Reviewerly/exHarmony huggingface dataset:
Crossing Disciplinary Borders to Improve Requirements Communication

Abstract Software requirements specifications (SRS) serve as an important source of information for a variety of roles involved in software engineering (SE) projects. This situation poses a challenge to requirements engineers: Different information needs have to be addressed, which are strongly dependent on the particular role(s) that SRS stakeholders have within a project. This chapter summarizes the contributions of a thesis that aimed to address and reduce role-specific defects in SRS that negatively influence the efficient usage and acceptance of these documents. To achieve this goal, we collected empirical data about role-specific information needs in a series of empirical studies that served as a baseline for a secondary analysis toward the definition of role-specific views. Moreover, we realized a proof-of-concept implementation that is capable of generating role-specific views on SRS. The results of a case study revealed that role-specific views have the potential to efficiently support SRS consumers during the analysis of a given SRS. Besides conducting further empirical studies in industry, future work aims to foster cross-disciplinary collaboration and requirements communication, especially in agile teams. Thereby, we are exploring synergy potential with best practices from non-SE disciplines.
Financial Stability of University as Factor Improving the Competitive Ability of the National Economy

This article considers the problem of ensuring financial stability at universities. In today's world, ensuring the competitive edge of the national economy in the global markets is only possible if the country has the highly skilled human resources required by the digital economy. Training such human resources depends on the qualifications of university teachers, the availability of adequate facilities and equipment, as well as research and development works of the world level. It is only possible to ensure the quality of education if the university can use its funds independently and determine the appropriate proportions within the budget. This article presents the acceptable proportions of university budgets that can help achieve efficient development and ensure the financial stability of the university. Maintaining these proportions is only possible if universities are autonomous.
3D Hand Pose Estimation for Guqin Performance

3D Hand Pose Estimations an important research content in the field of human-computer interaction, virtual reality, augmented reality and other gesture interaction. In this paper, 3D hand pose surface estimation based on personalized hand features is proposed and applied to guqin performance. We constructed the database of basic finger-pointing for guqin performance,and based on the Mask R-CNN and FPN network structure, a new MMFPN structure is proposed, which can not only realize the three-dimensional surface estimation of basic finger-pointing, but also effectively solve the problem of self-occlusion.
Aggregation with Feature Detection

Aggregating features from different depths of a network is widely adopted to improve the network capability. Lots of modern architectures are equipped with skip connections, which actually makes the feature aggregation happen in all these networks. Since different features tell different semantic meanings, there are inconsistencies and incompatibilities to be solved. However, existing works naively blend deep features via element-wise summation or concatenation with a convolution behind. Better feature aggregation method beyond summation or concatenation is rarely explored. In this paper, given two layers of features to be aggregated together, we first detect and identify where and what needs to be updated in one layer, then replace the feature at the identified location with the information of the other layer This process, which we call DEtect-rePLAce (DEPLA), enables us to avoid inconsistent patterns while keeping useful information in the merged outputs. Experimental results demonstrate our method largely boosts multiple baselines e.g. ResNet, FishNet and FPN on three major vision tasks including ImageNet classification, MS COCO object detection and instance segmentation.
An Empirical Study on Bias Reduction: Clipped Double Q vs. Multi-Step Methods

In actor-critic setting, Twin Delayed Deep Deterministic policy gradient algorithm (TD3) decelerates the overestimation bias by introducing a clipped variant of Double Q- leaning and achieves better results than Deep Deterministic Policy Gradient (DDPG). Another method proposed for bias reduction is Multi-step DDPG (MDDPG) which has comparable performance and is more computationally efficient than TD3. This paper builds on top of Soft Actor-Critic (SAC), which incorporates the clipped double Q target estimates and entropy maxi-mization, and proposes Multi-step SAC with Single Q target estimate (MSAC- Single) for empirical study on the effect of removing clipped double Q estimates from SAC and whether using multi-step method instead can get the same performance. Additionally, this paper also examines the performance of multi-step SAC (MSAC) with both bias reduction methods and makes run time comparisons.
Applying Data Mining of Association Rules as Decision Making In Coffee-Shop: a Case Study

