Reviewerly/exHarmony huggingface dataset:
Digitalization of Electronic Textbook Based on OPENCV

The traditional digitization method of electronic textbooks is limited by text data and illustration layout, and the data processing effect is poor. In order to adapt to the complex and changeable data formats, this paper proposes an adaptive data partitioning technique. We divide all the texts and illustrations in the textbooks into independent data blocks, locate and cut them, and use OCR technology to identify the information of each area to make the processing goals more clear. Experiments were conducted on the junior middle school history textbooks in terms of data recognition rate. The experimental results show that the method proposed in this paper has a good effect on the digitalization of electronic textbooks.
Automation of Change Impact Analysis for Python Applications

Software applications have to be updated on an ongoing basis as per the requirements of the user or the client or to fix any bugs. The cost of this maintenance phase of a software application typically costs more than 50% of all other software development life cycle (SDLC) phases. When a change request (CR) is received from the client, developers have to work upon the request. It may be possible that as a result of addressing this CR, it may impact other areas of the existing application functionality. This mandates that developer working on this CR should have complete knowledge of the application codebase and working scenarios. Quite often, developer may be lacking this extensive expertise and also may not have the time required to spend in doing the detailed impact analysis. There are some change impact analysis (CIA) tools which can aid developer in finding out almost all impacted elements for a given CR. But no such tool or prototype exists for Python applications which can aid developer in finding out impacted elements for a CR. This paper aims at developing an impact analysis tool (HETeye) which will help the developer to know what all elements of the Python source code will be affected and the dependencies of affected elements upon giving a CR and blacklist words which help in filtering the results even further.
Blockchain Model Implementation to Select the Best Bid in an Industrial Supply Chain

This paper presents an intelligent model which uses Artificial Intelligence within the Industry 4.0. It is of utmost importance to be able to choose the best supplier in order to identify a competitiveness model associated with the reduction of direct and indirect costs. The present investigation describes a novel Blockchain that uses big data for adequately determining critical factors of the purchasing department and then using a hashing model verifying and validating with real data from a manufacturing company as a components supplier within the automotive industry. This research focuses mainly on specifying the mathematical modelling involved in Blockchain and its implementation using innovative factors from the financial area.
Comparing Partitions: Metric Characterizations, Mean Partition, and Distortion

Measures for quantifying the distance between two partitions are involved in many current applications, including machine vision, image processing and understanding, image and video segmentation, biology and genetics, among others. This article investigates three fundamental aspects of the comparison of partitions: the characterization of metrics for comparing partitions; their role in average-based consensus of partitions; and how the different metrics distort the spatial organization of the partitions of a finite data set. In particular, we significantly reduce number of properties in the existing characterizations of metrics for comparing partitions such as Variation of Information and Mirkin metric. Moreover, we compile the main results describing the impact of these metrics on the construction of average-based consensus functions, as well as we compute the exact reduction of the search space that the different pruning criteria (quota rules) provide. Finally, we compute the distortion value for different $$\beta $$ -entropy metrics.
Analysis of the possibilities of optimizing SQL queries

Most of today's web applications run on relational database systems. Communication with them is possible through statements written in Structured Query Language (SQL). This paper presents the most popular relational database management systems and describes common ways to optimize SQL queries. Using the research environment based on fragment of the imdb.com database, implementing OracleDb, MySQL, Microsoft SQL Server and PostgreSQL engines, a number of test scenarios were performed. The aim was to check the performance changes of SQL queries resulting from syntax modication while maintaining the result, the impact of database organization, indexing and advanced mechanisms aimed at increasing the eciency of operations performed, delivered in the systems used. The tests were carried out using a proprietary application written in Java using the Hibernate framework.
Latin to Balinese Script Transliteration: Lessons Learned from the Computer-based Implementation

The decreasing use of Balinese Script, including its Latin-to-Balinese Script transliteration knowledge, has caused concern over the threat of extinction of this part of Balinese culture. This research is aimed to preserve that knowledge through the technological approach by collaboration between Engineering and Language disciplines. It provided lessons learned from the analysis of the computer-based implementation of Latin to Balinese Script transliteration on a relatively large document. This study has never been conducted yet and added certain perspectives also strengthened computer-based transliteration knowledge, as part of Balinese Language ubiquitous learning. The analysis was conducted on pioneering Aksara Bali Simbar Dwijendra (SD) web application that basically receives Latin text input and outputs Balinese Script based on Bali SD font. Through the experiment, ten categorized lessons were exposed and learned. Future work to enrich this transliteration knowledge is by conducting more exploration on computer-based implementation using dedicated Balinese-Unicode fonts.
A Modified Q-Learning Algorithm for Control of Two-Qubit Systems

