Reviewerly/exHarmony huggingface dataset:
Fair, Responsible, Secure and Private – The Changing Dimensions of AI

There has been an ever-increasing footprint of AI and machine learning in our business value chain and processes. Many process automations that consume machine leaning algorithms are being rolled with the aim of reaching the last mile of automation. Automations are getting smarter, learning from user actions, predicting business decisions and recommending choices. With all the evident positives, there is a question being asked as to how can we be sure that these algorithms are taking or recommending the right decision? Leading banking, life science and healthcare corporations are already invested into policy formulation towards controlling AI and machine learning algorithms-based programs with expectations of conformance to these policies. Accuracy cannot be the only parameter to evaluate effectiveness of machine learning algorithms simply because algorithms learn based on training but cannot identify if the training data itself is faulty. This paper evaluates methods that can be used to make machine learning based systems fair, responsible and ethical.
Enhanced Attentive Generative Adversarial Network for Single-Image Deraining

The problem of image rain removal has drawn widespread attention as the blurry images caused by rain streaks can degrade the performance of many computer vision tasks. Although exiting deep learning-based methods outperform most traditional methods, there are still unresolved issues in terms of performance. In this paper, we propose a novel enhanced attentive generative adversarial network named EAGAN to effectively remove the rain streaks and restore the image structural details at the same time. As rain streaks have different sizes and shapes, EAGAN utilizes a multiscale aggregation attention module (MAAM) to produce an attention map to guide the subsequent network to put conscious attention to rain regions. A symmetrical autoencoder with long-range skip-connections, squeeze-and-excitation (SE) modules, and non-local operation is further utilized to enhance the representation of the network. Finally, spectral normalization and a relativistic generative adversarial network (GAN) are further applied to improve the training stability and deraining performance. Both qualitative and quantitative validations on synthetic and real-world datasets demonstrate that the proposed approach can achieve a competitive performance in comparison with the state-of-the-art methods.
Knowledge Graph of Mergers and Acquisitions

Context driven decision making is a key factor to make critical decisions in business applications. We present the design and application of a knowledge graph to aid the context driven decision making for studying the patterns in Mergers and Acquisitions (M&A) activities in the industry. Using text data from news articles we make use of a Natural Language Processing pipeline to extract entities and relations to build a knowledge graph. The entity recognition model was 90.97% accurate in detecting the entities. The graph is further enriched with metadata from Wikipedia. We finally demonstrate two use cases to showcase the utility of the knowledge graph for making business decisions.
Reducing Negative Impact of Noise in Boolean Matrix Factorization with Association Rules

Boolean matrix factorization (BMF) is a well-established data analytical method whose goal is to decompose a single large matrix into two, preferably smaller, matrices, carrying the same or similar information as the original matrix. In essence, it can be used to reduce data dimensionality and to provide fundamental insight into data. Existing algorithms are often negatively affected by the presence of noise in the data, which is a common case for real-world datasets. We present an initial study on an algorithm for approximate BMF that uses association rules in a novel way to identify possible noise. This allows us to suppress the impact of noise and improve the quality of results. Moreover, we show that association rules provide a suitable framework allowing the handling of noise in BMF in a justified way.
Training Students for Work with Emerging Technologies in a Technology Park Environment

This paper discusses the training of secondary school students (oriented on working with emerging technologies) currently offered by the technology park of Kuban State Technological University in Krasnodar, Russia. Such training is integrated with research work in the technology park innovative training centers called quanta. Being constituent parts of the technology park environment, these quanta are multidisciplinary educational sites, specific interest clubs where multidimensional educational activities and supplementary education programmes are provided. Quanta promote the modernization of the country’s education system making it possible to accelerate the technical development of students, to train scientific personnel for the country’s economy, and to develop and implement modern methods of training (such as digital learning). All these innovations consider (adaptation for) working with emerging technologies. The technology park itself provides educational environment where students both acquire professional skills and undergo psychological adaptation for dealing with technology in its current and future state.
Research on Teaching Quality Assurance Based on the Combination of Theoretical System and Evaluation Model

