Reviewerly/exHarmony huggingface dataset:
Single Image Dehazing Based on Generative Adversarial Networks

Optical imaging technologies generally provide inferior images in foggy conditions resulting in low contrast, significant color distortion, and severe loss of image details for real world application. Most defogging methods uses atmospheric scattering models to estimate atmospheric light, however, the complexity of the atmospheric environment prevents them from obtaining parameters reliably. Learning-based strategies yield more natural results, but still suffer color distortion and incomplete defogging. This paper presents a generative adversarial network based single-image defogging model to end-to-end remove haze directly, where multi-scale structure is employed. An attention mechanism is also introduced to improve haze removal efficiency with edge loss functions. The network outperforms some classic defogging models on some datasets.
Integrating Knowledge Graph and Bi-LSTM for Drug-Drug Interaction Predication

To solve the problem of ADDI extraction, the construction and implementation of knowledge graph provide a suitable solution for medical knowledge storage and management. This paper designs the construction and implementation of a knowledge graph based on deep learning. Named entity recognition and relationship extraction are carried out on the text of social media data, and then the graph database is used to store medical knowledge and construct the knowledge graph (KG). A DDI prediction method is proposed by combing knowledge graph (KG) and Bi-directional long-short-term memory network (Bi-LSTM) with attention. The multiple DDI sources are integrated by KG, and then are transformed into vectors by the knowledge representation model HolE. Finally, the implicit features of DDI are extracted by Bi-LSTM, and DDI is identified by Softmax classifier. The proposed method effectively combines the advantages of KG, BI-LSTM and attention mechanism, which can not only extract the global information of DDI, but also extract the sequence information of DDI. The experimental results on the DDI corpus dataset validate that the proposed method is effective and feasible for DDI prediction even with the reasonable architecture.
Development and Application of Augmented Reality System for Part Assembly Based on Assembly Semantics

Currently, most assembly guidance augmented reality systems focus on assembly geometry relationship using traditional interaction methods, leading to poor interactivity. To improve the situation, firstly, an augmented reality system framework for part assembly based on assembly semantics is proposed. Secondly, the intelligent extraction of semantic information of assembly is realized, based on SolidWorks secondary development technology. Then the granularity modeling problem of the assembly scenario is studied. And finally, an augmented reality system for part assembly based on assembly semantics is developed, which verifies the effectiveness of the assembly guidance based on assembly semantics, and improves the human-computer interaction.
General Univariate Estimation-of-Distribution Algorithms

We propose a general formulation of a univariate estimation-of-distribution algorithm (EDA). It naturally incorporates the three classic univariate EDAs compact genetic algorithm, univariate marginal distribution algorithm and population-based incremental learning as well as the max-min ant system with iteration-best update. Our unified description of the existing algorithms allows a unified analysis of these; we demonstrate this by providing an analysis of genetic drift that immediately gives the existing results proven separately for the four algorithms named above. Our general model also includes EDAs that are more efficient than the existing ones and these may not be difficult to find as we demonstrate for the OneMax and LeadingOnes benchmarks.
Progress Rate Analysis of Evolution Strategies on the Rastrigin Function: First Results

Abstract A first order progress rate is derived for the intermediate multi-recombinative Evolution Strategy $$(\mu /\mu _I, \lambda )$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>μ</mml:mi> <mml:mo>/</mml:mo> <mml:msub> <mml:mi>μ</mml:mi> <mml:mi>I</mml:mi> </mml:msub> <mml:mo>,</mml:mo> <mml:mi>λ</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> -ES on the highly multimodal Rastrigin test function. The progress is derived within a linearized model applying the method of so-called noisy order statistics. To this end, the mutation-induced variance of the Rastrigin function is determined. The obtained progress approximation is compared to simulations and yields strengths and limitations depending on mutation strength and distance to the optimizer. Furthermore, the progress is iterated using the dynamical systems approach and compared to averaged optimization runs. The property of global convergence within given approximation is discussed. As an outlook, the need of an improved first order progress rate as well as the extension to higher order progress including positional fluctuations is explained.
A Harmonized Information Security Taxonomy for Cyber Physical Systems

