Reviewerly/exHarmony huggingface dataset:
A Span-Based Distantly Supervised NER with Self-learning

The lack of labeled data is one of the major obstacles for named entity recognition (NER). Distant supervision is often used to alleviate this problem, which automatically generates annotated training datasets by dictionaries. However, as far as we know, existing distant supervision based methods do not consider the latent entities which are not in dictionaries. Intuitively, entities of the same type have the similar contextualized feature, we can use the feature to extract the latent entities within corpuses into corresponding dictionaries to improve the performance of distant supervision based methods. Thus, in this paper, we propose a novel span-based self-learning method, which employs span-level features to update corresponding dictionaries. Specifically, the proposed method directly takes all possible spans into account and scores them for each label, then picks latent entities from candidate spans into corresponding dictionaries based on both local and global features. Extensive experiments on two public datasets show that our proposed method performs better than the state-of-the-art baselines.
Research on Tactical Intention Recognition Method Based on Probabilistic Network Ontology Language

According to the characteristics of uncertainty, coordination and distribution in tactical intention recognition, this paper proposes a process of tactical intention recognition oriented to intelligent command and control decision support, which combines the perceptual ability of deep learning, the decision ability of reinforcement learning and the reasoning ability of multi-entity Bayesian network, with complementary advantages. At the same time, in order to realize the exchange, sharing and reuse of information, improve the representation and reasoning ability of traditional Bayesian network for the uncertainty in the field of tactical intention recognition, a method of tactical intention recognition based on probabilistic network ontology language is proposed. Simulation results verify the effectiveness and feasibility of the method.
Research on Industrial Data Desensitization Algorithm Based on Fuzzy Set

With the rapid development of internet technology, informatization and digitalization have penetrated into every link of human social life. A large amount of sensitive data has been accumulated and is still being generated within the enterprise. These sensitive data runs through the daily operation of enterprises and is widely used in business analysis, development and testing, and even some outsourcing business scenarios, which are increasing the possibility of sensitive data leakage and tampering. In fact, due to the improper use of data and the lack of protective measures and other reasons, data leakage events have happened again and again. Therefore, by introducing the concept of fuzzy set and using the membership function method, this paper proposes a desensitization technology framework for industrial data and a data desensitization algorithm based on fuzzy set, and verifies the desensitization effect and protective action on sensitive data of this algorithm through the test data desensitization experiment.
Research on Differential Power Analysis of Lightweight Block Cipher LED

LED algorithm is a new lightweight encryption algorithm proposed in CHES 2011, which is used for IOT to protect the communication security of RFID tags and smart cards. It has been found that it is possible to retrieve secret key of algorithm by appropriate analysis of the devices’s power consumption, and the differential power analysis is the most powerful attack. This paper proposed a method of differential power analysis on LED algorithm. Combined with hardware circuit, it designed and implemented differential power analysis of the LED encryption system. The experimental results show that method which is proposed in this paper was cracked the 64 bits key of LED. Thus, without protection, the cipher LED would be difficult to resist differential power analysis. and according this, We provide a general analysis method of differential power analysis on other lightweight cryptographic algorithms.
Training of transport specialists and self-regulation of teachers and managers of educational institutions as human capital development strategy

Abstract Improving the quality of human capital in transport is closely related to the development of the ability of transport specialists to self-regulation. The training of modern, qualified transport specialists (as human capital development strategy) requires no less highly qualified teachers and managers, capable of not only managing others, but also themselves. In modern socio-cultural space, researchers pay special attention to the preservation and development of man as the highest value, the recognition and upholding of him as the highest value on earth. Such an understanding assumes the professionalism of consultative interaction with the client as a person, partner and professional, based on a detailed analysis of the characteristics and properties, processes and results of human activities. Appeal to followers in understanding the human mental life is associated with well-known methods for diagnosing and transforming the most complex internal world of a person, as well as the features, processes and results of the interaction of the internal and external worlds surrounding a person. Psychological comprehension of a person, penetration into the matrix structures of his personal, interpersonal and professional formation and development is sometimes serious difficulties, so our appeal to the psychodynamic and behavioural models of self-regulation, as well as in social and individual psychology and their methods, is dictated by these ideas. The appeal to classical conceptual constructs, in particular, modern and traditional domestic and foreign psychology, allows us to comprehend and practically develop methods and methods of immersion in the mental world of a person, his research, as well as subsequent transformation and development.
Implementation for Geological Environment Sub-node Intelligent Search of Geological Cloud 2.0

