Reviewerly/exHarmony huggingface dataset:
BUILDING UP SCHEDULES IN MULTIPROJECT DESIGN MANAGEMENT SYSTEMS

The article focuses on the problem of algorithmizing the process of building schedules in various spheres of human activity by using the modern mathematical apparatus, as well as achievements in the field of systems analysis, game theory, and graph theory. Nowadays, there have been analyzed and determined the boundaries of the effective application of many well-known heuristic and metaheuristic algorithms, which have shown good results in practice. However, despite the achievements in the discrete optimization, scheduling and network planning, the new problems of drawing up so-called coordinated schedules in the field of multi-project planning, which take into account the preferences (requests, wishes) of specific schedule executors, are still of practical interest. There have been considered the approaches and main stages of solving the problems of constructing coordinated schedules in multi-project planning, which is relevant for the development of new generation software and tools
Data Driven Detection of Technological Trajectories

The paper presents a text mining approach to identifying and analyzing technological trajectories. The main problem addressed is the selection of documents related to a particular technology. These documents are needed to detect a trajectory of technology. The approach includes new keyword and keyphrase detection method, word2vec embeddings-based similar document search method and fuzzy logic-based methodology for revealing technology dynamics. USPTO patent database was used for experiments. The database contains more than 4.7 million documents from 1996 to 2020. Self-driving car technology was chosen as an example. The result of the experiment shows that the developed methods are useful for effective searching and analyzing information about given technologies.
Automate Clinical Evidence Synthesis by Linking Trials to Publications with Text Analytics

Randomized clinical trials are the core data source for meta- analyses and relative efficacy analyses. As of today, there are missing links between large portions of trials registered in clinical trial registries and their reported outcomes published in scientific journals. This missing citation information makes it difficult to identify all relevant publications for evidence synthesis and decision support. Therefore, we propose a novel natural language processing-based system to establish links between clinical trials and their published outcomes in literature. Different approaches leveraging information retrieval and machine learning with embedding features were systematically developed and evaluated. Results show that shallow machine learning approach with embeddings provide promising results indicating the value it can add to circumvent the limitations of manual search and analyses.
Research on Information Visualization Design Based on Information Weight- a Case Study of Information-Based Scene Design of the Airport

Objective-Information visualization can enhance the effectiveness and efficiency of information communication by decreasing people’s cognitive load. Thus, the effectiveness of human cognitive information will affect the accuracy and efficiency of information communication. To speed up the individual decision-making process, this thesis attempts to work out a new design method and improve the information visualization design from the cognitive characteristics and information weight. Methods-Firstly, the relationship between information weight and cognitive capability is explored through literature analysis. From this part the researcher know that the course of information communication will be affected by information sources and presentation modes and information receivers. To learn about the impact degree of the three factors on information visualization, the researcher made four types of visualized information boards based on the weight distribution of various flight information, and launched visual cognition experiments on passengers of different ages. During the experiment, the reaction time of each passenger to find information on different boards was recorded. Finally, the average response differences of four types of flight information boards facing two groups of travelers of different ages are analyzed through correlation analysis and variance analysis and significant difference method. Outcome-Both groups present the same rule, that is, the higher the information weight, the smaller the average response difference. Beside, the main effects of age group and information weight level are highly significant. Conclusion-Information weight can accelerate the cognitive speed of receiver by affecting the structure of information presentation; age influence the way the information receiver collects information; the dependence of information receiver on his or her established experience will affect the efficiency of information communication.
Encryption of pseudorandom number generator logic circuits

The paper presents obfuscation methods for logic circuits of pseudorandom number generators (PRNG) on shift registers with linear and nonlinear feedback, which are based on the PRNG additional logic elements for protection against reverse engineering. The encryption of the PRNG logic circuit changes its design in the way that the device works correctly only if the signals at the additional key inputs of the PRNG take correct values. Even with a small bit capacity of PRNG, a huge number of PRNG implementations with a different number of states and different properties can be provided. The concept of (М + 1)-sequence generator is introduced. The possibility of transforming generators of (M – 1)-sequence and (M – 3)-sequence into generators of (M + 1)-sequence is demonstrated.
Designing a Marker Set for Vertical Tangible User Interfaces

