Reviewerly/exHarmony huggingface dataset:
Intellectual Cognitive Technologies for Cyber-Physical Systems

The paper presents information on the cognitive modeling of complex systems and provides considerations on its place within the framework of research and development of cyber-physical systems. Theoretical and practical results of the application of cognitive modeling of complex systems can be attributed to the intellectual decision support systems in the socio-economic and industrial areas. Mathematical and software apparatus of cognitive modeling of complex systems is designed to describe the structure and behavior of a complex system in the face of uncertainty, implements an interdisciplinary approach to decision-making problems. For the future, it can be recommended for use in the direction of "Cognitive city". A number of results of cognitive modeling of a regional socio-economic system are presented. The cognitive map of the system, the results of the study of its structural and dynamic properties, the results of scenario modeling and recommended management decisions are presented.
Domain-Invariant Latent Representation Discovers Roles

Discovering the roles of nodes in a network is important for solving various social issues. Role discovery aims to infer nodes’ roles from a network structure, and it has received considerable attention recently. The conventional methods of role discovery mainly use unsupervised learning, but due to the lack of information, it is difficult to discover the roles we want or to ascertain the results. In this paper, we attempt to improve accuracy through using supervised information. Specifically, we adopt transfer learning using adversarial learning. As a result of computational experiments, we show that the proposed model discovers a node’s role more effectively than do the conventional methods. Furthermore, we found that domain-invariant features lead to higher accuracy, the proposed method discovers roles better even with different network sizes, and the proposed method works well even if the networks have nodes of various structures.
Modeling and Control of UAV Quadrotor

Since the advances in technologies and the ability to manufacture miniature sensors and controllers using the Micro-Electro-Mechanical Systems (MEMS) technologies, there have been a lot of advances in the Unmanned Aerial Vehicle (UAV) area. A lot of the research conducted focused on the quadrotor due to its previously mentioned advantages of easier manufacturing, compactness and maneuverability among others. a detailed description of the dynamic modelling of the Quadrotor is presented. Various control strategies like the Proportional Derivative Control, the Sliding Mode Control and the Backstepping Control methods have been elucidated and implemented in MATLAB and SIMULINK. Simulations have been carried out and the results have been presented.
Voice pattern recognition using Mel-Frequency Cepstral Coefficient and Hidden Markov Model for bahasa Madura

Abstract Voice recognition is one part of an application that allows a device to recognize spoken words by digitizing words and matching digital signals with a particular pattern stored in a device. Spoken words are converted into digital signals by converting voice waves into a set of numbers which is then compared with the voice pattern to identify the words. MFCC can be an alternative method to solve the problem of voice extraction because this method is reliable for recognizing the unique features of human voice. Hidden Markov Model is used to recognize the voice pattern, so it can be used to compare the voice signal obtained from e-learning with the trained voice signal. Bahasa Madura is a regional language used by ethnic Madurese to communicate daily. Currently the number of Madurese people who understand this language is reduced so that the use of Bahasa Madura is also reduced. Therefore, it is necessary to conduct speech recognition research in Madura Language as one of effort to preserve and develop the use of Regional Language. The experimental results show that the average accuracy for testing the system with one model is 85% and the average accuracy for testing the system with multi model is 90%.
Triple Factorization-Like Symmetric and Nonnegative Latent Factor Models for Undirected, Sparse and Large-Scaled Networks

Non-negative latent factor (NLF) models have great capabilities of extracting useful knowledge from a symmetric, sparse and high-dimension (SHDiS) matrix with positive constraints. For the purpose of obtaining the NLFs, the tradition SNLF model is constructed based on the doublefactorization (DoF)-based matrix factorization (MF) technique, however it suffers from the low prediction accuracy. In order to improve the performance, an improved SNLF model in terms of triple factorization MF technique is proposed. Whereas, such a better prediction accuracy might be obtained at an evident cost of the time efficiency. Aiming at finding a fine balance between the model performance and the time cost, a novel triple factorization-like SNLF (TFL-SNLF) model is developed by introducing a pre-given non-negative symmetric matrix and a non-negative and symmetric LF matrix. Based on this established model, a single latent factor-dependent nonnegative additive gradient descent (AGD) update algorithm is designed for obtaining desired LFs. Experiments on two actual industrial data sets illustrate that the novel TFL-SNLF model can not only have a better performance of the prediction accuracy on missing data than the DoF-based SNLF model, but also is superior at the efficiency over the TrF-based SNLF. Hence, the proposed TFL-SNLF model is more applicable according to the industrial engineering.
3D Hand Movement Measurement Framework for Studying Human-Computer Interaction

