Reviewerly/exHarmony huggingface dataset:
AesTech - A Deep Learning Based Image Evaluation Model

Selection of the optimum number of best quality images and sequencing them are critical for online conversion in the Hospitality Industry. In this sector, critical decisions of assessing image quality and sequence are generally based on human judgments. Objective of the work is to develop an algorithm which scores every property for quality and quantity of images which is then used to qualify property for launch and sequence images on OYO and its partners’ online platforms. AesTech, the algorithm developed provides integrated property-level insights on the basis of quality and quantity parameters for each individual image of the property. Deep Learning based Image Scoring model provides part of the solution and brings objectivity in assessing quality of images. We evaluated the models with multiple evaluation criteria at both image and property level. Automated Property Image Scoring model is a necessity for organizations like OYO. Apart from saving time in launching new properties on different platforms, the model has helped in improving CTR (click through rates) and ultimately conversion. The success of AesTech across different geographies and different types of properties indicates immense potential in driving customer's motivation for the final decision of booking in industries such as travel, hospitality etc.
Research on the Innovation and Development of Data Mining Algorithm

The era of big data poses a severe challenge to education. In order to keep up with the development of the times, future education must go with the times. Schools in the 21st century should teach students to master and use 21st century skills to understand and solve the challenges of the real world. This paper studies the innovation and development of art teacher education under the data mining algorithm. The Art Literacy with vision, modeling, space, creativity and aesthetic feeling as the core corresponds to the 21st century skills. Through school art education, every teenager can obtain the essential core literacy for survival and communication in a highly complex world - Art Literacy, Through school art education, we can help young people master 21st century skills to base themselves on life and make positive contributions to their community and society.
Benchmark for anonymous video analytics

Abstract Out-of-home audience measurement aims to count and characterize the people exposed to advertising content in the physical world. While audience measurement solutions based on computer vision are of increasing interest, no commonly accepted benchmark exists to evaluate and compare their performance. In this paper, we propose the first benchmark for digital out-of-home audience measurement that evaluates the vision-based tasks of audience localization and counting, and audience demographics. The benchmark is composed of a novel, dataset captured at multiple locations and a set of performance measures. Using the benchmark, we present an in-depth comparison of eight open-source algorithms on four hardware platforms with GPU and CPU-optimized inferences and of two commercial off-the-shelf solutions for localization, count, age, and gender estimation. This benchmark and related open-source codes are available at http://ava.eecs.qmul.ac.uk .
An incentive compatible reward sharing approach for shard-based blockchains

Sharding is a way to solving the scalability problem in the blockchain. The sharding method uses traditional consensus algorithms, such as PBFT. In traditional consensus algorithms, network nodes can register blocks by working together. In these algorithms, an important issue is how to motivate group nodes to participate in the consensus algorithm so as not to cause collusion or free-riding. In this paper, we first examine the previous solutions that try to solve the incentive mechanism in sharding blockchain and show that these solutions do not consider the leader's role in involving other nodes in the consensus algorithm. By the game theory, we show that the leader for increasing his benefit prevents some nodes from participating in the consensus algorithm, which creates collusion. We propose a solution to motivate group nodes in the sharding method and by the game theory prove that this solution is incentive compatible and also does not causes any collusion.
Multifeatured Image Retrieval Techniques Based on Partial Differential Equations for Online Shopping