Tangible User Interfaces (TUI)s extend the domain of reality-based human-computer interaction by providing users the ability to manipulate digital data using physical objects which embody representational significance. Whilst various advancements have been registered over the past years through the development and availability of TUI toolkits, these have mostly converged towards the deployment of tabletop TUI architectures. In this context, markers used in current toolkits can only be placed underneath the tangible objects to provide recognition. Albeit being effective in various literature studies, the limitations and challenges of deploying tabletop architectures have significantly hindered the proliferation of TUI technology due to the limited audience reach such systems can provide. Furthermore, available marker sets restrict the placement and use of tangible objects since if placed on top of the tangible object, the marker will interfere with the shape and texture of the object limiting the effect the TUI has on the end-user. To this end, this paper proposes the design and development of an innovative tangible marker set specifically designed towards the development of vertical TUIs. The proposed marker set design was optimized through a genetic algorithms to ensure robustness in scale invariance, the capability of being successfully detected with distances of up to 3.5 meters and a true occlusion resistance of up to 25%, where the marker is recognized and not tracked. Open-source versions of the marker set are provided through research license on www.geoffslab.com/tangiboard_marker_set.
Comparative Analysis for E-Services Based on Chi-Square Test

In the edge of Information Technology, where most of the persons having smart phones, almost every organization either private or public wants to delivered their services through the online medium. Indian Government is also focusing on making Digital India; Government is launching their social welfare schemes through e-Services also.E-services (electronic services) are services which use of information and communication technologies (ICTs). If we talk about the medical, health related specific e-Services, lots of opportunities are still left, like in Chhattisgarh and Bihar, e-Services are their but it required lot of improvements, the parameters on which the said states are facing challenges, have been taken care in this study and analysis. In this paper the study and analysis is done for a Rohilkhand region, one of the largest regions of another Indian state i.e. Uttar Pradesh (largest state of Indian). This research paper consist the study, analysis for effectiveness of e-Services in terms of medical assistance, for the same we have conducted a survey for the comparison among the three e-Services. The analysis has been done on the surveyed data through the Chi-Square test; the result of the said test has been mentioned in the conclusion title.
A Tangible Interaction Projection Tabletop for Experiments

A tangible interaction projection tabletop system is developed for middle school experiments. The system supports both tangible interaction of physical objects, which are printe d by 3D printers, and finger pointing interaction on the tabletop. Object textures, as well as invisible experiments phenomena, which cannot be easily observed in real experiments (e.g. magnetic induction line), are projected by projectors. Compared with completely virtual experiments, the system has natural and tangible experimental operations. Compared with real experiments, the system can enhance the display of objective phenomena which are difficult to be observed in real experiment.
Mathematical Foundation of Artificial Intelligence

In this chapter we present some of the mathematical tools used in AI, mostly related to decision making. One of the main tools are general aggregation functions (operators) with some special important cases as triangular norms and copulas. We present briefly some of their important applications. Important extension of the classical field of real numbers is related to the so called pseudo-operations: pseudo-addition and pseudo-multiplication. Here we present some of the important cases. Then corresponding pseudo additive measures and corresponding integrals are introduced, which make the base for the so called pseudo-analysis. The usefulness of the pseudo-analysis, which is a approach to nonlinearity, uncertainty and optimization, is illustrated with a short overview of some important applications. Very important tools in modeling decision making with overlaped events are general fuzzy measures and corresponding integrals as Choquet integral. We present a fuzzy integral approach to Cumulative Prospect theory. Further, we present many different generalizations of the Choquet integral.
T<sub>D</sub> – Spaces And Its Proprerties

Abstract In this work we try to learm T D – space and reach to relation between T D – space with T0 – spaces and T0 - Alexandroff spaces and then try to get some properties of T D – spaces and prove them.
Combining Siamese Network and Correlation Filter for Complementary Object Tracking

Fully-Convolutional Siamese Network (SiamFC) is convolutional neural networks (CNNs) model-based tracking method. This method learns a similarity map from the cross-correlation between the feature representations of the search image and the target image extracted using CNNs, and tracks based on it. The tracking performance of SiamFC tends to degrade when there are similar distractors to the object or when the target object is deformed. On the other hand, recent object tracking methods using the correlation filter (CF) drift under some scenarios such as fast motion and complete occlusion. The analysis showed that although these two approaches have very different structures, they tend to have complementary characteristics. In this work, we propose a complementary tracking framework that parallel connects SiamFC with the CF-based tracker. In the proposed framework, to detect tracking failures, we evaluate the response map output from SiamFC using the confidence score defined in this paper. When a tracking failure is detected, the CF-based tracker provides relative correlated correction. Experiments on the OTB2015 show that our tracker obtains up to more than 5.7%/5.5% (precision score/success score) relative improvements over the original SiamFC and CF-based tracker on the OTB2015, and competitive performance with advanced trackers.
The Construction of Knowledge Base on Pre-Qin Chinese Reduplication