This study aims to transactions analysis from a coffee shop transaction in six months to define consumer purchases pattern with the development of a model of association rules. One of the coffee shops in Jakarta, the 8th Bean Cafe, was used as a study case in this study. The problems that occurred when deciding not based on data analysis, so they lost for 3 sales periods. This cafe has 80 menus that keep changing every time according to the owner’s wishes without knowing the favorite menu, the most frequently purchased menu, and so on. They never analyze what menu that consumers are interested in buying. Based on the results found 3 association rules, namely {CLASSIC $\Rightarrow$ SIGNATURE}, {FRIED RICE AND PASTA $\Rightarrow$ LIGHT BITES}, {LIGHT BITES FRIED RICE AND PASTA} from sales transaction data mining. The rules provide information that two types of 2-itemset combinations tend to buy together.
Styled Comic Portrait Synthesis Based on GAN

Although there are many studies on NPR (Non-Photorealistic Rendering) image synthesis using GANs (Generative Adversarial Networks), it is still difficult to create high-quality comic portraits of a real person. Moreover, there are few studies focused on the painting styles of comic authors, which is the style that makes a comic visually unique. It is expected that for comic readers, the synthesized comic portraits can be more attractive and meaningful if the portraits are presented in the user-preferred comic styles. Therefore, this paper proposes a styled comic portraits synthesis system based on CycleGAN and PIX2PIX. By integrating Deep Learning and NPR techniques, the proposed system aims to transform user’s real pictures into comic portraits with features preserved and defined painting style presented. CNN (Convolutional Neural Networks) is trained to classify the painting styles of comic authors. After that, two sets of GANs are trained with classified and augmented dataset, which is generated by mapping comic characters’ 2D texture onto perturbed and deformed 3D facial models. The experiment results show that the proposed system can successfully create clear and vivid comic portraits, which has a great potential to serve as a useful tool for social network and comic industry.
Simple but Efficient Results-Fusion Method for Object Detection

Each algorithm learns the location and label of the target by designing various network structures, but different models have different feature extraction methods for the same input resulting in different detection results. We notice that some target positions detected by different models are different and many algorithms always lose targets. Therefore, in order to find these targets and improve the detection performance, this paper proposes a results-fusion method using soft non-maximum suppression algorithm to solve results fusion of different model, which takes the detection output of the two models as the input. We take the face detection algorithm as an example to verily the effectiveness of the method and prove it can increase detection performance.
Functional Literacy in the Russian Scientific Dimension

The article examines the current state and prospects of the scientific and methodological aspects of the functional literacy formation. It is noted that the relevance of the purposeful formation and monitoring of functional literacy is determined by considerations of the economic efficiency of human activity in the modern world. The problem area of functional literacy is considered. The data obtained as a result of content analysis of text collections on functional literacy in the scientific electronic library eLIBRARY.ru show that the number of publications on the topic of functional literacy continues to grow. However, the nature of the predominantly cited sources of the carriers and directions of functional literacy considered in publications demonstrates discrepancies in understanding. On the one hand, there is no difference between the phenomenon of functional literacy and the corresponding scientific concept (construct). On the other hand, the use of the concept of "functional literacy" in scientific discourse is distinguished by universalism and intuitionism. It was revealed that in the center of the supertext “Functional literacy” in the Russian scientific discourse there is a link of the concepts “activity” and “learning,” which is bi-directionally connected by the concept of process. It is based on general pedagogical ideas, which are concretized in various aspects, leaving the integrative properties of personality (competence) and result (quality) in the last terms of priority. The process of interaction as an aspect of functional literacy is outside the focus of researchers’ attention, which negatively affects the results of pedagogical efforts. The purposeful development of communicative functional literacy as the basis of human interaction with a rapidly changing world is proposed.
Deep Learning Classification System based on User Guidance

Currently, deep learning models are widely used in many applications. When utilizing these models to different environments, the experience like the distribution of objects in an environment can be used to further increase the accuracy of classification. In this paper, we carried out user guidance based deep learning classification task that is named as DLC-UG, which efficiently utilizes this kind distribution in an environment to increase the accuracy of classification. Firstly, we train the deep learning model on the training set. Secondly, the user can input the distribution of each label in corresponding environment by the experience of himself or advice from others. Thirdly, we can get the probability of labels on each sample by this trained model. Finally, the distribution information selectively cooperates with the probability of labels on testing samples. We select six popular deep learning models on three real datasets for the evaluation. The experimental results show that our method can increase the accuracy of classification tasks, which is obviously higher than state-of-art methods.
MSCRC:Multi-layer Sidechain with Compressed Rootchain Blocks

