Reviewerly/exHarmony huggingface dataset:
Visual path following with obstacle avoidance for quadcopters in indoor environments

In this paper, we propose a hierarchical visual servo control scheme for following visual paths with quadcopters in indoor environments. The control scheme is able to deal with three tasks simultaneously, considering as order of hierarchy: first, collision avoidance task, second visibility task (keep point features in the camera’s field of view), and finally, visual servoing task. The strategy allows a commitment of active tasks (also the smooth deactivation of tasks) for avoiding conflicts between them. The proposed scheme uses a monocular RGB front-facing camera as single sensor, i.e., only 2D information is required for the control law computation. That information is used for online reactive visual path following, i.e., no 3D reconstruction of the environment is required, and no path or motion planning for obstacle avoidance is performed while the robot follows the visual path. The effectiveness of the proposed control scheme is supported by a stability analysis and experiments with a low-cost commercial quadcopter in scenarios with static obstacles.
Examination of the usability of Tinkercad application in educational robotics teaching by eye tracking technique

Abstract Like all sectors, the education sector has been negatively affected by the Covid-19 pandemic. Considering the decision to conduct face-to-face training in schools remotely, teachers had many difficulties in moving course content to the online platform. Teachers who perform robotic coding applications are looking for ways to do these activities remotely. Simulators enable real objects to be animated in a computer environment. There are many paid and free platforms that simulate robotic coding tools. Arduino and Micro:bit, can be simulated on the Tinkercad platform. This study tests the usability of the Tinkercad platform by teachers. In usability tests, users are expected to complete the authentic tasks they must perform on the tested platform. In this study, 12 Information Technology teachers tried to complete 10 authentic tasks in the Circuits section of the Tinkercad platform. Eye movements have been recorded and analyzed while participants perform tasks. Surveys were applied to the participants and data were collected with the observation form during the tests. Consequently, the teachers who completed the usability tests stated that it is appropriate for the platform to be used by teachers and students over 10 years of age and that they can use the platform in their activities.
Candidate Set Expansion for Entity and Relation Linking Based on Mutual Entity–Relation Interaction

Entity and relation linking are the core tasks in knowledge base question answering (KBQA). They connect natural language questions with triples in the knowledge base. In most studies, researchers perform these two tasks independently, which ignores the interplay between the entity and relation linking. To address the above problems, some researchers have proposed a framework for joint entity and relation linking based on feature joint and multi-attention. In this paper, based on their method, we offer a candidate set generation expansion model to improve the coverage of correct candidate words and to ensure that the correct disambiguation objects exist in the candidate list as much as possible. Our framework first uses the initial relation candidate set to obtain the entity nodes in the knowledge graph related to this relation. Second, the filtering rule filters out the less-relevant entity candidates to obtain the expanded entity candidate set. Third, the relation nodes directly connected to the nodes in the expanded entity candidate set are added to the initial relation candidate set. Finally, a ranking algorithm filters out the less-relevant relation candidates to obtain the expanded relation candidate set. An empirical study shows that this model improves the recall and correctness of the entity and relation linking for KBQA. The candidate set expansion method based on entity–relation interaction proposed in this paper is highly portable and scalable. The method in this paper considers the connections between question subgraphs in knowledge graphs and provides new ideas for the candidate set expansion.
Small-Scale Zero-Shot Collision Localization for Robots Using RL-CNN

For safety reasons, in order to ensure that a robot can make a reasonable response after a collision, it is often necessary to localize the collision. The traditional model-based collision localization methods, which are highly dependent on the designed observer, are often only useful for rough localization due to the bias between simulation and real-world application. In contrast, for fine collision localization of small-scale regions, data-driven methods can achieve better results. In order to obtain high localization accuracy, the data required by data-driven methods need to be as comprehensive as possible, and this will greatly increase the cost of data collection. To address this problem, this article is dedicated to developing a data-driven method for zero-shot collision localization based on local region data. In previous work, global region data were used to construct the collision localization model without considering the similarity of the data used for analysis caused by the assembly method of the contact parts. However, when using local region data to build collision localization models, the process is easily affected by similarity, resulting in a decrease in the accuracy of collision localization. To alleviate this situation, a two-stage scheme is implemented in our method to simultaneously isolate the similarity and realize collision localization. Compared with the classical methods, the proposed method achieves significantly improved collision localization accuracy.
Enhancing Utility in Anonymized Data against the Adversary’s Background Knowledge