This paper investigates quantum control problems using tabular Q-learning. A modified tabular Q-learning algorithm based on dynamic greedy method is proposed and the proposed algorithm succeeds for finding control sequences to drive a two-qubit system to a given target state with high fidelity. The modified algorithm also shows improved performance over the traditional Q-learning for solving quantum control problems on continuous states space. Moreover, the modified tabular Q-learning algorithm is compared with stochastic gradient descent and Krotov algorithms for solving quantum control problems. Simulation results on a two-qubit system demonstrate the effectiveness of the proposed algorithm.
Dementia Wandering Recognition using Classical Machine Learning and Deep Learning Techniques with Skeletal Trajectories

Wandering is considered to be one of the most common behavioral symptoms of dementia. Designing robust models which are capable of detecting wandering episodes in people living with dementia would allow the prevention of the consequences of this behavior. To tackle this problem, this study proposes a framework where the skeletal trajectories are used to extract patterns from the movements of the participants. These patterns are utilized to classify a movement between wandering and non-wandering behavior using three machine learning methods. The proposed models were assessed based on two datasets collected in different environments consisting of trajectories that are associated with lapping, pacing, and random movements that represent wandering episodes. The predictive model based on the LSTM network achieved the best classification results in terms of macro F1-scores on both datasets with an overall accuracy higher than 70%. The findings of this study present the potential of LSTM-based predictive models in addressing the wandering recognition problem in a real-world scenario with patients suffering from dementia.
Quantifying interdisciplinarity: Subject librarians as research collaborators

Interdisciplinary research presents unique challenges and unique opportunities for collaboration, but measuring the impact of interdisciplinary research creates particular opportunities for subject librarians. Researchers working in the field of applied zooarchaeology collect data about ancient animal populations by examining animal remains found at archaeological sites. These data provide historic insights that would be of use to scholars working in a variety of disciplines, but the question has lingered among applied zooarchaeologists as to whether their colleagues in other disciplines have been discovering and citing the published data. The authors of the current study designed a citation analysis to measure the impact of applied zooarchaeological publications beyond the traditional disciplinary boundaries of anthropology/archaeology. The authors then reflect on the implications of the findings for not just applied zooarchaeology, but for interdisciplinarity, for discovery and collection management, and for collaboration and demonstrating value.
Strategic Paradigms of Programming, Which Was Initiated and Supported by Academician Andrey Petrovich Ershov

The report is devoted to the description of the inconspicuous line of scientific activity of the USSR programming leader A.P. Ershov in the field of manifestation and development of programming paradigms. In science, leadership means the ability to recognize new problems and expand the space for practical solutions. A.P. Ershov was able to see unexpected key programming problems as a new area of research and purposefully expand the space for solving such problems. (Quotes from previously published materials are provided for readability.)
Integrating a Voice Analysis-Synthesis System with a TTS Framework for Controlling Affect and Speaker Identity

This paper reports an experiment exploring how a voice analysis-synthesis system, GlórCáil, can be used to add expressiveness to the synthetic voice in text-to-speech (TTS) systems. This implementation focuses on the Irish ABAIR TTS voices, where such voice control would facilitate many current/envisaged applications. GlórCáil allows voice control of synthesized speech, and for this experiment was integrated into a DNN-based TTS framework. Utterances were generated with f0, voice quality and vocal tract parameter manipulations targeting shifts in speaker identity and in the affective coloring of utterances. Scaling factors used for the manipulations were suggested in an earlier study. They involved global changes without sentence-internal dynamic variation, with a view to ascertain whether such global shifts might alter listeners' perception of speaker identity and affect. Results demonstrate affect shifts compatible with expectations. However, there were confounding factors. The female/child voices were poorly differentiated, which was expected given the similarity in the scaling factors used. The affect transformations suggest the baseline voice used had an intrinsically sad quality so that there is weak differentiation between the sad and no emotion stimuli. Male angry voice was the least successful, suggesting that dynamic, within-utterance variation is essential for the signaling of certain affects.
A Distributed Location Trusted Service Achieving k-Anonymity against the Global Adversary