In the wave of innovation and entrepreneurship in the whole society, colleges and universities have been committed to the construction of "double first-class" and strive to become the incubator and cradle of innovation and entrepreneurship. To achieve this goal, improve the quality of teaching and cultivate innovative talents is the fundamental method and key to solve the problem. Based on the macro education policy guidance, micro education system guarantee and specific teaching quality and effect guarantee, this paper establishes the corresponding teaching quality comprehensive evaluation model. The evaluation model consists of two parts: theoretical model and practical model. The analysis method of the theoretical evaluation model is mainly qualitative analysis of the teaching quality of the micro system guarantee part in this paper, which involves the establishment of teaching system requirements, the setting of corresponding functional departments, the guarantee of capital investment, the investment of software and hardware, and the investment of management cost. The analysis method of the actual evaluation model is mainly in a certain micro level On the basis of system guarantee, it is a kind of quantitative teaching quality analysis to sample, quantify and model the actual teaching data. The results of data experiment show that: compared with a single evaluation model, the comprehensive evaluation method combining theoretical model and practical model can get more scientific and accurate evaluation results. Finally, it helps the steady improvement of teaching quality and the reform and innovation of teaching mode.
Research On Adaptive Object Detection Method Of Kernel Correlation Filtering

Aiming at the problem that the KCF algorithm cannot adaptively track the object and even the object may be lost when the object scale changes, this paper proposes a kernel correlation filter algorithm that can perform adaptive object detection. The KCF algorithm is adopted to calculate the center position of the object, and the object is divided into sub-blocks for applying the KCF classifier idea to each sub-block at the same time. Then the center position of each sub-block is calculated for estimating the scale change ratio of the object according to the relative change of the center position of the sub-block. And the scale information of the object is finally determined for following tracking. On this basis, the HOG feature and the CN feature are combined to make full use of the image pixel information to promote accuracy of the object prediction. Finally, public dataset is used for comparing the proposed method with multiple classic algorithms. The experimental result illustrate that the proposed method can obtain better accuracy and overlapping ratio, and the processing speed can meet the real-time requirements, which verifies the effectiveness of the proposed algorithm.
Analysis and Countermeasures of higher education supply under the background of sustainable development

With the sustainable development of economy and society and the acceleration of the popularization of higher education, Henan Province, as a big province of education, has developed rapidly, but it also has some drawbacks. As an important mission of higher education, the supply of higher education is a systematic problem with regularity, which is closely related to the regional social and economic development. The problems of higher education supply seriously affect the energy of higher education. Based on this, this paper takes the supply of higher education as the starting point. On the basis of sorting out the current situation and problems of the supply evolution of undergraduate colleges in Henan Province, this paper puts forward some policy suggestions to improve the effective supply of higher education in Henan Province. This has a certain theoretical and practical significance to promote the effective supply of higher education in Henan Province and improve the supply quality of higher education in Henan Province.
Smart Programmed Light Source Calibrated and Software-Based Measuring Tool [SPLSC]

In engineering and technological field, to express or explain any idea, concept or principle, drawing is taken as an expressing medium. And for drawing, measuring phenomena plays vital role which obviously needs measuring tool. Whether it is physics, chemistry, or biology measurement is the key. So measurement is the phenomenon which is present everywhere. As increasing modern technology various measuring instruments are introduced but they have certain limitations like one instrument can measure maximum up to 4–5 quantities, materials failures like errors due to wear and tear, decreased accuracy based on prolonged time period, etc. To overcome such disadvantages and limitations we have introduced a unique multipurpose measuring instrument which works on the principle of light source directed by a programmed chip and software based on modern technology which is compact in size, more accurate, compatible, reliable and give ease in measurement.
Lithosphere of the West Transbaikalian Sector of the Central Asian Fold Belt According to Electromagnetic Studies

This paper presents the results of magnetotelluric (MT) studies performed within the Western Transbaikalia segment of the Central Asian fold belt along the Selenga River delta-Krasnyi Chikoi Village profile. The data are interpreted using the results of recent geological, petrological, geothermal, tectonic, and geochronological studies of the Mongolia-Transbaikalian part of the Central Asian fold belt, including large heterochronous provinces and zones. The studies have shown that the investigated area has a complex geologic and tectonic structure produced by extensive rifting leading to the formation of large crustal blocks and by intense magmatic fluid activity along deep fault zones. The investigated by a combination of blocks with different types of geoelectric section – mountain range and intermontane basins separated by long-lived deep fault zones. Significantly less tectonic activity of the West Transbaikalian rift zone, compared with the Baikal region of modern rift formation, was reflected in the contrast of the resistivity distribution and heat flow values in the North-Western and South-Eastern parts of the studied profile.
Service Oriented Computing for Humans as Service Providers

