Reviewerly/exHarmony huggingface dataset:
Multi-Agent Path Planning and Trajectory Generation for Confined Environments

Planning the collision-free, simultaneous movement of multiple agents is a fundamental problem in multirobot systems and becomes particularly challenging in highly confined environments. This paper addresses this problem by first searching for collision-free paths in a discretized environment and then optimizing the agents' dynamically feasible trajectories along the discrete paths with respect to energy and time. Our approach extends the available movement options at each planning step of the enhanced conflict-based search and results in shorter path lengths, faster planning times, and reduced number of waiting events for agents at waypoints. We compare our new approach with the original algorithm in a simulation study and investigate the performance in confined spaces.
A Laguerre-based Distributed Nonlinear Model Predictive Control Scheme for Dynamic Obstacle Avoidance on Multi-Rotor UAVs

This paper presents a Distributed NMPC approach for the problem of Dynamic Obstacle Avoidance based on Laguerre Polynomials combined with potential fields. The proposed approach aims at tackling the computational burden by using a combination of different methods to achieve real-time performance. Moreover, the approach benefits from being able to represent the projected paths of each UAV using a significantly reduced amount of information, thus reducing the bandwidth required for a practical implementation. The paper presents a complete derivation of the proposed approach along with an algorithm for its implementation, and discusses aspects specifically related to Multi-Rotor UAV models which can help to reduce the computational burden even further. The performance of the proposed approach is demonstrated through a simulation of 50 UAVs sharing common airspace.
Near minimum time trajectory planning for surveying using UAVs

In this paper, we present a motion planning strategy for UAVs that generates a time-optimal trajectory to survey a given target area. There are several situations where completing an aerial survey is time sensitive, such as gaining situational awareness for first responders, surveying hazardous environments, etc. One of the challenges in such cases is to plan a time-optimal trajectory for the drone. To this end, we present an autonomous aerial survey framework that minimizes the time taken to completely explore a target area or volume using drones. In this work, (i) we present an approach, where for a known flight survey pattern, the planner can generate time-optimal flight paths in 3-D (ii) we frame the planning problem as a discrete non-linear program, and reduce the time taken to compute its solution by using an SOCP relaxation (iii) The given path is then executed using a simple trajectory tracking controller on a quadrotor to demonstrate its capability on hardware.
Dynamic and Directionally Scaled Geocages for Containment of UAS Operations

The operation of Unmanned Aircraft (UA) in the European Union Aviation Safety Agency (EASA) specific category requires the operator to put risk-minimizing measures in place, where an operation poses a risk to third parties. A geocaging functionality contains the UA operation inside a pre-determined flight geography and defines boundaries, at which mitigative actions are triggered to ensure that the operation is returned to control or terminated safely. This paper focuses on the development of a computationally efficient geocaging algorithm with superior performance characteristics over the state-of-the-art. While the outermost boundaries of the operational volume are maintained, the presented approach leverages performance heuristics to adapt the geocage’s mitigation boundaries to the current vehicle state and direction of flight such that the operational volume is maximized and significantly faster velocities are achieved in narrow passages. Simulation results for flights in convex and concave geocage geometries, as well as benchmarks against a polygon scaling method are presented.
Autonomous Obstacle Avoidance for UAV based on Point Cloud

This paper presents an autonomous obstacle detection, avoidance, and path-finding algorithm for UAV (un-manned aerial vehicle) in outdoor applications by using a stereo depth camera. The obstacle avoidance part is divided into two steps: process the point cloud, remove noise and points of ground, and extract clusters to represent different objects; perform and define detectors, to detect whether the UAV has a collision at different distances and in various directions around the nearest obstacle, even behind the free escape space respectively. Finally, the flight decision is selected with the direction with highest votes and publish the flight command to be executed by UAV. A voting mechanism for judgments during a period is implemented, to avoid the misjudgment by a single frame, as well as to enhance the plausibility of flight decision. The algorithm is evaluated in Gazebo-based simulation environment as well as in field tests, has been approved that the UAV can successfully avoid obstacles in dense woods and find new obstacle-free paths, even in extreme environments with huge buildings, which indicated that the algorithm achieved its initial purpose.
Elastic Consistency