Cyber physical systems (CPSs) are found in many aspects of daily life, and they control and protect energy production, manufacturing and even healthcare. Due to long lifecycles and the use of legacy technologies, its associated security comes with many challenges. Security taxonomies are useful to classify and communicate security-related information and elements. Despite the existence of numerous taxonomies, they are fragmentary, limited to only specific lifecycle phases or cover only specific aspects. A harmonized taxonomy must be applicable to all lifecycle phases of the CPS. This paper presents well-established taxonomies that are combined into a single comprehensive and harmonized taxonomy and allows application throughout the different lifecycle phases. Application of the taxonomy to real-world scenarios requires a consistent implementation methodology. The use of the harmonized taxonomy methodology is demonstrated by applying it to an actual incident case study. The taxonomy is used to identify information security gaps through its implementation in the industrial facility in question. The identified gaps are then addressed as part of the security lifecycle of the CPS. The harmonized taxonomy can be expanded to apply it to industries with specific requirements.
A Semi-Supervised Learning Approach for Automatic Detection and Fashion Product Category Prediction with Small Training Dataset Using FC-YOLOv4

Over the past few decades, research on object detection has developed rapidly, one of which can be seen in the fashion industry. Fast and accurate detection of an E-commerce fashion product is crucial to choosing the appropriate category. Nowadays, both new and second-hand clothing is provided by E-commerce sites for purchase. Therefore, when categorizing fashion clothing, it is essential to categorize it precisely, regardless of the cluttered background. We present recently acquired tiny product images with various resolutions, sizes, and positions datasets from the Shopee E-commerce (Thailand) website. This paper also proposes the Fashion Category—You Only Look Once version 4 model called FC-YOLOv4 for detecting multiclass fashion products. We used the semi-supervised learning approach to reduce image labeling time, and the number of resulting images is then increased through image augmentation. This approach results in reasonable Average Precision (AP), Mean Average Precision (mAP), True or False Positive (TP/FP), Recall, Intersection over Union (IoU), and reliable object detection. According to experimental findings, our model increases the mAP by 0.07 percent and 40.2 percent increment compared to the original YOLOv4 and YOLOv3. Experimental findings from our FC-YOLOv4 model demonstrate that it can effectively provide accurate fashion category detection for properly captured and clutter images compared to the YOLOv4 and YOLOv3 models.
AAC-IoT: Attribute Access Control Scheme for IoT Using Lightweight Cryptography and Hyperledger Fabric Blockchain

The Internet of Things (IoT) is an integrated environment as it merges physical smart objects to the Internet via wireless technologies to share data. The global connectivity of IoT devices brings the needs to ensure security and privacy for data owners and data users. In this paper, an attribute-based access control scheme for IoT (AAC-IoT) using Hyperledger Fabric (HLF) blockchain is proposed to address the security challenges. In the AAC-IoT scheme, data owners are registered and authenticated using identities, certificates and signatures. Data users, however, are registered with identities, certificates, signatures and physical unclonable function (PUF); then a credence score is computed for users to predict the originality during authentication. For access control, attribute-based access control (ABAC) is used, and the number of attributes is selected based on the sensitivity of the data. In accordance with the attributes count, the access control policies are generated. The novel concept of attribute count is determined from a fuzzy logic method using data type and preference. Hyperledger Fabric (HLB) blockchain is presented to manage meta-data and security credentials from data owners and data users, respectively, using a lightweight hashing algorithm. The AAC-IoT model using HLF blockchain is developed with Java programming language and iFogSim simulator. The performance metrics are measured based on latency, throughput and storage overhead, and the results show better outcome than the previous research work.
Population Diversity Leads to Short Running Times of Lexicase Selection

In this paper we investigate why the running time of lexicase parent selection is empirically much lower than its worst-case bound of $$O(N \cdot C)$$ . We define a measure of population diversity and prove that high diversity leads to low running times $$O(N + C)$$ of lexicase selection. We then show empirically that genetic programming populations evolved under lexicase selection are diverse for several program synthesis problems, and explore the resulting differences in running time bounds.
Learnability of the Boolean Innerproduct in Deep Neural Networks

In this paper, we study the learnability of the Boolean inner product by a systematic simulation study. The family of the Boolean inner product function is known to be representable by neural networks of threshold neurons of depth 3 with only 2n+1 units (n the input dimension)-whereas an exact representation by a depth 2 network cannot possibly be of polynomial size. This result can be seen as a strong argument for deep neural network architectures. In our study, we found that this depth 3 architecture of the Boolean inner product is difficult to train, much harder than the depth 2 network, at least for the small input size scenarios n≤16. Nonetheless, the accuracy of the deep architecture increased with the dimension of the input space to 94% on average, which means that multiple restarts are needed to find the compact depth 3 architecture. Replacing the fully connected first layer by a partially connected layer (a kind of convolutional layer sparsely connected with weight sharing) can significantly improve the learning performance up to 99% accuracy in simulations. Another way to improve the learnability of the compact depth 3 representation of the inner product could be achieved by adding just a few additional units into the first hidden layer.
Methodology for Determining Time Intervals by Video Recordings