In today’s rapid development of network and multimedia technology, the booming of electronic commerce, users in the network shopping species of images and other multimedia information showing geometric growth, in the face of this situation, how to find the images they need in the vast amount of online shopping images has become an urgent problem to solve. This paper is based on the partial differential equation to do the following research: Based on the partial differential equation is a kind of equation that simulates the human visual perception system to analyze images; based on the summary of the advantages and disadvantages of multifeature image retrieval technology, we propose a multifeature image retrieval technology method based on the partial differential equation to alleviate the indexing imbalance caused by the mismatch of multifeature image retrieval technology distribution. To improve the search speed of the data-dependent locally sensitive hashing algorithm, we propose a query pruning algorithm compatible with the proposed partial differential equation-based multifeature image retrieval technology method, which greatly improves the retrieval speed while ensuring the retrieval accuracy; to implement the data-dependent partial differential equation algorithm, we need to distribute the data set among different operation nodes, and to better achieve better parallelization of operations, we need to measure the similarity between categories, and we achieve the problem of distributing data among various categories in each operation node by introducing a clustering method with constraints. The purpose of this article for image recognition is for better shopping platforms for merchants. This algorithm has trained multiple samples and has data support. The experimental results show that our proposed data set allocation method shows significant advantages over the data set allocation method that does not consider category correlation. However, the image features used in image retrieval systems are often hundreds or even thousands of dimensions, and these features are not only high in dimensionality but also huge in number, which makes image retrieval systems encounter an inevitable problem—“dimensionality disaster.” To overcome this problem, scholars have proposed a series of approximate nearest neighbor methods, but multifeature image retrieval techniques based on partial differential equations are more widely used in people’s daily life.
Path Planning Algorithm for Two-dimensional Raster Maps based on Deep Reinforcement Learning

Path planning is one of the main research contents of decision control. The problem of path planning can be described as finding a collision free path from the initial state to the target state in the environment. Raster-based path planning is a basic problem in robotics, games and other fields. Traditional path planning methods need to model the environment in advance, and then carry out path planning on the basis of known map information. However, there is a lot of dynamic information in the environment, such as pedestrians, movable objects. It is difficult for traditional algorithms to carry out path planning in the dynamic unknown environment. With the development of deep learning and reinforcement learning, such methods provide a new way to solve the problem of path planning. Aiming at resolving the shortcomings of traditional path planning algorithms, this paper adopts deep reinforcement learning algorithm to carry out path planning for two-dimensional raster maps, and finally validates and evaluates the performance of the algorithm within an efficient simulation environment.
Security Analysis and Optimization Methods of LSFR Generating Function for MIFARE Classic CARD

MIFARE CLASSIC CARD (hereinafter referred to as Mifare Classic) produced by NXP is the most common contactless smartcard on the market. Mifare Classics uses NXP's licensed patent stream cipher algorithm, CRYPTO-1, whose internal structure and algorithm parameters are commercial secrets and unknown to the outside world. At present, there is much research on the security of Mifare Classics. We analyze the security of the CRYPTO-1 algorithm in the Mifare Classics from the point of generating function of the linear feedback shift register (Linear feedback shift register, hereinafter referred to as LFSR) of the card and propose an optimization method for the vulnerability of generating function to improve the security of the Mifare Classics. Furthermore, the optimization method is generally applicable to the encryption algorithm based on the LFSR generating of pseudo-random sequences.
Exploring the Effect of Dynamic Drive Balancing in Open-ended Learning Robots

This paper seeks to explore the effect and possibilities of autonomously balancing drives in a motivational architecture aimed at open-ended learning robots. These types of robots are very useful in unconstrained human robot interaction settings or when uncontrolled dynamic scenarios that are unknown at design time must be addressed. Designing a robot under these conditions implies that it must be endowed with some primary operational purpose and some additional self-preservation objectives whose fulfillment depend on the characteristics of the particular domain it is facing each moment in time. Domains that are not known beforehand and for which no a priori goal or skill structure can be designed in. Thus, an approach to the design and engineering of motivational structures to endow robots with specific purposes is proposed and tested here. We concentrate on the drive structure of a motivational system and the effects of its autonomous adaptation to changing circumstances. To provide for this adaptation, a simple evolutionary strategy is defined for the autonomous regulation of multiple drives seeking to optimize long-term operation. The proposal is tested on a Baxter robot performing an industrial task and the results confirm the potential of autonomous dynamic drive balancing as a tool in open-ended settings.
Contrastive Graph Representation Learning via Maximizing Mutual Information

With the expansion of graph data in the real world, unsupervised graph representation learning shows greater potential. Unsupervised graph representation learning is mainly about extracting high-level representation from the rich properties and structures of graphs. In this paper, we propose Graph Representation Contrastive Learning Framework based on mutual information(CgI).It extracts the effective topology structural information and context of the graph through maximizing the node-level and graph-level mutual information in two perspectives respectively. Node-level mutual information mainly focuses on local association information between nodes, while graph-level mutual information is more concerned with the guiding role of global information. CGI combines contrastive learning and mutual information into feature extraction. The experimental results confirm that the proposed model performs outstanding improvements contrast with the-state-ofthe-art models, and it has better representative learning ability.
Proactive Leadership in Team Building and Investment Evaluation in Human Capital

