Reviewerly/exHarmony huggingface dataset:
Deep Neural Network Guided Evolution of L-System Trees

Lindenmayer systems (L-systems) are mathematical formalisms used for generating recursive structures. They are particularly effective for defining realistic tree and plant models. It takes experience to use L-systems effectively, however, as the final rendered results are often difficult to predict. This research explores the use of genetic programming (GP) and deep learning towards the automatic evolution of L-system expressions that render 2D tree designs. As done before by other researchers, the L-system language is easily defined and manipulated by the GP system. It is challenging, however, to determine a fitness function to evaluate the suitability of evolved expressions. We train a deep convolutional neural network (CNN) to recognize suitable trees rendered in the style of the L-system language. Experiments explore a number of deep CNN strategies. Results in some experiments are very promising, as images conforming to specified styles of tree species were often produced. We found that underspecifying or over-complicating the training requirements can arise, and the results become unsatisfactory in such cases. Our results also confirm that of other researchers, in that deep learning can be fooled by evolutionary algorithms, and the criteria for success learned by deep neural networks might not conform with those of human users.
Span Representation Generation Method in Entity-Relation Joint Extraction

Relation extraction (RE) is an important part of knowledge graph construction. The span-based entity-relation joint extraction model is an emerging model for Relation extraction. In the span-based entity-relation joint extraction model, the method of generating span representation vectors is usually relatively simple, and the semantic representation ability is insufficient. This paper studies the impact of four different span vector representation methods on the performance of the entity-relation joint extraction model, and enriches the features of span representation vectors by combining multiple span semantic representation methods. Compared with the baseline model, the combined span representation method can effectively improve the performance of the model on the CoNLL04 data set. Named entity recognition has achieved an F1 score of 89.37%, and relation extraction has achieved an F1 score of 72.64%. Compared with the baseline model, it has increased by 0.43% and 1.17% respectively.
Analysis of alcohol abuse using improved artificial intelligence methods

Abstract In this work, artificial intelligence (AI) based method has been proposed for analysis of alcohol abuse (AA) to evaluate drug risk and find an optimal method for analysis of AA. To detect alcohol abuse, twelve input features are selected. As the number of positive classes and negative classes are imbalanced, input features are processed using synthetic minority oversampling technique (SMOTE). The selected input features are given to the AI based method to predict whether an individual uses alcohol or not. Three AI methods (logistic regression, Naïve Bayes and gradient boost) have been used from which the most suitable method is chosen as optimal method for analysis of AA. Methods used for validation of the proposed AI based method are leave one out cross validation and test sample estimate. Among the three methods used, gradient boost method performs better than other method. Gradient Boost based AI method has 97.55% accuracy to predict alcohol user. Significant achievement of the proposed work is that the accuracy (nearly 97%) to detect alcohol abuse is much more than previously (nearly 70%) suggested methods. This is due to the efficient design of AI method implemented in this work. All the feature processing work and AI algorithm design work has been done using python software.
Using Graphs in Processing of Light Microscope Medical Images

We consider graph theoretical approach to image processing, especially to segmentation of the biological images. At first, we provide a brief overview of methods from graph theory used for to image segmentation. Next, we focus on graph cuts method and its application in image processing. We show how we transform an image to a graph and corresponding segmentation network. Then, we show how we deal with segmentation of the real biological data. We define a completely new method for pre-processing data to get realistic results. We bring new pre-processing algorithm for preparing data for segmentation and also postprocessing counting of results and verifying the hypotheses. The chapter fullfils expectations of medical and biological researchers to get mathematical methods and tools for analysing the results and verifying the hypotheses. It refers to user-defined requirements of data processing, numerical evaluation, counting of cells or minerals, percentual performing, counting of minerals in the tissue. Our own contribution is the “software”, is created on special properties, requirements and requests on biological and medical researchers, because free and open software commonly available on Internet does not give them satisfying results. We want to show that graphs and its applications bring enough good tools for processing iron in medical and biological data. We used biological data in the connection with Alzheimer disease to achieve our goal.
Coherent Homodyne Synaptic Interconnect with Sign- and Weight-Tunable Detection

We experimentally investigate a synaptic interconnect with low-complexity emitter and coherent receiver. Phase tuning of the LO enables the signed multiplication of the detected signal, which is demonstrated for 130-ps spikes at 1-GHz repetition rate.
Geometrically Based Collision Avoidance for Quadrotors under Short Sensing Distance Conditions