Video recording as a product of informational and communication technologies has a specific place in the development of the society. The problem of forensic analysis of video recordings has a long history and transforms along with the development of technical means. Video recordings are a valuable source of factual data on cases involving traffic accidents, when in addition to describing the contentrelated side of the case, it is necessary to make certain calculations of temporal and spatial characteristics.The paper aims to form a unified methodological approach to establishing the temporal characteristics of events recorded on a video as part of forensic analysis of video footage. The authors present a framework for selecting a correct source of data when determining time intervals, describe a step-by-step sequence of actions for an expert to solve the question put to him, and the methods to determine time intervals.
Comparison Framework for Blockchain Interoperability Implementations

Blockchain interoperability has gained importance in practice, is increasingly discussed in literature, and serves as basis for new use cases such as manufacturing and financial services. However, many of the blockchain interoperability solutions discussed in literature are still in the design phase, are unpopular or have a small developer community. Therefore, this study proposes a comparison framework and examines implemented public blockchain interoperability solutions, focusing on data from published GitHub repositories. The results show that these implementations vary significantly in terms of popularity, their developer communities as well as their source code, indicating differences in quality. The insights gained in this work facilitate the selection of an appropriate implementation to enable blockchain interoperability use cases.
Synthetic Data in Automatic Number Plate Recognition

Machine learning has proven to be an enormous asset in industrial settings time and time again. While these methods are responsible for some of the most impressive technical advancements in recent years, machine learning and in particular deep learning, still heavily rely on big datasets, containing all the necessary information to learn a particular task. However, the procurement of useful data often imposes costly adjustments in production in case of internal collection, or has copyright implications in case of external collection. In some cases, the collection fails due to insufficient data quality, or simply availability. Moreover, privacy can be an ethical as well as a legal concern. A promising approach that deals with all of these challenges is to artificially generate data. Unlike real-world data, purely synthetic data does not prompt privacy considerations, allows for better quality control, and in many cases the number of synthetic datapoints is theoretically unlimited. In this work, we explore the utility of synthetic data in industrial settings by outlining several use-cases in the field of Automatic Number Plate Recognition. In all cases synthetic data has the potential of improving the results of the respective deep learning algorithms, substantially reducing the time and effort of data acquisition and preprocessing, and eliminating privacy concerns in a field as sensitive as Automatic Number Plate Recognition.
Towards Practical Secure Privacy-Preserving Machine (Deep) Learning with Distributed Data

A methodology for practical secure privacy-preserving distributed machine (deep) learning is proposed via addressing the core issues of fully homomorphic encryption, differential privacy, and scalable fast machine learning. Considering that private data is distributed and the training data may contain directly or indirectly an information about private data, an architecture and a methodology are suggested for A biomedical application example is provided to demonstrate the application potential of the proposed methodology.
Using Virtual Learning Objects in Educational Content

Today, virtual learning objects and virtual learning environments are influencing changes in educational content as well as the emergence of new learning models. The improvement of teaching methods and the integration of information technology into education are priorities today. The research conducted a systematic review of available articles published in the Web of Science, Google Scholar, and Scopus databases. The main result of the research shows that virtual objects can be teaching aids that help students learn better. In addition, during the pedagogical experiment based on augmented reality technologies, digital educational resources were developed, the impact of the developed virtual learning objects on the learning content and learning outcomes were studied. The adaptation of teaching methods to the modern digital generation by improving the content of teaching and the introduction of information technologies in education reflects the perspective of the research direction, which today is a priority task of a pedagogical science and the EdTech industry. In this context, virtual and augmented realities were concretized not only as an object of learning but also as a mechanism of the carrier of the learning content, as a means of assessing the acquired knowledge, as directions for future research of approaches to the modernization of the learning content.
Research on the Changes of College Students' Cognitive Level under the Broadcast Teaching Mode during the COVID-19 Pandemic Period

