Reviewerly/exHarmony huggingface dataset:
Attribute Similarity and Relevance-Based Product Schema Matching for Targeted Catalog Enrichment

Many eCommerce catalogs rely on structured prod-uct data to provide a good experience for customers. For large scale services, product information is provided by millions of different manufacturer and vendor schemas. Due to inherent heterogeneity of this data, unifying it to a consistent catalog schema remains a challenge. Schema matching is the problem of finding such correspondences between concepts in different distributed, heterogeneous data sources. Most approaches in automated schema matching assume either a small number of source schemas, attributes, and contexts (i.e., matching movie attributes from media knowledge bases). By contrast, schema matching in product catalogs encounter the problem of scaling across millions of noisy, heterogenous schemas spanning thou-sands of categories and attributes. In this paper, we introduce a scalable schema matching framework that utilizes unsupervised domain-specific attribute representations and general attribute similarity metrics. Our method first identifies relevant attributes for a given product based on existing customer signals, and then prioritizes among candidate attributes to consolidate only those relevant product facts from multiple manufacturers and vendors with little to no labeled data. We demonstrate value by experiments that enriched catalog data containing millions of attribute enumer-ations sourced from tens of thousands of schemas across a wide range of product categories. Experimental results show reduced manual annotation efforts by 75% from competing schema matching efforts by automating schema matching on targeted product facts, resulting in high accuracy, precision, and recall for important attributes that contribute to customer interest. We also demonstrate performance improvements of 8% MRR using our approach compared against two well-established approaches to unsupervised schema matching.
FIRM

Recent years have witnessed a growing interest in applying few-shot learning algorithms to personalized recommendation, and text classification tasks. In this paper, we propose a Fused Information Re-optimization Method based few-shot learning algorithm (FIRM) which can realize the fusion of label description information and sample instances. With the fusion strategy, FIRM can fully use the commonality between categories, the characteristic information of each category, and training model parame-ters with both generalization ability and classification characteristics. Further, to evaluate the validity of the proposed algorithm on the defense domain, a few-shot dataset (i.e., the defense science and technology indicators dataset) is constructed. Experimental results on the dataset validate our proposal and indicate the superior capability in classification tasks than a prototype network and MAML. Our main contributions are:
Extraction and Merging of Stroke Structure of Chinese Characters

Chinese characters are complex graphics with strokes as the basic unit. In order to analyze their structure, stroke extraction is the first step. This paper presents an automatic extraction method of Chinese character strokes, which regards the extraction of Chinese character strokes as finding the optimal path and merging. Based on the superpixel network, the path network is applied to enumerate all possible stroke segments. Then, the repeated strokes are merged according to the Intersection over Union. Experimental results show that the method can effectively extract accurate strokes and obtain single strokes with high-level semantic features and complete information.
Adaptive Power Control Scheme for Noise Suppressing in Quantum-Secured Distribution Communication Networks

To provide a future-proof security solution, we investigate the integration of quantum key distribution (QKD) with distribution communication networks and propose a QKD over TWDM-PON scheme. Some former researches reduced the launch power of classical signals to the level of error-free transmission to mitigate the noise impairments on QKD. However, this sacrifices the transmission reliability of classical signals and is not suitable for distribution communication networks whose reliability influences the distribution grid operation. In this paper, we propose an adaptive power control (APC) scheme, in which the launch power is dynamically optimized by considering different data rate of data traffics and the balance of generating and consuming key resources in quantum key pool. Simulation results verify that APC scheme can obtain a good trade-off between the secure key providing and the reliability of classical signal transmission.
Highly Accurate Optical Encryption for Image Security Applications Using FFT and Block Swapping

Nowadays, safe data transmission is one of the challenging tasks due to intelligent hackers in the transmission medium. Especially, digital image transmission plays a major role in various applications such as biomedical engineering, Remote sensing, Robotics, and industrial automation, etc. Previously, various techniques have been proposed to perform the optical image encryption process with a high degree of security. The main issue of previous works are flexibility and computational cost. The Least Significant Bit (LSB) replacing technique is used to perform secret data hiding process in the cover image. Fast Fourier transform (FFT) and blocks the swapping technique is proposed in this work to increase the security level further with improved flexibility and reduced computational cost. The reverse process is performed on the receiver side for image decoding process. To evaluate the performance, standard performance measures such as Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), and correlation coefficient (CC) are considered in various noise conditions. From the performance measures, it is clear that this work achieved 1.8s for compression and improved quality metrics when compared to previous works.
Learning on a Budget via Teacher Imitation