Reduplication is an important manifestation of Chinese morphological change. While there are numerous reduplications among polysyllabic words of Pre-Qin Chinese, there is no dedicated knowledge base on reduplication. This project designs a reduplication extraction and annotation framework, extracts reduplications from 29 Pre-Qin texts, and annotates their unique attributes to construct a syntactic and lexical semantic knowledge base of Pre-Qin Chinese reduplication. More specifically, the annotated attributes include base attributes, reduplication information, lexical meaning, grammatical function, additional meanings, collocation information, archaic Chinese phonology, documentary evidence and so on. The proposed knowledge base will play a role in reduplication retrieval, reduplication research, Pre-Qin Chinese information processing, lexicographic compilation, Chinese teaching and learning.
Applied Aspects of Socio-Psychological Training for Conflict Resolution in Electronic Environment

The COVID-19 pandemic has put the educational institutions in front of new challenges. Teaching shifted from real-time offline environment into online digital one. Thus, the social process that allows students to learn from their teachers and lecturers should be preceded at the level of the electronic environment. This has put social and emotional competencies like conflict management, critical thinking, emotions management, team work on the background – all of them important for students’ adaptation and their realization within the society. The authors were provoked to test psychologic-pedagogical experiment with implementing social-psychological training in the pandemic online conflict resolution context.
Multisensory Digital Experiences

Whether we go to a restaurant or jogging, almost all the experiences in our daily lives consist of what we see, hear, smell, taste, and feel. Lately, especially in the COVID-19-affected world, many of these multisensory experiences have been transformed and capitalized through invents in technology. This chapter looks at the technological advancements in the area of new interactive technologies and multisensory experiences. This chapter describes the basics of multisensory experiences, the relationship between the human senses and technologies. It discusses the concepts that help analyze and explain how the senses interact with each other. Further, this chapter highlights the difference between virtual reality, augmented reality, and mixed reality using the reality-virtuality continuum. In the end, this chapter underscores some ethical concerns, our responsibility towards it, and what the future of those multisensory experiences may hold for us.
Real-time GP-based Wheelchair Corridor Following

In this paper, we present a novel GP-based visual controller. The HOG features are used as a global representation of the observed image. The Gaussian Processes (GP) algorithm is trained to learn the mapping from the HOG feature vector onto the velocity variables. The GP training is achieved using corridor images collected from different places, these images are labeled using velocity values generated by a geometric-based control law and robust features. A hand-based verification of the features is done to ensure the accuracy of the ground truth labels. Experiments were conducted to explore the capabilities of the developed approach. Results have shown R Squared metric with more than ninety percent on the trained GP model in noisy conditions.
Studying the impacts of loop unrolling and pipeline in the FPGA design of the Simon and RoadRunneR lightweght ciphers

In this work, the impacts of the loop unrolling and pipeline in the FPGA implementation of the Simon and RoadRunneR lightweight ciphers is studied. To achieve this, two basic architectures were used. In the first architecture only loop unrolling is applied while, the second one supports both the loop unrolling and pipeline. Using these architectures, three designs versions were developed, for each algorithm. The first and second ones concern individually the encryption and decryption procedures while, the third version is a hybrid one that supports both encryption and decryption. The metrics that were studied the area, frequency, throughput, throughput/area and energy consumption. The produced designs were implemented in Xilinx (Kintex-7) FPGA technology. Based on the implementation results, a detailed study on the above-mentioned design metrics was performed and important outcomes were derived.
Characteristics Influencing Digital Technology Choice in Digitalization Projects of Energy Industry

Abstract Digitalization projects are actively underway in the energy industry, such as the power industry and oil and gas industry. However, there has been no in-depth and quantitative analysis of the relationships between the participants, industry, and technology of digital projects. Therefore, this study focused on which technologies are invested according to key characteristics such as the types of participants and industries driving digitalization projects. This study also examined whether there are differences in technology choices depending on the degree of clean energy exposure. Based on statistics from Bloomberg New Energy Finance (BNEF), a total of 711 projects were analysed using multinomial logistic regression (MNLR). As a result, the proportion of Analytics software was generally higher in the whole industry, and the energy industry was more likely to invest in Analytics software than in other industries. Comparing the power, oil and gas sectors, there was a high probability of investment in Internet of Things (IoT) in the power sector and Automation in the oil and gas sector. In the type of cooperation between energy companies and industrial companies, the probability of investing in Analytics software was significantly higher. In the case of cooperation between energy companies and information and communications technology (ICT) companies, in the oil and gas sector, Analytics software and Cloud/Data accounted for a large proportion. This study provides insight into the effect of characteristics of energy digitalization projects on the technology choice.
A Questionnaire Data Clustering Method Based on Optimized K-Modes Algorithm

