Reviewerly/exHarmony huggingface dataset:
Simplifying Fitness Landscapes Using Dilation Functions Evolved With Genetic Programming

Several optimization problems have features that hinder the capabilities of searching heuristics. To cope with this issue, different methods have been proposed to manipulate search spaces and improve the optimization process. This paper focuses on Dilation Functions (DFs), which are one of the most promising techniques to manipulate the fitness landscape, by “expanding” or “compressing” specific regions. The definition of appropriate DFs is problem dependent and requires a-priori knowledge of the optimization problem. Therefore, it is essential to introduce an automatic and efficient strategy to identify optimal DFs. With this aim, we propose a novel method based on Genetic Programming, named GP4DFs, which is capable of evolving effective DFs. GP4DFs identifies optimal dilations, where a specific DF is applied to each dimension of the search space. Moreover, thanks to a knowledge-driven initialization strategy, GP4DFs converges to better solutions with a reduced number of fitness evaluations, compared to the state-of-the-art approaches. The performance of GP4DFs is assessed on a set of 43 benchmark functions mimicking several features of real-world optimization problems. The obtained results indicate the suitability of the generated DFs.
$\ell _{2,p}$-Norm and Mahalanobis Distance-Based Robust Fuzzy C-Means

Fuzzy C-means (FCM) is a kind of classic cluster method, which has been widely used in various fields, such as image segmentation and data mining. Euclidean distance is a frequently used distance metric in FCM, but it is only suitable for data with spherical structure. As an extension of Euclidean distance, Mahalanobis distance has been used in Gustafson–Kessel FCM and its variants to tackle ellipsoidal data. For the convenience of optimizing, most existing Mahalanobis distance-based FCM algorithms only focus on squared Mahalanobis distance. However, squared Mahalanobis distance may not be the best distance metric for FCM because it is easy to enlarge the influence of outliers. In this article, we propose a novel <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{2,p}$</tex-math></inline-formula> -norm and Mahalanobis distance-based FCM model, abbreviated as LM-FCM, which can help FCM improve the ability of tackling ellipsoidal clusters and outliers. Then, in order to reduce computational complexity, we propose a more simplified model, abbreviated as SLM-FCM. Furthermore, we develop an iteratively reweighted optimization algorithm to optimize the proposed models and provide a rigorous monotonous convergence proof. Finally, compared with the existing state-of-the-art FCM algorithms, we conduct extensive experiments on both synthetic and real-world datasets to manifest the superior clustering performance and robustness of the proposed algorithms.
Face Recognition using Machine Learning and Deep Learning

Facial Recognition is the biometric method used in face detection. Confirming or identifying a face from multi-media pictures is done using the facial recognition technique. With the development of a forward-thinking society, the requirement for face identification has been really important. Detection and identification of faces have grown globally. It owes to the request for security such as permission, national safety, and other dynamic situations. Face can be detected using various algorithms. This paper aims to present the assessment of two face recognition techniques Haar Cascade and Local Binary Pattern. As anoutcome, the accuracy of Haar Cascade is better than the Local Binary Pattern but the execution time in Haar Cascade is better than Local Binary Pattern.
Hastha: Online Learning Platform for Hearing Impaired

Sign language is the primary means of communication for the hearing-impaired community. Introducing a learning platform can result in many ways to make learning more accessible for the hearing-impaired community of Sri Lanka. Although many approaches are being made to build such systems, the learning platform “Hastha” aims to provide a more interactive outcome with a component that converts Youlhbe videos to sign language and a Chatbot component that acts as an intermediary between a hearing-impaired user and a Google Search Engine. Furthermore, it includes a game-based learning platform and a gesture translation component from Sri Lankan to American Sign Language while the results are displayed to the users in the form of an animation. The proposed methodology is achieved by using Natural Language Processing, speech recognition, and machine learning techniques. This web-based application enables increased interaction between the student and the system making it an effective learning environment for the hearing impaired.
Generalized net model of the connections between different types of transport in Bulgaria

The article describes a generalized net model of the connections between the different types of transport in a transport network encompassing six major Bulgarian towns. The crossings marked with letter indices represent six major towns in Bulgaria where there are real connections between the different types of transport networks (public and freight). The so created model offers possibilities for optimization and synchronization of the connections between different types of transport. Some possible applications of the model are also discussed.
Fuzzy Linguistic Summaries for Explaining Online Semi-Supervised Learning