With the development of blockchain technology, people are safety and privacy guaranteed to own digital assets benefited from digital economy system. Meanwhile, the problems have risen that how can we make interactions between different digital economy systems, and, more importantly, how could our assets kept safe as it claims to be. To address this problem, we present our protocol based on a trusted root chain, Ethereum for example. Organization or Government agencies with independent digital economy systems are connected in a tree-like hierarchy with smart contracts to the root chain. User assets can freely flow from root chain to any digital economy system with safety guaranteed and timely withdraw strategy. We release the burden of root chain by periodic write headers through smart contracts. A fraud proof challenge solution and security deposit strategy are presented to solve the data access problem. With this protocol, diversity of digital assets system can be built without efforts to earn trusts from public, and the connections between them are easily implemented since they are inherited from the same root chain.
The Integration of Blockchain and Enterprise Network: a Distributed Operation Solution

As a collaborative and associated system of industrial clusters, enterprise networks has encountered trust problems in distributed business scenarios, and the service providers or clients of its operational solutions may leave due to their own interests. Traditional trustworthiness methods are gradually losing their advantages due to the shackles (single point of failure and fairness query) or elbows (multiple iterations and performance bottlenecks) of the trust intermediary. Undoubtedly, the trustworthiness method of blockchain and the mechanism for resolving disputes are extremely important to the distributed operation mode of enterprise networks. We proposed three modes of blockchain and enterprise network integration and focused on the private chain mode of blockchain and star structure enterprise network integration, the alliance chain mode of blockchain and linear structure enterprise network integration. It also proposes applicative and distributed operation solutions based on blockchain because of its characteristic like decentralization and non-repudiation for enterprise network autonomy scenarios. Our research introduces the blockchain technology into the construction of the logistics and information flow ecosystem to master the laws of information resource flow, optimize the management of information resources, realize the innovation and development of industrial clusters, and provide theoretical reference and practical guidance.
Data Center New Energy System under the “Peak Carbon Dioxide Emissions and Carbon Neutrality” Background and Comprehensive Evaluation Analysis Thereof

The targets of “peak carbon dioxide emissions and carbon neutrality” put forward the requirements of high efficie ncy, high reliability and low carbon emission for the data center. A new totally direct current data center energy system with totally direct current supply and access to renewable energy and large-scale energy storage is proposed through analyzing the features and requirements in each sub-link of the data center “power supply-grid-load-energy storage” in the paper. The analytic hierarchy process and fuzzy comprehensive evaluation method are utilized for comparative evaluation analysis between the new totally direct current data center energy system and the traditional alternating current data center energy system through establishing an index system considering system efficiency, availability, economy and environmental protection. The results show that the new totally direct current data center energy system proposed in this paper has higher efficiency and lower carbon emission than the traditional data center energy system. The comprehensive evaluation result is still better although the economy is slightly worse. It belongs to a new plan which can be considered in the future.
A Gradually Linearizing Kalman Filter Bank Designing for Product-Type Strong Nonlinear Systems

Our study aimed to improve the poor performance of existing filters, such as EKF, UKF and CKF, that results from their weak approximation ability to nonlinear systems. This paper proposes a new extended Kalman filter bank focusing on a class of product-type strong nonlinear systems composed by system state variables, time-varying parameters and non-linear basic functions. Firstly, the non-linear basic functions are defined as hidden variables corresponding to system state variables, and then the strong nonlinear systems are described simplistically. Secondly, we discuss building two dynamic models between their future values of parameters, as well as hidden variables and their current values based on the given prior information. Thirdly, we recount how an extended Kalman filter bank was designed by gradually linearizing the strong nonlinear systems about system state variables, time-varying parameters and hidden variables, respectively. The first extended Kalman filter about future hidden variables was designed by using these estimates of the state variables and parameters, as well as hidden variables at current. The second extended Kalman filter about future parameters variables was designed by using these estimates of the current state variables and parameters, as well as future hidden variables. The third extended Kalman filter about future state variables was designed by using these estimates of the current state variables, as well as future parameters and hidden variables. Fourthly, we used digital simulation experiments to verify the effectiveness of this method.
Information Extraction Method of Power Grid Operation Mode Based on Double Layer Labeling Model

