Reviewerly/exHarmony huggingface dataset:
A comprehensive review on bio-inspired flower pollination algorithm

AbstractAbstractSince time immemorial, the complex and highly nonlinear nature of various optimization problems in science and engineering has motivated researchers to develop sophisticated solutions. Along with traditional algorithms, in recent years, many computationally intelligent meta-heuristic techniques have also grabbed attention due to their effectiveness and flexibility. Flower Pollination Algorithm (FPA) is one such biologically-motivated procedure, which is influenced by the fertilization procedure of flowering species of plants. It has been effectively incorporated to fix many real-world problems in fields like wireless sensor networking, signal processing, power and communications, rain fall prediction, global function optimization and many more. Keeping in mind, its growing popularity due to its simplicity, adaptability and cross domain applicability, in this paper, a comprehensive review on FPA is presented. It throws light on its biological inspiration, algorithm, variants and applications in various industries.Subject Classification: 68T30Keywords: Meta-heuristicOptimizationNature-inspiredFPA
An Ensemble Auto-Encoder for Age Estimation Based on Human Facial Expressions

The age estimation from facial expression is most rapid topic of interest, these days because of its great applications. It is a challenging task because of huge intra-class facial expression differences due to age. In this study, a methodology has been proposed to estimate the age depending upon the expressions of face of a human. The proposed methodology initiates its process with clarification of facial expression which is followed by the clustering process. The images of the database are clustered based on expressions. For this purpose, six facial expressions (Happiness, Disgust, Anger, Fear, surprise and sadness) are considered in this study. For getting better accuracy in age estimation, the features are extracted cluster wise using Binarized Statistical image feature (BSIF) in multi-layer fashion. The final feature vector is feed to ensemble auto-encoder (EAE) to train and estimate age by considering min Error value. The age categories considered are four: child (0-10), young (11-30), Middle (31-50) and senior citizen (50+). To estimate age, this study considered two databases namely: MORPH-II, PAL. The test results on the chosen databases perform better in terms of estimation of age when contrasted with other methods of the state of art.
WFA-IRL: Inverse Reinforcement Learning of Autonomous Behaviors Encoded as Weighted Finite Automata

This paper presents a method for learning logical task specifications and cost functions from demonstrations. Constructing specifications by hand is challenging for complex objectives and constraints in autonomous systems. Instead, we consider demonstrated task executions, whose logic structure and transition costs need to be inferred by an autonomous agent. We employ a spectral learning approach to extract a weighted finite automaton (WFA), approximating the unknown task logic. Thereafter, we define a product between the WFA for high-level task guidance and a labeled Markov decision process for low-level control. An inverse reinforcement learning (IRL) problem is considered to learn a cost function by backpropagating the loss between agent and expert behaviors through the planning algorithm. Our proposed model, termed WFA-IRL, is capable of generalizing the execution of the inferred task specification in a suite of MiniGrid environments.
Trend for development of the material and technical base of grain production in the Russian Federation

Expanded reproduction in agriculture is largely determined by the content and structure of the material and technical base, the platform of which in the grain subcomplex is the machine and tractor fleet of agricultural producers. Its optimal composition ensures the timeliness and quality of the mechanized technological processes carried out in the industry, has a direct impact on the cost of manufactured products, and is a necessary condition for reduction of its labour intensity. The low level of technical support for agricultural production led to a simplification of the technology for growing grain crops, and the area under crops has been reduced. In its’ turn, the high price of new modern machines does not allow agricultural producers to buy equipment, since the peculiarities of the seasonal factor of agricultural production do not always allow to pay for it within one year. There are also difficulties with the introduction of samples of modernized technology, which ensures the use of innovative (in particular resource-saving) technologies. In this regard, it is necessary to take programmatic measures that would contribute to the provision of grain farming with modern agricultural technology.
Research on Web Components Association Relationship Based on Data Mining

The security of web components is increasingly concerned by the industry, and identifying web components is of great significance to both network attack and defense. To solve this problem, first, a positive and negative association rule mining method based on the DW-SCCI framework is proposed. The DW-SCCI framework performs weighted frequent itemsets mining based on dynamic item weights, and then performs positive and negative association rule mining based on indicators such as interest. Second, an association rule quality evaluation method based on the weight of the common rule index is proposed, which is used to evaluate the quality of association rules mined by different algorithms. Third, on the basis of mining association rules, the application scenarios of association rules are analyzed and studied. The experimental results show that the positive and negative association rules mined by the DW-SCCI framework are of high quality, and have good results in Web component identification and abnormal website mining.
Investigating the Parallel Tractability of Knowledge Graph Reasoning via Boolean Circuits

