Reviewerly/exHarmony huggingface dataset:
METRIC—Multi-Eye to Robot Indoor Calibration Dataset

Multi-camera systems are an effective solution for perceiving large areas or complex scenarios with many occlusions. In such a setup, an accurate camera network calibration is crucial in order to localize scene elements with respect to a single reference frame shared by all the viewpoints of the network. This is particularly important in applications such as object detection and people tracking. Multi-camera calibration is a critical requirement also in several robotics scenarios, particularly those involving a robotic workcell equipped with a manipulator surrounded by multiple sensors. Within this scenario, the robot-world hand-eye calibration is an additional crucial element for determining the exact position of each camera with respect to the robot, in order to provide information about the surrounding workspace directly to the manipulator. Despite the importance of the calibration process in the two scenarios outlined above, namely (i) a camera network, and (ii) a camera network with a robot, there is a lack of standard datasets available in the literature to evaluate and compare calibration methods. Moreover they are usually treated separately and tested on dedicated setups. In this paper, we propose a general standard dataset acquired in a robotic workcell where calibration methods can be evaluated in two use cases: camera network calibration and robot-world hand-eye calibration. The Multi-Eye To Robot Indoor Calibration (METRIC) dataset consists of over 10,000 synthetic and real images of ChAruCo and checkerboard patterns, each one rigidly attached to the robot end-effector, which was moved in front of four cameras surrounding the manipulator from different viewpoints during the image acquisition. The real images in the dataset includes several multi-view image sets captured by three different types of sensor networks: Microsoft Kinect V2, Intel RealSense Depth D455 and Intel RealSense Lidar L515, to evaluate their advantages and disadvantages for calibration. Furthermore, in order to accurately analyze the effect of camera-robot distance on calibration, we acquired a comprehensive synthetic dataset, with related ground truth, with three different camera network setups corresponding to three levels of calibration difficulty depending on the cell size. An additional contribution of this work is to provide a comprehensive evaluation of state-of-the-art calibration methods using our dataset, highlighting their strengths and weaknesses, in order to outline two benchmarks for the two aforementioned use cases.
Research on the influence of pilot workload on local route path planning of helicopters in low-altitude penetration

Abstract Targeting the problem of high pilot workload caused by trajectory planning of low altitude penetration, this paper proposed a trajectory planning method considering the pilot workload. Firstly, by decomposing the three-dimensional trajectory into horizontal and vertical directions, the trajectory tree method based on the dynamic programming technique is used for horizontal trajectory planning, and the comprehensive slope-curvature limitation method is used for altitude trajectory planning. Then, a method was proposed to quantify the control inputs and pilot workload based on trajectory tracking simulation. The pilot workload cost, together with flight altitude cost and route length cost, was added to the local planning of a reference trajectory. To solve the problem of dimension explosion in trajectory tree search, a segmented optimal route tree algorithm is proposed. Finally, the simulation results show that the proposed method can effectively consider the pilot workload in trajectory planning, which is helpful to improve the flight ability of the planning trajectory and reduce the pilot workload at the same time.
How Economic Indicator Drive Crime? Empirical Study in Developing Country, Indonesia

More offenses are committed in society as the crime rate rises, which is a sign that the safety of society is deteriorating. The purpose of this research is to examine how the Gini ratio, per capita income, unemployment, poverty, and population density affect the number of crimes committed in Indonesia between 2012 and 2020 in 34 Indonesian provinces, this research used secondary data from the years 2012 until 2020. Multiple linear regression using a panel data approach is the study methodology. The study's findings demonstrate that the fixed effect approach is the most effective. Unemployment had a positive and significant impact on the amount of crimes, according to partial testing, while overcrowding and internet access had a negative and significant impact. In Indonesia between 2012 and 2020, the Gini ratio, per capita income, and poverty did not affect crime, according to partial testing, which also found that unemployment had a positive and significant impact on the number of crimes. Overcrowding and internet access also had a negative and significant impact on the number of crimes. According to this study, it is hoped that the government will establish learning facilities that put an emphasis on developing skills, offer counseling regarding family planning initiatives, and create a fair digital infrastructure to raise the standard of internet access.
Analysis of the development level of Internet teaching in primary and secondary school teachers - -Based on a survey of more than 40,000 teachers in China

Under the background of “Internet +”education, Internet teaching has become a teaching way to leverage the teaching reform and promote the development of students. It is found that there are significant differences in the development of teachers in the eastern, central and western regions, and teachers in primary and primary schools teachers’ Internet teaching are the best. It need to improve the infrastructure, to carry out related training, selecting pilot schools, further enhance the level of national basic education teachers Internet teaching development.
DEVELOPMENT OF A LABORATORY STAND FOR MONITORING AIR POLLUTION

This paper describes the importance of laboratory work in the process of teaching students, the tasks of developing a laboratory
Hypersonic target interception based on short-range damage estimation

Abstract Aiming at the interception problem of hypersonic target end phase, the flight interception simulation based on semi-active radar guidance is firstly established, and a warhead based on fragment damage is designed. Secondly, research proposes an effective method of target damage and verifies the interception effect by simulation. AUTODYN was used to establish the 3D finite element model based on the nonlinear flight dynamics model, to simulate the damage process and fragmentation dispersion. In this paper, the probability of success of missile interception is estimated by using conditional probability and the distribution of fragment field combined with the miss distance in the interception process and the fragment explosive dispersion law of the steel ball, so as to ensure the destruction of the terminal hypersonic incoming weapon within the safe range.
Minimum consistent subset of simple graph classes