Deep Reinforcement Learning (RL) techniques can benefit greatly from leveraging prior experience, which can be either self-generated or acquired from other entities. Action advising is a framework that provides a flexible way to transfer such knowledge in the form of actions between teacher-student peers. However, due to the realistic concerns, the number of these interactions is limited with a budget; therefore, it is crucial to perform these in the most appropriate moments. There have been several promising studies recently that address this problem setting especially from the student's perspective. Despite their success, they have some shortcomings when it comes to the practical applicability and integrity as an overall solution to the learning from advice challenge. In this paper, we extend the idea of advice reusing via teacher imitation to construct a unified approach that addresses both advice collection and advice utilisation problems. We also propose a method to automatically tune the relevant hyperparameters of these components on-the-fly to make it able to adapt to any task with minimal human intervention. The experiments we performed in 5 different Atari games verify that our algorithm either surpasses or performs on-par with its top competitors while being far simpler to be employed. Furthermore, its individual components are also found to be providing significant advantages alone.
Beyond a Strong Baseline: Cross-Modality Contrastive Learning for Visible-Infrared Person Re-Identification

Matching cross-modality pedestrian images ( i.e ., visible images and infrared images) has been considered a useful and important application in the person re-identification (reID) field due to its potential to retrieve a person cross daytime and nighttime in real-life practice. However, it is a challenging task. Two main difficulties arise here. 1-Different imaging principle of spectrum cameras leads to a large cross-modality discrepancy between visible images and infrared images. 2-The various views of the large-scale distributed surveillance cameras also result in within-class variations. Along with challenges for the task itself, a strong baseline is also an important integral part of the research field, for the sense that the baseline can be the chief reason for the considerable progress on benchmarks and a strong baseline can evaluate the effectiveness of algorithms fairly. To address the aforementioned issues, this paper first systematically studies the training methods and presents a strong baseline network for the visible-infrared person reID task. Beyond using the existing training methods, we further propose a novel cross-modality contrastive learning (CMCL) scheme, which contains intra-modality contrastive loss and inter-modality contrastive loss, to improve the embedding quality for images of two modalities. Thorough experiments show the superior performance of baseline network and CMCL. For instance, on the RegDB dataset, our work outperforms state-of-the-art values by 7.56% / 4.60% on Rank-1 / mAP metrics.
Stacked AMR Parsing with Silver Data

Lacking sufficient human-annotated data is one main challenge for abstract meaning representation (AMR) parsing.To alleviate this problem, previous works usually make use of silver data or pre-trained language models.In particular, one recent seq-to-seq work directly fine-tunes AMR graph sequences on the encoder-decoder pre-trained language model and achieves new state-of-the-art results, outperforming previous works by a large margin.However, it makes the decoding relatively slower.In this work, we investigate alternative approaches to achieve competitive performance at faster speeds.We propose a simplified AMR parser and a pre-training technique for the effective usage of silver data.We conduct extensive experiments on the widely used AMR2.0 dataset and the results demonstrate that our Transformer-based AMR parser achieves the best performance among the seq2graph-based models.Furthermore, with silver data, our model achieves competitive results with the SOTA model, and the speed is an order of magnitude faster.Detailed analyses are conducted to gain more insights into our proposed model and the effectiveness of the pre-training technique.
Image Dehazing Via Cycle Generative Adversarial Network

Recovering a clear image from single hazy image has been widely investigated in recent researches. Due to the lack of the real hazed image dataset, most studies use artificially synthesized dataset to train the models. Nonetheless, the real word foggy image is far different from the synthesized image. As a result, the existing methods could not defog the real foggy image well, when inputting the real foggy images. In this paper, we introduce a new dehazing algorithm, which adds cycle consistency constraints to the generative adversarial network (GAN). It implements the translation from foggy images to clean images without supervised learning, that is, the model does not need paired data to training. We assume that clear and foggy images come from different domains. There are two generators that act as domain translators, one from foggy image domain to clean image domain, and the other from foggy image to clean image. Two discriminators in the GAN are used for assessing each domain translator. The GAN loss, combined with the cycle consistency loss are used to regularize the model. We carried out experiments to evaluate the proposed method, and the results demonstrate the effectiveness in dehazing and there is indeed difference between the real-fog images and the synthetic images.
Pyramid HBAO — a Scalable Horizon-based Ambient Occlusion Method