For the past twenty years, Service Oriented Computing has changed the way in which information technology was understood. The approach involves not only technological advances that have influenced the development of Software Engineering, such as Service Oriented Architecture, Web services, Service Choreography, or Microservices. In addition, it has also provided the pillars for the development of Cloud Computing, which has transformed how the business in Information and Communication Technology is developed. In that context, this work focuses on how Service Oriented Computing can also drive the integration of humans in the Internet of Things and Crowd Sensing loops by enabling them to act as service providers. The key to this is the deployment of services on mobile devices, in particular smartphones. The enormous penetration of these devices in today’s society, together with the personal nature of the information they handle, open a new horizon for the development of services. Through them individuals are able to make personal information available to others. This paper depicts Human Microservices, an architecture that allows humans to be considered as service providers, and discusses the open challenges in the field that conforms one of the next frontiers for Service Oriented Computing.
Towards Discovery of Differences of Source Code Through Semantic Difference Checking

Abstract Source code difference analysis plays an important role in software development, maintenance and regression testing. Many existing research methods are text-based, however, they are subject to code movement and alignment limitations, resulting in imprecise difference analysis. In addition, some methods are lexical and syntax-based, which ignore the key semantic information of the code and make the analysis results imprecise. In this paper, we propose a source code difference analysis technique based on semantic difference checking to improve the problem of inaccurate difference analysis. We implement the prototype, and exploit static analysis and textual difference detection tools to pro-process the programs, then perform semantic difference checking on the code with textual differences, and define the semantic differences. We conduct experiments on two C programs with differences and the results show that our approach can effectively and accurately detect code differences.
GraphGuess: Approximate Graph Processing System with Adaptive Correction

Graph-based data structures have drawn great attention in recent years. The large and rapidly growing trend on developing graph processing systems focuses mostly on improving the performance by preprocessing the input graph and modifying its layout. These systems usually take several hours to days to complete processing a single graph on high-end machines, let alone the overhead of pre-processing which most of the time can be dominant. Yet for most graph applications the exact answer is not always crucial, and providing a rough estimate of the final result is adequate. Approximate computing is introduced to trade off accuracy of results for computation or energy savings that could not be achieved by conventional techniques alone. In this work, we design, implement and evaluate GraphGuess, inspired from the domain of approximate graph theory and extend it to a general, practical graph processing system. GraphGuess is essentially an approximate graph processing technique with adaptive correction, which can be implemented on top of any graph processing system. We build a vertex-centric processing system based on GraphGuess, where it allows the user to trade off accuracy for better performance. Our experimental studies show that using GraphGuess can significantly reduce the processing time for large scale graphs while maintaining high accuracy .
A Big Data Intelligence Marketplace and Secure Analytics Experimentation Platform for the Aviation Industry

The unprecedented volume, diversity and richness of aviation data that can be acquired, generated, stored, and managed provides unique capabilities for the aviation-related industries and pertains value that remains to be unlocked with the adoption of the innovative Big Data Analytics technologies. Despite the large efforts and investments on research and innovation, the Big Data technologies introduce a number of challenges to its adopters. Besides the effective storage and access to the underlying big data, efficient data integration and data interoperability should be considered, while at the same time multiple data sources should be effectively combined by performing data exchange and data sharing between the different stakeholders. However, this reveals additional challenges for the crucial preservation of the information security of the collected data, the trusted and secure data exchange and data sharing, as well as the robust data access control. The current paper aims to introduce the ICARUS big data-enabled platform that aims provide a multi-sided platform that offers a novel aviation data and intelligence marketplace accompanied by a trusted and secure analytics workspace. It holistically handles the complete big data lifecycle from the data collection, data curation and data exploration to the data integration and data analysis of data originating from heterogeneous data sources with different velocity, variety and volume in a trusted and secure manner.
A Novel Data Set for Information Retrieval on the Basis of Subgraph Matching

