Reviewerly/exHarmony huggingface dataset:
Path Planning of UAVs Under Dynamic Environment based on a Hierarchical Recursive Multiagent Genetic Algorithm

Path planning is a key technology to realize the automatic navigation of unmanned aerial vehicles (UAV), which has great significance both in theory and practical application. Evolutionary algorithms (EAs) are a type of nature-inspired computational methodologies for addressing complex real-world problems that cannot be solved well by mathematical or traditional modeling. However, the huge calculation of each iteration in the EAs greatly reduces the efficiency of the algorithm. In this paper, to accelerate the search speed of EAs and consider the dynamics of the environment, we propose a hierarchical recursive multi-agent genetic algorithm that can perform path planning in real time, termed as HR-MAGA. We used a strategy of layer-by-layer optimization on 3D maps with different resolution in the optimization process. The local search ability of the algorithm is improved by competition and self-learning process. In addition, the hierarchical recursive optimization process can greatly reduce the amount of computation and effectively deal with the dynamic characteristics of the environment. The experimental results show that HR-MAGA not only has strong global optimization ability, but more importantly, is able to generate collision free paths in real time after considering the physical limitations of the UAV and the dynamics of the environment.
Large-Scale optimization via Evolutionary Multitasking assisted Random Embedding

Evolutionary algorithms (EAs) often lose their superiority and effectiveness when applied to large-scale optimization problems. In the literature, many research studies have been proposed to improve the search performance of EAs, such as cooperative co-evolution, embedding, and new search operator design. Among those, memetic multi-agent optimization (MeMAO) is a recently proposed paradigm for high-dimensional problems by using random embeddings. It demonstrated high efficacy with the assumption of “effective dimension However, as prior knowledge is always unknown for a given problem, this method may fail on the large-scale problems that do not have low effective dimensions. Taking this cue, we propose an evolutionary multitasking (EMT) assisted random embedding method (EMT-RE) for solving large-scale optimization problems. Instead of conducting a search on the randomly embedded space directly, we treat the embedded task as the auxiliary task for the given problem. By performing EMT with both the given problem and the randomly embedded task, not only the useful solutions found along the search can be transferred across tasks toward efficient problem solving, but the effectiveness of the search on problems Without a low effective dimensionality is also guaranteed. To evaluate the performance of newly proposed EMT-RE, comprehensive empirical studies are carried out on 8 synthetic continuous optimization functions with up to 2,000 dimensions.
SEADer++ v2: Detecting Social Engineering Attacks using Natural Language Processing and Machine Learning

Social engineering attacks are well known attacks in the cyberspace and relatively easy to try and implement because no technical knowledge is required. In various online environments such as business domains where customers talk through a chat service with employees or in social networks potential hackers can try to manipulate other people by employing social attacks against them to gain information that will benefit them in future attacks. Thus, we have used a number of natural language processing steps and a machine learning algorithm to identify potential attacks. The proposed method has been tested on a semi-synthetic dataset and it is shown to be both practical and effective.
Diagnostic evaluation model of English learning based on machine learning

The diagnostic evaluation model of English learning is difficult to judge the subjective factors in student learning, so some diagnostic evaluation models of English learning are difficult to apply to English learning practice. In order to improve the effect of English learning, based on machine learning technology, this study combines the needs of English evaluation to build a diagnostic evaluation model of English learning based on machine learning. Moreover, this study compares the methods of random forest, Bayesian network, decision tree, perceptron, K-nearest neighbor and multi-model fusion, and selects the best algorithm for diagnostic analysis. The diagnostic evaluation model of English studies constructed in this paper mainly evaluates and judges the errors in students’ English learning. In addition, this study validates the methods proposed in this study through controlled experiments. The research results show that the method proposed in this study has a certain effect.
Research on athlete’s action recognition based on acceleration sensor and deep learning

Due to the difficulty of athletes’ motion recognition, there are few studies on athletes’ specific motion recognition. Based on this, this study uses the acceleration sensor as the carrier, and uses human-computer interaction to transform the action of the athlete into a machine-identifiable action unit. At the same time, this paper combines the actual situation of human body motion to construct a human body motion model and builds a corresponding computer hardware and software platform. Moreover, this paper designs a classification recognition algorithm that can recognize the movement of athletes and builds SVM model based on machine learning for classification and recognition. In addition, in this study, the effectiveness of the algorithm was studied through experimental comparison. Finally, the simulation analysis was carried out to obtain the corresponding research results, and the results were analyzed by combing statistics. The research shows that the proposed algorithm can classify and recognize the collected motion data, and it has certain effects on the theoretical analysis of athletes’ motion recognition. Moreover, the algorithm can perform motion quality analysis and provide theoretical reference for subsequent related research.
On the exact recovery conditions of 3D human motion from 2D landmark motion with sparse articulated motion

