Reviewerly/exHarmony huggingface dataset:
Personalized Recommendation System Design for an Online B2B Platform

We formulate the problem of designing a personalized recommendation system for an online business-to-business (B2B) marketplace, propose a method to solve it, and evaluate results using field experiments. In this problem, buyers place requests for quotation (RFQs) to the platform, sellers respond by accepting or declining those RFQs, and the objective of the platform is to design a recommendation system by matching RFQs with sellers based on likelihood of acceptance. Our research is conducted in collaboration with IndiaMart, the dominant online B2B platform in India serving approximately 119 million buyer firms and 6.4 million seller firms in more than 71 million products and services. The main challenges in our problem are short timespan of RFQs, high-dimensional, sparse, and heterogeneous transaction data, and class imbalance such that the volume of `accepted' records is significantly larger than `declined' records.We construct an attribute-based logit classifier to solve this problem, apply feature engineering to address high-dimensionality, and propose a new resampling approach, Panel Data Augmentation Technique, to counter class imbalance. To synthesize the trade-off between canonical predictive metrics and determine the optimal resampling strategy, we propose a novel aggregate measure based on operational costs. Our results demonstrate a high out-of-sample predictive accuracy and a significant gain in the recall for minority class in offline testing. Two controlled field experiments conducted at IndiaMart show that our methods lead to significant improvements in the top-5, top-10, and top-25 contributions from the recommendations page. Our method has been adopted by the platform for all their customers.
Adaptively Fusing Complete Multi-resolution Features for Human Pose Estimation

Multi-resolution features are important for image-based human pose estimation. In this paper, we present a method to exploit complete information from feature maps of neural network in different resolutions to improve the accuracy of human pose estimation. The proposal, namely Adaptively Complete Multi-Resolution Feature Fusion (AdaCMRFF), is based on a high-resolution network (HRNet). AdaCMRFF fuses all feature maps based on the adaptive parameters which can preserve useful information of different resolution feature maps when fusing into a specific resolution feature map. Firstly, different resolution feature maps are resized to the same shape by sampling and convolution strategies. The fused weight parameters are then generated through 1 \(\times \) 1 convolutions and softmax function which operate on above feature maps. Finally, the feature maps and fused parameters are added to make a new feature map. AdaCMRFF is equipped on all the stages of HRNet to retain handy information of all the feature maps. A series of experiments are conducted on two mainstream human pose estimation datasets, includes COCO2017 and CrowdPose dataset present the effect of the proposed method.
Research on the Design of Primary School English Learning Resources Based on Cognitive Model

Attention is one of the key cognitive factors that affect English learning. This paper analyzes the distribution and transfer of learners’ attention in the process of English learning by using eye movements data, and explores the design principles of English learning resources. According to different English learning characteristics, three sets of learning resources are designed. Through the analysis of eye movements data, the design principles of picture learning resources and audio-visual multi-mode learning resources are obtained. Picture learning resources should emphasize the corresponding relationship between pictures and English vocabulary, so as to help learners establish and improve the second language system. The pictures in the learning resources should help learners form the overall cognition of English, rather than establish the corresponding Chinese meaning in English. Audio-visual multi-mode learning resources have a positive effect on the improvement of learners’ English learning ability, but they need to be designed according to different English learning features. At the same time, the coordination of visual and auditory stimuli needs to be changed according to learners’ learning ability.
MODERN TECHNOLOGIES FOR TEACHING GEOMETRY IN CONDITIONS OF DISTANCE LEARNING

The article is devoted to the use of web quests in geometry lessons, the possibilities of introducing this educational technology into educational process are revealed in order to improve quality of educational services. The advantages of using web quests in educational process are considered. An example of a geometry web quest for grade 7 students is provided.
A Position-Sensitive Regression Network for Multi-Oriented Scene Text Detection

Over the past few years, scene text detection has attracted significant attention, and numerous efficient multi-oriented scene text detectors have emerged. However, there still exist challenges while detecting the long text instance due to the limitation of the receptive field. In this work, we propose a position-sensitive regression network, which predicts three scores of each pixel according to whose relative position in the text instance, representing text core, head, and tail, respectively. Only the pixel belongs to text head or tail is used for predicting the vertex coordinates offset to form the text bounding boxes, which is able to improve the accuracy of detecting the long text instance. In post-processing, we propose a text line reconstruction module (TLRM) to alleviate the effect of missing prediction of the head or tail of the text instance. Experiments on ICDAR 2015 and MSRA-TD500 verified the effectiveness of our method. Our proposed method achieves an F-score of 0.828 and a precision rate of 0.854 on ICDAR 2015 dataset.
Basic Ensemble Learning of Encoder Representations from Transformer for Disaster-mentioning Tweets Classification