In the long-term power grid construction process, the grid dispatching department keeps many adjustment notices of grid operation mode, which record important grid equipment models and their corresponding operating status requirements. In order to facilitate dispatchers to quickly obtain information about abnormal equipment and its operating status when facing with emergencies, it is necessary to study the information extraction method to obtain grid operation rules from such texts. Due to the linguistic characteristics of the entities in the adjustment notices of power grid operation mode such as diverse expressions, uncertain lengths, complex structures and unclear boundaries, a double layer labeling (DLL) model is proposed. By constructing an independent deep learning network at each layer, DLL model first recognizes the outer-layer entities of power grid equipment model and operating state. Then, the key inner-layer entities which make up the power grid equipment model entity was recognized to map the power grid model entity to the corresponding ID. Finally, the unstructured adjustment notices of operation mode can be stored as structured information. The above method is a promising tool to help dispatchers reasonably arrange the operation mode of the power grid and eliminate the hidden dangers of the safe and stable operation of the power grid in time. The DLL model based on two independent Bidirectional Encoder Representation from Transformers and Long Short-Term Memory and Conditional Random Fields networks (BERT-BiLSTM-CRF) is proposed in the article, and the F1 values in the test data set reach 0.932 and 0.963, respectively. The experiment results validate the efficiency of the proposed model for extracting information from the adjustment notices.
Multi-font Curved Text Scene for Enhanced OCR Readability

Newspapers contain artistic components like headings, advertisements and different stylish posters, which are printed in such a way that text lines are oriented in differently and contain multi-font curved images. WordArt in Microsoft office is one such widely used tools for text multi-orientation, which can also stretch, skew, bend or modify the shape of the text. This type of text is not horizontal and stylized through multi-font properties which makes it difficult to recognize through conventional optical character recognition (OCR) systems. In this paper, a novel technique is proposed which transforms multi-line and multi-font curved text images into single font horizontal format. This method uses vertical and horizontal projection bar threshold techniques to divide text through line segmentation and word segmentation. Each character is then individually reshaped through its centroid in order to align it with the vertical axis. This approach yields high recognition accuracy for text strings which OCR fails to recognize before alignment.
VSCnet: A View Sematic Contexutal Based Network for Multi-label Classification of Cell

Medical image analysis is one of the most important applications of computer vision. Because of the complexity of medical images, classifiers need not only efficient feature extractors, but also information of different of vision semantic context to make prediction. As a result, many common deep learning models do not perform well in medical images. In this paper, we propose a novel architecture based on multi-view learning, which generate the view of label semantics context and view of global spatial context to make supplement each other’s blind spots. This method archive state of art performance in our dataset.
A high‐precision adaptive blind estimation method for chaotic time series

To improve the estimation accuracy of chaotic time series and reduce the computational complexity, a high-precision adaptive blind estimation method is proposed. By introducing the pre-treatment method, the adaptive identification model is established, and the linear estimation equation is derived. Through the organic combination of the model and the least square mechanism, the complexity of the model is limited. When changing the type and length of the sequence, the parameters can be changed adaptively, so as to construct the corresponding trajectory equation and realize the blind estimation of chaotic time series. The analysis and experimental results show that the estimation accuracy of this method can reach 10–16 order of magnitude.
“Do you mean I was wrong?” A Preliminary Approach on a Graph-based Framework for Suggesting Alternate Interpretations on Japanese Conversations

When a person cannot predict how his or her speech will be interpreted by others, communication problems will happen in person-to-person communications. In the case of communication at workplaces, junior staff may receive his or her supervisor’s words as verbal violence even if the supervisor spoke no offense words. This research aims to achieve the method that shows the candidates of other person’s interpretations in advance. If the interpretations were shown in advance, we can avoid speaking the words eliciting misunderstanding. As a concrete application, this research focuses on the conversation on text chat software. The text chat software shows the candidates of text interpretation which the other person will feel. An opinion mining research has reported that building a semantic tree is effective for text meaning recognition. The research of misinformation detection also has reported the effectiveness of graph data use. In this study, we construct a semantic tree to recognize Japanese text conversations. We also implement the function that transforms the text based on the grammar to show malicious meaning the receiver may perceive. The evaluation showed that the proposed method can transform texts into other texts that clearly express malicious meanings. A translation process was done in practical time, which was 0.32 seconds on average.
A Study on Class Imbalance Problem Using Genetic Algorithm

Class imbalance is a problem faced in machine learning and data science, which means classes are unevenly distributed across datasets. Machine learning algorithms assume that the datasets are uniformly distributed and thus gains more precision by avoiding the characteristics of the minority classes. The purpose of this article is to discuss the different techniques to solve this problem and compare them by applying on different datasets like Credit card fraud detection, medical diagnosis, fault detection, and device failure. Although there are two approaches such as data level and algorithmic level, the primary focus of this work is on data level techniques. The results show that Gensample has performed better than all other techniques, which means creating synthetic minority instances driven by evolutionary processes works best.