Recent studies have shown that data are some of the most valuable resources for making government policies and business decisions in different organizations. In privacy preserving, the challenging task is to keep an individual’s data protected and private, and at the same time the modified data must have sufficient accuracy for answering data mining queries. However, it is difficult to implement sufficient privacy where re-identification of a record is claimed to be impossible because the adversary has background knowledge from different sources. The k-anonymity model is prone to attribute disclosure, while the t-closeness model does not prevent identity disclosure. Moreover, both models do not consider background knowledge attacks. This paper proposes an anonymization algorithm called the utility-based hierarchical algorithm (UHRA) for producing k-anonymous t-closed data that can prevent background knowledge attacks. The proposed framework satisfies the privacy requirements using a hierarchical approach. Finally, to enhance utility of the anonymized data, records are moved between different anonymized groups, while the requirements of the privacy model are not violated. Our experiments indicate that our proposed algorithm outperforms its counterparts in terms of data utility and privacy.
Estimation of Cloudiness Data Based on Multiple Linear Regression Model

This study estimates cloudiness data using meteorological parameters which include climatic variables and air quality index. Daily average observed values of all meteorological parameters used in this study were transformed to monthly mean data for 1990-2015 period. The monthly mean values of cloudiness were estimated by using the other climatic elements and the value air quality index at urban area in Kayseri. Multiple Linear Regression model was built to determine the mathematical relationships for predicting cloudiness. It has been shown that meteorological parameters affect cloudiness the most in May and October, and the least in September and January. Additionally, according to the estimated models, air quality index value has effect on cloudiness data on January, July, October and November as statistically significant.
Unsupervised Video Summarization Based on Deep Reinforcement Learning with Interpolation

Individuals spend time on online video-sharing platforms searching for videos. Video summarization helps search through many videos efficiently and quickly. In this paper, we propose an unsupervised video summarization method based on deep reinforcement learning with an interpolation method. To train the video summarization network efficiently, we used the graph-level features and designed a reinforcement learning-based video summarization framework with a temporal consistency reward function and other reward functions. Our temporal consistency reward function helped to select keyframes uniformly. We present a lightweight video summarization network with transformer and CNN networks to capture the global and local contexts to efficiently predict the keyframe-level importance score of the video in a short length. The output importance score of the network was interpolated to fit the video length. Using the predicted importance score, we calculated the reward based on the reward functions, which helped select interesting keyframes efficiently and uniformly. We evaluated the proposed method on two datasets, SumMe and TVSum. The experimental results illustrate that the proposed method showed a state-of-the-art performance compared to the latest unsupervised video summarization methods, which we demonstrate and analyze experimentally.
Illegal Domain Name Generation Algorithm Based on Character Similarity of Domain Name Structure

Detecting and controlling illegal websites (gambling and pornography sites) through illegal domain names has been an unsolved problem. Therefore, how to mine and discover potential illegal domain names in advance has become a current research hotspot. This paper studies a method of generating illegal domain names based on the character similarity of domain name structure. Firstly, the K-means algorithm classified illegal domain names with similar structures. Then, put the classified clusters into the adversarial generative network for training. Finally, through a specific result verification method, the experiment shows that the average concentration of the generation algorithm is 23.82%, the effective concentration is 63.54%, and the expansion rate is 7.5. By comparing the results with the enumeration algorithm, the generation algorithm has greatly improved in terms of generation efficiency and accuracy.
Tiny object detection model based on competitive multi-layer neural network (TOD-CMLNN)

Tiny Object Detection (TOD) is a fundamental and difficult task in computer vision. Current state-of-the-art detectors like RCNN, Fast RCNN, Faster RCNN, SSD, and YOLO can't find small objects using single-stage or multi-stage methods. With the exponential growth of deep learning, several researchers have drawn attention to advances in tiny object detection approaches. This study proposes a TOD-CMLNN (Tiny Object Detection Competitive Multi-Layer Neural Network) architecture with three sub components first competitive multi-layer network, second TOD auxiliary and third multi-level continue features aggregation for accurately detecting small objects. Competitive learning for object detection is the basis of the proposed architecture. Comparison study with existing RCNN, Fast RCNN, Faster RCNN, SSD and YOLO shows significant improvement in the results. TOD-CMLNN receives 72.46 % accuracy in terms of mAP which is impressive as compared to state-of-the-art detectors.
Object tracking and detection techniques under GANN threats: A systemic review