Ensemble learning is a system which used to train multiple learning models and combine their results, treating them as a “committee” of decision makers. To explore effect of ensemble learning, this paper applied two basic ensemble systems of encoder to natural language processing. To compare the individual models and ensemble systems, this paper varied the number models which used to calculate ensemble accuracies. The result is that the decision of the model, with all models combined, usually have better overall accuracy, on average, than any single model. It shown that ensemble system used all models usually have better performance. This paper given explanation in the conclusion section of this result.
Power-law mixtures of bayesian forests for value added tax audit case selection

Tax authorities need to maximize the yield of the limited tax audits they afford to perform each year. Thus, they need to predict the likelihood of a candidate audit resulting in a satisfactory yield; this predictive process is usually referred to as audit case selection. Random Forests (RFs) constitute a standard method for Value Added Tax (VAT) audit case selection. Despite, though, their success, their predictive performance is still below the expectations of tax authorities, that need to timely detect cases of significant audit yield potential. This lackluster performance is mainly attributed to the fact that RFs cannot deal with data that entail non-stationary nature, multiple modalities, or discontinuities. These are common characteristics of real-world datasets; thus, the incapacity to properly address them is a major suspect for undermining their performance. This work addresses these issues by considering a generative non-parametric Bayesian model with power-law behavior, capable of generating distinct (Bayesian) RFs over the observations space of the modeled data. This way, our approach enables capturing an indefinite number of distinct classification patterns, while being able to effectively handle outliers. The latter advantage is of paramount importance for the effectiveness of the modeling procedure in cases where few large parts of the observations space can be modeled by few RF classifiers, yet there is a large number of small parts of the observations space that require distinct RFs to be properly modeled (power-law nature). We provide an efficient algorithm for model inference, based on the variational Bayesian framework, and prove its efficacy using real-world datasets.
Supersingular Isogeny-based Ring Signature

The increasing demand for secure and anonymous transactions raises the popularity of ring signatures, which is a digital signature scheme that allows identifying a group of possible signers without revealing the identity of the actual signer. This paper presents efficient supersingular isogeny-based ring signature and linkable ring signature schemes that will find potential applications in post-quantum technologies. We develop the ring signature scheme by applying the Fiat-Shamir transform on the sigma protocol for a ring which we obtain from the supersingular isogeny-based interactive zero-knowledge identification scheme by adopting the scheme for a ring. We also extend our ring signature protocol with an additional parameter, i.e., a tag that provides to detect if a signer issues two signatures concerning the same ring by preserving anonymity and linkable anonymity. The signature size of our ring signature protocols increases logarithmically in the size of the ring thanks to the Merkle trees. We show the security proofs and efficiency analyses of the protocols offered. Moreover, we provide the implementation results of the supersingular isogeny-based ring signature, which offers small signature sizes for NIST post-quantum security levels.
Smart Device to Detect Social Distancing Violations during COVID-19 Pandemic

The COVID-19 pandemic has increased the risk of contracting a deadly virus in public places such as malls, streets, or local shops. These essential places have proven to be hotspots for the dissemination of the coronavirus. One of the most efficient ways to curb the spread is to maintain social distancing. Currently, due to the lack of strict supervision, social distancing is not being followed. The paper provides a solution to implement a smart device meant to detect social distancing violations in public areas. The paper also compares three detection approaches, namely the You Only Look Once (YOLO) approach and its variants, the Histogram of Oriented Gradients – Support Vector Machine (HOG-SVM) approach, and the top view camera angle approach to detect these violations. The most practical approach, i.e., the YOLOv3-tiny, was loaded onto the Raspberry Pi to make a fully automated device. The paper also addresses the design flow, the working of the different approaches, and the future scope in advancements.
PSTNet: Crowd Flow Prediction by Pyramidal Spatio-Temporal Network

Crowd flow prediction in high density urban scenes is involved in a wide range of intelligent transportation and smart city applications, and it has become a significant topic in urban computing. In this letter, a CNN-based framework called Pyramidal Spatio-Temporal Network (PSTNet) for crowd flow prediction is proposed. Spatial encoding is employed for spatial representation of external factors, while prior pyramid enhances feature dependence of spatial scale distances and temporal spans, after that, post pyramid is proposed to fuse the heterogeneous spatio-temporal features of multiple scales. Experimental results based on TaxiBJ and MobileBJ demonstrate that proposed PSTNet outperforms the state-of-the-art methods.
Towards a Blended Programme for Arabic and Other Less Commonly Taught Languages (LCTLs) in the South African Higher Education Context