The "Geological Cloud 2.0" intelligent search system is an engine for fast search of data resources and systems on the cloud. The geological environment sub-node of "Geological Cloud 2.0" as one of distributed sub-nodes on the cloud provides data information for intelligent search. In order to serve better the "Geological Cloud 2.0" intelligent search, the metadata and exchange format of the geological environment entity database was defined to construct intelligent search indexing. And the metadata content interactive push process was introduced from the geological environment sub-node entity database to "Geological Cloud 2.0" intelligent search system. The results show that the geological environmental sub-node search information for Geological Cloud 2.0 intelligent search is synchronized with entity database and has attention, which provides experience for the construction and upgrade of the "Geological Cloud" intelligent search in the future.
Generating Emotional Social Chatbot Responses with a Consistent Speaking Style

Emotional conversation plays a vital role in creating more human-like conversations. Although previous works on emotional conversation generation have achieved promising results, the issue of the speaking style inconsistency still exists. In this paper, we propose a Style-Aware Emotional Dialogue System (SEDS) to enhance speaking style consistency through detecting user’s emotions and modeling speaking styles in emotional response generation. Specifically, SEDS uses an emotion encoder to perceive the user’s emotion from multimodal inputs, and tracks speaking styles through jointly optimizing a generator that is augmented with a personalized lexicon to capture explicit word-level speaking style features. Additionally, we propose an auxiliary task, a speaking style classification task, to guide SEDS to learn the implicit form of speaking style during the training process. We construct a multimodal dialogue dataset and make the alignment and annotation to verify the effectiveness of the model. Experimental results show that our SEDS achieves a significant improvement over other strong baseline models in terms of perplexity, emotion accuracy and style consistency.
L-measure evaluation metric for fake information detection models with binary class imbalance

Fake information in social media frequently causes social issues. The amount of fake information is smaller than that of real information, this leads to class imbalance. Some improved classification methods and metrics to resolve the imbalance and evaluate model performance have been proposed, respectively. However, the existing metrics for classification methods have many limitations. This paper proposes the robust metric, L-measure, that can reasonably evaluate all models with binary class imbalance with different IRs. L-measure also require less computation than the Matthews correlation coefficient. Finally, this paper demonstrates the validity of the proposed metric under different IRs with examples from UCI and Kaggle.
Proposal of a Framework for Information Migration from Legacy Applications in Solidarity Financial Sector Entities

Software development tools evolve rapidly, which allows the design and implementation of new software applications that optimize every time the automation of processes required in any type of business sectors. New applications replace legacy systems including also the upgrade of operating systems and infrastructure. However, information is a resource that cannot be replaced and requires the cleaning, improvement and movement from the legacy system to the new target application; this process is called information migration. In literature, migration focused on infrastructure and information is limited only to the creation of tools for Extraction-Transform-Load, which in most of the cases is enough. In developing countries, Solidarity Financial Sector Entities are entities that grow quickly and often require the migration of software systems and therefore their corresponding information. In this context, we propose a framework called Financial Migration Framework that exposes guidelines for the process of information migration that protects the legacy information and ensures the quality of information for correct operation of the target application. For validation purposes, the framework was applied in two Entities, allowing the evaluation of the financial information clients’ privacy and the process duration. To visualize these results, surveys were applied to the stakeholders who participated in each activity, and the results indicate for both Entities the quality of the migrated information allows the correct operation of the new target system, even though the process duration did not meet their expectations.
Completely Blind Image Quality Assessment with Visual Saliency Modulated Multi-feature Collaboration

It has been proved that opinion-unaware (OU) blind image quality assessment (BIQA) methods are of good generalization capability and practical usage. However, so far, no OU-BIQA methods have demonstrated a very high prediction accuracy. In this paper, we proposed a novel OU-BIQA method which incorporated a collection of quality-aware statistical features that are highly related to the distortions of image local structure, the hue, the contrast and the natural scene statistics (NSS). Then, to take the benefit from the psychophysical characteristics of the human visual system (HVS), a new fitting model from the visual saliency modulated multivariate gaussian distribution is proposed to fit these features to predict the perceptual image quality by comparing with a standard model learned from natural image patches. The experimental results show that the proposed method has excellent performance and good generalization capacity.
An Algorithm to Detect Variations in Writing Styles of Columnists After Major Political Changes

