with user interaction. We have also applied 3D-printed markers to the development of a modular playset for exploring the Guia Fortress cultural heritage site in Macau SAR, China. We report some of the explorations of 3D-printed markers and describe the Virtual Guia Fortress playset.
Improving The Seventh Grade Students' Reading Comprehension in Narrative Texts by Using Comic Strip at SMP N 6 Pupuan

IMPROVING THE SEVENTH GRADE STUDENTS’ READING COMPREHENSION IN NARRATIVE TEXTS BY USING COMIC STRIPS AT SMP N 6 PUPUAN Belinda Fingki Seldi Putri, NIM 15212006 English Education Department, IKIP Saraswatie-mail: belindafingkiseldiputri@gmail.com ABSTRACT The research aimed at improving students’ reading comprehension of narrative text through comic strips. The research subjects were the tenth grade students of SMP N 6 Pupuan in the academic year 2019/2020 consisting of 30 students, 14 students are male and 16 students are female. This research was conducted by using Classroom Action Research (CAR). The technique of data analysis was qualitative and quantitative data using observation, study documentation, tests, and questionnaire. Comic strips used as an interested way to improve students reading comprehension on the material of narrative text. By using narrative-based comics based on learning technology, is expected students are able to understand the learning, and students can learn independently, easily, fun and achieve learning goals. This research was accomplished in two cycles. Each cycles consisted of four steps. They are planning, action, observation and reflection. Before the cycle I was applied, the researcher held a pre-test first to determine the ability of students’ before the implementation of comic strips in student learning. The first cycle consisted of two meetings including giving material and post-test I. The second cycle consisted of two meetings including giving material and post-test II. Before using comic strips, the students’ comprehension in reading narrative text was low. It can be seen from the result of the score. The mean score of pre-test was 60, the mean score of post-test I increased to 69, and the mean score of post-test II reached until 80. The students who passed the Minimum Mastery Criterion/Kriteria Ketuntasan Minimal (KKM) only 9 students in pre-test, became 15 students in post-test I, and increased up to 30 students in post-test II. Based on the result of the research, it could be concluded that teaching reading narrative text by using comic strips could improve the students’ reading comprehension on narrative text. Keywords : Reading, Reading Comprehension, Narrative Text, and Comic Strips
Visual Analysis of the Research Status of Intelligent Question Answering System

As a new type of question retrieval method, intelligent question answering can provide users with answers to the questions they need in a short time. The rapid development of the Internet makes the emergence of intelligent question answering systems inevitable and lays the foundation for their extensive application in various fields. This article uses Citespace to visually analyze more than 500 academic papers in the field of intelligent question and answer from 2010 to 2020 included in Web of Science and IEEE access, including the distribution of countries, institutions, and authors, as well as keyword and research topic clustering, etc., in order to obtain the Field research hotspots and future development trends. On this basis, it focuses on the summary of intelligent question answering based on knowledge graph and intelligent question answering with sentiment analysis, providing a reference for the close integration of intelligent question answering with knowledge graph and sentiment analysis.
Asymmetric Mutual Learning for Unsupervised Cross-Domain Person Re-identification

Unsupervised domain adaptation in person re-identification is a challenging task. The performance of models trained on a specific domain generally degrades significantly on other domains due to the domain gaps. State-of-the-art clustering-based cross-domain methods inevitably introduce noisy labels. The negative effects of noisy labels gradually accumulate during iterative training. Besides, optimizing with conventional triplet loss could make the model stuck in local optima in the late stage of domain adaptation. To mitigate the effects of noisy labels, this paper proposes an asymmetric mutual learning framework which cooperates two models with asymmetric labels. The learned asymmetric information is helpful for the two models to complement with each other. Specifically, we propose a merging clusters algorithm to generate asymmetric labels. We also introduce a similarity weighted loss which can further adapt the model to target domain. Extensive experiments demonstrate that our approach outperforms the state-of-the-art methods on three popular person re-identification datasets.
Construction of University Financial Information Platform under Cloud Computing Environment

With the continuous development of “Internet + ”, big data, cloud computing and other intelligent scientific and technological concepts, it brings opportunity and challenge to the construction of university informatization. Information transmission will determine the decision-making, so the construction of universities financial information is an urgent matter. At the same time, with the introduction of the government accounting system, higher requirements have been put up for the accounting of administrative institutions. At present, the system of financial accounting in colleges and universities in China is still not perfect enough, the information platform construction still has some shortages, however, the university financial accounting check and calculates is the important constituent of university development, strengthening and consummating the university finance is imminent in the information construction. On the basis of analyzing the current situation and problems of college informatization, this paper puts forward the theoretical framework of financial informatization sharing platform based on cloud computing environment to meet the needs of college teaching, scientific research and management, some thoughts on the financial transformation of colleges and universities in the later period.