Reviewerly/exHarmony huggingface dataset:
Image Caption Based on Bigru and Attention Hybrid Model

To solve the problem that the detail features of image are not comprehensive enough to grasp the key semantic information, which leads to a great difference between the generative description result and the actual content of the image, an image semantic description method based on the BiGRU and Attention hybrid model is proposed by the author. Firstly, extract the global features of the image are extracted by the Convolutional Neural Network (CNN), and then each feature is obtained by the Channel Attention Mechanism. A weight value will be assigned to each feature channel based on its importance, so that the neural network can focus on certain channels. Secondly, the processed feature information is input to the Bidirectional Gated Recurrent Unit (Bidirectional Gated Recurrent Unit, BiGRU), capturing bidirectional semantic information to generate semantic information that can reflect the content of the image. Finally, an attention mechanism is added after BiGRU to effectively extract image features while focusing on more important information. The experimental results on the three public data sets of MSCOCO, Flicker8k and Flicker30k show that the proposed model is more effective than traditional image semantic description methods, and can effectively improve the accuracy of image semantic description.
Micro-level Assessment Procedure for Digital Competencies in the Context of Economy Digitalization

The digitalization of society is characterized by the widespread of information technology, the Internet, social media, and the Internet of Things in everyday lives. The dramatic digitalization of the last decade transformed many aspects of working life and routines for many people. Managing, accessing, interpreting, and using data on the Internet require specific skills. All of the more important skills people need to master are related to digital literacy that is required to assess the quality of the information received and analyze it. Digital competencies are becoming essential for survival in the modern world. This article presents the assessment procedure for digital competencies developed by the authors. The authors reviewed research works on digital skills and presented their interpretation of those. They also present some global definitions of digital economy competencies. Moreover, the authors compared the results of the research works on the digital skill divide. The article presents the results of digitalization level analysis in the Russian Federation. The authors formulated the principles to divide the population of the Russian Federation into groups to conduct research and determined the prominence of digital skills on the micro-level in the context of the digitalization of the economy.
Correction: Majeed, A.; Lee, S. Towards Privacy Paradigm Shift Due to the Pandemic: A Brief Perspective. Inventions 2021, 6, 24

The authors made a mistake while publishing this paper [...]
Scale-Aware Network with Scale Equivariance

The convolutional neural network (CNN) has achieved good performance in object classification due to its inherent translation equivariance, but its scale equivariance is poor. A Scale-Aware Network (SA Net) with scale equivariance is proposed to estimate the scale during classification. The SA Net only learns samples of one scale in the training stage; in the testing stage, the unknown-scale testing samples are up-sampled and down-sampled, and a group of image copies with different scales are generated to form the image pyramid. The up-sampling adopts interpolation, and the down-sampling adopts interpolation combined with wavelet transform to avoid spectrum aliasing. The generated test samples with different scales are sent to the Siamese network with weight sharing for inferencing. According to the position of the maximum value of the classification-score matrix, the testing samples can be classified and the scale can be estimated simultaneously. The results on the MNIST and FMNIST datasets show that the SA Net has better performance than the existing methods. When the scale is larger than 4, the SA Net has higher classification accuracy than other methods. In the scale-estimation experiment, the SA Net can achieve low relative RMSE on any scale. The SA Net has potential for effective use in remote sensing, optical image recognition and medical diagnosis in cytohistology.
Prognostic Model of a Photovoltaic Power Plant

Economically viable integration of such variable renewable energy (VRE) resources as wind and solar power into local, regional, and national electric energy systems is impossible without active balancing of the whole energy market. In this context, hourly prediction of electric energy generation by means of the renewable sources one day in advance is one of the most topical problems. It should be mentioned that despite the availability of numerous proposed algorithms, programs, and program systems the problem has not any adequate universally recognized solution. Electric energy generation by means of photovoltaic power stations depends directly upon weather changes becoming more and more unpredictable. Strictly speaking, the problem of photovoltaic solar energy conversion amounts to a problem of developing a mathematical model able to reflect the most accurate dependence of energy production upon each influencing factor. The paper analyzes certain approaches to develop the taught prognostic models of photovoltaic power stations.
Changes in Hiring and Recruiting process at Physics Mindboggler in the new normal.

