Reviewerly/exHarmony huggingface dataset:
Preventing Overfitting of LSTMs using Ant Colony Optimization

Overfitting is a general problem for neural networks, where they overfit to only the training data and get low generalization performance. Long Short-Term Memory (LSTM) is a type of neural network which can handle sequences of data, and it often suffers from overfitting because of its complex and expressive structure. There are several methods to prevent overfitting of neural networks, such as dropout or dropconnect. Nevertheless, they fail in some cases. This article studies the usage of ant colony optimization (ACO), a swarm intelligence algorithm for combinatorial optimization problems, on optimizing the structure of LSTMs and preventing overfitting. The proposed method optimizes the structure with ACO while training the LSTM with backpropagation. It was applied to time-series prediction tasks on three real-world datasets, and it performed better than other traditional methods.
Analog Hardware Neural Networks using Logarithmic Four-Quadrant Multiple Circuits

In the artificial intelligence, machine learning and neural network fields, many practical use models have been proposed. However, these models are based on a digital Von Neumann model&#x0027;s computer. There are a minority of studies in the field of analog learning neural networks. In our previous study, we used multiple circuits as connecting weights. Multiple circuits calculate the product of the two input values, input signals and connecting weights. However, the working range is very small because of the semiconductor characteristics. When constructing a network, one of the disadvantages is that the input and output range is limited. Furthermore, the circuit operation becomes unstable because of the characteristics of the multiple circuit. In this study, we proposed logarithmic four-quadrant multiple circuits as a connecting weight. From the experimental result, it is shown that this circuit operates stably over a wide range. The model works using pure analog electronic circuits. The learning time is quite short compare to a digital process computer. We improved the number of units and network layers. Moreover, we suggest the possibility of the realization of the hardware implementation of the deep learning model.
University Reform for Competency-Based Education using Eduinformatics by Significant Other Groups

Kobe Tokiwa University established its first university reform team in 2014. The team developed a list of 19 Tokiwa competencies, using which it started new curricula since 2017. Now, for the new curriculum for 2022, a second university reform is underway. In this study, we evaluate the validity of the 19 Tokiwa competencies through cooperation with significant other groups. A missing data analysis and exploratory factor analysis showed that the competencies can be reduced from 19 to 13. One problem with Kobe Tokiwa university&#x0027;s curriculum is the large number of competencies. However, it is not easy to reduce the number of competencies because of their interrelationships. Further research is needed to make effective use of competencies in the future.
Estimating Semantic Relationships between Sentences Using Word Embedding with BERT

In this study, we focus on conjunctions between sentences to estimate the semantic relations between sentences. As a method for estimating the types of hidden conjunctions, we propose a method using a word embedding with bidirectional encoder representations from the transformer (BERT), which has shown high accuracy in various natural language processing tasks. By using Japanese newspaper articles, we have confirmed the effectiveness of the proposed method in estimating the presence or absence of conjunctions and the types of conjunctions. There was a difference in the accuracy by changing the estimator used to input word embedding. The result varied greatly depending on the conjunction.
Microteaching technology: an effective tool for building and assessing future teachers' professional competencies

The purpose of the study – to explore the possibility of modernizing the traditional form of university education through the integration of micro-learning technology and traditional lecture classes. The article discusses the effectiveness of the use of micro-learning technology as a tool for the formation and evaluation of professional competencies of future foreign language teachers. The scientific novelty of the study lies in the fact that it proves the effectiveness of using micro-learning as a means of implementing the principle of activity-based learning and activating the role of students in the educational process. As a result of the qualitative analysis of the conducted micro-lessons, the skills of future teachers were identified, which by the time of the end of the course of the methodology were sufficiently formed, and those that still needed to be improved. Thus, it was proved that micro-training is an effective tool for monitoring and evaluating the level of professional competence of future teachers.
Patent Litigation Research Trends and Trend Analysis

The purpose of this study is to provide useful points to companies by performing an analysis about patent litigation (dispute) trends that is recently coming to the fore as an important issue and also the solution measures. In this study, network analysis method will be used. For this, data was collected from dissertation database and Twitter, and data preprocessing was conducted before performing the analysis. The data analysis was performed by applying a data analysis tool called R and a network analysis tool. First of all, the analysis results of Twitter showed that patent and dispute, Nokia, litigation and patent, coronavirus, legal, and developments appeared as important factors because of the high weight value compared to other factors. The analysis results of Scopus showed that patent and litigation, infringement, system, payment, and settlement appeared as important factors because of the high weight value compared to other factors. The results of this study can provide a practical guideline to the researchers that intend to carry out research about patent dispute and also to the persons in charge of company that need prior knowledge about patent dispute.
Learning to Write Descriptive Texts Based on Batik Culture of BIPA Students at the Indonesian Embassy in London