Intelligent systems for the medical domain often require processing data streams that evolve over time and are only partially labeled. At the same time, the need for explanations is of utmost importance not only due to various regulations, but also to increase trust among systems’ users. In this work, an online data-driven learning method with focus on the explainability of evolving models equipped with incremental semi-supervised learning algorithms is considered. The proposed method combines: (i) the Dynamic Incremental Semi-Supervised Fuzzy C-Means (DISSFCM) algorithm to incrementally classify subsets of data; with (ii) Linguistic Summarization, which provides explanations of the classification results in terms of short sentences in a natural language. The approach has been illustrated for streaming data collected from voice calls of patients affected by Bipolar Disorder. The results show the effectiveness of the proposed method in classifying instances belonging to healthy and affective states, and explaining the approximate reasoning behind the classification of new acoustic data related to patients.
Generalized Net Model of a Parallel Composition of Services with Intuitionistic Fuzzy Estimations of Uncertainty

In [1], [2], a novel approach to the representation of uncertainty in service systems is proposed. It extends the traffic characterization in the documents of the International Telecommunication Union (ITU) with the notions of Intuitionistic Fuzzy (IF) flow, time and traffic characterizations. The three proposed IF characterizations are used to estimate the uncertainty of various compositions of services. In the present paper, a Generalized net model of a parallel composition of two services is proposed. The IF characterizations of the composition are obtained in the form of characteristics of some special tokens of the GN. The proposed model can be used to define new Quality of Service (QoS) indicators of a parallel composition of services.
OEIS: Knowledge Graph based Intelligent Search System in Ocean Engineering

The knowledge graph can link massive fragmented data, transform data into knowledge and provide services. However, there is a lack of related applications and research in the field of ocean engineering. In order to solve the above problems and fill the gap in the application research of related knowledge graphs in the field of ocean engineering, we design and implement knowledge graph based intelligent search system in ocean engineering. We define the ontology of the ocean engineering field and adopt a top-down approach to construct the knowledge graph. First, for processing unstructured data, we use BERT-BiLSTM-CRF for named entity recognition and use R-BERT for relationship extraction. Then, the knowledge graph is stored in the Neo4j graph database. Finally, the intelligent search system uses BERT-CRF and Lexicon Matching to parse the query and provide search services through Cypher sentence generation. In the entity extraction experiments of query parsing, we evaluated the BERT-CRF model performance for 100 to 575 project entities, and the experiments continue to improve as the number of project entities is increasing. Knowledge graph based intelligent search system in ocean engineering (OEIS) has been applied to Jiangsu’s comprehensive ocean management and monitoring. With the use of the system, we are able to collect more entity data, which will also effectively improve the performance of our intelligent search system.
DNN Training Optimization with Pipelined Parallel Based on Feature Maps Encoding

The pipeline system is available to process multiple training batches simultaneously. Thereby, it can allow forward and backward propagation tasks for each batch to be performed across multi-time units on multi-compute nodes. The pipeline system can ensure better utilization of computing resources. However, a large amount of memory consumption during training is attributable to the large number of feature maps, leaving the performance of the pipeline system not fully utilized. To address the problem of high memory usage in pipeline parallel training, we propose a pipelined memory optimization approach based on feature maps encoding (FePipe). FePipe encodes the feature maps in the computing intervals between forward and backward propagation. Then, to reduce the memory usage, it uses binarization encoding to store the redundant elements of the input feature maps in the combined Relu-Pooling layer and CSR encoding to store the sparse feature maps in the combined Relu-Conv layer. It can be seen from the experimental results that, compared with the data parallel approach BSP and the pipeline parallel training approach PipeDream, the average memory usage of FePipe decreases by 1.51 times and 1.33 times, respectively. Besides, FePipe can effectively complete the training task with a small loss of accuracy.
Scheme of Peer-to-Peer Trade in Multi-Microgrid Based on Blockchain

With the advent of the era of power trading, how to balance the interests of all parties has become the key to the development of electric power information. The Multi-Microgrid model has gradually become an important part of power trading due to its reliability and efficiency, but how to balance the profit game among the Multi-Microgrid has gradually become a difficult problem for market development. In view of the distributed transaction environment of multi-micro network, we propose to use blockchain technology for multi-party transaction, use Stackelberg game model to achieve the best balance of interests of all participants, and zero-knowledge proof combined with secret sharing scheme is used to achieve effective access control in transactions. We simulate and test the performance indexes of the scheme, and the results show that our scheme is feasible.
TeCNTS: A Robust Collaborative Filtering Recommendation Scheme Based on Time-effective Close Neighbor Trusted Selection Strategy