When location-based services (LBS) are delivered, location data should be protected against honest-but-curious LBS providers, them being quasi-identifiers. One of the existing approaches to achieving this goal is location k-anonymity, which leverages the presence of a trusted party, called location trusted service (LTS), playing the role of anonymizer. A drawback of this approach is that the location trusted service is a single point of failure and traces all the users. Moreover, the protection is completely nullified if a global passive adversary is allowed, able to monitor the flow of messages, as the source of the query can be identified despite location k-anonymity. In this paper, we propose a distributed and hierarchical LTS model, overcoming both the above drawbacks. Moreover, position notification is used as cover traffic to hide queries and multicast is minimally adopted to hide responses, to keep k-anonymity also against the global adversary, thus enabling the possibility that LBS are delivered within social networks.
Person re-identification based on frequency channel attention networks under the surveillance scenario

Abstract Under the surveillance scenario, due to the influence of occlusion, deformation and illumination, the accuracy of person re-identification (ReID) task will be greatly affected. The extraction of effective pedestrian features has become the key of person ReID task. In view of this problem, this paper creatively uses frequency channel attention (FCA) network to carry out person ReID task. FCA network solves the problem of insufficient information representation caused by global average pooling (GAP) in traditional channel attention network. In addition, this paper conducts experiments on two datasets. The ablation experimental results show the effectiveness of FCA network in person ReID task, and the results of contrastive experiment show the superiority of FCA network in person ReID task.
Show Me the Universe! Perceived Usability and Task Load of an AR Mobile-App in Secondary School Learning

Modern digital artefacts not only affect the way we work but increasingly also the way we learn and teach. The Internet, smart devices, as well as innovative forms of human-content interaction open up new pathways for successful knowledge transfer. The work presented in this paper aims to explore one of these recent developments, i.e. the use of Augmented Reality in school learning. We report on an experimental study in which two cohorts of secondary school students used a smartphone-based AR mobile-app to learn about astronomy and the solar system. The goal of the experiment was to evaluate the app’s perceived usability and task load associated with a set of particular learning exercises. Results point to an average perceived usability and moderate task load, independent of the students’ gender. Further, we found a negative correlation between the perceived usability and task load, even though this was only significant for male students.
Dynamic Shared Limbs: An Adaptive Shared Body Control Method Using EMG Sensors

An inter-personal shared body is an extended body that allows an avatar or robot to be controlled by multiple people. It is possible for operators to share control over the actions of the shared body. A shared body can be operated well when the same actions are performed by its operators. However, when their actions are not the same, we need to have an adaptive control method for the operators to maintain a sense of agency and ownership. In this study, we propose dynamic adjustment methods to control a shared body using EMG sensors and movements of the operators.
Secure Threshold Ring Signature Based on SM9

Nowadays, it becomes a major issue that has become increasingly prominent to ensure the privacy and security of information. With the wide application of SM9 algorithm in various fields, it is particularly important to improve the security of SM9 algorithm. Therefore, many researchers use the SM9 algorithm for supporting the underlying cryptography. And group signature, ring signature and two-party signature are introduced in succession to design the related scheme with significant effecting. In order to further improve the signature security and promote the promotion of SM9 algorithm. First, the SM9 identification and cipher algorithm is improved by using multi-KGC(Key Generation Center) to generate system parameters to achieve the purpose of improving the security of the key in this paper. Second, a threshold ring signature scheme based on SM9 is proposed by combining SM9 with threshold ring signature. The analysis shows that, compared with the existing schemes, the proposed scheme has more advantages in security, such as higher anonymity, unforgeability and non-repudiation, while resisting malicious user attacks and malicious KGC attacks. This scheme extends SM9 from single user signature to multi-users signature, and increases the application scenarios of SM9, which plays a positive role in the promotion of SM9.
Check-in at the Robo-desk: Effects of automated social presence on social cognition and service implications

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Autonomous Groups Particles Swarms Optimisation (AGPSO) to Optimise the Fuzzy Membership Function in the Automatic Watering Plant Case Study