Screen-space Ambient Occlusion (SSAO) methods have become an integral part of the process of calculating global illumination effects in real-time applications. The use of ambient occlusion improves the perception of the geometry of the scene, and also makes a significant contribution to the realism of the rendered image. However, the problems of accuracy and efficiency of algorithms of calculating ambient occlusion remain relevant. Most of the existing methods have similar algorithmic complexity, what makes their use in real-time applications very limited. The performance issues of methods working in the screen space are particularly acute in the current growing spreadness of 4K (3840 x 2160 pixels) resolution of the rendered image. In this paper we provide our own algorithm Pyramid HBAO, which enhances the classic HBAO method by changing its calculation complexity for high resolution.
Modify the Quantization Table in the JPEG Header File for Forensics and Anti-forensics

As Joint Photographic Experts Group (JPEG) compression is widely used in image processing and computer vision, the detection of JPEG forgery has become an important issue in digital image forensics, and many related works have been reported. However, these works all assume that the quantization table in the JPEG header file is real, and corresponding research is carried out based on this assumption. This assumption leaves a potential flaw for those wise forgers to confuse or even invalidate the current JPEG forensics detectors. Taking double JPEG compression forensics as an example, if the quantization table in the header file is modified, it will cause the algorithm to fail. However, the tampering of header files not only brings negative effects, it can also improve the forensics performance of the algorithm. According to our analysis and experiments, increasing the step in the quantization table in the header file can lead to the failure of the forensics algorithm, and reducing the step in the quantization table in the header file can improve the performance of the existing algorithm. Based on this observation, we propose a general forensics and anti-forensics model by replacing the quantization table in the header file. The experimental results on the UCID database show that the scheme is effective for obfuscating and improving the three typical double JPEG compression forensics work.
Modelling of optimal parking for a wheeled robot

We consider a kinematic model for a differential wheeled robot. The corresponding optimal control problem is stated in the general form with an arbitrary domain for the controls and an arbitrary minimized functional. The known specifications of the problem lead to the classical models given by Markov-Dubins problem, Reeds-Shepp problem, Euler’s elastic problem and the sub-Riemannian problem on the rototranslation group. We describe the algorithms for constructing optimal trajectories of the robot for the given boundary conditions. These algorithms are implemented in an interface software developed in Wolfram Mathematica system. Additionally, we allow to equip the robot with a trailer. Trailer trajectories are computed for each model. We assume that the robot stays at the initial and final positions with zero velocities and accelerates to the given maximum linear velocity along the way. The interface program animates the movement of the robot (with a trailer) along the chosen optimal paths.
INNOVATIVE TECHNOLOGIES FOR IMPLEMENTATION OF ADDITIONAL PROFESSIONAL EDUCATION PROGRAMS

Innovative technologies are constantly used in education, since education is a developing area that must quickly respond to changes and to the emergence of new technologies in industry, science, culture and society. The choice of innovative technologies is determined by: modern conditions and challenges of education; by the specifics of additional professional education; by target audience. The article analyses the innovative technologies that are relevant to additional vocational education. The tasks of the study are to determine the specifics of innovative technologies’ applications, to describe the experience of their application at the Institute of Continuing Professional Development of South Ural State University. The areas and experience of using innovative technologies for the implementation of programs of additional professional education are described. Based on the analysis of the best education practices in the use of innovative technologies in continuing education at SUSU and other education institutions the leading trends have been identified.
Is it a Case Study? - Support for Ensuring and Assuring a Study is a Case Study

The term "case study" is not used consistently when describing studies and, most importantly, is not used according to the established definitions. Given the misuse of the term "case study", we analyse articles that cite case study guidelines and report case studies. Based on our experiences of conducting that analysis, we formulate support for ensuring and assuring case studies. We propose a new study type, i.e., the contrived study, to distinguish studies that have properties as case studies but are not formally case studies. We also develop two checklists. The first checklist targets alignment with the definitions of case study; the second checklist aims to distinguish contrived studies from case studies. Finally, we develop and evaluate a smell indicator to automatically suggest when a self-reported case study is not actually a case study. In conclusion, approximately 50% of the studies labelled "case study" are not case studies according to the established formal definitions. Based on the findings, two checklists and an indicator for non-case studies are suggested as support for ensuring and assuring a study is a case study.
AI-Based Fall Detection Using Contactless Sensing

