Reviewerly/exHarmony huggingface dataset:
Data-efficient Weakly-supervised Learning for On-line Object Detection under Domain Shift in Robotics.

Several object detection methods have recently been proposed in the literature, the vast majority based on Deep Convolutional Neural Networks (DCNNs). Such architectures have been shown to achieve remarkable performance, at the cost of computationally expensive batch training and extensive labeling. These methods have important limitations for robotics: Learning solely on off-line data may introduce biases (the so-called domain shift), and prevents adaptation to novel tasks. In this work, we investigate how weakly-supervised learning can cope with these problems. We compare several techniques for weakly-supervised learning in detection pipelines to reduce model (re)training costs without compromising accuracy. In particular, we show that diversity sampling for constructing active learning queries and strong positives selection for self-supervised learning enable significant annotation savings and improve domain shift adaptation. By integrating our strategies into a hybrid DCNN/FALKON on-line detection pipeline [1], our method is able to be trained and updated efficiently with few labels, overcoming limitations of previous work. We experimentally validate and benchmark our method on challenging robotic object detection tasks under domain shift.
People Counting Based on Multi-scale Region Adaptive Segmentation and Depth Neural Network

People counting based on surveillance camera is the basis of the important tasks, such as the analysis of crowd behavior, the optimal allocation of resources and public security. Aiming at the low accuracy of the people counting method based on object detection, a people counting method based on multi-scale region adaptive segmentation and deep neural network is proposed in this paper. The idea originates from the analysis and research of multi-scale objects, and it is found that the detection accuracy will be improved if the multi-scale objects match the size of multi-scale anchors. In this method, K-means is used to cluster the detection results of Faster-RCNN model. Then the image is segmented adaptively according to the clustered results. Finally, Faster-RCNN model is used to detect the segmented images. The experimental results show that the average accuracy of this method is 45.78% on mall dataset, which is higher than Faster-RCNN about 3.59%.
GlosysIC Framework: Transformer for Image Captioning with Sequential Attention

Over the past decade, the field of Image captioning has witnessed a lot of intensive research interests. This paper proposes “GlosysIC Framework: Transformer for Image Captioning with Sequential Attention” to build a novel framework that harnesses the combination of Convolutional Neural Network (CNN) to encode image and transformer to generate sentences. Compared to the existing image captioning approaches, GlosysIC framework serializes the Multi head attention modules with the image representations. Furthermore, we present GlosysIC architectural framework encompassing multiple CNN architectures and attention based transformer for generating effective descriptions of images. The proposed system was exhaustively trained on the benchmark MSCOCO image captioning dataset using RTX 2060 GPU and V100 GPU from Google Cloud Platform in terms of PyTorch Deep Learning library. Experimental results illustrate that GlosysIC significantly outperforms the previous state-of-the-art models.
Logistics of Pedagogical Innovations: Paradigms of Development in Educational Holding

The transition of Russian society to the post-industrial stage actualizes the problem of developing educational systems based on innovations and the search for a relevant methodology for managing pedagogical innovations. Its basis is the logistics of pedagogical innovations as the theory and practice of flow control in educational systems. The purpose of the study is to determine the paradigms of logistic development of pedagogical innovations as a methodology and mechanism for managing innovation in an educational holding. The hypothesis is presented by the assumption that these paradigms form value, content and instrumental frameworks for the implementation and development of logistics of pedagogical innovations. The study is based on an analysis of primary sources, including 1492 cases, which include reports on the results of innovative activities of educational organizations of Ulyanovsk Oblast, which are innovative sites that are part of a regional educational holding (100 educational organizations annually). The materials were prepared during the period from 2003-2004 to 2017-2018 academic years. We introduced the concept of “logistics of pedagogical innovations”, which describes the theory and practice of managing flows (pedagogical, infrastructural, integrative, interference) in an educational holding in order to ensure a steady flow of pedagogical innovations that are the result of its activities and development factor. We identified directions of development of pedagogical innovations logistics, which are defined by five paradigms: axiological (value of innovation), humanitarian (a person as a subject of logistics), interaction paradigm (focus on the development of interaction in the holding’s internal and external environment), meaningful (priority in the study of flows in an educational holding) and technological (focus on improving methods and means of logistics). Each of them sets priorities for improving the educational holding.
An Integrated Conceptual Model of 360-Degree Performance Appraisal and Candidate Forecasting Using Adaptive Neuro-Fuzzy Inference System