In this paper, we address the problem of exact recovery condition in retrieving 3D human motion from 2D landmark motion. We use a skeletal kinematic model to represent the 3D motion as a vector of angular articulation motion. We address this problem based on the observation that at high tracking rate, regardless of the global rigid motion, only few angular articulations have non-zero motion. We propose a first ideal formulation with ℓ0-norm to minimize the cardinal of non-zero angular articulation motion given an equality constraint on the time-differentiation of the reprojection error. The second relaxed formulation relies on an ℓ1-norm to minimize the sum of absolute values of the angular articulation motion. This formulation has the advantage of being able to provide 3D motion even in the under-determined case when twice the number of 2D landmarks is smaller than the number of angular articulations. We define a specific property which is the Projective Kinematic Space Property (PKSP) that takes into account the reprojection constraint and the kinematic model. We prove that, for the relaxed formulation, we are able to recover the exact 3D human motion from 2D landmarks if and only if the PKSP property is verified. We further demonstrate that solving the relaxed formulation provides the same ground-truth solution as the ideal formulation if and only if the PKSP condition is filled. Results with simulated sparse skeletal angular motion show the ability of the proposed method to recover exact location of angular motion. We provide results on publicly available real data (Human3.6M, Panoptic, MPI-I3DHPand 3DPW) and show that our method is able to achieve state-of-the-art 3D reconstructions.
Text-Convolutional Neural Networks for Fake News Detection in Tweets

Sinha, Harsh Sakshi Sharma, YashvardhanWith the widespread use of online social networking websites, user-generated stories and social network platform have become critical in news propagation. The Web portals are being used to mislead users for political gains. Unreliable information is being shared without any fact-checking. Therefore, there is a dire need for automatic news verification system which can help journalists and the common users from misleading content. In this work, the task is defined as being able to classify a tweet as real or fake. The complexity of natural language constructs along with variegated languages makes this task very challenging. In this work, a deep learning model to learn semantic word embeddings is proposed to handle this complexity. The evaluations on the benchmark dataset (VMU 2015) show that deep learning methods are superior to traditional natural language processing algorithms.
Learning representation from multiple media domains for enhanced event discovery

In this paper, we focus on event discovery by utilizing data distributed in multiple media domains, such as news media and social media. To this end, we propose an in-domain and cross-domain Laplacian regularization (ICLR) model, which can learn effective data representation for both textual news reports contributed by journalists in news media domain, and image posts shared by amateur users in social media domain. The achieved data representation can be used by classification and clustering strategies for existing and new event discovery, respectively. More specifically, ICLR constructs respective Laplacian regularization terms considering the property of inter-domain and intra-domain label consistency, which can be optimized by employing an alternating optimization strategy with theoretical guarantee for convergence. In particular, we collect and release a multi-domain and multimodal dataset for evaluations and public use.
SNoRe: Scalable Unsupervised Learning of Symbolic Node Representations

Learning from complex real-life networks is a lively research area, with recent advances in learning information-rich, low-dimensional network node representations. However, state-of-the-art methods are not necessarily interpretable and are therefore not fully applicable to sensitive settings in biomedical or user profiling tasks, where explicit bias detection is highly relevant. The proposed SNoRe (Symbolic Node Representations) algorithm is capable of learning symbolic, human-understandable representations of individual network nodes, based on the similarity of neighborhood hashes which serve as features. SNoRe's interpretable features are suitable for direct explanation of individual predictions, which we demonstrate by coupling it with the widely used instance explanation tool SHAP to obtain nomograms representing the relevance of individual features for a given classification. To our knowledge, this is one of the first such attempts in a structural node embedding setting. In the experimental evaluation on eleven real-life datasets, SNoRe proved to be competitive to strong baselines, such as variational graph autoencoders, node2vec and LINE. The vectorized implementation of SNoRe scales to large networks, making it suitable for contemporary network learning and analysis tasks.
A multi-faceted optimization scheduling framework based on the particle swarm optimization algorithm in cloud computing