Writers tend to follow a certain style that can be detected or at least sketched by an appropriate algorithm. Columnists in newspapers, being also writers, follow their specific style. The style tends to be stable once writers reach maturity, but it is subject to change when internal or external circumstances differ. Here, we apply a bag-of-words approach to approximate the style of several journalists working in Mexican newspapers, and we track their style for a long period of time with the aim of detecting changes when external circumstances, in particular political ones, change. This provided us with an environment for detecting variations in stylomics, which is the closest we can get to an experiment. In particular, we collected hundreds of writings of ten Mexican columnists from different newspapers, both previous to the Presidential Mexican elections of 2018 and posterior to it. We processed these documents on different supervised and not supervised learning algorithms, such as random forest, principal component analysis, and k-means. Likewise, we implemented different validation procedures. As a result, we detected that the style in all studied columnists suffered tangible changes in the frequency of use of some particular words, particularly at specific times, some of which may be related to the 2018 Mexican presidential elections.
Effectiveness of Peer Group Discussion Plus: Adolescent’s Knowledge and Attitude Toward Smoking and Alcohol Abuse

Negative socio-cultural environments are risky factors for adolescents in unhealthy behavior. Major health problems in adolescents include smoking, alcohol use, drug use, pre-marital sex, mental, and emotional problems. The number of smokers and consumers of alcoholic beverages among Indonesian adolescents is quite high. The study aims to determine the influence of peer group discussion plus-modules against adolescent knowledge and attitudes about smoking and alcohol abuse . This research is conducted in the village Pakunden Kediri (East Java, Indonesia) by using quasy-Experiment’s research design with the design of Two group pre-post-test control design and simple random sampling type probability sampling technique . The Number of respondents per group is 15, making it the total of 30 people in the control group and their treatment. The variables studied were the health Education Methods of the Peer Group Discussion Plus module as independent variables as well as the knowledge and attitudes about smoking and alcohol abuse in adolescents as dependent variables. Using research Instruments in the form of Learning event units (SAP) and questionnaires. There is a treatment group before the peer group discussion plus module. There are 66.65% of teenagers who have good knowledge and 60% of teenagers who have positive attitudes. After intervention, there was increased knowledge of respondents to 80% and increase in the attitude of positive respondents to 73.3%. Peer Group Discussion Plus modules can increase adolescent knowledge significantly, but at the establishment of a positive attitude takes time so there is no significant change in positive attitudes towards respondents.
An In-Sight Into How Compression Dictionary Architecture Can Affect the Overall Performance in FPGAs

This paper presents a detailed analysis of various approaches to hardware implemented compression algorithm dictionaries, including our optimized method. To obtain comprehensive and detailed results, we introduced a method for the fair comparison of programmable hardware architectures to show the benefits of our approach from the perspective of logic resources, frequency, and latency. We compared two generally used methods with our optimized method, which was found to be more suitable for maintaining the memory content via (in)valid bits in any mid-density memory structures, which are implemented in programmable hardware such as FPGAs (Field Programmable Gate Array). The benefits of our new method based on a “Distributed Memory” technique are shown on a particular example of compression dictionary but the method is also suitable for another use cases requiring a fast (re-)initialization of the used memory structures before each run of an algorithm with minimum time and logic resources consumption. The performance evaluation of the respective approaches has been made in Xilinx ISE and Xilinx Vivado toolkits for the Virtex-7 FPGA family. However the proposed approach is compatible with 99% of modern FPGAs.
Intelligent Algorithms of Automatic text Formation Based on Knowledge Point Association Analysis and Fuzzy Clustering

Intelligent algorithms for college automatic formation of English test paper based on the knowledge point association analysis and fuzzy clustering are designed in this manuscript. As a kind of evolutionary algorithm, differential evolutionary algorithm is based on real-number coding. The mathematical model of this paper mainly focuses on the three kinds of the operations of variation, crossover and selection. The biggest difference between differential evolutionary algorithm and genetic algorithm is that the variation operation in differential evolutionary algorithm is based on the difference vector of the chromosomes. Aiming at the risk that the association between data will be damaged due to the hard association rule processing of the traditional Boolean mining algorithms, fuzzy theory is introduced into the association rule mining model. The proposed method can achieve the test paper automatic formation based on the knowledge point association analysis. The performance is validated through the experimental analysis.
Graph: An Efficient Data Structure to Represent and Interpret Semantic Information