Machine learning models can match or surpass humans on specialized tasks such as image classification [20, 14], speech recognition [37], or complex games [39]. One key tool behind this progress has been a family of optimization methods which fall under the umbrella term of stochastic gradient descent (SGD) [35], which are by and large the method of choice for training large-scale machine learning models.
Towards Building Resilient Ensembles against Training Data Faults

In this talk, we describe our approach to construct resilient ML ensembles against training data faults [1]. First, we demonstrate how ensembles tolerate faulty training data. Then, we show how we could use analytical modelling to help ML practitioners build resilient ensembles without the need for resource intensive fault injection experiments.
SbrPBert: A BERT-Based Model for Accurate Security Bug Report Prediction

Bidirectional Encoder Representation from Transformers (Bert) has achieved impressive performance in several Natural Language Processing (NLP) tasks. However, there has been limited investigation on its adaptation guidelines in specialized fields. Here we focus on the software security domain. Early identification of security-related reports in software bug reports is one of the essential means to prevent security accidents. However, the prediction of security bug reports (SBRs) is limited by the scarcity and imbalance of samples in this field and the complex characteristics of SBRs. So motivated, we constructed the largest dataset in this field and proposed a Security Bug Report Prediction Model Based on Bert (SbrPBert). By introducing a layer-based learning rate attenuation strategy and a fine-tuning method for freezing some layers, our model outperforms the baseline model on both our dataset and other small-sample datasets. This means the practical value of the model in BUG tracking systems or projects that lack samples. Moreover, our model has detected 56 hidden vulnerabilities through deployment on the Mozilla and RedHat projects so far.
Multi-authoritative Users Assured Data Deletion Scheme in Cloud Computing

With the rapid development of cloud storage technology, an increasing number of enterprises and users choose to store data in the cloud, which can reduce the local overhead and ensure safe storage, sharing, and deletion. In cloud storage, safe data deletion is a critical and challenging problem. This paper proposes an assured data deletion scheme based on multi-authoritative users in the semi-trusted cloud storage scenario (MAU-AD), which aims to realize the secure management of the key without introducing any trusted third party and achieve assured deletion of cloud data. MAU-AD uses access policy graphs to achieve fine-grained access control and data sharing. Besides, the data security is guaranteed by mutual restriction between authoritative users, and the system robustness is improved by multiple authoritative users jointly managing keys. In addition, the traceability of misconduct in the system can be realized by blockchain technology. Through simulation experiments and comparison with related schemes, MAU-AD is proven safe and effective, and it provides a novel application scenario for the assured deletion of cloud storage data.
Blockchain-Based Incentive and Arbitrable Data Auditing Scheme

The private data possession of proof mechanism is an important means to ensure the security of cloud data. Using the private data possession of proof mechanism may cause disputes between users and cloud storage service providers. There is no credible proof to determine the wrong party, it will affect the public’s confidence in using cloud storage technology. Therefore, this paper designs an incentive and arbitrable data audit protocol based blockchain. This protocol uses the quadratic residue theorem to achieve efficient auditing. The smart contract is introduced as a fair arbitrator, the blockchain is used as a self-recording channel to provide credible proof for the contradiction between users and cloud storage service providers, avoid disputes and realize dispute-free arbitration. A set of reward and punishment measures are designed using power functions to restrain the malicious behavior of untrusted users and cloud storage service providers. Efficiency analysis and experiments show that the proposed scheme has lower computational and communication overhead.
Effects of a Gamified MALL Application on Developing EFL Preparatory School Students’ Speaking and Enjoyment and Reducing Speaking Anxiety

The present study investigated the effect of utilizing Duolingo, a MALL (Mobile-Assisted Language Learning) gamified application, on Egyptian second year preparatory school students' EFL speaking skills.The study also explored the impact of Duolingo on boosting foreign language enjoyment and alleviating speaking anxiety.The study examined the correlation between speaking, foreign language enjoyment, and speaking anxiety.To participate in the study, 60 EFL students from a preparatory female school were randomly assigned into two groups of 30 each: one experimental and one control.A speaking test and a questionnaire were administered as part of the mixed-methods study.The post-test results demonstrated a statistically significant difference in the mean scores of the overall speaking competence and five sub-skills between the experimental and control groups in favor of the experimental group.In the post-administration of the questionnaire, there was a statistically significant difference between the mean scores of the two groups in language enjoyment, favoring the experimental group.The results also indicated that the participants in the control group were more anxious than their counterparts in the experimental group.Moreover, there was a negative correlation between speaking anxiety and enjoyment, a positive correlation between speaking performance and enjoyment, and a strong negative correlation between speaking anxiety and speaking.According to qualitative data from open-ended questions in the questionnaire and interviews, the experimental group perceived Duolingo favorably in terms of reducing speaking anxiety, enhancing foreign learning enjoyment, and developing their speaking skills.The current study provided implications and recommendations for future research.
Research on Interactive Conceptual Network of Historical Vocabulary for International Chinese Learning and Teaching