In order to develop better touch and gesture user interfaces, it is important to be able to measure how humans move their hands while interacting with technical devices. The recent advances in high-speed imaging technology and in image-based object tracking techniques have made it possible to accurately measure the hand movement from videos without the need for data gloves or other sensors that would limit the natural hand movements. In this paper, we propose a complete framework to measure hand movements in 3D in human-computer interaction situations. The framework includes the composition of the measurement setup, selecting the object tracking methods, post-processing of the motion trajectories, 3D trajectory reconstruction, and characterizing and visualizing the movement data. We demonstrate the framework in a context where 3D touch screen usability is studied with 3D stimuli.
Use of Smartapps for administrative service based paperless system

Abstract This study aims to streamline the use of information systems in supporting the reduction in the use of paper media as/for administrative services. Smartapps is an integrated information system or one-door service aimed at providing information service related to administration including correspondence, attendance checks, and attendance report printing. Paperless is a method used to reduce paper use by storing documents and data digitally and stored in digital storage. The Smartapps system is used to address/ease mail administration services, check attendance, and attendance reports that still use paper media. The results obtained are to facilitate correspondence services, check attendance, and attendance reports by reducing paper use or paperless systems. With the Smartapps system, manual activities in correspondence, attendance checking, and attendance reports can reduce several stages of administrative services, so that services can be made effective with a 50% effectiveness rate. This reduction in manual stages results in speed, reliability and effectiveness of administrative services.
Reinforcement Learning in Card Game Environments Using Monte Carlo Methods and Artificial Neural Networks

Artificial intelligence has wide range of application areas and games are one of the important ones. There are many applications of artificial intelligence methods in game environments. It is very common for game environments to include intelligent agents. Having intelligent agents makes a game more entertaining and challenging for its players. Reinforcement learning methods can be applied to develop artificial intelligence agents that learn to play a game by themselves without any supervision and can play it at a high level of expertize. Supervised learning methods, on the other hand, can be applied to develop agents that play a game by imitating the supervisor players. The purpose of this study is to develop self-learning agents for a card game, namely Batak, using reinforcement learning combined with supervised learning. Batak is a trick-taking card game popular in Turkey. Results of the study reveal that the developed agents are better at gameplaying and similar at bidding compared to some rule based Batak playing agents.
Cryptocurrencies Price Index Prediction Using Neural Networks on Bittrex Exchange

Cryptocurrencies have become fairly popular in the market since they were first introduced in the early 2000s. Cryptocurrencies are used primarily outside existing banking and governmental institutions and are exchanged over the Internet. Cryptocurrency exchanges allow customers to trade cryptocurrencies for other assets, such as conventional fiat money, or to trade between different digital currencies. This paper proposes a method to predict fluctuations in the prices of cryptocurrencies, which are increasingly used for online transactions worldwide. A Multi-Layer Perceptron (MLP)-based Non Linear Autoregressive with Exogenous Inputs (NARX) cryptocurrencies price forecasting model using the closing past prices together with volume. The model is evaluated based on price data collected from Bittrex Exchange, a US-based famous cryptocurrency exchange. Validation tests and Prediction test indicate that the proposed model is suitable for predicting prices on collected data.
A Discussion on the Service System of College Smart Libraries in the “Internet +” Age

Abstract Since the State Council proposed the “Internet +” action plan in 2015, various industries have been developing their “Internet +” action plans. “Internet +” has already begun to affect the library community: In terms of service method, library services need to be transformed from online model to offline model and from manual services to smart self-service ways for users; in terms of service accuracy, libraries should solve the following problems: how to be as accurate as possible to meet the needs of users, how to exactly position the library services and products, and how to build an effective service platform. This requires the library to re-explore the new service system in the “Internet +” environment. Based on the author’s learning and practical experience, this article first analyzed the connotation and characteristics of “Internet +” and then discussed the reform of library services led by “Internet +”. Then it discussed the smart service system of college libraries and finally built a smart service system of college libraries based on “Internet +”
Multi-layer LSTM Network Statement Generation Based on Mixed Input