Learning Indonesian as a Foreign Fuller (BIPA) is carried out along with the introduction of culture to learners. Apart from language learning, the introduction of culture can also provide a more concrete picture of Indonesian identity. This article focuses on the study of writing skills, in particular, on writing descriptive texts. The learning to write based on Indonesian culture, especially batik culture. Using qualitative methods, this research was conducted for approximately three months in the period of September-December 2019. The data of this study were the ability to write descriptive texts of BIPA students at the Indonesian Embassy in London. The research subjects were 9 BIPA students at the Indonesian Embassy in London. The results of this study are descriptions of the learning to write descriptive texts of BIPA students and descriptions of culture-based learning of BIPA students at the Indonesian Embassy in London. The descriptive text is used as an alternative approach in learning to write for BIPA because it makes it easier for the students to learn vocabulary as well as culture.
Psycholinguistic aspect of polylingualism

This article reveals the importance of multilingual knowledge in modern society and the need to introduce multilingual education. This article deals with the complexity of the process of development of polylingual personality, the influence of psychological factors on multilingual, the interconnection between linguistics and psychology. The components of the linguistic aspect of polylingualism and the essence of the concept of “psycholinguistics” are revealed. The article describes how language, speech, and comprehension correlate and interact with psychological processes. The results of theoretical generalizations of the subject and the importance of psycholinguistics are presented. The components of psycholinguistics and their significance are outlined. The stages of formation of psycholinguistics as science are investigated. The article substantiates the importance of developing a polylingual personality taking into account human psychological characteristics. Thanks to scientific conversation, it is shown that the understanding, processing and production of one and two languages in the human brain are significantly different from the same process in the brain of a multilingual man. These aspects are considered in view of the needs of multilingual education and to detect a number of psycholinguistic factors that are significant for the methodology of teaching several foreign languages.
Assessment of Systems Requirements Specification Skills Based on an Industry Body of Knowledge

Contribution - The acquisition of systems requirements knowledge and skills by systems developers is a desirable outcome of a STEM graduate program in technology management. In this paper, we assess whether the learning outcomes of such a STEM program are being met. We present a process to align the program curriculum with an industry-standard to ensure the acquisition of those competencies. Background - We selected the International Institute of Business Analysts (IIBA) as a vendor-neutral body of knowledge for the Business Analyst profession. We used a well-documented process to align a university curriculum to the industry needs, following earlier for project management and data analytics. Research questions - To align the curriculum to the IIBA BABOK (Business Analysis Body of Knowledge), we asked: (1) whether students graduating from the program had acquired good business analysis and systems requirements competencies; and (2) If they had not, how the curriculum may be modified to help students acquire those competencies. Methods - The curriculum was reviewed, and we assured proper topical coverage by pertinent course to the IIBA BABOK knowledge. Results - Using the existing curriculum, we found that most students were able to pass the assessment based on the IIAB BABOK. We identified some deficiencies by knowledge domain. The assessment results were sufficiently granular to make modifications to the curriculum and course contents to improve student’s acquisition of systems requirements specifications skills.
Augmented Reality Technology Projects of Tea Culture for China’s Secondary Students

AR technologies has becoming an uprising yet challenging technology to foster K-12 students’ learning. Previous research has reported contrasting but positive conclusions about the effect of integrating AR technology in science, technology, engineering and mathematics (STEM) education. This research selected tea culture, which is a profound and long-lasting culture in China and the world, and designed and developed AR tea culture resources to facilitate secondary student’s learning. In order to further examine the effect of utilizing AR tea culture resources, this study designed a quasi-experiment and utilized a mixed-method approach to examine students’ knowledge acquisition, learning experience, and learning attitude towards tea culture during instruction through AR project. The analysis results show that students in experimental group achieved a better grade in knowledge acquisition, and significant improvement in learning experience and learning attitude towards tea culture than students receiving lecture through traditional teacher-led PowerPoint instruction. It is becoming more necessary to educate future developers and educators of the development and integration of AR technologies in formal education, where our experience could provide some implications for future STEM educational community.
Darknet jako przedmiot badań nauk społecznych