The traditional collaborative filtering recommendation (CFR) scheme assumes that the data is static and usually ignores the dynamic phenomenon in the sample data. To solve this issue, many CFR schemes used dynamic information such as user interest change and dynamic trust relationships in their strategies. However, most of them did not handle the malicious changes in user interests and inadvertent fluctuation in users’ trust degree. When suffering from shilling attack, they still cannot get high performance in prediction accuracy and anti-attack. In order to improve CFR robustness, we present a robust CFR scheme named TeCNTS based on time-effective close neighbor trusted selection strategy. We deal the malicious changes in user interest with a user time-effective similarity measure function and inadvertent fluctuation of users’ trust degree with a reliable trust evaluation model, respectively. Based on the MovieLens dataset, we evaluate the performance of TeCNTS. The results show that TeCNTS is superior to the baseline schemes in prediction accuracy, accuracy stability, anti-attack and attacker filtering, and it is a robust CFR scheme.
Personalized tourism recommendation algorithm integrating tag and emotional polarity analysis

The matrix factorization algorithm has achieved good results in the field of scenic spot recommendation, but there are still many problems. Most of the studies only refer to the user’ s score of the tourist attraction to represent the user ’s overall evaluation of the attraction, thus ignoring the correlation between the online review text information of the attraction and the attraction, which eventually leads to the low accuracy of the entire recommendation system. This paper proposes a personalized tourism recommendation algorithm that integrates scenic spot labels and sentiment polarity analysis. On the basis of constructing feature labels for various scenic spots, the sentiment polarity analysis of user comments is carried out through natural language text information processing, and implicit emotions are given to the labels contained in scenic spots. Secondly, it is introduced into the factor vector of matrix decomposition model algorithm, and finally the potential relationship between users and feature labels and scenic spots is deeply explored. Through the user ’s preference for emotional feature labels, the user ’s approximate rating value for attractions is predicted. Experiments show that compared with the user-based collaborative filtering scenic spot recommendation algorithm, the average absolute error (MAE) and root mean square error (RMSE) are reduced by 70.28 % and 65.23 % respectively. Compared with the tag-based collaborative filtering attraction recommendation algorithm, MAE and RMSE are reduced by 65.02 % and 63.93 %, respectively. Compared with the matrix factorization recommendation algorithm of scenic spot labels, MAE and RMSE are reduced by 34.02% and 29.93 % respectively. To sum up the performance of the algorithm is significantly higher than the existing attractions recommended algorithm can be more accurate to provide users with recommended options.
Implementing Canny Edge Detection Algorithm for Different Blurred and Noisy Images

In the field of image processing, edge detection of visual data is one of the most important operations of image processing research. This operation is very useful in many applications like object recognition, feature detection of image, motion analysis in computer vision field, etc. Canny detector is an algorithm of these operations of edge detection which constitutes more information about object to other edge detection algorithms. This work consists of a comparison study of image edge detection methods by focusing on the Canny algorithm to present the efficiency and perfection of this methods compared to other. Moreover an improved execution of this methods for blurred and noisy images.
Generalized Net Model of Waste from Wastewater plants for Domestic Wastewater Treatment

In the current paper a Generalized Net (GN) model of waste generated from wastewater plant for domestic treatment is presented. The technological scheme demonstrates the main processes of collection, treatment, decontamination, recycling, and disposal of the waste and sludge generated. The proposed GN-model allows for more exhaustive following and managing the individual steps in the different stages of the process of collection and treatment. Moreover, the GN-model can aid tracing, analysis and tuning of some parameters related to the operation of the equipment.
Aspect-based Sentiment Analysis on Mobile Application Reviews

With the popularity of smartphones, mobile application (A.K.A Mobile App) development has become a booming industry all across the World. One of the main hurdles that app developers are facing, is understanding users’ needs and catering their products to satisfy the users. Though Users are one of the main stakeholders of the App development process it is harder to incorporate them into the requirement elicitation process. Numerous studies have shown that incorporating user reviews in the requirement elicitation process paves the way to a better understanding of user needs which, in turn, helps developers develop better apps that satisfy the targeted audience of the app. In this paper, we introduce a CNN-based approach to analyze user reviews using Aspect-based Sentiment Analysis (ABSA). The results show that our approach could achieve 87.88%, 93.75%, and 31.25% improvements in aspect category classification and 16.43%, 23.35%, and 3.72% improvements in aspect sentiment classification over the baseline results for AWER dataset in productivity, social networking, and game domains respectively.
MRCO: A Multi-ring Convergence Oscillator-based High-Efficiency True Random Number Generator