The practice of recruiting, hiring, deploying, and managing an organization's personnel is known as human resource management (HRM). Human resource management (HRM) is a term that is frequently used interchangeably with the term "Human resources". Human Resource Management is concerned with the principles, methods, and technology that are used to increase an organization's productivity. HR professionals accomplish this by implementing tactics and policies that boost employee productivity. Employees or staff are also known as human capital, the lifeblood, and the most valuable resource in most businesses.Physics Mindboggler (PM) is a social venture launched in 2016 and supported by Bennett Hatchery, the on-campus incubator at Bennett University, a Times Group initiative. Fascinated by physics, They strive to share its beauty and scope with the young minded generation. Practicing recruiting, hiring, deploying, managing and other human resource activities at Physics mindboggler is totally a different task as it creates and bridge the gap between two totally different streams, As at PM we promote science and physics while being on the path of management and entrepreneurship. Usually these two are totally different streams but at PM we promote science by using management functions including marketing, human resource management, finance, operations and many more.Physics Mindboggler is a Bennett University based Tech-Startup, founded in the year 2016 as a blogging platform for physics and science lovers, then converted into full-fledged social venture in the year 2018. Here at PM, They do not promote the culture or behaviour as owner and employee relationship in the company rather than that they work as partners with equal interest and stake in the company. PM is a dynamic Team of energetic go-getters with big dreams. With constant support from its eminent Advisers, It is committed to embedding curiosity and imagination to stimulate scientific and economic development. PM’s present services include blogs, vlogs, books, science kits & tools and research around Physics. PM’s future plans include inspirational initiatives for the student community and professional initiatives for the academic and research community.
Some Thoughts on Proof Discovery

This chapter mentions some of the exciting new efforts to speed up the search process in Automated Theorem Proving (ATP), and expresses the belief that these new “speed demons” will play a crucial role in modern technology, where a great deal of reasoning power is needed. It then argues that speed alone can never cope with the really difficult theorems that arise from mathematics and challenging application areas, theorems that are nevertheless fairly easy for gifted mathematicians. The authors suggests that a part of the research effort in ATP be directed toward methods which have proven useful (powerful) for human mathematicians, but which have been largely neglected by the ATP community. The field of AI has long recognized the importance of knowledge, and knowledge bases which contain “how-to” methods as well as facts. This is the key ingredient of Expert Systems, which attempt to duplicate expert behavior.
What You See Is What You Get: Practical Effect Handlers in Capability-Passing Style

Abstract Structuring control flow is an essential task almost every programmer faces on a daily basis. At the same time, the control flow of software applications is becoming increasingly complicated, motivating languages to include more and more features like asynchronous programming and generators. Effect handlers are a promising alternative since they can express many of these features as libraries. To bring effect handlers closer to the software engineering practice, we present capability passing as an implementation technique for effect handlers. Capability passing provides the basis for the integration of effect handlers into mainstream object-oriented programming languages and thereby unlocks novel modularization strategies. It also enables programmers to apply lexical reasoning about effects and gives rise to a new form of effect polymorphism. Finally, it paves the path for efficient compilation strategies of control effects.
MODERN TRENDS IN THE DEVELOPMENT OF MATHEMATICAL MODELING IN THE EPOCH OF DIGITAL TECHNOLOGIES

The article analyzes the concept and significance of mathematical modeling.Particular importance is given to the trends of recent years, which is caused by the sharp development of digital technologies.As an example, a mathematical model is presented, which can solve the problem of forecasting the development of the municipality.In the framework of the work, a number of regression equations of the model are presented, which make it possible to forecast the key indicators of the socio-economic development of the municipality.
Wheeler’s delayed-choice experiment based on Rydberg atoms

Abstract The delayed choice gedanken-experiment initially proposed by J A Wheeler reveals the nontrivial features of wave–particle duality in quantum world, that is, the choice of whether to observe the particle/wave character of a quantum system at a delayed time can affect its evolution history. In this work, the Wheeler’s delayed-choice experiment is conducted and tested in a new regime based on a Rydberg-ground level atomic Mach–Zehnder interferometer with optical coherent manipulation. Our quantum system exhibits an excellent single-excitation of the collective atomic state due to the Rydberg blockade effect, and the delayed choice of the interferometer configuration is realized by a quantum random number generator based on high-quality Rydberg single-photon source. Our experimental results demonstrate the Wheeler’s nontrivial time order inversion in this quantum system and confirm the Copenhagen interpretation of the physical reality of quantum state, which means the particle/wave character can only be ascribed to a quantum system when it was measured. This work is encouraging toward testing the foundations of quantum mechanics using the Rydberg atoms.
Weil polynomials of abelian varieties over finite fields with many rational points