Quadrotors have seen a surge in popularity in many applications such as medical deliveries, surveying tasks, mapping tasks and much more. Urban operations, such as deliveries of parcels, require the quadrotor, referred to as the ownship in this paper, to be capable of autonomous navigation and collision avoidance, relying only on its onboard sensors. The focus of this paper is on the maneuvering of the ownship to avoid a potential collision with another neutral vehicle pursuing its own mission. We present a geometrical collision avoidance scheme based on the original 3DVO algorithm. The novelty of the improved 3DVO (I-3DVO) lies in its abilities to select an optimal collision avoidance plane and to vary the speed of the ownship to achieve collision avoidance under the short sensing distance conditions. We validate our proposed algorithm via a Matlab simulation accounting for the often neglected dynamics of the quadrotor ownship.
Visual Sentiment Analysis on Social Media Data

Visual sentiment analysis is the way to automatically recognize positive and negative emotions from images, videos, graphics, stickers etc. To estimate the polarity of the sentiment evoked by images in terms of positive or negative sentiment, most of the state-of-the-art works exploit the text associated to a social post provided by the user. However, such textual data is typically noisy due to the subjectivity of the user which usually includes text useful to maximize the diffusion of the social post. Proposed system will extract and employ an Objective Text description of images automatically extracted from the visual content rather than the classic Subjective Text provided by the user. The proposed System will extract three views visual view, subjective text view and objective text view of social media image and will give sentiment polarity positive, negative or neutral based on hypothesis table.
Criticality of E-Privacy and Data Leakage Amid the Pandemic

The global pandemic has led to an undeniable surge in using digital technologies due to the social distancing norms and nationwide lockdowns. Firms and organizations are conforming to the new culture of work and life. The use of internet services, digital devices, and cloud systems has seen surges in usage from 40% to 100%, compared to pre-lockdown levels. With the rapid growth of this technological use, people are exposing their digital assets, presence, and behavior out to the binary world where AI-driven data analysis algorithms, data-gathering systems, and spyware are continuously monitoring their behavior. These subconsciously exposed data are then carried forward for delivering customized ads and recommend features.
A Semi-supervised Method to Identify Urban Anomalies through LTE PDCCH Fingerprinting

In this paper we advocate the use of mobile networks as sensing platforms to monitor metropolitan areas. In particular, we are interested in detecting urban anomalies (e.g., crowd gathering) by processing the control information exchanged among the base stations and the mobile users. For this, we design an anomaly detection framework based on semi-supervised learning, which enables the automatic identification of different types of anomalous events without any a-priori information. The proposed approach uses unsupervised learning techniques to gain confidence in real mobile traffic demand patterns from the city of Madrid in Spain and build an ad-hoc ground truth. A recurrent neural network is then trained to detect contextual anomalies and identify different types of urban events. Simulation results confirm the better performance of the semi-supervised method compared to pure unsupervised anomaly detection frameworks.
Klasterisasi Kasus Kekerasan Terhadap Anak dan Perempuan Berdasarkan Algoritma K-Means

Violence is action or threats against themselves alone, a group of people or community a group of people or community, loss psychologist, trauma, or deprivation of rights. District Karawang is on of the district that exist in the province of Jawa Barat. Violence that befell children and women in the area of Karawang bloom occurs, such as the lacj awareness of the victim to follow up cases that happened. The purpose of knowing the results of the cluster of cases of violence against children and women into three clusters are statterd in every sub-district in the District Karawang with category level of hardness low, medium or high in order that the government Karawang can provide treatment that is defferent and more targeted and focused on the results ot the analysis for each-each district. Data mining is the process of extracting data to obtain new information. In this study using CRIPS-DM methodology.Research is doing computation algorithm k-means clustering on the data of case of violence against children and women in 2016-2020. Results of testing using tools WEKA 3.8 earnded three cluster or the three categories of the level of violence that is cluster 0 there are 4 members who categorized the level of violence high, cluster 1 there are 2 members categorized the level of violence medium, and cluster 2 there are 24 members who categorized the level of violence low, the results of clustering is evaluated using equation testing purity measure, generate value purity 0,617, case that shows the cluster is quite good.
Authentication Key-Exchange Using SMS for Web-Based Platforms