1. Choudhury S.R., Kharpal A., The ‘deep web’ may be 500 times bigger than the normal web. Its uses go well beyond buying drugs, CNBC, https://www.cnbc.com/2018/09/0.... Google Scholar
Design science research approach in studying e-negotiations: models, systems, experiments

Abstract Inspired and led by Dr. Gregory E. Kersten, a number of research projects have been conducted at the InterNeg Research Centre. This paper intends to acknowledge Dr. Kersten’s unique role as a pioneer in e-negotiation research, particularly in exploring and integrating various elements in e-negotiations. From the design science research perspective, this paper reviews a series of relevant research works in e-negotiation modeling, system design and development, and experimental studies. This provides an integrative view of interconnected elements in this field, and also helps framing the various studies into different aspects and stages of e-negotiation research. The paper then suggests several guidelines and directions for future design science research in e-negotiations.
Przestępstwa komputerowe w polskim Kodeksie karnym

1. Clifford R.D. (red.), Cybercrime. The Investigation, Prosecution and Defense of a Computer-related Crime, Durham 2011. Google Scholar
Secure and efficient multifunctional data aggregation without trusted authority in edge-enhanced IoT

Since the rapid development of Internet of Things (IoT) has promoted the dramatic growth of data, data aggregation has received considerable attention, which can collect the sensed data from IoT devices and analyze them for high-quality information to greatly benefit our life. Nevertheless, these sensed data may be related to individual sensitive information and are vulnerable to security threats. To solve the problem, many secure data aggregation schemes have been proposed. However, most of them either have a high overhead, only support sum aggregation, or need the involvement of a trusted third party. They cannot strike a good balance between security, efficiency and functionality. In this paper, we propose a Secure and Efficient Multifunctional Data Aggregation without Trusted Authority (TA) in Edge-Enhanced IoT (SEMDA). In our method, combined with lightweight cryptographic techniques, a robust, multifunctional and fine-grained data aggregation scheme is constructed. In addition, considering the potential privacy disclosure caused by deep association between IoT data, we extend SEMDA to support differential privacy. A comprehensive security analysis of SEMDA is conducted under the honest-but-curious model, and the results show that our scheme not only achieves confidentiality and privacy, but also guarantees integrity and authentication. Meanwhile, extensive theoretical analyses and experimental evaluations demonstrate that SEMDA performs more efficiently with respect to computation and communication while retaining more desired properties.
Automated detection of offshore wave power using machine learning techniques

Various statistical methods are used for estimating sea state and wave characteristics using digital wave models. Several machine learning techniques help to develop the computational difficulties of these methods. This study aims to estimate the wave power with Extreme Learning Machine (ELM), Regression, Restricted Boltzmann Machine (RBM), and RBM-ELM methods using nonlinear wave input parameters at different depths. Furthermore, the performance criteria are improved by applying the Relief feature selection algorithm to these methods. The input bias and input weights for the ELM have been determined using the RBM. In the RBM-based presented method, the highest estimation values were obtained using Relief feature selection and without Relief as 96.96% and 94.02%, respectively. The highest accuracy rate based on ELM is 76.86% in the estimation of wave power without Relief. In the same way, the accuracy rate with Relief was calculated as 92.16%. These values were increased to 90.10% (without Relief) and 95.73% (with Relief) using the RBM-ELM method. This study demonstrates that the performance of the presented methods with Relief feature selection was improved.
One step further: evaluating interpreters using metamorphic testing

The black-box nature of the Deep Neural Network (DNN) makes it difficult for people to understand why it makes a specific decision, which restricts its applications in critical tasks. Recently, many interpreters (interpretation methods) are proposed to improve the transparency of DNNs by providing relevant features in the form of a saliency map. However, different interpreters might provide different interpretation results for the same classification case, which motivates us to conduct the robustness evaluation of interpreters.
Pytest-Smell: a smell detection tool for Python unit tests

Code quality and design are key factors in building a successful software application. It is known that a good internal structure assures a good external quality. To improve code quality, several guidelines and best practices are defined. Along with these, a key contribution is brought by unit testing. Just like the source code, unit test code is subject to bad programming practices, known as defects or smells, that have a negative impact on the quality of the software system. As a consequence, the system becomes harder to understand, maintain, and more prone to issues and bugs. In this respect, methods and tools that automate the detection of the aforementioned unit test smells are of the utmost importance.
B‐SCORE – A blockchain based hybrid chaotic signatures for medical image encryption in an IoT environment