Disruptive technologies are widely used in education today. They aim to develop the knowledge, skills, and competencies of students. The field of applied linguistics, in general, and foreign language teaching, in particular, have benefited immensely from the developments taking place in computer-assisted language learning (CALL) and mobile-assisted language learning (MALL). However, meaningful learning cannot be achieved by using technology indiscriminately; an understanding of educational theories and key instructional design models is urgently required. The present study argues that the adoption of established instructional design models will yield effective learning materials not only for the less commonly taught languages (LCTLs) but also for language classrooms in general. It investigates the use of ADDIE instructional design model for designing and developing a blended syllabus for teaching Arabic as a foreign language in South African institutions of higher learning. The study also deals with the attitudes of the students towards the designed blended syllabus. The proposed syllabus is based on a wide range of web-based tools and e-learning specifications such as Learning Tools Interoperability (LTI) and Shareable Content Object Reference Model (SCORM). This study serves as a guideline for developing instructional materials for teaching Arabic, as well as other languages.
Reduced-Reference 3D Image Quality Measurement via Spatial to Gradient Domain Feature Aggregation

Objective quality measurement of a three-dimensional (3D) image is a challenging issue in various 3D visual applications. In this paper, we proposes a novel reduced-reference (RR) 3D quality assessment evaluator to deal with the characteristics of 3D images. Specifically, in spatial domain, the generalized Gaussian density (GGD) fits of luminance wavelet coefficients, and the correlations of luminance and disparity wavelet coefficients are used to represent the statistical characteristics of 3D image. Furthermore, in gradient domain, the enhanced gradient magnitudes are computed by using neighborhood phase congruency (PC) information to weight the gradient magnitudes (GM) in a locally adaptive manner. Afterward, the entropy differencing of discrete wavelet transform coefficients of enhanced gradient magnitudes are extracted as the perceptual features of HVS. Finally, we combined the quality indexes of both the statistical characteristics of 3D image and the perceptual properties of HVS to yield 3D image quality index. Experiments are performed on published 3D image quality assessment database show that our proposed R3DQAE model achieves highly competitive performance as compared with the state-of-the art some typical full-reference (FR) and RR models.
Bayesian Optimization for Backpropagation in Monte-Carlo Tree Search

In large domains, Monte-Carlo tree search (MCTS) is required to estimate the values of the states as efficiently and accurately as possible. However, the standard update rule in backpropagation assumes a stationary distribution for the returns, and particularly in min-max trees, convergence to the true value can be slow because of averaging. We present two methods, Softmax MCTS and Monotone MCTS, which generalize previous attempts to improve upon the backpropagation strategy. We demonstrate that both methods reduce to finding optimal monotone functions, which we do so by performing Bayesian optimization with a Gaussian process (GP) prior. We conduct experiments on computer Go, where the returns are given by a deep value neural network, and show that our proposed framework outperforms previous methods.
Attention-based Dynamic Filters for Anchor-free Instance Segmentation

The convolution operation is the core of convolutional neural networks (CNNs). To make CNNs more efficient, existing works construct multi-scale representation by utilizing different filter sizes or expanding filter sizes with dilated convolutions. However, these filters have fixed parameters after training so that they are not adaptive to the input image during inference. To address this issue, we propose an attention-based dynamic filter, which is a novel design that adaptively generates filters based on image contents. We apply the proposed dynamic filter to the mask branch, named Attention-based Adaptive Context-guided mask (ACG-Mask) branch, which is added to anchor-free one-stage object detector (FCOS). Besides, we design a multi-scale head, which contains an improved Receptive Field Block (iRFB) to enhance the discriminability and robustness of the feature. We name our model as Attention-based Dynamic Filters for anchor-free Instance Segmentation (ADFInst). Extensive experiments on the fine-annotation Cityscapes and COCO datasets reveal the effectiveness of the proposed method. ADFInst achieves a new record 37.9% AP and 63.3% AP50 on the fine-annotation Cityscapes dataset and achieves 37.8% AP, 58.7% AP50, and 40.5% AP75 on COCO dataset.
Fault Diagnosis Expert System for Top Drive System Based on FTA

Top driver system (TDS) is a complex structure of mechanical, electrical and hydraulic integration drilling equipment, which is a high-tech equipment in modern drilling equipment. However, TDS in the process of drilling operation will inevitably have faults, which has high requirements for maintenance personnel. Shortening maintenance cycle, reducing cost and improving operation efficiency have become an important problem for engineering and technical personnel. Based on fault tree analysis (FTA), a fault diagnosis expert system for TDS was designed. According to FTA, the expert knowledge base was established, and the design of the inference engine was realized according to the production rules in the knowledge base and the forward reasoning strategies. Finally, Visual Studio 2015 and SQL Server 2012 were used to develop this expert system, which can effectively diagnose the fault of TDS and has good convenience and practicability.
The Post-Industrial Turn