Abstract There is still a challenge for efficient watering, considering the exact time and exact watering intervals are unknown. The advances in fuzzy technology and microcontroller technology offer valuable solutions. By using this technology, automatic watering plant based on Humidity and Temperature sensor. However, the application of fuzzy technology requires the optimal determination of membership function. The optimal membership function previously developed using the genetic algorithm. Since the genetic algorithm is time-consuming, many researchers develop optimal membership function using Particles Swarm Optimisation. The modified Particles Swarm Optimisation provides a better solution. Eventually, Autonomous Groups Particles Swarm Optimisation invention offers an optimal solution to search for several mathematical problems. The basic concept of Autonomous Groups Particles Swarm Optimisation algorithm influenced by individuals dissimilarity in bugs swarming or bird flocking. In typical colonies, each insect or bird is not similar in terms of perception and capability, but they all do their commitment as members of the colonies. The Autonomous Groups Particles Swarm Optimisation algorithm discovery has tested for 23 mathematical test functions, but not to the real world. Therefore, the possibility of searching for optimal solutions needs to implement to the real world. In this study, the optimisation applies to fuzzy membership function, so that the automatic plant watering can be more optimal and efficient. Preliminary studies show that optimisation using AGPSO for Gaussian membership function provide a positive result.
Blockchain Driven Access Control Mechanisms, Models and Frameworks: A State of the Art Review

Access Control or authorization is referred to as the confinement of specific actions of an entity to perform an action. Blockchain driven access control mechanisms have gained considerable attention since applications for blockchain were found beyond the premises of cryptocurrencies. However, there are no systematic efforts to analyze existing empirical evidence. To this end, we aim to synthesize literature to understand the state-of-the-art in blockchain driven access control mechanisms with respect to underlying platforms, utilized blockchain properties, nature of the models and associated test-beds & tools. We conducted the review in a systematic way. Meta Analysis and thematic synthesis was performed on the findings and results from the relevant primary studies in order to answer the research questions in perspective. We identified 76 relevant primary studies passing the quality assessment. A number of problems like single point of failure, security, privacy etc were targeted by the relevant primary studies. The meta analysis suggests the use of different blockchain platforms, several application domains and different utilized blockchain properties. In this paper, we present a state of the art review of blockchain driven access control systems. In hindsight, we present a taxonomy of blockchain driven access control systems to better under the immense implications this field has over various application domains.
Multi-UAV Cooperative Path Planning via Mutant Pigeon Inspired Optimization with Group Learning Strategy

This paper proposes a mutant pigeon-inspired optimization algorithm with group learning strategy (MGLPIO), for multi-unmanned aerial vehicle(UAV) cooperative path planning. The group learning strategy is introduced in map and compass operator to reduce computation complexity and enhance the global search ability. At the same time, the triple mutations strategy is employed in landmark operator to enhance swarm diversity. What’s more, in order to synchronize multi-UAV, the time stamp segmentation technique is designed to prove waypoints, which can simplify the cost function by reducing the number of independent variables. Besides, we geometric the threat sources to quantify their dangerous level. The coordination costs can guarantee collision-free flight and real-time communication. Finally, the proposed method is applied to path planning in set scenarios. The simulation results indicate that our model is feasible and effective, and the MGLPIO algorithm can have a good balance between exploration and exploitation by comparing with other four algorithms.
Providing contexts for classification of transients in a wide-area sky survey: An application of noise-induced cluster ensemble

With new sensor systems that capture sky survey at high quality level, analyzing the resulting data within a limited time frame appears to be the next challenge. Specific to the GOTO project, this task proves to be crucial to discover new transients from a pool of large candidates. Initial works based on the feature-based approach design this detection as imbalance classification, where a data-level method can be used to resolve the difference in cardinality between classes. This paper presents a context generation framework to complement the previously proposed model. In particular, samples are clustered to form data contexts to which different learning strategies may be applied. To ensure the quality of data clustering, a noise-induced cluster ensemble technique that has been recently introduced in the literature is employed here. The results with simulated data and algorithms of NB, C4.5 and KNN have shown that the proposed framework can filter out some negative samples quickly, while making classification of the rest more effective. In particular, it enhances predictive performance of basic classifiers by lifting F1 scores from less than 0.1 to around 0.3–0.5. Besides, parameter analysis is also given as a guideline for its application.
GLEAM

Efforts to evolve modularity in genetic programming are as old as the field itself. While many techniques have been proposed to evolve programs that make use of simultaneously-evolved modules, the general problem of evolving large-scale, modular software still stands as a central challenge to the field. In this paper, we present GLEAM, a mechanism of achieving modularity, whereby an evolving program uses a local library of modules. The program can refer to the modules during execution and the ones not used by the program are eventually replaced by code segments extracted from the program itself. Modules can also be absorbed by the program when their references in the program get expanded to the full code segments. We show that this simple mechanism improves the success rate on a number of program synthesis problems.