Natural language generation(NLG) for the network domain is an important part of network public opinion control. Traditional natural language generation systems are based on rules and templates, such methods are suitable in situations where the human language style has not changed, not suitable for the language style used in the network field. Due to the network language style is different from person to person, and has no fixed rules for some use of words. So the model training in this article is based on characters which let model self to learn the language styles. Under actual circumstances, network language involves many popular internet slang. In order to further improve the effect, we collected internet slang and made a network slang dictionary, then mixed character and these words as input to the model. During the training phase, we selected and improved the popular Adam optimization algorithm to make our model produce better results. We deepened the number of network layers to enhance the learning effect of the model, and proposed a multi-layer LSTM network statement generation system based on mixed input. Experiments show that mixed input can produce more ideal sentences than just character-level input, improved Adam algorithm can further optimize the effect of our model. At last, in the experiment step, comparison with other models confirms the effectiveness of our model.
Keyword-Search Interval-Query Dynamic Symmetric Searchable Encryption

Searchable Symmetric Encryption (SSE) enables clients to securely store data on untrusted server while keeping the ability to search and update over the encrypted data efficiently. For practical purposes, we also need several additional properties to make our search function more versatile. In this paper, we introduce an extension type of SSE which we called Keyword-search Interval Dynamic SSE (KIDSSE). In KIDSSE we can search for encrypted files containing a keyword that occurs in a queried range. We also construct a solution for KIDSSE and show that our scheme maintains all important security properties such as forward privacy, backward privacy.
The Dr-KGQA System for Automatically Answering Medication Related Questions in Chinese

As the public medical resources becomes more and more scarce due to the aging of a large population in China, online medical consultation become a popular alternative. However, existing online medical consultation providers or forums are either labor-intensive, thus expensive, or free but unreliable with respect to the quality of answers. With the aim of providing a convenient, instant and reliable tool for people in need of simple medication related consultations, we developed a question and answering system (Dr-KGQA) to automatically answer the questions from users in an interactive manner, with the help of our powerful Chinese medical knowledge graph (Med-KG). Our system employs a pipeline of deep learning models including named entity recognition (NER) and relation matching. We conduct a series of experiments to demonstrate the performance of our system, on a human annotated dataset we collect. We have piloted our system on a family doctor platform in Chongqing, China, and the results show that our system is promising for real-world applications.
On completely regular and completely transitive supplementary codes

Given a parity-check matrix Hm of a q-ary Hamming code, we consider a partition of the columns into two subsets. Then, we consider the two codes that have these submatrices as parity-check matrices. We say that anyone of these two codes is the supplementary code of the other one. We obtain that if one of these codes is a Hamming code, then the supplementary code is completely regular and completely transitive. If one of the codes is completely regular with covering radius 2, then the supplementary code is also completely regular with covering radius at most 2. Moreover, in this case, either both codes are completely transitive, or both are not. With this technique, we obtain infinite families of completely regular and completely transitive codes which are quasi-perfect uniformly packed.
Topic Representation using Semantic-Based Patterns

Topic modelling is the state of the art technique for understanding, organizing, and extracting information from text collections. Traditional topic modeling approaches apply probabilistic techniques to generate the list of topics from collections. Nevertheless, human understands, summarizes and discovers the topics based on the meaning of the content. Hence, the quality of the topic models can be improved by grasping the meaning from the content. In this paper, we propose an approach to identify sets of meaningful terms based on ontology, called Semantic-based Patterns, which represent the content of a collection of documents. A set of related semantic-based patterns can be used to represent a latent topic in the collection. The proposed Topic Representation using Semantic-based Patterns aims to generate semantically meaningful patterns based on ontology rather than term co-occurrence as what existing topic modelling methods do. The semantically meaningful patterns were evaluated by applying the information filtering to semantic-based topic representation. The semantic based patterns were used as features for information filtering and were evaluated by comparing against popular information filtering baseline systems. Topic quality was evaluated in terms of topic coherence and perplexity. The experimental results verified that the quality of the proposed patterns was better than features used in baseline systems for information filtering. Further, the quality of topic representation outperforms the generated topics of other topic modeling approaches.
Research and Application of Question Answering System Based on Subject Knowledge Base