The purpose of this article is to identify the role of proactive leadership in the formation of a team based on a metaprograms approach to assessing the human capital of an organization and the formation of a methodology for evaluating the effectiveness of investments made in it. This goal is achieved by solving two tasks: first, the analysis of the concepts of leadership and proactive leadership, as well as the related concepts of human capital and human asset, and second, studying the relationship between the concepts of human capital, proactive leadership and organizational effectiveness. The authors have analyzed the concept of the leadership concept, focused on the phenomenon of proactive leadership. Qualitative characteristics of proactive leadership have been highlighted, and the approach to personnel evaluation based on metaprograms and behavioral patterns has been analyzed. The author’s view on the structure of human capital and methods for evaluating the effectiveness of investment in its development are proposed for discussion.
Impact of ELM Parameters and Investment Horizon for Currency Exchange Prediction

The foreign exchange market is of the utmost importance for many sectors of the economy, therefore attempts to forecast changes in currency price levels are the research area of many practitioners and theorists. The article aims at examining the impact of settings of various neural network parameters on the results of currency forecasts. The three currency pairs the US dollar, British pound, and Swiss franc to EUR were selected for the analysis. The forecast results for different network settings are examined with three different indicators: forecast error, the ratio of correctly forecasted changes in the course direction and the potential profit generated. The neural network used for the study is Extreme Learning Machine and the forecast horizons taken into account are in the range of one to ten days. The better-quality forecasts based on price levels than on rates of return was shown and good quality forecasts for two out of three currency pairs was obtained in the study. The article also presents the relationship between the results generated by the neural network and the settings of these networks - in particular, the impact of the number of delays on forecast errors and the number of hidden nodes on all three assessment parameters.
Communicative Features of Undergraduate Training in Advertising and Public Relations in Technical Higher Educational Institutions

Currently, the issue of the quality of professional education and compliance of the competencies of university graduates with the requirements of professional community is acute. New technologies require new approaches to the educational process and teaching methods . The development of humanitarian training in technical universities was initiated by economic considerations related to transformations in the economy and the restructuring of higher education. However, the high efficiency of such educational programs is now confirmed if they combine special competencies and knowledge of the characteristics of a specific industry, which becomes a competitive advantage of graduates in the labor market. A good example confirming this approach is the training of bachelors in the field of Advertising and Public Relations in technical universities. The authors evaluated the effectiveness of communication features of bachelors training of a humanitarian profile in technical universities on the basis of such criteria as (1) the use of the scientific and educational potential of the university and its resources; (2) the interest caused by this direction among prospective students; (3) the specifics of the educational process; (4) the uniqueness of the competencies that graduates of a technical university have, (5) their demand in the labor market and socio-professional assessment. This study finalized in a poll of fourth (final) year undergraduate students studying in training profile “Advertising and Public Relations in the Aerospace Industry”. As the poll showed, graduates of humanitarian fields of training in technical universities can be characterized by lateral thinking, erudity, knowledge of industry specifics, and motivation to work in the chosen field. An important advantage of such training it the perception of students consists in application of knowledge gained in other industries. It is important to note that the high level of training is confirmed by the social and professional accreditation of the faculty.
Evaluation of Wizard-of-Oz and Self-Play Data Collection Techniques for Turkish Goal-Oriented Dialogue Agents