Current developments in object tracking and detection techniques have directed remarkable improvements in distinguishing attacks and adversaries. Nevertheless, adversarial attacks, intrusions, and manipulation of images/ videos threaten video surveillance systems and other object-tracking applications. Generative adversarial neural networks (GANNs) are widely used image processing and object detection techniques because of their flexibility in processing large datasets in real-time. GANN training ensures a tamper-proof system, but the plausibility of attacks persists. Therefore, reviewing object tracking and detection techniques under GANN threats is necessary to reveal the challenges and benefits of efficient defence methods against these attacks. This paper aims to systematically review object tracking and detection techniques under threats to GANN-based applications. The selected studies were based on different factors, such as the year of publication, the method implemented in the article, the reliability of the chosen algorithms, and dataset size. Each study is summarised by assigning it to one of the two predefined tasks: applying a GANN or using traditional machine learning (ML) techniques. First, the paper discusses traditional applied techniques in this field. Second, it addresses the challenges and benefits of object detection and tracking. Finally, different existing GANN architectures are covered to justify the need for tamper-proof object tracking systems that can process efficiently in a real-time environment.
Passenger Flow Detection in Subway Stations Based on Improved You Only Look Once Algorithm

Passenger flow detection plays an important role in guaranteeing passenger safety. It contributes to the efficiency of passenger flow control at stations. However, passenger flow detection in subway stations has the problems of target size inconsistency and poor detection effects of small targets. To solve this problem, an improved You Only Look Once algorithm (Improved-YOLO) is proposed based on the YOLOv4 for passenger flow detection in subway stations. The repeatable bidirectional feature fusion (BiFF) module was designed to combine with the adaptively spatial feature fusion (ASFF) module to replace the feature fusion network of the YOLOv4. To verify the effectiveness of the Improved-YOLO, the passenger flow dataset of Nanning Metro Line 1 was used for the experiment. Augmentation and transfer learning were then used to improve the performance of the model. Compared with the YOLOv4, the results showed that the mean average accuracy (mAP) of the Improved-YOLO increased from 89.63% to 92.96%, and the detection time of a single frame image reduced from 61 ms to 52 ms. Compared with other classical models, the Improved-YOLO showed satisfactory performance in passenger flow detection in subway stations. These experimental results can provide reference and theoretical support for real-time passenger flow detection and passenger behavior recognition in subways.
Decentralized power grid fault traceability system based on internet of things and blockchain technology

With the economic and social development of China, the scale of the power grid continues to expand. Rapid location and diagnosis of power failures have become significant for China to maintain its stable development of power system. In recent years, the Internet of Things (IoT) based on 5G technology has been applied to power grid more widely. Meanwhile, given the fact that the blockchain is traceable and tamper-resistant, the combination of the blockchain and IoT is considered to locate power failures quickly and assist professional maintenance personnel to deduce the cause of failures, minimizing economic loss. With the foundation of IoT sensor node data, this paper designs a decentralized electronic certificate scheme based on blockchain and Interplanetary File System (IPFS) to collect data of each node of the power system and store it in the blockchain. The model of data sharding, storage and certificate optimizes the utilization of storage space of the blockchain, reducing the time required for system access to nodes. Traceability of data stored on blockchain data is employed to quickly and accurately trace faults of the power system, providing strong technical support for the safe and stable operation of China’s power system.
A practical approach for technical debt prioritization based on class‐level forecasting

Abstract Monitoring technical debt (TD) is considered highly important for software companies, as it provides valuable information on the effort required to repay TD and in turn maintain the system. When it comes to TD repayment, however, developers are often overwhelmed with a large volume of TD liabilities that they need to fix, rendering the procedure effort demanding. Hence, prioritizing TD liabilities is of utmost importance for effective TD repayment. Existing approaches rely on the current TD state of the system; however, prioritization would be more efficient by also considering its future evolution. To this end, the present work proposes a practical approach for prioritization of TD liabilities by incorporating information retrieved from TD forecasting techniques, emphasizing on the class‐level granularity to provide highly actionable results. Specifically, the proposed approach considers the change proneness and forecasted TD evolution of software artifacts and combines it with proper visualization techniques, to enable the early identification of classes that are more likely to become unmaintainable. To demonstrate and evaluate the approach, an empirical study is conducted on six real‐world applications. The proposed approach is expected to facilitate developers better plan refactoring activities, in order to manage TD promptly and avoid unforeseen situations long term.
Exploring the relationship between IT development, poverty and cybercrime: an Armenia case study