The combination of the Internet and education brings us a series of new technologies, which transcends the boundaries between schools and classes, provides a new educational model for individual learners and forms a brand-new educational environment. International Chinese Education is now going through a major transformation from face-to-face teaching to smart online teaching. From the student's point of view, Chinese History is an attractive but difficult course, there are overtly abundant contents of Chinese history, and made even more so if the course looks closer into dynasties. It is necessary to extract the essentials and systematically display it to students. For this reason, based on the Connectivism learning theory and the schema theory, we made an experimental corpus, collecting Chinese history textbooks and history reading materials for foreign students. We use K-means to cluster the text topics quickly and accurately, synthesize the word co-occurrence relationship in the corpus, and use Networkx to build the word relationship network diagram. Then, we generate a JSON file to facilitate the production of interactive web pages. In the web page, we use d3.js to display the network. By using Ajax, we dynamically update the data to achieve sentence retrieval. Finally, we use the VUE framework to update the data list synchronously on the web page.
Research on the Cultivation of Teaching Design Ability of English Normal in the Background of Educational Informationization and Computer Education 

Informatization instructional design is an inevitable trend in the curriculum design of elementary education. The cultivation of teaching design ability of English majors is also the focus of the training of normal majors and the conscious behavior of the normal development of normal education students. Based on the elaboration of the basic concepts of information-based instructional design, the article analyzes the structure of English teachers informatization instructional design ability, and points out that the ability of normal students to integrate information technology and disciplines is the process of cultivating the informatization of English subjects. At the same time, this paper also analyzes the influencing factors of English teaching students' teaching design ability under the information technology and computer education environment, expecting to have a more comprehensive basic understanding of the English teaching students' teaching design ability under the informational and computer education background.
Driving forces in joint training of industry-education graduate students under regional innovation ecosystem

Under the regional innovation ecosystem, combined with the actual development of Yibin city, this paper discusses the factors influencing the joint cultivation of industry-education integration graduate students in Yibin from the perspectives of industry requirements, policy guarantee, education development, benefits and responsibilities, and points out that the joint cultivation of industry-education integration graduate students is driven by multiple factors. At the same time, it is necessary to further the dynamic mechanism of graduate joint training to improve the construction level of the regional innovation ecosystem and enhance regional development vitality.
Historical knowledge self-testing and training based on control decision

This article uses the control strategy learning of Markov decision process to realize self-testing of historical knowledge, improve the efficiency of historical knowledge learning, and evaluate and diagnose according to the actual situation, adaptively recommend relative test topics and learning materials, thereby improving the history The efficiency and quality of knowledge to achieve the goal of history teaching. In this process, the teacher does not need to participate in the process and only needs to set a minimum expected accuracy value for the test answer submitted by the learner of history knowledge. This project takes the historical knowledge teaching of Shenzhen junior high school as the research object, uses the control strategy of Markov decision-making process, and relies on the reinforcement learning theory to study the fundamental theories and key technologies of the adaptive historical knowledge self-test and evaluation system, and improve the historical knowledge self Testing and evaluation methods, improving the efficiency and quality of historical knowledge learning, and perfecting the theories and methods of adaptive historical knowledge self-testing and training systems.
A Research on Vocabulary Size Test for Chinese Second Language E-learning Platform Based on Word Co-occurrence Network

The vocabulary size is one of the important factors that affect the learning effect of Chinese Second Language learners (L2 Chinese learners). As far as L2 Chinese E-learning platform is concerned, many adaptive functions are dependent on L2 Chinese learners’ vocabulary levels. Based on existing related researches, this paper proposes a method for measuring L2 Chinese learners’ vocabulary levels for use on the Chinese E-learning platform: by using a Chinese textbook corpus to build a Chinese word co-occurrence knowledge, vocabulary test is regarded as a test of actual collocation knowledge before and after the target word, thus the required word collocation knowledge can be extracted from the word co-occurrence network. By recruiting L2 Chinese learners to take part in the Chinese vocabulary test, and through a statistical analysis of the test data, it is verified that the test has achieved its expected reliability and validity. This vocabulary size test method for L2 Chinese E-learning platform based on the word co-occurrence network proposed in this research plays a vital role in improving the functions of the E-learning platform because it can generate questions automatically, offer personalized and adaptive tests for the learners, and get accurate results.
Comparison of Different Feature Selection Approaches on Breast Cancer Dataset