As with all natural language processing tasks, the lack of open-source training data required for the development of dialogue agents is a major obstacle to research studies in the field. Especially languages that are not widely studied, such as Turkish, suffer more from this problem. This article introduces a comparison of Wizard-of-Oz and self-play data collection techniques for Turkish goal-oriented dialogue system generation. Three data sets have been prepared and introduced to the researchers by using these techniques. Being the first publicly available human-to-human Turkish dialogue data sets, although open for development, the created resources from the restaurant domain are very valuable for further research on Turkish dialogue systems. The mentioned methods are quantitatively compared on the produced data sets, in terms of dialog act classification and slot identification scores. Since it is costly to collect data with methods like Wizard-Of-Oz in every domain, an open-source flexible and easy-to-use framework is also provided implementing self-play which may be used to create machine-to-machine dialogue outlines and speed data collection for low-resource languages like Turkish. Besides, designed templates of annotation screens for crowdsourcing are provided for future studies.
Matryoshka: A Coalesced Delta Sequence Prefetcher

To learn complex memory access patterns effectively, many spatial data prefetchers have been proposed that characterize the patterns as fixed-length delta sequences. However, because complex patterns are variable in workloads, it is difficult for fixed-length delta sequences to recognize them with both high competitive coverage and accuracy. That is, longer delta sequences increase accuracy at a lower probability of pattern matching, while shorter delta sequences increase coverage at a higher probability of false predictions. A classical strategy is to introduce the multiple matching mechanism associated with variable-length delta sequences, but sequences have to be redundantly stored in multiple tables.
Computational Recovery of Sample Missings

Many problems in the real world include uneven label distributions where the proportion of samples of one type in the data is greatly different from the proportion of samples of other types. Due to the difficulty in the data collection, this data imbalance problem widely exists. For life science issues, this data sampling missing is difficult to alleviate by improving data-collection methods. Thus the problem of data imbalance poses a challenge to traditional machine learning methods. In response to this problem, this chapter proposes structure-aware and rebalancing learning. Through the analysis of internal structure of the data, the proposed method tries to rebalance the unbalanced data. On the association analysis and prediction tasks, we demonstrate the strucure-aware rebalancing method can efficiently improve the analysis of imbalanced data.
Implementation of Application Asset Analysis and Management Tool Based on Zowe

With the development and progress of the society, information changes at an amazing speed and customers leave more and more program codes on the mainframe. As society’s demands on information technology continue to grow and mainframe customers’ work items change, some program assets are shelved, deleted, hidden, or even forgotten. When customers need them again to build new projects, they can’t find where the code is, which obviously makes code reuse difficult. Based on the needs of users for the reuse of project assets, this paper designs and develops a web graphics tool based on Zowe to help users explore project assets deeply, find the location and value of program code, and make old code get better use in new projects, so as to effectively solve the related pain points of users.
Fixed Representatives and Variable Features: From Regularized Deep Clustering to Classic Clustering

The clustering problem in machine learning is often tackled using classic/traditional models, like k-means and mixtures of Gaussians, but in the past few years, the deep learning community has also developed algorithms for unsupervised learning and for clustering specifically. A very common pattern in these new algorithms is to map the data into a new space via a neural network and at the same time, to perform clustering with these new data representations. This general idea comes with a cost: in order to avoid trivial/unsatisfactory solutions one should regularize the model. The purpose of this study is to create a new clustering algorithm by simplifying this framework (as opposed to adding complex regularization schemes) such that a clustering is learnable, i.e. we do not obtain trivial solutions. We retained a multilayer perceptron and added a simple regularization technique composed of two characteristics of the new space: it has fixed representatives, e.g. the centroids for k-means or the distributions for the mixture of Gaussians are fixed, and the features have high variance. We compared the new model to classic and deep clustering algorithms on three datasets, and the empirical results show that the proposed method is close to the classic ones in terms of performance.
Estimativa do Tempo de Resolução de Issues no GitHub Usando Atributos Textuais e Temporais

Estimating issues resolution time is one of the most important steps in software maintenance processes. However, although the subject is covered in the literature, there are few specific models for GitHub. This platform is very popular mainly in the open source context but its issue tracking system is not bureaucratic and issues are registered in a very simple way, which makes the process of building predictive models even more challenging. This work aims to develop machine learning models to estimate the resolution time of issues from GitHub. To handle the data scarcity, we propose textual attributes to capture issues characteristics; and temporal attributes to provide information about the time of issue events. Neural networks were used in classification algorithms and proved to be more suitable for solving this problem. To validate the proposed models we compared them with a reference from literature through different metrics and the results were positive with a significant improvement in accuracy.