A popular Data structure Graph is a quite a useful structure to model variety of real-life problems. In language understanding domain, Semantic analysis makes an attempt to map syntactic structures of a language such as sentences and paragraphs as a whole, to their language-independent meanings. Main focus of Semantic analysis mainly focuses the context window frame surrounding individual words. To represent the context between words in the form of relationships between two words, Graph data structure is quite suitable. A Knowledge Base (KB) is a special type of graph which stores data in the form of entities as nodes and relation between entities as edges. Knowledge Bases majorly follow Resource Description Framework (RDF) standard to store relational data. Semantic Knowledge Graphs automatically identify relationships between entities to form a compact graphical representation from a data corpus to represent a knowledge in the given domain.
Optoelectronic III-V nanowire implementation of a neural network in a shared waveguide

Neural node components consisting of III-V nanowire devices are introduced. This allows for the construction of a small footprint specialized neural network. A broadcasting strategy is developed which removes the need for inter-node wiring. As a model system, an insect brain navigational circuit is chosen and successfully emulated using the introduced nodes and network architecture. The results are based on electronic transport simulations in each device as well as finite-difference time-domain simulations for the broadcasting of optical signals.
Maintainability and Portability Evaluation of the React Native Framework Applying the ISO/IEC 25010

Creating native mobile applications generates twice as much work for developers, as a solution appears Cross-Platform development. React Native (RN) is a framework developed by Facebook and is proposed as an alternative to develop native applications for iOS and Android. However, there are still unknowns such as how easy it is to modify and transfer a software product from one environment to another. The present project proposes the evaluation of the maintainability and portability of RN through a case study using an existing standardized quality model; we specifically focus on that proposed in the ISO/IEC 25010 standard. All Sub-characteristics were evaluated using metrics generating quantitative results which show how maintainable and portable is the RN framework. The study also yields data that enables good development practices that significantly increase the quality of the software product.
The Ecological Model and Global optimization Algorithm for Service Internet

With the further development of service Internet, how to effectively manage, control and optimize the entire service Internet system has become a research hotspot. However, the current research on service Internet optimization mostly focuses on service selection and service composition. And few studies have provided the global optimization strategy for service Internet. So, in this paper, first of all, we propose an ecological model of service Internet by using ecological theory and business process characteristics. Then, based on this model, considering the time-varying uncertainty of service demands, a global service Internet optimization policy learning algorithm called GSIOA is proposed, which uses deep reinforcement learning to automatically formulate global management, control, and optimization strategies of service Internet. Finally, through the comparison of simulation results, it can be concluded that the performance of GSIOA is better than other baseline methods. Our algorithm based on deep reinforcement learning can well solve the global optimization problem of service Internet.
Sentiment Analysis Of Full Day School Policy Comment Using Naïve Bayes Classifier Algorithm

Sentiment analysis is an important and emerging research topic today. Sentiment analysis is done to see opinion or tendency of opinion to a problem or object by someone, whether it tends to have a negative or positive view. The main purpose of this study is to find out public sentiment on Full Day school's policy comment from Facebook Page of Kemendikbud RI and to find out the performance of the Naïve Bayes Classifier Algorithm. In this study, the authors used the Naïve Bayes Classifier algorithm with trigram and quad ram character feature selection with two different training data models and labeling of training data using Lexicon Based method in the classification of public sentiment toward the Full day school policy. The result of this research shows that public negative sentiment toward Full Day School policy is more than positive or neutral sentiment. The highest accuracy value is the Naïve Bayes Classifier algorithm with trigram feature selection of 300 data training models with a value of 80%. The greater of training data and feature selection used on the Naïve Bayes Classifier Algorithm affected the accurate result.
A Novel Web Service Search System Using WSDL

The proposed system investigates SOAP based web services. This search system dynamically discovers the web services and integrates the required web services to satisfy the user requests. Using search engine, WSDL files are retrieved and these files are processed to select the required web services. In this system, user requested functional words are searched using search engine. Horizontal, vertical, inter-cluster and intra-cluster searches are performed on these search results. All elements of WSDL files are compared with user given functional word using weight matrix and selected web services are executed to satisfy the user request. Performance of different search systems are analyzed. Due to dynamic nature of web services, stored data set is not used for implementation. Instead WSDL links are retrieved during run time which is novelty of the proposed system.