Abstract The intelligent subject knowledge base question-and-answer system has a positive effect on improving the self-learning ability of the students, and designs a subject knowledge base question-and-answer system based on man-machine interaction. The system design includes the design of the question and answer algorithm of the subject knowledge base and the software development and design of the system. The semantic ontology structure mapping method is adopted to control the semantic information in the question-and-answer process of the subject knowledge base, and the expert subject knowledge base is constructed. The semantic concept tree analysis is carried out in combination with the feature construction method of the subject word feature, the human-computer interaction design of a subject knowledge base question-and-answer system is carried out by adopting a networked information interaction mode, and a corpus of words, phrases and sentences of a subject knowledge base question-and-answer system is constructed, in that concept of the semantic subject word, the sentence division of the question and answer of the subject knowledge base is realized, and the subject word characteristic matching method is adopted to realize the algorithm design of the network subject knowledge base question and answer, and the intelligence of the question and answer of the subject knowledge base is improved. In the embedded industrial control host, the system development and human-computer interaction design of the subject knowledge base are carried out, and the system is mainly composed of an information processing module, a subject knowledge base information acquisition module, a DMA controller, an output module and the like. The test results show that the system can effectively realize the question-and-answer of the network subject knowledge base and improve the reliability and intelligence of the subject-and-answer system of the subject knowledge base.
Wolf pack algorithm for solving VLSI design tasks

Abstract The paper deals with the development of new and modified heuristic mechanisms of searching optimal solutions, which is considered as one of the main problems of artificial intelligence. One of the promising areas of artificial intelligence development is application of methods and models of biological systems behavior for solving NP-complete and NP-difficult optimization tasks. The paper presents the statement of the placement task in designing very large-scale integration circuits (VLSI). The authors propose the algorithm for solving this task on the basis of biological system behavior in nature, e.g. wolf pack. Wolves are considered as typical social animals having clear separation of social work. The paper describes the actions and rules of wolf pack behaviour in nature. Based on the wolves’ behaviour rules and actions, the authors present the modified wolf pack algorithm. The benefits of the developed modified algorithm include the ability to improve each following iteration of the placement task. The wolf pack algorithm is implemented as a computer software on Java. To estimate the effectiveness of the proposed approach, the authors use the well-known IBM benchmarks to compare with the developed algorithm. The comparison is implemented with the results of the following algorithms: Capo 8.6, Feng Shui 2.0, Dragon 2.23. The results show that the wolf pack algorithm is more effective than the analogues.
A Method to Prevent Tracking Browsing History with the Use of Browser Extension

Almost all popular search engines, social networks and other sites collect information about users and their browsing history, which may be considered a privacy violation. Users should be able to prevent it. This research suggests a new method to prevent these kind of violations implemented in the form of a browser plug-in. The testing of this extension demonstrated that the method does not require much time and computing resources. There is an overview of other privacy protection solutions with comparison to the suggested approach in this research.
Recovering Internal States of Grain-v1

In this paper, we analyze the non-linear part of the output function h of Grain-v1 and use a guess and determine strategy to recover 33 state bits from 33 consecutive keystream bits of Grain-v1 by fixing 45 bits and guessing 82 bits. This reduces the conditional sampling resistance of Grain-v1, which is best till now. We apply the Time-Memory-Data Trade-Off (TMDTO) attack on Grain-v1 with this conditional sampling resistance to get a trade-off curve which improves the pre-processing time complexity and online time complexity with improved memory.
A Hybrid Approach to Retrieve Knowledge from a Document

The task of retrieving the theme of a document and presenting a shorter form compared to the original text to the user is a challenging assignment. In this article, a hybrid approach to extract knowledge from a text document is presented, in which three key sentence level relationships in association with the Markov clustering algorithm is used to cluster sentences in the document. After clustering, sentences are ranked in each cluster and the highest ranked sentences in each cluster are merged. In the end, to get the final theme of the document, the Gradient boosting technique XGboost is used to compress the newly generated sentence. The DUC-2002 data set is used to evaluate the proposed system and it has been observed that the performance of the proposed system is better than other existing systems.
A Machine Learning Benchmark with Meaning: Learnability and Verb Semantics

Just over thirty years ago the prospect of modelling human knowledge with parallel distributed processing systems without explicit rules, became a possibility. In the past five years we have seen remarkable progress with artificial neural network (ANN) based systems being able to solve previously difficult problems in many cognitive domains. With a focus on Natural Language Processing (NLP), we argue that the progress is in part illusory because the benchmarks that measure progress have become task oriented, and have lost sight of the goal to model knowledge. Task oriented benchmarks are not informative about the reasons machine learning succeeds, or fails. We propose a new dataset in which the correct answers to entailments and grammaticality judgements depend crucially on specific items of knowledge about verb semantics, and therefore errors on performance can be directly traced to deficiencies in knowledge. If this knowledge is not learnable from the provided input, then it must be provided as an innate prior.