360-degree evaluations are popular performance appraisal method that involves evaluation input from multiple levels within the firm as well as external sources. However, subordinate can be bias the ratings. The performance appraisal process is far from accurate and objective, sometimes resulting in rating errors. This article proposes an approach to minimizing subjective judgement in the effective employee evaluation in the existence of the multi-factor competency-based measures in a hierarchical structure using ANFIS and ANOVA algorithm. This study supports the ideas that the best MSE (0.0056) calculation is achieved by Using Adaptive Neuro-Fuzzy Inference System (ANFIS) with ANOVA selection features. The method suggested helps Human Resource (HR) managers to make more objective decisions for put right man on right time. The predicting behavioural competencies of employees to make investment in human development more effective. It is also a change in the decision-making concept of Human Resource Development (HRD) from feelings about the decision based on more information.
Analysis of entanglement source based on coherent feedback control

基于非测量的量子相干反馈控制系统不会引入额外的噪声, 可以用于稳定、操控和改善多种量子系统的性能. 利用相干反馈的方法可以操控非简并光学参量放大器, 在一定条件下能够增强其输出Einstein-Podolsky-Rosen (EPR)纠缠态光场的纠缠度. 相干反馈控制系统中的核心光学元件是控制耦合镜, 其透射率的选取直接影响反馈控制的效果. 本文针对控制耦合镜对偏振相互垂直的种子光场透射率不同的情况, 从理论上分析了该情况对相干反馈控制效果的影响, 得出相干反馈的正作用达到最佳时对控制镜透射率的要求, 理论分析与实验结果相吻合. 同时分析了相干反馈控制效果随其他物理参量的变化关系, 得出系统进一步优化的实验条件. 为今后相干反馈控制系统中物理参量的选择提供依据, 也为利用相干反馈操控更多的量子系统提供参考.
A self-adaptive global harmony search based extreme learning machine for classification problem

A very important self-adaptive global harmony search combined with extreme learning machine (SGHS-ELM) is used for pattern classification in this paper. It is seen that, for improving the classification performance of neural classifiers trained with extreme learning machine (ELM) and reducing the number of hidden nodes, the input weights and hidden biases of the neural classifiers can be adjusted with the SGHS mechanism to ensure that the features of input patterns can be linearly separable in the feature space. The output weights are then optimally designed through minimizing the sum of output error squares and the sum of output weight squares to balance and reduce the empirical and structural risks. The simulation results illustrate the good robustness and classification performance of the neural classifier.
A Generic Model for Software Documentation and its Application in Embedded Systems Developed with Scrum

Software development life cycle (SDLC) depicts the necessary software development activities while applying methods in different software types. Previous works documented the software activities either by taking into account the development method or the software type. However, the documentation of development activities depends on the choice of the method as well as the software type. This paper presents a generic model of software documentation as well as its case study that applies Scrum in the development of an embedded system. The model provides guidelines for engineers to well choose and structure the development of software documents.
Markets for Crypto Tokens, and Security under Proof of Stake

Cryptocurrency systems based on proof of stake (PoS) grant governance rights to the holders of currency tokens and therefore are vulnerable to attack by adversaries who buy tokens in order to gain control. To evaluate the robustness of PoS cryptocurrencies to such attacks, we model the market for tokens and determine how the cost of attacking the system depends on the level and shape of token supply and demand. We show that, contrary to popular belief, the appreciation of tokens in response to demand by attackers plays a small role in securing the system. In particular, stablecoins can be less vulnerable to attack than cryptocurrencies that are freely floating. Moreover, PoS cryptocurrencies that primarily function as mediums of exchange are vulnerable to attack if the velocity of money is high.
Data Privacy Maturity Assessment Practice of Digital Transformation Enterprises under the COVID-19

The Internet under the COVID-19 has brought more revolutionary changes to global trade and data flows. It tears apart national boundaries and further promotes exchanges and trade on a global scale. In this context, data privacy issues have been paid more attention, especially in the context of the epidemic situation, trade data privacy relying on digital transformation enterprises needs to be regulated and controlled. Taking an industrial company in Xiamen as an example, this article conducts online and offline research on companies through literature research and expert interviews, and refers to the Intel privacy maturity model, and constructs 12 indicators in three areas: platform risks, corporate behaviors, and external threats, so as to apply the data privacy maturity of an industrial enterprise in Xiamen. Empirical evidence shows that the maturity of data privacy of this enterprise is in the intermediate stage of institutionalization, and the overall maturity needs to be further developed in the field of adjusting enterprise behavior and preventing external threats. This evaluation system has good applicability to the data privacy issues of digital transformation companies under the epidemic, and can provide reference for the development of corporate data privacy maturity in the domestic manufacturing industry.
Fuzzy Regression Analysis using Trapezoidal Fuzzy Numbers