A variety of smart education evaluation methods have emerged in the era of information technology. This study proposes an improved Bayesian Cognitive Tracing Model (BKT-IRT) based on the working environment of classroom response system. The college physics classroom response data set is analyzed to compare the cognitive level under broadcast teaching mode during the COVID-19 epidemic period and traditional classroom teaching mode. The results show the student cognitive level under broadcast teaching mode is lower compared with that of the traditional mode. The BKT-IRT method can show the differences in cognitive level more clearly compared with IRT method and questionnaire survey method.
Ontology-driven detection of redundancy in short texts and its visualization

Nowadays, a lot of content is being moved to digital form, which allows its better processing and analysis. However, in some cases, loose redundancy or duplicity can be a problem. In this paper, redundancy even duplicity of short texts based on ontology-related approaches and their visualization is presented. The paper presents several attempts even forms of visualization used to display information about possible redundancy and duplicity of content plus specially in the domain of educational content engineering.
Complex-Valued Crow Search Algorithm for 0–1 KP Problem

The crow search algorithm is a novel swarm intelligence optimization algorithm. Aiming at the shortcomings of the crow search algorithm, a complex coding crow search algorithm (CCSA) is proposed to enhance its detection ability. A greedy algorithm is introduced into the algorithm to balance detection and development and enhance the optimization accuracy. And use the Sigmoid function to discretize the CCSA algorithm to solve the 0–1 knapsack problem.
Application of Improved Fruit Fly Optimization Algorithm in Three Bar Truss

Fruit fly algorithm (FOA) is a new swarm intelligence algorithm based on fruit fly foraging behavior. It has the advantages of less adjustment parameters, fast running speed and easy to use. However, the original algorithm also has some problems, such as poor stability, low convergence accuracy and easy to fall into local optimization. Because the exponential function, logarithmic function and inverse proportional function have different variation characteristics, they have good parameter regulation performance. In order to improve the optimization performance of FOA, the above three mechanisms are introduced into the original algorithm. Firstly, the variable step size mechanism (exponential function) is applied in the initialization of FOA, which can improve the stability of FOA; Secondly, the logarithmic mechanism is used for iterative optimization to improve the optimization accuracy of the original algorithm; Thirdly, the disturbance coefficient (inverse proportional function) is used to replace the fixed step value, so as to improve the global optimization ability of the original algorithm. Therefore, this paper proposes a multi mechanism improved fruit fly algorithm (IFOA), which is to reduce the blindness and disorder in the optimization process and promote the optimization ability of the algorithm. Finally, 15 test functions and an engineering example are selected to simulate experiment. The results show that IFOA has better performance.
Classification of Spoken English Accents Using Deep Learning and Speech Analysis

Accent detection which is also known as dialect recognition represents an emerging topic in speech processing. The classification of spoken accents can provide details about people background and their demographic information which can help in several domains. In this research, convolution neural network is utilised for the detection of three accent of English speakers including the American, British, and Indian accents. The data was collected from publicly available resources with speech samples from required English accents. Experimental results indicated the robustness of deep learning algorithms for the classification of English spoken accents which has the potential to be utilised in diverse applications specifically, within the security domain.
A Novel DCT-Based Video Steganography Algorithm for HEVC

With the increasing preference for information exchange in the form of video, steganography technology based on video will have huge application requirements in military communications, industrial Internet and other fields. High Efficiency Video Coding (HEVC) has received extensive attention for its high compression efficiency since its release in 2013, and after recent years of development, it has been applied to many products, so it is of great significance to research the video steganography algorithm based on the HEVC coding standard. This paper presents a HEVC video steganography method based on intra-frame QDCT coefficient. The secret information is embedded into the coupling coefficient of the selected 8 × 8 luminance QDST blocks to avert the intra-frame distortion drift. And the (7, 4) Hamming code is used to reduce the modification of these embedded blocks. The experimental results show that the proposed algorithm can effectively increase the visual quality and get good embedding capacity.
Human Resource Management Effects Functioning on Balanced Scorecard

The “speed” of modern business—as well as changes in the environment—have led administrative organizations on the path of additional challenges. These organizations tirelessly strive for the realization of advanced systems, adapting their business to modern ideas and trends and gaining market advantage. In this way, organizations become modern and contemporary in their understandings of internal functioning. The study presented by the authors in the chapter aimed to determine the impact of human resource management (HRM) functions on the balanced scorecard in Jordanian public joint-stock companies. In the research, adequate statistical and descriptive methods were used. The results of the study recommend the creation of an organizational culture that promotes the optimal and effective use of HRM functions in the organizational environment.