Cloud Computing emerged from Grid Computing empowered with the concept of virtualization. The advantage of switching to a virtual environment is quite beneficial, however in cloud computing there are significant problems pertaining to performance and cost optimization because of various kinds of resource requirement and execution of multiple jobs. Thus the level of performance and criteria of SLA to be maintained becomes very arduous due to such constraints. To overcome these constraints and amend the solution quality, an integrated approach of scheduling model and resource cost timeline model labelled as Multi-Faceted Optimization Scheduling Framework (MFOSF) has been endeavouring in this paper in a timely manner. Resource Cost timeline model manifest the relation between user budget and producer cost while scheduling Model based on optimization in performance and cost can be achieved by the help of PSO. Some simulation has been made to evaluate this framework by applying four different metrics a) Cost b) the Makespan c) Deadline d) Resource Utilization. On the basis of aforesaid metrics, experiment outcomes show MFOSF-PSO method is more effective than the other models peculiarly increases 57.4 % in best case scenario.
TensorFlow solver for quantum PageRank in large-scale networks

Google PageRank is a prevalent algorithm for ranking the significance of nodes or websites in a network, and a recent quantum counterpart for PageRank algorithm has been raised to suggest a higher accuracy of ranking comparing to Google PageRank. The quantum PageRank algorithm is essentially based on quantum stochastic walks and can be expressed using Lindblad master equation, which, however, needs to solve the Kronecker products of an O(N4) dimension and requires severely large memory and time when the number of nodes N in a network increases above 150. Here, we present an efficient solver for quantum PageRank by using the Runge-Kutta method to reduce the matrix dimension to O(N2) and employing TensorFlow to conduct GPU parallel computing. We demonstrate its performance in solving quantum stochastic walks on Erdös-Rényi graphs using an RTX 2060 GPU. The test on the graph of 6000 nodes requires a memory of 5.5 GB and time of 223 s, and that on the graph of 1000 nodes requires 226 MB and 3.6 s. Compared with QSWalk, a currently prevalent Mathematica solver, our solver for the same graph of 1000 nodes reduces the required memory and time to only 0.2% and 0.05%. We apply the solver to quantum PageRank for the USA major airline network with up to 922 nodes, and to quantum stochastic walk on a glued tree of 2186 nodes. This efficient solver for large-scale quantum PageRank and quantum stochastic walks would greatly facilitate studies of quantum information in real-life applications.
Combined inner and outer loop feedback in an intelligent tutoring system for statistics in higher education

Abstract Intelligent tutoring systems (ITSs) can provide inner loop feedback about steps within tasks, and outer loop feedback about performance on multiple tasks. While research typically addresses these feedback types separately, many ITSs offer them simultaneously. This study evaluates the effects of providing combined inner and outer loop feedback on social sciences students' learning process and performance in a first‐year university statistics course. In a 2 x 2 factorial design (elaborate inner loop vs. minimal inner loop and outer loop vs. no outer loop feedback) with 521 participants, the effects of both feedback types and their combination were assessed through multiple linear regression models. Results showed mixed effects, depending on students' prior knowledge and experience, and no overall effects on course performance. Students tended to use outer loop feedback less when also receiving elaborate inner loop feedback. We therefore recommend introducing feedback types one by one and offering them for substantial periods of time.
Hash Tree-Based Device Fingerprinting Technique for Network Forensic Investigation

Forensic investigation of cybercrimes in ecommerce and banking portals is becoming increasingly difficult due to the inability of existing Internet protocols, to collect the necessary information as digital evidences. Present tools for investigation on Internet protocols tracks the attackers up to the ISP, only and the actual attacker’s location and machine may be tracked based on the information of ISP which is not primary evidence and also not acceptable evidence. According to the Indian Evidence Act Section 62 and also others, international act for evidence says, primary evidences are most superior class of evidences and admissible and acceptable at first place. This is only possible, if investigator proves that attacks have been occurred by using the device and evidence should prove that it has been happened by using the investigated device. Our proposed device fingerprinting technique uses the concept of hash tree and generates the device fingerprints which can be used as forensically sound evidence. In disputes, which has been caused by external attacks or the denial of client and business provider will be solved by analyzing the fingerprint collected in the form of digital evidence with every transaction. The fingerprint generated by the proposed system has all the characteristics stipulated by the court of law.
Understanding the Interactions between the Scrum Master and the Development Team: A Game-Theoretic Approach