One of the advantages of One Time Password (OTP) is that it's free from brute force, replay, and shoulder attacks.The codes may originate from different entropy attributes and schemes, such as true random and digital random number generators.Businesses, organizations, and academic institutions have adopted OTP methods for credit card transaction confirmation, recalling forgotten passwords, and validating web portal accounts.This paper proposed a new method in authenticating login credentials using a 3 × 3 matrix and random system key as Two-Factor Authentication (2FA) with an SMS-enabled feature.We used the 6-codes pseudorandom method and a 4-codes validation to allow mobile flexibility and ensure that the user has the required access.The page link and evaluation form are sent to students, IT professionals, and researchers.The results showed respondents are satisfied in terms of functionality, usability, efficiency, and reliability.The developed system could safeguard information, disallow unauthorized access, and impose acceptable data protection measures and minimal system requirements to use the system.
Category-Based Strategy-Driven Question Generator for Visual Dialogue

GuessWhat?! is a task-oriented visual dialogue task which has two players, a guesser and an oracle. Guesser aims to locate the object supposed by oracle by asking several Yes/No questions which are answered by oracle. How to ask proper questions is crucial to achieve the final goal of the whole task. Previous methods generally use an word-level generator, which is hard to grasp the dialogue-level questioning strategy. They often generate repeated or useless questions. This paper proposes a sentence-level category-based strategy-driven question generator (CSQG) to explicitly provide a category based questioning strategy for the generator. First we encode the image and the dialogue history to decide the category of the next question to be generated. Then the question is generated with the helps of category-based dialogue strategy as well as encoding of both the image and dialogue history. The evaluation on large-scale visual dialogue dataset GuessWhat?! shows that our method can help guesser achieve 51.71% success rate which is the state-of-the-art on the supervised training methods.
A Differential Evolution with Multi-factor Ranking Based Parameter Adaptation for Global Optimization

The performance of differential evolution (DE) algorithm depends critically on the setting of mutation factor F and crossover rate CR. In this paper, a multi-factor ranking based parameter adaptation scheme is proposed to properly set the value of F and CR. The proposed adaptation scheme includes a parameter storage and distribution mechanism. The parameter storage mechanism is used to produce a suitable value by considering the multi-factor ranking scheme, which can help the algorithm enhance its exploration ability in the early stage. The parameter distribution mechanism is a layered parameter calculation strategy, in which the individuals generate new parameters based on multi-factor ranking properties. This mechanism is helpful to improve the exploitation capability of DE. The performance of the proposed method has been evaluated on CEC2014 and CEC2015 test suites and compared with related methods. The results show our method could achieve good performance and outperform related methods.
Using Color Histograms for Shrunk Images Comparison

The possibility of using of color histograms formed in HS* spaces for image recognition was investigated. The comparison of the color histograms was performed via cross correlation coefficient calculation. The dependencies of the cross correlation coefficient of histograms on the shrinking degree and interpolation method are obtained.
Autonomous object manipulation and transportation using a mobile service robot equipped with an RGB-D and LiDAR sensor

This paper presents the implementation of a mobile service robot with a manipulator and a navigation stack to interact and move through an environment providing a delivery type service. The implementation uses a LiDAR sensor and an RGB-D camera to navigate and detect objects that can be picked up by the manipulator and delivered to a target location. The robot navigation stack includes mapping, localization, obstacle avoidance, and trajectory planning for robust autonomous navigation across an office environment. The manipulator uses the RGB-D camera to recognize specific objects that can be picked up. Experimental results are presented to validate the implementation and robustness.
Fuzzy Influence in Fuzzy Semantic Similarity Measures

The field of Computing with Words has been pivotal in the development of fuzzy semantic similarity measures.Fuzzy semantic similarity measures allow the modelling of words in a given context with a tolerance for the imprecise nature of human perceptions.In this work, we look at how this imprecision can be addressed with the use of fuzzy semantic similarity measures in the field of natural language processing.A fuzzy influence factor is introduced into an existing measure known as FUSE.FUSE computes the similarity between two short texts based on weighted syntactic and semantic components in order to address the issue of comparing fuzzy words that exist in different word categories.A series of empirical experiments investigates the effect of introducing a fuzzy influence factor into FUSE across a number of short text datasets.Comparisons with other similarity measures demonstrates that the fuzzy influence factor has a positive effect in improving the correlation of machine similarity judgments with similarity judgments of humans.
From Informal to Formal: Connecting Learning Experiences in Smart Learning Environments

Learners have ubiquitous informal learning opportunities, but it is difficult to take advantage from them and relate them to their formal education. The connection of formal and informal learning is one of the aims of SLEs, but how to do it is still a question. This paper explores such connection by integrating the mobile application Casual Learn to the SLE SCARLETT and it discusses the challenges faced in such integration.
Research on the Advantages of 5G Network to online Learning