Symbolic based logic reasoning is always an important service for correctness sensitive applications built on knowledge graphs. To make reasoning efficient in practice, current research focuses on designing parallel reasoning algorithms or employing high-performance computing architectures, like neural networks. No matter what architecture we choose, the computational complexity of reasoning is upper-bounded by the PTime-completeness or higher ones that are not parallelly tractable. This means that the task of reasoning can be inherently sequential in the worst cases. In this paper, we investigate the parallel tractability of knowledge graph reasoning from the theoretical perspective. We focus on datalog rewritable knowledge graphs and work to identify the classes of datalog programs, for which, reasoning complies with the NC complexity that is parallelly tractable. To this end, we utilize the tool of Boolean circuit to obtain all the theoretical results given in this paper. One can utilize our results to check the parallel tractability of a given knowledge graph. Further, the Boolean circuits proposed in this paper can also be used to construct neural networks to perform knowledge graph reasoning.
The current state of ICT security at Czech primary schools

This research study deals with an overview of the findings on the state of information and communication technology (ICT) security in primary schools in the Czech Republic. Due to the constantly developing field, the ever-closer connection between ICT and education, and the number of threats from malware or network attacks, there is a need for sufficient security in schools. The research surveyed the state of security in a sample of 100 schools and analyzed its level. In this respect, the study provides innovative and previously unpublished findings.
Research on Online on-Demand Platform of Piano Repertoire Based on Streaming Media Technology

Video-on-demand is a new way to disseminate multimedia information, transmitting the video content clicked or selected by the user to the requested user, providing the user with real-time, high-quality on-demand services. Streaming is to split the video data to be transmitted into several small data blocks, and the mobile terminal adopts the way of playing while downloading, which has the advantages of low latency and high flexibility compared with complete downloading and playing. We developed an online on-demand platform for piano repertoire to make up for the shortcomings of online teaching of piano courses. The platform uses the new standard of video coding based on streaming technology, the data storage structure design based on MySQL, and the online on-demand video playing interaction process based on flowchart design to provide a complete solution for the system development.
Informed Sampling-based Collision Avoidance with Least Deviation from the Nominal Path

This paper addresses local path re-planning for n-dimensional systems by introducing an informed sampling scheme and cost function to achieve collision avoidance with minimum deviation from an (optimal) nominal path. The proposed informed subset consists of the union of ellipsoids along the specified nominal path, such that the subset efficiently encapsulates all points along the nominal path. The cost function penalizes large deviations from the nominal path, thereby ensuring current safety in the face of potential collisions while retaining most of the overall efficiency of the nominal path. The proposed method is demonstrated on scenarios related to the navigation of autonomous marine crafts.
Non-uniform Piecewise Linear Activation Functions in Deep Neural Networks

Recently, PWLU has been proposed to learn specialized activation functions with straightforward piecewise linear definition and SOTA performance in different vision tasks and neural networks. However, the uniformly distributed intervals strongly limit the flexibility of PWLU, and the definition of PWLU requires the statistic-based realignment method to handle the misalignment between PWLU and input data. This paper proposes a new piecewise linear activation function called Nonuniform Piecewise Linear Unit (N-PWLU). N-PWLU has two advantages to overcome the drawbacks in PWLU. First, non-uniformly distributed intervals are used to increase flexibility. Second, the cumulative definition establishes close connections to the parameters in different intervals, which helps alleviate the misalignment issue. With these advantages, N-PWLU significantly outperforms PWLU, especially with fewer intervals. For example, on ImageNet classification dataset, 4-interval N-PWLU outperforms 4-interval PWLU with 1.15% top-1 accuracy in MobileNet-V3. Besides, the expressivity of 4-interval N-PWLU is compatible with 16-interval PWLU in different datasets and architectures. Fewer intervals simplify the computation of N-PWLU, which is friendly to be deployed on edge devices. We believe that our N-PWLU gets a step further in learning better parametric activation functions.
Learning Linguistic Association Towards Efficient Text-Video Retrieval

Text-video retrieval attracts growing attention recently. A dominant approach is to learn a common space for aligning two modalities. However, video deliver richer content than text in general situations and captions usually miss certain events or details in the video. The information imbalance between two modalities makes it difficult to align their representations. In this paper, we propose a general framework, LINguistic ASsociation (LINAS), which utilizes the complementarity between captions corresponding to the same video. Concretely, we first train a teacher model taking extra relevant captions as inputs, which can aggregate language semantics for obtaining more comprehensive text representations. Since the additional captions are inaccessible during inference, Knowledge Distillation is employed to train a student model with a single caption as input. We further propose Adaptive Distillation strategy, which allows the student model to adaptively learn the knowledge from the teacher model. This strategy also suppresses the spurious relations introduced during the linguistic association. Extensive experiments demonstrate the effectiveness and efficiency of LINAS with various baseline architectures on benchmark datasets. Our code is available at https://github.com/silenceFS/LINAS .
An Optimization Algorithm for the Game of NoGo based on MCTS