As a widely used method, regression analysis plays an increasingly important role in creating statistical models and making forecasts in the field of economics and finance. The use of traditional regression for modeling socio-economic processes is not sufficiently substantiated in some situations. Currently, a new direction is being actively developed, associated with fuzzy regression analysis and its application as an alternative to classical methods for modeling economic phenomena. Fuzzy regression methods are based on the theory of fuzzy sets. A number of methods and their modifications are proposed for constructing fuzzy regression models, but most of them use triangular fuzzy symmetric numbers. In this paper, we propose a new method for constructing linear fuzzy regression using trapezoidal fuzzy numbers. The method is based on dividing the sample using a regression model which is estimated by using the ordinary least squares. Two fuzzy regressions using triangular numbers are estimated from the formed samples, on the basis of which a fuzzy model with trapezoidal fuzzy numbers is constructed. Basing on the proposed method, a linear fuzzy model of the gross regional product as an indicator of the economic development of the Republic of Tatarstan of Russia is constructed depending on a number of factors. A comparative assessment of the quality of fuzzy regression models using triangular and trapezoidal numbers was performed.
Vehicle-Level Traffic Accident Detection on Vehicle-Mounted Camera Based on Cascade Bi-LSTM

In this paper, we propose a traffic accident detection on vehicle-mounted camera. In the proposed method, the minimum bounding box coordinates the central coordinates on the bird"s eye view and motion vectors of each vehicle object, and ego-motions of the vehicle equipped with dash-cam are extracted from the dash-cam video. By using extracted 4 kinds features as the input of Bi-LSTM (bidirectional LSTM), the accident probability (score) is predicted. To investigate the effect of each input feature on the probability of an accident, we analyze the performance of the detection the case of using a single feature input and the case of using a combination of features as input, respectively. And in these two cases, different detection models are defined and used. Bi-LSTM is used as a cascade, especially when a combination of the features is used as input. The proposed method shows 76.1% precision and 75.6% recall, which is superior to our previous work.
Disambiguation of Potentially Idiomatic Expressions with Contextual Embeddings

The majority of multiword expressions can be interpreted as figuratively or literally in different contexts which pose challenges in a number of downstream tasks. Most previous work deals with this ambiguity following the observation that MWEs with different usages occur in distinctly different contexts. Following this insight, we explore the usefulness of contextual embeddings by means of both supervised and unsupervised classification. The results show that in the supervised setting, the state-of-the-art can be substantially improved for all expressions in the experiments. The unsupervised classification, similarly, yields very impressive results, comparing favorably to the supervised classifier for the majority of the expressions. We also show that multilingual contextual embeddings can also be employed for this task without leading to any significant loss in performance; hence, the proposed methodology has the potential to be extended to a number of languages.
Effect on Quality of 3D Model of Plant with Change in Number and Resolution of Images Used: An Investigation

3D reconstruction of plants is a challenging task. It is essential in non-destructive plant growth monitoring systems and important to reconstruct plant features precisely. Two parameters which critically affect quality of the 3D model are change in the number of input images and the resolution of the images. In this paper, the effect on 3D models of these parameters is analysed. This analysis enables an appropriate number of input images and resolution of the images to be determined for a precise 3D model. To validate the results, the plant stem height and number of leaves extracted from reconstructed 3D models were compared with ground truth values. We used descriptive statistics for validation and achieved high correlation between extracted and ground truth values.
Experiencers, Stimuli, or Targets: Which Semantic Roles Enable Machine Learning to Infer the Emotions?

Emotion recognition is predominantly formulated as text classification in which textual units are assigned to an emotion from a predefined inventory (e.g., fear, joy, anger, disgust, sadness, surprise, trust, anticipation). More recently, semantic role labeling approaches have been developed to extract structures from the text to answer questions like: “who is described to feel the emotion?” (experiencer), “what causes this emotion?” (stimulus), and at which entity is it directed?” (target). Though it has been shown that jointly modeling stimulus and emotion category prediction is beneficial for both subtasks, it remains unclear which of these semantic roles enables a classifier to infer the emotion. Is it the experiencer, because the identity of a person is biased towards a particular emotion (X is always happy)? Is it a particular target (everybody loves X) or a stimulus (doing X makes everybody sad)? We answer these questions by training emotion classification models on five available datasets annotated with at least one semantic role by masking the fillers of these roles in the text in a controlled manner and find that across multiple corpora, stimuli and targets carry emotion information, while the experiencer might be considered a confounder. Further, we analyze if informing the model about the position of the role improves the classification decision. Particularly on literature corpora we find that the role information improves the emotion classification.
DOME-T: adversarial computer vision attack on deep learning models based on Tchebichef image moments