Managing the technology turn of the post-industrial era is the focal point for profit generation. Turn is a short name for a radical change meant to last long. Industrial turns characterise paradigm shifts concerning the way goods emerge in the market. We are in the middle of an industrial turn enabled by technology. Nothing new here since technology was always responsible for changes, in general, within society. Although it is not society that drives technology, it is the society ordered by technology to use it to create more value for itself and, by doing so, motivates the development of further technology that recursively reorders society. Technology and, in general, innovations are not tools but media that release new possibilities within industrial Enterprises. New opportunities are related to how products are defined, designed, produced, sold and distributed, and how all these activities may complement each other to increase profit-facilitating complementarities. Under these premises, technology and especially information technology, mainly artificial intelligence, overturns industrial production in the fourth industrial revolution. Digitisation does not create profits automatically, but digitisation may create new possibilities for profit-making. The question addressed in this chapter is how to do it. The significant distinction between intelligence and information is that information in-forms, but to form, one needs to know how and what to include, transform and complement.
Lockable Obfuscation from Circularly Insecure Fully Homomorphic Encryption

In a lockable obfuscation scheme, a party called the obfuscator takes as input a circuit $$C$$ , a lock value y, and a message m, and outputs an obfuscated circuit. Given the obfuscated circuit, an evaluator can run it on an input x and learn the message if $$C(x) = y$$ . For security, we require that the obfuscation reveals no information on the circuit as long as the lock y has high entropy even given the circuit $$C$$ . The only known constructions of lockable obfuscation schemes require indistinguishability obfuscation ( $$i\mathcal {O}$$ ) or the learning with errors (LWE) assumption. Furthermore, in terms of technique, all known constructions, excluding $$i\mathcal {O}$$ -based, are build from provably secure variations of graph-induced multilinear maps. We show a generic construction of a lockable obfuscation scheme built from a (leveled) fully homomorphic encryption scheme that is circularly insecure. Specifically, we need a fully homomorphic encryption scheme that is secure under chosen-plaintext attack (IND-CPA) but for which there is an efficient cycle tester that can detect encrypted key cycles. Our finding sheds new light on how to construct lockable obfuscation schemes and shows why cycle tester constructions were helpful in the design of lockable obfuscation schemes. One of the many use cases for lockable obfuscation schemes are constructions for IND-CPA secure but circularly insecure encryption schemes. Our work shows that there is a connection in both ways between circular insecure encryption and lockable obfuscation.
BSLKT: A Bagging Model with Self-Attention and LightGBM for Knowledge Tracing

Knowledge tracing(KT) is a hot topic in computer-supported education. It’s a task that can model a sequences of learning activities that students engage with, so as to trace the students’ continuously developing state of knowledge. One important purpose of knowledge tracing is that it can evaluate students’ mastery of knowledge concepts(KCs) according to their performance in learning activities, in order to customize personalized learning plans for students to help them get better learning plans. In recent years, many knowledge tracing methods have been proposed, which can be divided into Recurrent Neural Networks(RNN) based methods and self-attention based methods. Methods based on RNN include Deep Knowledge Tracing(DKT), Dynamic Key-Value Memory Network(DKVMN), etc. These RNN methods have better performance than traditional knowledge tracing methods. However, they are not suitable for dealing with sparse data, that is, situations where students interact with few KCs. The Self-Attention model for Knowledge Tracing(SAKT) uses the self-attention mechanism, which can identify the KCs from the learning activities that students participated in and are related to a given KC, so as to predict the mastery of the given KC. Therefore, SAKT can deal with the sparse data problem that the RNN methods are not suitable for processing. LightGBM is a gradient boosting framework that uses a tree based learning algorithm and is one of the current mainstream machine learning algorithms. In order to make the prediction more accurate, we propose the BSLKT model, which fuses the SAKT model and the LightGBM model in bagging manner. In addition, we performed feature engineering preprocessing on the experiment dataset. The experiment compared the accuracy of SAKT model, LightGBM model and the BSLKT model and the results show that BSLKT can achieve better prediction performance.
Improvement in SHA-3 Algorithm Using Different Internal Methods and Operations

In this paper, we propose a novel approach to change the internal architecture of schoolbook implementation of SHA-3 algorithm. With this new architecture and internal operations, we aim at decreasing the time taken by the original algorithm. Specifically, we reduce the number of internal rounds of the algorithm, and to compensate for the loss in confusion and diffusion, we make SHA-3’s internal operations more complex. We change the algorithm on its core bit level and make its internal operation more interdependent on the neighbouring bits to achieve more diffusion which results in better confusion.Jain, Vanita Bansal, Rishab Swami, Mahima Saini, Dharmender