Scrum methodology is widely used in the information technology (IT) industry for the purposes of team-based iterative software development. However, limited studies have been conducted to explore the nature of interactions between a Scrum Master and other team members and the effect of these interactions on team effectiveness. The aim of this study is to understand the interactions between the Scrum Master and other team members in an educational setting and propose and demonstrate an application of cooperative game theory for the same. Cooperative game theory can model scenarios where other team members can benefit from cooperating. Through the lens of the cooperative game-theoretic model, we investigated the strategies employed by the Scrum Master and other team members when involved in a semi-capstone IT project. Specifically, the study explored the team interaction between a Scrum Master and other team members at three different levels of team effectiveness: least effective, partially effective, and most effective. Our results indicate that a Scrum Master should be active to maximize their payoff as well as the teams’ overall payoff. Contrary to this, other team members should be active in the most and partially effective teams, while being passive in the least effective teams at higher costs of interpersonal relations and the processes. The results of the study represent a novel application of game-theoretic modeling for understanding the Scrum Master and other team member interactions. These results are applicable not just in an educational setting but also to the wider area of software development by identifying the right set of strategies by the Scrum Master, and other team members in order to help IT professionals to maximize their payoff.
CSENet: Cascade semantic erasing network for weakly-supervised semantic segmentation

Weakly-supervised semantic segmentation based on image-level annotations has difficulty exploring pixel-level information. Most approaches adopt Class Activation Maps (CAM) to localize initial object regions, called seeds. To cover more potential object parts, seeds-expansion methods raise concern for artificial mask generation. Due to the seeds simply focus on discriminative regions, it is a challenge to spread seeds to the integral object. To tackle this problem, we propose a Cascade Semantic Erasing Network (CSENet) to expand seeds effectively and reasonably. In particular, CSENet sequentially stacks the semantic erasing stage to erase discriminative areas progressively. It forces the network to exploit relevant feature response for non-discriminative object districts. Moreover, CSENet directly suppresses seeds on the Class Activation Maps (CAM), which have stronger semantics, rather than on the Intermediate Feature Maps (IFM). Under semantic guidance, proposed erasing strategy correctly spreads seeds regions to the intra-class regions and meanwhile, prohibits from extending to the unexpected inter-class areas. Extensive experiments demonstrate the effectiveness of proposed CSENet. More specifically, our approach achieves 62.3% and 63.4% mIoU on PASCAL VOC 2012 validation and test set, respectively.
UUV Path Planning for Collision Avoidance Based on Ant Colony Algorithm

In this paper, the ant colony algorithm used to study the path planning for collision avoidance of unmanned underwater vehicle (UUV) in a large-scale marine environment. Multi-beam forward-looking sonar’s simulation detection model based on the grid environment model established in it. In order to overcome the limitation that the ant colony can only choose to walk on adjacent grids, the concept of virtual visualization introduced in the last step of the ant colony. This paper uses a non-linear penalty mechanism in the objective function of ant colony optimization to avoid large-angle turning movements in online path decision making. Simulation results show that the proposed method has short planning time, high optimization efficiency, and smooth paths.
Learning information recommendation based on text vector model and support vector machine

The difficulty of knowledge point recommendation based on the learning diagnosis model lies in how to perform feature recognition and selection of recommended knowledge points. At present, the recommendation system has certain problems in the accuracy of recommended knowledge points. Based on this, this study mainly studies the personalized problem recommendation of middle school students in the field of education. Moreover, this study takes the answer records of students’ exercises as data, and combines the characteristics of the field of education to propose an exercise recommendation algorithm based on hidden knowledge points and an exercise recommendation method based on the decomposition of student exercise weight matrix. In addition, in order to verify the effectiveness of this research algorithm, this paper selects the accuracy rate and recall rate as evaluation indicators to analyze the recommendation results of this algorithm and the current more advanced CF algorithm, and the statistical experiment results are drawn into charts. The research results show that the method proposed in this paper has certain advantages and can be used as one of the subsystems of the learning system.
Adversarial Attack: A New Threat to Smart Devices and How to Defend It

This article introduces adversarial attack, a recently-unveiled security threat to consumer electronics, especially those utilizing machine learning techniques. We start with the fundamental knowledge including what are adversarial examples, how to realize such attacks, and common defense methods. Adversarial training enhances models' resilience to adversarial attacks by taking both regular and adversarial examples for training. However, applying adversarial examples under a single adversarial strength provide defense in a very limited effective range. We propose a multiple-strength adversarial training method. A random walk algorithm is adopted to optimize the selection of adversarial strengths, which is closely related to the design cost and training time. We also analyze the hardware cost and quantization loss to guide future consumer electronics designs.