In this paper, a novel black box adversarial computer vision attack is proposed. The introduced attack is based on removing from images some components described by their Tchebichef discrete orthogonal moments, rather than to perturb them. The contribution of this work is focused on the addition of one more clue, supporting the critical hypothesis that computer vision systems fail because they support their decisions not only in robust features but also in others non-robust ones. In this, context non-robust image features described in terms of Tchebichef moments are excluded from the original images and the approximated reconstructed versions of them are used as adversarial examples in order to attack some popular deep learning models. The experiments justify the effectiveness of the proposed adversarial attack in terms of imperceptibility and recognition error rate of the deep learning classifiers. It is worth noting that the top-1 accuracy of the attacked models was degraded by a factor between 9.48%-70.89% for adversarial images of 65dB to 57dB PSNR values. The corresponding degradation of the top-5 models’ accuracy was between 6.9% and 55.14% for the same quality images. Moreover, the proposed attack seems to have more strength than the Fast Gradient Sign Method (FGSM) attacking method traditionally applying in most cases. These results reveal that the proposed attack is able to exploit the vulnerability of the deep learning models’ towards degrading their generalization abilities. In this paper, a novel black box adversarial computer vision attack is proposed. The introduced attack is based on removing from images some components described by their Tchebichef discrete orthogonal moments, rather than to perturb them. The contribution of this work is focused on the addition of one more clue, supporting the critical hypothesis that computer vision systems fail because they support their decisions not only in robust features but also in others non-robust ones. In this, context non-robust image features described in terms of Tchebichef moments are excluded from the original images and the approximated reconstructed versions of them are used as adversarial examples in order to attack some popular deep learning models. The experiments justify the effectiveness of the proposed adversarial attack in terms of imperceptibility and recognition error rate of the deep learning classifiers. It is worth noting that the top-1 accuracy of the attacked models was degraded by a factor between 9.48%-70.89% for adversarial images of 65dB to 57dB PSNR values. The corresponding degradation of the top-5 models’ accuracy was between 6.9% and 55.14% for the same quality images. Moreover, the proposed attack seems to have more strength than the Fast Gradient Sign Method (FGSM) attacking method traditionally applying in most cases. These results reveal that the proposed attack is able to exploit the vulnerability of the deep learning models’ towards degrading their generalization abilities.
Automatic Topological Field Identification in (Historical) German Texts

For the study of certain linguistic phenomena and their development over time, large amounts of textual data must be enriched with relevant annotations. Since the manual creation of such annotations requires a lot of effort, automating the process with NLP methods would be convenient. But the required amounts of training data are usually not available for non-standard or historical language. The present study investigates whether models trained on modern newspaper text can be used to automatically identify topological fields, i.e. syntactic structures, in different modern and historical German texts. The evaluation shows that, in general, it is possible to transfer a parser model to other registers or time periods with overall F1-scores >92%. However, an error analysis makes clear that additional rules and domain-specific training data would be beneficial if sentence structures differ significantly from the training data, e.g. in the case of Early New High German.
Technological platform for forming resources of the electronic library “Scientific Heritage of Russia”

The article describes the structure of the technological complex for forming the electronic library "Scientific heritage of Russia" elements. The presented solutions can be used in the design process of a common digital knowledge space.
A Survey of Algorithms and Applications Related with Graph Embedding

The network is a crucial form of representing the relationship between entities. Recently, with the rapid development of machine learning, the application of graph embedding in complex network systems such as social networks and information networks has become more and more widespread. From early Euclidean distance of the network structure to the shallow word embedding model, to the present deep auto-encoder that combines with additional information of nodes, graph embedding technology is undergoing a great leap. Its core idea is to compress network information into low-dimensional dense vectors, and then use the obtained feature vectors for practical network analysis tasks. The survey introduces the current development status of graph embedding technology and the upcoming challenges brought to us by the limitations of processing data. We take the time sequence as a rough baseline to systematically classify and summarize the traditional and current mainstream graph embedding methods based on different fundamental principles. In addition, we also enumerate the applications of the algorithm in various scenarios such as link prediction, node classification. As the frontier topic and the cutting-edge technology in the field of network representation learning, graph embedding possesses extremely vital academic value and application prospects.
Practical Fully Dynamic Minimum Cut Algorithms

We present a practically efficient algorithm for maintaining a global minimum cut in large dynamic graphs under both edge insertions and deletions. While there has been theoretical work on this problem, our algorithm is the first implementation of a fully-dynamic algorithm. The algorithm uses the theoretical foundation and combines it with efficient and finely-tuned implementations to give an algorithm that can maintain the global minimum cut of a graph with rapid update times. We show that our algorithm gives up to multiple orders of magnitude speedup compared to static approaches both on edge insertions and deletions.
RETRACTED: Study of the bullet rifling linear traces matching technology based on deep learning

This article has been retracted, and the online PDF has been watermarked “RETRACTED”. The retraction notice is available at https://doi.org/10.3233/JIFS-219327.