We consider the finite set of isogeny classes of [Formula: see text]-dimensional abelian varieties defined over the finite field [Formula: see text] with endomorphism algebra being a field. We prove that the class within this set whose varieties have the maximal number of rational points is unique, for any prime even power [Formula: see text] big enough and verifying mild conditions. We describe its Weil polynomial and we prove that the class is ordinary and cyclic outside the primes dividing an integer that only depends on [Formula: see text]. In dimension [Formula: see text], we prove that the class is ordinary and cyclic and give explicitly its Weil polynomial, for any prime even power [Formula: see text].
Distributed Information Flow Control in Serverless Computing

To secure a serverless system, an approach using Distributed Information Flow Control is proposed to be constructed. For a system to be secure at the programming level, it is imperative that confidentiality and integrity are enforced. While confidentiality is described as the virtue of a system to protect its sensitive data from being disclosed to unauthorized parties, whereas integrity can be explained as the ability of a system to prevent an information from being manipulated or modified by unauthorized parties. All the essential ideas are elaborated in this section as follows: 1.1 introduces Serverless Computing, whereas 1.2 talks about the information flow control model. Furthermore, 1.3 describes the phenomena of non-interference in security, and 1.4 describes two types of information flow control enforcements, Termination Insensitive Non-Interforence (TINI), and Termination Sensitive Non-Interforence (TSNI) where TSNI is described formally. Finally, 1.5 discusses the existing approaches that employ IFC to secure serverless systems.
Feature Recognition Technology of Substation Inspection Image

The task of target detection is to locate and classify the detected objects in the image. Applying it to the power inspection reduces the manpower and time consumption, improves the efficiency of the inspection work, and is of great significance to ensure the reliability of the power system operation. In this paper, the Faster R-CNN method is applied to the state detection of cotter pins. And the detection process is divided into two parts. The first part is the image preprocessing part, which will expand the date set by adding noise, sharpening, and shadowing on the original date. The cotter pins can be marked by the labeling tool Labelimage. The second part realizes the state detection of the cotter pin by Faster R-CNN model. The target candidate area is closer to the true value because of the unique RPN technology of the Faster R-CNN network. On the basis of the Faster R-CNN network, the update weight adopts L2 regularity to reduce the impact of over-fitting, which also solves the problems of gradient dispersion and gradient explosion to a certain extent. The Tensorflow's deep learning framework is applied in the experiment test to realize the location and classification of the four states: normal cotter pin, cotter pin not split, cotter pin single leg out, and cotter pin missing. The experimental results verify the correctness and effectiveness of the proposed method.
Student Facial Expression Recognition Based On Multi-scale Residual Network

The monitoring of student learning status is a hot topic in the field of visual analysis such as intelligent classroom and digital teaching. Its essence is the students’ facial expression recognition. In this paper, we proposed an expression recognition method based on multi-scale residual network to solve the problems of different degrees of occlusion in the classroom. The model realizes the fusion of multi-scale features for expression recognition by integrating different scale features, and effectively inhibits the influence of partial occlusion in the classroom scene. To verify the effectiveness of the model, the data-set of students’ facial expressions was established based on the actual classroom scene. The average accuracy of the test reached 93.6%. The results of the experiments show that the propose model could accurately identify students’ facial expressions in class.
Graph space: using both geometric and probabilistic structure to evaluate statistical graph models

Abstract Statistical graph models aim at representing graphs as random realization among a set of possible graphs. To evaluate the quality of a model $M$ with respect to an observed network $G$, most statistical model selection methods rely on the probability that $G$ was generated by $M$, which is computed based on the entropy of the associated microcanonical ensemble. In this article, we introduce another possible definition of the quality of fit of a model based on the edit distance expected value. We show that adding a geometric structure to the microcanonical ensemble induces an alternative perspective which may lead to select models which could potentially generate more different graphs, but whose structure is closer to the observed network. Finally, we introduce a statistical hypothesis testing methodology based on this distance to evaluate the relevance of a candidate model with respect to an observed graph.
A Graph-based Feature Selection using Class-Feature Association Map (CFAM)