Falls are a major health concern for the elderly as it threatens their livelihood and independence. Nearly 50% of the older adults, aged over 65 years old, fall in a span of 5 years, with 62% sustaining injuries and 28% extensive protracting injuries. This paper presents a high accuracy contactless falls detection framework based on channel state information extracted from software-defined radios. The aim is to develop a system capable of detecting whether an individual subject is present within the sensing area, or if the subject is falling, and, finally, if the subject is performing one of three other activities, including sitting, standing, and walking. The results showed a promising detection accuracy of 95.6% and 98%, using the 10-fold cross-validation and train-test split methods, based on the Random Forest classifier, respectively. Furthermore, we present a real-time analysis of the system to highlight its capability to detect, analyze, and report falls in real-time.
Model-based Poisson co-clustering for Attributed Networks

Co-clustering aims at simultaneously partitioning both dimensions of a data matrix. It has demonstrated better performances than one-sided clustering for high-dimensional data. In this paper, we focus on the task of attributed network clustering and propose to consider the edges of these networks as a form of pairwise semi-supervision between data points. We present a probabilistic model for co-clustering based on the Poisson Latent Block Model (LBM) and incorporate probabilistic Must Link relationships in the model using Hidden Markov Random Fields (HMRF). We present two inference algorithms based on Variational and Classification EM. Experiments on the task of node clustering in attributed networks confirm the interest of our approach and demonstrate the effectiveness of our algorithms.
STUDENTS’ LEARNING MOTIVATION OF DIFFERENT YEARS OF STUDY

Motivation of students' educational activity is one of the important conditions for training modern specialists at higher education institutions. The problem is insufficiently studied concerning the dynamics of student motivation for different years of study. The purpose of the article is to study the parameters of students’ learning motivation in different years of study. We solved the following main tasks: conducted an anonymous electronic survey of 800 bachelor's degree students (1-4 years) and 271 master's students (1-2 years), identified and compared parameters of learning motivation (internal motivation, external positive motivation, external negative motivation) on the basis of Uzhhorod National University. The study (2020) shows that the majority of bachelor's and master's students of different years of study (80-84%) have optimal motivational complexes. Either internal or external positive motivation dominates, or they are the equal, but higher than external negative motivation. We observed the predominance of indicators of external negative motivation for some students (16-20%). These students have the most common motivational complexes: external negative motivation, or its value is equal to the internal, external positive motivation. Similar results occur in the case of comparing bachelor's and master's students in general: about 82% have optimal motivational complexes, about 18% have external negative motivation. The c2-criterion (p ≤0,05) confirmed the formation of similar motivational complexes by students. Internal motivation prevails mostly among the first-year bachelor's (55,20%) and first-year master's (51,66%) students. The influence of external factors increases in the following years of study, the number of students with external positive motivation grows. We found a slightly greater impact of negative motivational factors on the first-year bachelor's and master's students, as among them there is the highest percentage of students with a predominantly external negative motivation. This parameter decreases in the following years of study.
Algorithms for Reading Line Classification

Eye-Tracking has been emerging as a useful tool in human-computer interaction. However, the state of the art in eye-tracking applications suffers from a significant amount of measurement noise. Also, the inherent nature of the eye-gaze movement adds to the difficulty of obtaining valuable information from eye-gaze measurements. In this paper, a novel classification approach is proposed to classify the lines being read based on eye-gaze measurements. The proposed approach consists of a novel Kalman smoother-based preprocessing procedure to separate eye-gaze data corresponding to different text lines and to reduce variance. The preprocessed data is then used to train two different classifiers, one based on Gaussian discriminants and the other based on support vector machines. The resulting line-classification approach is shown to be superior in performance compared to other recent approaches.
Studies on the Influence of Baking Oven Temperature on Squeak Noise of Body Sheet Metal

The problem of squeak noise of body A/B-pillar in the process of road test of a passenger car is studied. By optimizing the parameters related to the baking oven temperature of coating electrophoresis (Heating rate, maximum furnace temperature and holding time), the glass transition temperature of sheet metal electrophoresis coating is improved, and the problem of squeak noise of body sheet metal is solved. The internal relationship between ambient temperature, electrophoretic oven temperature, electrophoretic coating glass transition temperature and squeak noise of body sheet metal are studied. The inner mechanism and solution of squeak noise of this kind of batch body sheet metal are summarized.
Analyzing the quality of responses of Pakistani software developers over stack overflow

A number of online Q&A systems have evolved over past few years for developers to collaborate and help each other in various technical issues. However, there is a passive participation of a great number of members over these forums. This paper analyzes the responses of software developers on stack overflow by Pakistani, Indian and US citizens in the past ten years. The data is extracted and analyzed to determine various trends such as the number of questions posed by developers, the number of answers, the number of accepted answers and the ratio of posed answers to the accepted answers. The paper also show the emerging technological trends in various countries.