When conducting user research, questionnaires are often used to collect user characteristics, attitudes, and other information. Data cluster analysis is often used to divide user groups. The traditional clustering algorithms are mostly only suitable for numerical attributes or disordered categorical attributes. However, questionnaire data is mainly composed of disordered and ordinal categorical attributes. To solve the questionnaire data clustering problem, based on the traditional K-Modes algorithm, a method that combines subjective weighting and objective clustering for questionnaire data analysis is proposed. This method first performs multiple-choice clustering questions to reduce dimensionality and then re-weighting ordinal categorical attributes to rationalize the distance measurement. An optimized mixed K-Modes algorithm for questionnaire data clustering is proposed. The dissimilarity measure between objects according to the two types of disorder and ordinal categorical attributes. In order to evaluate the clustering results, an effective cluster validity index is also defined in this paper. Using a bank user survey questionnaire as a case proved the effectiveness of this data clustering method.
Designing a Learner Model for Use in Training Analysts in a Social Media Practice Environment

This paper describes the characteristics, design and architecture of a learner model, the GTRI Learner Assessment Engine (GLAsE), that is designed to operate within a practice environment for teaching the analysis of social media feeds. The purpose of GLAsE in the practice environment is to provide a state of the learner’s competency or proficiency for the information and use by the learner and instructor to guide the learning activities, e.g., the experience of the learner in the practice environment. The Learner Model is represented as a curriculum overlay model with additional annotations and background information. The contents of the Learner Model consist of items represented in the Curriculum Model as learning objectives: Concepts, Skills, and Problem-solving approaches. This Learner Model is called an overlay model because the Learner Model has the same representation as the expert domain knowledge, i.e., the Curriculum Model—it is overlaid on that representation. The Learner Model will provide mastery or proficiency scores for each of the learning objectives that represent the model’s best estimate of the state of the learner proficiency for that objective. The learner assessment process will identify and prioritize any concepts in which the learner has deficiency and will, whenever it is appropriate in a learning or practice system, provide information to other system components that could be used to direct the learner to particular activities to help increase proficiency in those concepts. Results of the learner assessment will be used to update the Learner Model.
Comparative Analysis of Inductive Density Clustering Algorithms Meanshift and DBSCAN

The article presents an inductive model of objective clustering based on the MeanShift clustering technique. The algorithm for breaking an assortment of original data into two evenly powerful subsets is employed. The balance criterion is handled as an external criterion. To test the functioning of the proposed model, the “Jain” and “Flame” data sets from the Computing School of the East Finnish University were employed. The inductive DBSCAN algorithm was adopted for matching the preliminary outcomes. Based on the simulation proceeds, the ways for further improvement of the proposed model are arranged in order to increase the clustering objectivity of the examined data.
An Enhanced GridLOF Approach for Image Filtering

As image noise filtering is very important to consider in many applications. Many research efforts have been seen in the literature trying to tackle its challenges. Among these research efforts are outlier detection approaches. However, there is still a need to extend these approaches in order to attain an improved performance. In this paper, an image noise filtering approach is proposed. The proposed approach use a boosted type of GRIDLOF algorithm proposed in a previous work. The efficiency of the proposed approach is assessed through simulations. The simulations results demonstrate that the developed approach overtakes other approaches in the literature.
Using BERT Model for Intent Classification in Human-Computer Dialogue Systems to Reduce Data Volume Requirement

User-intent classification is a sub-task in natural language understanding of human-computer dialogue systems. To reduce the data volume requirement of deep learning for intent classification, this paper proposes a transfer learning method for Chinese user-intent classification task, which is based on the Bidirectional Encoder Representations from Transformers (BERT) pre-trained language model. First, a simulation experiment on 31 Chinese participants was implemented to collect first-handed Chinese human-computer conversation data. Then, the data was augmented through back-translation and randomly split into the training dataset, validation dataset and test dataset. Next, the BERT model was fine-tuned into a Chinese user-intent classifier. As a result, the predicting accuracy of the BERT classifier reaches 99.95%, 98.39% and 99.89% on the training dataset, validation dataset and test dataset. The result suggests that the application of BERT transfer learning has reduced the data volume requirement for Chinese intent classification task to a satiable level.