The quality and size of the feature space play an essential role in the main process of machine learning and directly affect the results of data mining. Feature selection as a preprocessing step in data mining is used to find the effective features while removing redundant and irrelevant ones from the initial set of features. One way to understand the relationships among the features is to represent them as a graph. In this paper, a graph-based method is proposed for feature selection. To represent the feature space as a graph, we use the Class-Feature Association Map (CFAM) concept, which considers both relevance and redundancy and is constructed using Spearman's correlation measure. Then we apply the Leiden algorithm for detecting communities of the graph and finally use eigenvector centrality and variance score to find the most significant features from the communities. According to experimental results, our algorithm reduces an average of 70.6% of features while achieving higher or competitive classification accuracy with the selected features.
Residual Networks with Channel Attention for Single Image Super-Resolution

With the development of convolution neural network (CNN), CNN-based methods also achieve a great success for image super-resolution tasks. Further, ResNet makes it possible that the network of super-resolution can be trained deeply. However, simply increasing the depth or width of the network has a scant improvement on the reconstruction quality. Therefore, we need to exploit some new mechanisms to boost the quality of the reconstructed SR images. In this paper, we utilize channel attention mechanism to rescale channel-wise features and extract the desired high-frequency information. Additionally, we add feature fusion structure into the network in order to make full use of all the extracted middle high-frequency information instead of that extracted only by the final layer. Experiments we have conducted show that the network we proposed could reconstruct high quality images with only a few parameters.
Exploration and Practice of Subject Service in Tianjin University Library under the Background of Supporting the Construction of

The original responsibilities of university library mainly focus on cultivating creative talents, scientific and technological innovation and knowledge discovery. The rapid development of new media application has brought unprecedented challenges to the traditional library information literacy education. Taking the library of Tianjin University as a case, this paper introduced the exploration and practice of subject service. In the face of multi-dimensional subject information needs and deep-seated knowledge service requirements in the digital scientific research environment, libraries should make use of their own advantages, take more methods to embed in the front line of scientific research, and provide a full range of subject services for teachers and students. At the same time, it also improves the level, ability and quality of subject services carried out by University Libraries in the new era.
Disclosure and Forecast of Stock Issuance Information Based on High Performance Computing and Blockchain Technology

Blockchain technology can improve the security and controllability of the blockchain system by controlling membership, but it cannot ensure that all parties will not conspire to tamper with the basic agreement and ultimately harm the interests of other participants. This article established a model: CNN network model based on the distributed mechanism of blockchain technology to predict the stock issuance. The research results show that only when the application cost of blockchain technology is controlled within a certain range, that is, when the BC is 36.01, can the losses caused by the many drawbacks of the traditional model be compensated, thereby promoting the active use of the technology in the securities market.
Supporting maintenance and testing for AI functions of mobile apps based on user reviews: An empirical study on plant identification apps

Abstract Despite the tremendous development of artificial intelligence (AI)‐based mobile apps, they suffer from quality issues. Data‐driven AI software poses challenges for maintenance and quality assurance. Metamorphic testing has been successfully adopted to AI software. However, most previous studies require testers to manually identify metamorphic relations in an ad hoc and arbitrary manner, thereby encountering difficulties in reflecting real‐world usage scenarios. Previous work showed that information available in user reviews is effective for maintenance and testing tasks. Yet, there is a lack of studies leveraging reviews to facilitate AI function maintenance and testing activities. This paper proposes METUR, a novel approach to supporting maintenance and testing for AI functions based on reviews. Firstly, METUR automatically classifies reviews that can be exploited for supporting AI function maintenance and evolution activities. Then, it identifies test contexts from reviews in the usage scenario category. METUR instantiates the metamorphic relation pattern for deriving concrete metamorphic relations based on test contexts. The follow‐up test dataset is constructed for conducting metamorphic testing. Empirical studies on plant identification apps indicate that METUR effectively categorizes reviews that are related to AI functions. METUR is feasible and effective in detecting inconsistent behaviors by using the metamorphic relations constructed based on reviews.