This paper explores the relationship between IT development, regional poverty, and cybercrime, through the case of Armenia. Armenia was selected as it is a former Soviet state that has sought to promote the development of its IT sector in recent years, which has occurred within a context of widespread regional poverty. The paper acknowledges the potentially cyber-criminogenic interactions between developed socio-technological and impoverished legitimate economic conditions, that the literature has noted in several high cybercrime nations. It then examines the case of Armenia by exploring potentially cyber-criminogenic conditions there and by constructing an overview of economic cybercrime trends in Armenia since 2010. The paper finds that, despite the promotion of IT development within the context of regional poverty, cybercrime in Armenia remains low. It explores, through a series of expert interviews, characteristics of the IT sector in Armenia that have mitigated against the cyber-criminogenic interactions between these two conditions. Finally, it identifies potentially transferable policy lessons, wider theoretical implications, and avenues of future research that emerge from this case.
A study on utilizing SPALT technology in the classroom

The objectives of this study are to identify the use of smartphone-assisted language teaching in the classroom and to explore its importance and advantages. Computers, mobile phones, etc. are introduced in the classroom and several studies are being carried out in this area. When compared to such studies, the present study focuses on the use of smartphones in language teaching and also explores its importance of it. For the present study data were collected from higher-educational institutions to identify the use of smartphones in classrooms using a structured questionnaire. 100 respondents participated. The findings of the study reveal that smartphone usage was found in classrooms and was useful for students.
Crowdsourcing to Tackle Online Child Sexual Exploitation: Europol’s ‘Stop Child Abuse—Trace an Object’ Platform

Abstract This article examines how Europol’s ‘Stop Child Abuse—Trace an Object’ (SCATO) platform uses crowdsourcing to collect tips from the public in order to tackle online child sexual exploitation. The article evaluates the platform's effectiveness based on three key factors: how easy it is for the public to access the platform, the transaction costs involved in gathering information from the public, and how trust is established between the public and law enforcement agencies. By analyzing Europol documents and conducting interviews with law enforcement officers and non-governmental organizations, the article argues that the SCATO platform is user-friendly and accessible to the public. However, in order to reduce the searching cost of intelligence gathering, the images shared on the platform should encourage high-value reporting without compromising victim anonymity. Additionally, the platform should also benefit from clear guidelines and feedback mechanisms to encourage more public participation.
EMRNet: Efficient Modulation Recognition Networks for Continuous-Wave Radar Signals

Automatic modulation recognition(AMR) of radar signals is a currently active area, especially in electronic reconnaissance, where systems need to quickly identify the intercepted signal and formulate corresponding interference measures on computationally limited platforms. However, previous methods generally have high computational complexity and considerable network parameters, making the system unable to detect the signal timely in resource-constrained environments. This letter firstly proposes an efficient modulation recognition network(EMRNet) with tiny and low latency models to match the requirements for mobile reconnaissance equipments. One-dimensional residual depthwise separable convolutions block(1D-RDSB) with an adaptive size of receptive fields is developed in EMRNet to replace the traditional convolution block. With 1D-RDSB, EMRNet achieves a high classification accuracy and dramatically reduces computation cost and network paraments. The experiment results show that EMRNet can achieve higher precision than existing 2D-CNN methods, while the computational cost and parament amount of EMRNet are reduced by about 13.93× and 80.88×, respectively.
Serverless Computing Platforms Performance and Scalability Implementation Analysis

Serverless computing and Platform as a Service (PaaS) are similar in that they are both tiny runtime containers that enable infrastructure-free execution of individual lines of code. In 2014, Amazon launched Lambda functions, an event-driven computing platform with a 25 concurrent maximum. Like Platform as a Service (PaaS), but without the overhead of managing the underlying infrastructure, with serverless computing, programs may be executed in a small runtime environment. In 2014, Amazon introduced event-driven computing with the Lambda functions. Functions are often designed for microservices and light workloads, although they are connected to concurrent distributed data processing. We claim that existing serverless computing platforms may allow dynamic applications to operate simultaneously if a partitioned operation can be finished on a small function instance. Results for compute performance in terms of throughput, network bandwidth, file I/O, and concurrent invocations are shown. Even though our solution is yet a prototype, we believe the suggested strategy has a lot of potential. We also talk about potential next steps for running scientific procedures on serverless infrastructures. This paper provides a cost and runtime throughput of several cloud service providers and computes with our prototype model. We do a cost analysis, time conception and performance analysis in the end and discuss the implications for scientific applications' resource management in general.