Since the appearance of 5G technology, it has provided convenience for all aspects of society and life. 5G technology has also made great contribution to education, and the help effect for education is very significant, especially for online learning. In March 2020, the epidemic situation of covid-19 in China is severe, and the concept of "classes suspended but learning continues" has become the trend of school teaching. In particular, colleges and universities, involving a wide range of population migration, basically adopt online learning methods to carry out teaching activities. Therefore, the traditional teaching mode with teachers in the classrooms no longer meets the current teaching needs, and online learning has become the mainstream. Compared with the traditional face-to-face teaching, there are many problems in online learning due to network speed and latency.Based on the above considerations, this paper studies the online learning system based on the 5G mobile communication technology network, as well as the traditional face-to-face teaching and online learning under other different networks, and compares the students' mastery of knowledge under different teaching methods through examination. In the current situation of complex network, this paper proposes the number of knowledge points and the number of effective interaction between teachers and students, and puts forward the prospect of online learning in the future.
Machine Learning for Determining the Transition Point in Hybrid Metaheuristics

High-level relay hybrids are among the most effective metaheuristics in multiple domains. However, the relay aspect of hybridization raises the problem of when to perform the transition from one algorithm to the next. This problem becomes more relevant in exploration-only exploitation-only hybrids, where each algorithm specializes in a specific task and performs rather poorly in the other. This paper presents a novel way of approaching the transition problem as a classification problem. Different classifiers are trained and tested on the MPS-CMAES hybrid; computational results are presented for the CEC'13 benchmark. The performance of the machine learning based hybrid confirms the effectiveness of the approach by achieving a significant improvement over the original hybrid.
User Visual Attention Behavior Analysis and Experience Improvement in Virtual Meeting

With the development of virtual reality technology, the application of virtual scenes is becoming more and more extensive. At present, when users perform tasks in virtual reality conference scenes, graphical interactive components are presented above the main scene often causing deep conflict between the two and reducing user attention. At the same time, complex scenes will affect the user's attention to the target entity and reduce work efficiency. In this study, in order to increase the user's attention to complete interactive tasks in virtual reality conference scenes, and to obtain a better user experience, two methods are used to increase user attention by adding aperture blur to interactive components and adding highlight reminders on the edges of target objects. We designed two sets of experimental tasks to verify the effectiveness of the above two methods. The results show that both methods can achieve the effect of improving user attention.
Research on the countermeasures of improving teachers' information teaching ability in higher vocational colleges based on big data

Under the modern education system, the teacher's informatization teaching ability plays a key role, directly related to the level of teaching quality. Using big data can provide high quality teaching service. Teachers should establish student data analysis model according to the actual situation of teaching, and give full play to the role of open teaching platform with information technology, build an integrated teaching model, promote the implementation of talent training plans, and deeply integrate large-scale and personalized education to guarantee the quality of education and teaching.
Practical Evaluation of Lyndon Factors via Alphabet Reordering

We evaluate the influence of different alphabet orderings on the Lyndon factorization of a string. Experiments with Pizza&amp;Chili datasets show that for most alphabet reorderings, the number of Lyndon factors is usually small, and the length of the longest Lyndon factor can be as large as the input string, which is unfavorable for algorithms and indexes that depend on the number of Lyndon factors. We present results with randomized alphabet permutations that can be used as a baseline to assess the effectiveness of heuristics and methods designed to modify the Lyndon factorization of a string via alphabet reordering.
Exact Quantum 1-Query Algorithms and Complexity

Deutsch–Jozsa problem (D–J) has exact quantum 1-query complexity (“exact” means no error), but requires super-exponential queries for the optimal classical deterministic decision trees. D–J problem is equivalent to a symmetric partial Boolean function, and in fact, all symmetric partial Boolean functions having exact quantum 1-query complexity have been found out and these functions can be computed by D–J algorithm. A special case is that all symmetric Boolean functions with exact quantum 1-query complexity follow directly and these functions are also all total Boolean functions with exact quantum 1-query complexity obviously. Then there are pending problems concerning partial Boolean functions having exact quantum 1-query complexity and new results have been found, but some problems are still open. In this paper, we review these results regarding exact quantum 1-query complexity and in particular, we also obtain a new result that a partial Boolean function with exact quantum 1-query complexity is constructed and it cannot be computed by D–J algorithm. Further problems are pointed out for future study.