Abstract Internet of things (IoT) has evolved exponentially in the recent years and its applications are also being explored in the medical field. Due to this, the volume of medical images transmitted has increased multifold. Usage of IoT networks for medical image transmission has significantly reduced the time needed for clinical diagnosis and thereby increasing treatment efficiency. However at present, IoT networks are open to various security threats, which may affect the sensitive and private information that are present in patient's medical image datasets. Existing studies reveal the need of improvisation for secured medical data transmission over IoT networks. In the context to IoT security issues, this research paper proposes blockchain architecture integrated with chaotic encrypted medical image transmission to ensure the high security in medical image transmission. The proposed system incorporates tri‐layered architecture such as Image Aware Segmentation (IAS), hybrid chaotic encryption scheme and finally blockchain environment. The extensive experimentation has been carried out in which the performance parameters such as entropy, NACI and UACI (Number of Pixel Change Ratio and Unified Average Changed Intensity) were calculated and analyzed. It is found that the proposed architecture has NPCR as 99.65%, UACI as 33.95% and entropy ideally close to 8. Encryption results show that the proposed architecture exhibited more randomness, which can defend the IoT security threats.
The G4i Analogue of a G3i Sequent Calculus

Abstract This paper provides a method to obtain terminating analytic calculi for a large class of intuitionistic modal logics. For a given logic with a cut-free calculus that is an extension of the method produces a terminating analytic calculus that is an extension of and equivalent to . was introduced by Roy Dyckhoff in 1992 as a terminating analogue of the calculus for intuitionistic propositional logic. Thus this paper can be viewed as an extension of Dyckhoff’s work to intuitionistic modal logic.
Leveraging a Neuroevolutionary Approach for Classifying Violent Behavior in Video

Security has become a critical issue for complex and expensive systems and day-to-day situations. In this regard, the analysis of surveillance cameras is a critical issue usually restricted to the number of people devoted to such a task, their knowledge and judgment. Nonetheless, different approaches have arisen to automate this task in recent years. These approaches are mainly based on machine learning and benefit from developing neural networks capable of extracting underlying information from input videos. Despite how competent those networks have proved to be, developers must face the challenging task of defining both the architecture and hyperparameters that allow such networks to work adequately and optimize the use of computational resources. In short, this work proposes a model that generates, through a genetic algorithm, neural networks for behavior classification within videos. Two types of neural networks evolved as part of this work, shallow and deep, which are structured on dense and 3D convolutional layers. Each network requires a particular type of input data: the evolution of the pose of people in the video and video sequences, respectively. Shallow neural networks use a direct encoding approach to map each part of the chromosome into a phenotype. In contrast, deep neural networks use indirect encoding, blueprints representing entire networks, and modules to depict layers and their connections. Our approach obtained relevant results when tested on the Kranok-NV dataset and evaluated with standard metrics used for similar classification tasks.
Data Encryption Technology Analysis of Robot Computer Network Information

In order to ensure the security of robot computer network information, a data encryption technology is proposed. Through the comprehensive research of symmetric cryptography algorithm, open cryptography algorithm, hybrid encryption algorithm, AES algorithm, quantum cryptography technology, and ECC algorithm, a data encryption method suitable for robot computer network information is proposed. It is found that when analyzing the entanglement characteristics of the four-particle cluster state, the anomaly will inevitably disturb its state. In the eavesdropping detection, the communication parties can cooperate to detect the existence of abnormal conditions. In addition, in the formal communication stage, trial photons are introduced, whose confidentiality characteristics are guaranteed by the principle of quantum noncloning and the uncertainty principle. Therefore, this technology further ensures the confidentiality of communication.
PlugSonic: a web- and mobile-based platform for dynamic and navigable binaural audio

Abstract PlugSonic is a series of web- and mobile-based applications designed to edit samples and apply audio effects (PlugSonic Sample) and create and experience dynamic and navigable soundscapes and sonic narratives (PlugSonic Soundscape). The audio processing within PlugSonic is based on the Web Audio API while the binaural rendering uses the 3D Tune-In Toolkit. Exploration of soundscapes in a physical space is made possible by adopting Apple’s ARKit. The present paper describes the implementation details, the signal processing chain and the necessary steps to curate and experience a soundscape. We also include some metrics and performance details. The main goal of PlugSonic is to give users a complete set of tools, without the need for specific devices, external software and/or hardware specialised knowledge, or custom development, with the idea that spatial audio has the potential to become a readily accessible and easy to understand technology, for anyone to adopt, whether for creative or research purposes.