The game of NoGo has been proposed in recent years. NoGo game and its related rules are firstly introduce. Then we discuss the problems exposed by the traditional MCTS algorithm at the beginning of the game, proposes an idea of creating a converse digraph and solves the shortcomings of the traditional MCTS (Monte-Carlo Tree Search) algorithm at the beginning of the game. Finally, the NoGo result between the proposed algorithm by this paper and other traditional algorithm is obtained. The experimental result show that our proposed algorithm are practical for NoGo.
The Importance of Multiple Temporal Scales in Motion Recognition: from Shallow to Deep Multi Scale Models

Studying human motion requires modelling its multiple temporal scale nature to fully describe its complexity since different muscles are activated and coordinated by the brain at different temporal scales in a complex cognitive process. Nevertheless, current approaches are not able to address this requirement properly, and are based on oversimplified models with obvious limitations. Data-driven methods represent a viable tool to address these limitations. Nevertheless, shallow data-driven models, while achieving reasonably good recognition performance, require to handcraft features based on domain-specific knowledge which, in this cases, is limited and does no allow to properly model motion- and subject-specific temporal scales. In this work, we propose a new deep multiple temporal scale data-driven model, based on Temporal Convolutional Networks, able to automatically learn features from the data at different temporal scales. Our proposal focuses first on over-performing state-of-the-art shallows and deep models in terms of recognition performance. Then, thanks to the use of feature ranking for shallow models and an attention map for deep models, we will give insights on what the different architectures actually learned from the data. We designed, collected data, and tested our proposal in custom experiment of motion recognition: detecting the person who draw a particular shape (i.e., an ellipse) on a graphics tablet, collecting data about his/her movement (e.g., pressure and speed) in different extrapolating scenarios (e.g., training with data collected from one hand and testing the model on the other one). Collected data regarding our experiment and code of the methods are also made freely available to the research community. Results, both in terms of accuracy and insight on the cognitive problem, support the proposal and support the use of the proposed technique as a support tool for better understanding the human movements and its multiple temporal scale nature.
PENGARUH SIKAP KECERDASAN LINGUISTIK TERHADAP KEMAMPUAN SISWA DALAM MENYELESAIKAN SOAL BENTUK NARASI

AbstractThe problem of narrative form will be easier to solve if students have linguistic intelligence. Linguistic intelligence is the ability to think to use words effectively both oral and written. This study aims to determine the influence of linguistic intelligence attitudes on students' ability to solve narrative problems in class IXF material at SMPN 3 Pontianak. The research approach used is a quantitative approach with a form of survey research. The sample number of 31 students in grade IXF. The research was conducted at SMPN 3 Pontianak. Data collection techniques using indirect communication techniques. Validate data using Pearson Product Moment. Questionnaires and test questions were analyzed using a simple one-way linear regression test. The results showed that the linguistic intelligence attitude of students in grade IXF SMPN 3 Pontianak belongs to the moderate category with a percentage of 42%. Problem solving ability in the form of narrative of students in grade IXF SMPN 3 Pontianak fall into the low category with a percentage of 39%. As well as the results of simple linear regression analysis showed that there is a significant influence between the attitude of linguistic intelligence and the resolution of narrative forms on the material opportunity of 19.2%.Keywords: Attitude of Linguistic Intelligence, Material Opportunities, Problem Solving Narrative Forms.
Toward Inclusion and Accessibility in Visualization Research: Speculations on Challenges, Solution Strategies, and Calls for Action (Position Paper)

Inclusion and accessibility in visualization research have gained increasing attention in recent years. However, many challenges still remain to be solved on the road toward a more inclusive, shared-experience-driven visualization design and evaluation process. In this position paper, we discuss challenges and speculate about potential solutions, based on related work, our own research, as well as personal experiences. The goal of this paper is to start discussions on the role of accessibility and inclusion in visualization design and evaluation.
Attention-based Emotion-assisted Sentiment Forecasting in Dialogue