We are facing the challenge of rapidly increasing amounts of data. Moreover, we observe that in many applications the underlying data contains strongly related entities making graphs the most appropriate structure for data modeling. When data is represented by means of a graph, querying corresponds to a graph matching problem. The present paper introduces a novel graph that models information from the medical domain with about 110,000 nodes and 220,000 edges. Additionally we present several basic benchmark queries, i.e. specific subgraphs, from different categories that can be found multiple times in the medical graph. Both the graph and the benchmark can be used to implement, test, and compare novel graph matching algorithms in a real world scenario.
Experimental research on mechanical performance of the lightweight composite slabs

Abstract Aimed at reliability model in the satellite communication system, the index weight of subjective problem, this paper proposes a method based on entropy weight - the combination of cloud model method, combining the expert’s subjective analysis in the process of modelling and model their own objective data to get a more complete set of index weight, and USES the fuzzy quantitative evaluation method for satellite communication system reliability model of the final evaluation results. The validity and feasibility of the algorithm are verified by an example, and the application of entropy weight-cloud model realizes the uncertain evaluation between the evaluation index and the evaluation level, which provides a new idea and method for the evaluation of the reliability model of satellite communication system.
Emotional RobBERT and Insensitive BERTje: Combining Transformers and Affect Lexica for Dutch Emotion Detection

In a first step towards improving Dutch emotion detection, we try to combine the Dutch transformer models BERTje and RobBERT with lexicon-based methods. We propose two architectures: one in which lexicon information is directly injected into the transformer model and a meta-learning approach where predictions from transformers are combined with lexicon features. The models are tested on 1,000 Dutch tweets and 1,000 captions from TV-shows which have been manually annotated with emotion categories and dimensions. We find that RobBERT clearly outperforms BERTje, but that directly adding lexicon information to transformers does not improve performance. In the meta-learning approach, lexicon information does have a positive effect on BERTje, but not on RobBERT. This suggests that more emotional information is already contained within this latter language model.
New performance measures for object tracking under complex environments

Various performance measures based on the ground truth and without ground truth exist to evaluate the quality of a developed tracking algorithm. The existing popular measures—average center location error (acle) and average tracking accuracy (ata) based on ground truth—may sometimes create confusion to quantify the quality of a developed algorithm for tracking an object under some complex environments (e.g., scaled or oriented or both scaled and oriented object). In this article, we propose three new auxiliary performance measures based on ground truth information to evaluate the quality of a developed tracking algorithm under such complex environments. Moreover, one performance measure is developed by combining both two existing measures (acle and ata) and three new proposed measures for better quantifying the developed tracking algorithm under such complex conditions. Some examples and experimental results conclude that the proposed measure is better than existing measures to quantify one developed algorithm for tracking objects under such complex environments.
AI-Based Enabled Performances Measurements for MOOCs

MOOC represents Massive Open Online Courses. There is a recent trend of attending online training where these MOOC courses are getting popular. The reason for the popularity is the ease of learning and teaching for both students as well as teachers. Students can learn anytime, anywhere as per their convenience and teachers can reach to a larger audience to impart their knowledge. There is a large number of enrollment for online courses, but it has also been noticed that there is a substantial amount of candidates dropping out of said course. The online courses conducted by Coursera, edX, Udemy and so on, is capturing higher enrollment from India. Learners from India account for 27% of edX, and 1.5 million users are registered to Coursera are Indians as in December 2016. By improving the online courses in India then we can attract those students enrolled in MOOC courses outside India. This project proposes to improve the MOOC courses by using novel technologies of the Machine Learning model. Personalized Learning Path, Recording Personalized Feedback, Performance Analyzer proposed in this project for MOOC courses improvement.
A FRAME OF REFERENCE FOR RESEARCH OF A BLOCKCHAIN-BASED SOLUTION TO CORPORATE GRC-MANAGEMENT

This society has faced many sorts of global challenges, especially the world of business and technological innovation, there's no greater aroused general argument nowadays than digital transformation (DT). Among production-oriented Corporates which many of them have begun to integrate digital technology into most areas of their business. How to well fulfill Corporate Governance, Risk and Compliance (GRC) while expanding existing businesses in the dramatic growth in DT environment has become a major challenge for all Corporates. Through a review of previous studying works and based on existing gaps the author evaluated whether there could be the feasibility of a blockchain-based technology being integrated as a solution to Corporate GRC-Management together with the theory of entire personnel's GRC responsibility system which would be parts of an agenda for the future research on this field and also contribute to Corporates in an overall strategic height level to embrace the coming of DT.