The entropy source structure with embedded XOR gates in a ring oscillator (RO) as a true random number generator (TRNG) can improve the speed of accumulating jitter in the oscillator. However, the XOR gate has a certain response time to the input change, and when the potential at the input flips too fast, the XOR gate will output short pulses. In this paper, we propose a TRNG design based on a multi-ring convergence oscillator (MRCO) making use of the characteristics of short pulses propagating in the circuit. We study the effect of jitter on the output of the XOR gate when the XOR gate has high-speed changing inputs. Excessively small short pulses will be eliminated in the circuit and suppresses the accumulation of jitter in the oscillator. Our proposed TRNG cleverly avoids the concatenation of the short pulses with the inverter so that the short pulses are only input to the XOR gate, affecting the oscillation of the circuit by interfering with the change of the XOR gate output by another input change. The proposed TRNG design is implemented in Xilinx Virtex-6 FPGA, making it comparable to the state-of-the-art TRNG also implemented on FPGAs. The results show that this TRNG has the highest ratio of throughput rate to hardware resources. The generated random sequence pass both NIST SP800-22 test and NIST SP800-90B test.
Sequential Models for Automatic Personality Recognition from Multimodal Information in Social Interactions

The task of automatic personality recognition has become very popular in recent years and it is considered a difficult one as we are trying to model human behavior that may not be visually obvious. Although state-of-the-art approaches have used deep learning architectures such as Transformers and some techniques such as Neural Architecture Search (NAS), some of these methods disregard valuable temporal information. In this paper, we approach the task by modeling it as a sequential problem, using a bimodal recurrent neural network, and exploiting the visual and textual modalities jointly. We report experimental results obtained in a novel corpus of dyadic interactions, outperforming state-of-the-art for the Extraversion personality trait. Another contribution of this paper is that we also analyze the regression to the mean problem that we think most state-of-the-art approaches could be facing when approaching the personality recognition task.
Bayesian Approach to Uncertainty Visualization of Heterogeneous Behaviors in Modeling Networked Anagram Games

Heterogeneous player behaviors are commonly observed in games. It is important to quantify and visualize these heterogeneities in order to understand collective behaviors. Our work focuses on developing a Bayesian approach for uncertainty visualization in a model of networked anagram games. In these games, team members collectively form as many words as possible by sharing letters with their neighbors in a network. Heterogeneous player behaviors include great differences in numbers of words formed and the amount of cooperation among networked neighbors. Our Bayesian approach provides meaningful insights for inferring worst, average, and best player performance within behavioral clusters, overcoming previous model shortcomings. These inferences are integrated into a simulation framework to understand the implications of model uncertainty and players’ heterogeneous behaviors.
Blind image deblurring via content adaptive method

Blind image deblurring aims to obtain a clear image and blur kernel from a blurred image. Most existing methods estimate the blur kernel through the entire image. However, different image information, such as image structure information, smooth area information and noise information, contribute differently to blur kernel estimation. The uniform processing of various image information will reduce the accuracy of blur kernel estimation. In this paper, we propose a new blind deblurring method based on the content-weighted data fidelity term, which can focus more on the sharp edge to restore image structure. Moreover, we construct a new image prior to constrain the weight matrix. However, the content-weighted data fidelity term is a non-convex function. In this work, we introduce the variable splitting method to replace content-weighted matrix, which can be optimized by alternating iteration method. A large number of experiments show that the proposed deblurring algorithm can obtain the best performance on natural images and text images.
Leveraging joint incremental learning objective with data ensemble for class incremental learning

A class-incremental learning problem is characterized by training data becoming available in a phase-by-phase manner. Deep learning models suffer from catastrophic forgetting of the classes in the older phases as they get trained on the classes introduced in the new phase. In this work, we show that the change in orientation of an image has a considerable effect on the model prediction accuracy, which in turn demonstrates the different rates of catastrophic forgetting for the different orientations of the same image, which is a novel finding. Based on this, we propose a data-ensemble approach that combines the predictions for the different orientations of the image to help the model retain information regarding the previously seen classes and thereby reduce the rate of forgetting in the model predictions. However, we cannot directly use the data-ensemble approach if the model is trained using traditional techniques. Therefore, we also propose a novel training approach using a joint-incremental learning objective (JILO) that involves jointly training the network with two incremental learning objectives, i.e., the class-incremental learning objective and our proposed data-incremental learning objective. We empirically demonstrate that JILO is vital to the data-ensemble approach. We apply our proposed approach to state-of-the-art class-incremental learning methods and empirically show that our approach significantly improves the performance of these methods. Our proposed approach significantly improves the performance of the state-of-the-art method (AANets) on the CIFAR-100 dataset by absolute margins of 3.30%, 4.28%, 3.55%, 4.03%, for the number of phases P=50, 25, 10, and 5, respectively, which establishes the efficacy of the proposed work.