Dialogue has received a lot of research. But there is very little research on dialogue sentiment forecasting, which aims to forecast the sentimental polarity of what the interlocutor is about to say and provides sentimental guidance for empathic dialogue generation. Since the sentence has not been spoken, the vector of the sentence can't be directly obtained. And according to cognitive science, emotions are different from sentiment, but there is an internal connection. Therefore, our paper proposes an Emotion-Assisted Sentiment Forecasting (EASF) model based on attention to integrating these goals. Our model uses attention to capture the significant content of emotions and sentiment, and emotion assistance can obtain the emotional change, then this change is used to assist in the analysis of the polarity of the sentiment. Experimental results show that EASF significantly outperforms all baselines.
A Safe and Self-Recoverable Reinforcement Learning Framework for Autonomous Robots

Reinforcement learning (RL) holds the promise of autonomous robots because it can adapt to dynamic or unknown environments by automatically learning optimal control policies from the interactions between robots and environments. However, the interactions can be unsafe to both robots and environments during the learning phase, which hinders the practical deployment of RL. Some safe RL methods have been proposed to improve the learning safety by using external or prior knowledge to guide safe actions, but it is difficult to assume having this knowledge in practical applications, especially in unknown environments. More importantly, considering failures are unavoidable in practice, current safe RL lacks the capability of recovering to safe states from failures so that the learning cannot be continued and finished. To solve these problems, we propose a safe and self-recoverable reinforcement learning framework that can predict and prohibit other unsafe actions based on known, explored unsafe actions during the exploration process, and can self-recover to a safe state when a failure occurs. The maze navigation simulation results show that our approach can not only significantly reduce the number of failures but also accelerate the convergence of reinforcement learning.
Multi-dimensional Data Quick Query for Blockchain-Based Federated Learning

Due to the drawbacks of Federated Learning (FL) such as vulnerability of a single central server, centralized federated learning is shifting to decentralized federated learning, a paradigm which takes the advantages of blockchain. A key enabler for adoption of blockchain-based federated learning is how to select suitable participants to train models collaboratively. Selecting participants by storing and querying the metadata of data owners on blockchain could ensure the reliability of selected data owners, which is helpful to obtain high-quality models in FL. However, querying multi-dimensional metadata on blockchain needs to traverse every transaction in each block, making the query time-consuming. An efficient query method for multi-dimensional metadata in the blockchain for selecting participants in FL is absent and challenging. In this paper, we propose a novel data structure to improve the query efficiency within each block named MerkleRB-Tree. In detail, we leverage Minimal Bounding Rectangle (MBR) and bloom-filters for the query process of multi-dimensional continuous-valued attributes and discrete-valued attributes respectively. Furthermore, we migrate the idea of the skip list along with an MBR and a bloom filter at the head of each block to enhance the query efficiency for inter-blocks. The performance analysis and extensive evaluation results on the benchmark dataset demonstrate the superiority of our method in blockchain-based FL.
Target tracking trajectory generation for quadrotors in static complex environments

This paper mainly focuses on the problem of target tracking trajectory generation for quadrotors in static complex environments. In this paper, a trajectory planning framework is proposed that can be used to address the occlusion problem caused by obstacles in target tracking situations. The main innovations are: First, a visibility function is introduced that is constructed based on a signed Euclidean distance field (ESDF) map. Based on this function, the favorable view direction is derived to track the target distinctly. Second, the distance between the target and chaser is optimized as one of the cost functions, resulting in a smooth and comfortable distance between them. Finally, the effectiveness of the algorithm is verified on the Rviz platform.
Handwriting Data Analysis from Crayonic KeyVault Smart Security Device

In this article, we discuss handwriting as a behavioral biometric modality. The main contribution focuses on biometric data acquired from handwriting using the pioneering Crayonic KeyVault device, which has a built-in gyroscope, accelerometer, and pressure sensor. Thanks to these sensors, this device can record the movements of the hands while handwriting the digits based on the prompts of the device itself. This data in raw format was sampled and then normalized and parameterized. The time-sampled data were visualized and the intrapersonal similarity was observed from multiple typing attempts of the same participant, and the interpersonal variability when the same number was written from different participants. The data was then divided into a test and a training set. The training set was used to train the machine learning models and the accuracy was verified with the test set. The 91.6% accuracy of the person model identification on the test set was achieved.
Stress Level Detection of IT Professionals Using Machine Learning

Due to the COVID-19 pandemic, to control pandemic situations and its spread, the government took a decision to shut all the educational institutions, which in turn creating a direct impact on many people by causing stress and mental illness. We propose a solution for organizations where they can know the levels of stress faced by the students and could calculate percentage of stress. So for this to be done, students can take up the survey through a google form which consist of the parameters which are helpful in collecting information about mental distress and many other psychological factors faced by the students. The data which is collected from the students is inputted into the model with results the stress levels of the students.