Breast cancer was found to be the second most common type of cancer which occurred around the world in the year 2018. It takes a huge amount of effort from the healthcare team to diagnose the type of breast cancer. It is crucial to correctly classify the tumor type, as misclassification might lead to the loss of life of the patient. This work aims at investigating different nuclear features present in the dataset and using this domain knowledge, determines the features which could assist in achieving the model with the highest accuracy. Also, select features using other feature selection algorithms such as Boruta, exhaustive search methods, and choose the best among them. Further, compare numerous model performances using domain knowledge and the selected feature selection approach, respectively.
VIARS: An Intelligent Voice Agent to Prevent Inappropriate Content Display for Restricted Users

Abstract The wide usage of voice intelligent assistants (VIAs), such as Siri, brings lots of convenience to our daily life. However, there are also kinds of issues related to VIAs displaying inappropriate contents to restricted users such as children. To address this problem, we developed a system that detects the inappropriate contents and rates them with defined ranks. In this manner, a threshold or suggestion could be given in order to avoid displaying inappropriate contents to restricted users. This evaluation system is called Voice Intelligent Aid Rating System (VIARS). With a developed VIARS prototype, we further tested its effectiveness with a designed experiment that consisted of several tasks. As a result, VIARS’s intelligent rating results were proven to be highly consistent with the experts’ manual rating results, indicating the reliability of the VIARS classification mechanism. It is also notable that when compared with experts’ manual rating, VIARS’s automatic rating mechanism showed significant advantages in response time.
Effective ways of Carrying Out Network Autopsy

By focusing on intrinsic network weaknesses and communication processes, network forensics techniques aid in the tracking of internal and external network attacks. However, when cyber criminals remove traces in order to evade detection, the investigation of cybercrime becomes more difficult. As a result, network forensics approaches use mechanisms to make inquiry easier by recording every packet and event that passes over the network. As a result, it is possible to determine the source of the assault by reconstructing the captured data. The purpose of this paper is to outline effectives way of carrying out network autopsy. Keywords: Network Autopsy, Network Forensics, Network Security, Packets, Data Capture. BOOK Chapter ǀ Research Nexus in IT, Law, Cyber Security &amp; Forensics. Open Access. Distributed Free Citation: Louis Opoku Gyamfi (2022): Effective ways of Carrying Out Network Autopsy Book Chapter Series on Research Nexus in IT, Law, Cyber Security &amp; Forensics. Pp 329-334 www.isteams.net/ITlawbookchapter2022. dx.doi.org/10.22624/AIMS/CRP-BK3-P52
Libforensics For Developing Digital Forensics Applications

The evolution of computers, increase in cybercrime and the demand by law requirements for the production of admissible forensic reports require the application of digital/computer technologies and require the development of a methodology to systematically search digital devices for significant evidence. Cyber and computer fraud are growing by the passing of the day with less than two percent of the reported cases resulting in confidence leading to securing justice and or convictions. This study explores the digital forensic applications and ease of integration of the existing forensic applications. It was the view of the researcher that there exists a gap of monolithic in forensic applications and the cyber universe. One requires different applications to conduct a forensic investigation into a crime revolving around different digital universes. The study proposed research into the development of a single enterprise digital forensic application capable of examining all aspects of the universe regardless and producing an admissible report in the court of law Keywords: Forensic, Cybercrime, Monolithic, Digital universe, Cyberspace, Cyber ecosystem BOOK Chapter ǀ Research Nexus in IT, Law, Cyber Security &amp; Forensics. Open Access. Distributed Free Citation: James Hebidzi Senanu (2022): Libforensics For Developing Digital Forensics Applications. Book Chapter Series on Research Nexus in IT, Law, Cyber Security &amp; Forensics. Pp 335-338 www.isteams.net/ITlawbookchapter2022. dx.doi.org/10.22624/AIMS/CRP-BK3-P53