In the minimum consistent subset (MCS) problem, a connected simple undirected graph G=(V,E) is given whose each vertex is colored by one of the colors {c1,c2,…,ck}, and the objective is to compute a minimum size subset C⊆V such that for each vertex v∈V, its set of nearest neighbors in C (with respect to the hop-distance) contains at least one vertex of the same color as v. The decision version of the MCS problem is NP-complete for general graphs. Even for planar graphs, the problem remains NP-complete. We will consider some simple graph classes like path, spider, caterpillar and comb; and present polynomial-time algorithms.
Lightweight-YOLOv5s for the camouflaged personnel detection in the woodland

Abstract Unmanned aerial vehicles(UAVs) technology and target detection technology are increasingly integrated. However, the target detection networks are cumbersome and complex, and it is hard to directly deploy these networks on the UAVs. To address this problem, a Lightweight-YOLOv5s which can be applied to the UAVs is proposed to detect the camouflaged personnel in the woodland. Depthwise separable convolutions are added to the YOLOv5s for reducing the parameters and computational effort. Then a new channel pruning method based on Batch Normalization(BN) layer scaling factor with adaptive sparsity training is proposed for network compression, which can balance the compression ratio and accuracy of the detection. The pruned network is then fine-tuned to detect the camouflaged personnel. In addition, a special dataset is built for the camouflaged personnel detection in the woodland. The quantitative results on this dataset prove that the proposed Lightweight-YOLOv5s greatly improve the YOLOv5s. Parameters from 7.06M to 1.86M, floating point operations from 16.4G to 9.9G and inference time from 8.16ms to 7.06ms. Besides, mAP of the Lightweight-YOLOv5s is 0.83, which shows the excellent detection performance of the proposed method.
A retrospective analysis and systematic review of the areas of entertainment computing and persuasive technologies for health

The areas of entertainment computing and persuasive technologies are interdisciplinary fields that have gained increasing attention in the last decades. Health is one of the domains that has leveraged the benefits of fun to improve the results of its technology-enabled interventions. Previous work has reviewed the area of health entertainment from many different perspectives; however, an integrative analysis across disciplines (health sciences and computer science and engineering) throughout the development and validation cycle of technologies in this domain is missing. Having such an in-depth retrospective analysis would shed light on how research on entertainment computing and persuasive technologies for health has evolved, acknowledging its contributions, recognizing its strengths and limitations, and, as a result, allowing for the definition of ways forward. This paper engages in an unprecedented systematic review of the work produced between 2004 and 2017 in this area. From an initial total of 10,350 retrieved results, a total of 1,307 full-texts were included in this review and were thoroughly examined to gain a retrospective understanding of the type of studies that have been produced. Among others, this systematic review reports on the trends, venues of publication, and the characteristics of the studies including methodologies, sample characteristics, study design, the type of solutions produced, the conditions and domains of application, and the purpose of the studies. Results show that there is a growing body of research in the area, with most studies being published in roughly the same venues, and where the lion's share of solutions fall into the area of health rehabilitation and motor conditions. With regards to the most addressed health domains, our review shows that most solutions produced are aimed at stroke and fitness, followed by balance training. Most studies (82.3%) are conducted with their target population, mostly adults (18–49 y), and are conducted either in the lab or in clinical settings. However, the median sample size of the studies has remained stable ( N = 20) in the last decades. Regarding technology, 2D and 3D solutions are equally used, and most systems employ movement sensors and are single-user. Finally, only 21.4% of the studies are performed using validated instruments.
Deep learning and gradient-based extraction of bug report features related to bug fixing time

Bug reports typically contain detailed descriptions of failures, hints at the location of the corresponding defects, and discussions. Developers usually resolve bugs using comments in descriptions and discussions. The time to fix a bug varies greatly. Previous studies have investigated bug reports, but the influence of comments on bug fixing time is not well understood. This study adopts a convolutional neural network (CNN) and gradient-based visualization approach called Grad-cam to elucidate the impact of comments on bug fixing time and extract features. A feature represents an observed characteristic in a bug report when processing via deep learning. Specifically, CNN classifies bug reports, and then Grad-cam visualizes the decision basis of CNN by identifying the top 10 word sequences used in the prediction. Here, the features are major word sequences extracted by Grad-cam. In an experiment, the proposed method classified more than 36,000 actual bug reports from Bugzilla with an accuracy of 75%–80%. Additionally, the visualization highlighted differences in the stack trace and word abstraction by bug fixing time. Bug reports with short bug fixing times are concrete, whereas those with a long bug fixing time are abstract.
Context-aware lightweight remote-sensing image super-resolution network

In recent years, remote-sensing image super-resolution (RSISR) methods based on convolutional neural networks (CNNs) have achieved significant progress. However, the limited receptive field of the convolutional kernel in CNNs hinders the network's ability to effectively capture long-range features in images, thus limiting further improvements in model performance. Additionally, the deployment of existing RSISR models to terminal devices is challenging due to their high computational complexity and large number of parameters. To address these issues, we propose a Context-Aware Lightweight Super-Resolution Network (CALSRN) for remote-sensing images. The proposed network primarily consists of Context-Aware Transformer Blocks (CATBs), which incorporate a Local Context Extraction Branch (LCEB) and a Global Context Extraction Branch (GCEB) to explore both local and global image features. Furthermore, a Dynamic Weight Generation Branch (DWGB) is designed to generate aggregation weights for global and local features, enabling dynamic adjustment of the aggregation process. Specifically, the GCEB employs a Swin Transformer-based structure to obtain global information, while the LCEB utilizes a CNN-based cross-attention mechanism to extract local information. Ultimately, global and local features are aggregated using the weights acquired from the DWGB, capturing the global and local dependencies of the image and enhancing the quality of super-resolution reconstruction. The experimental results demonstrate that the proposed method is capable of reconstructing high-quality images with fewer parameters and less computational complexity compared with existing methods.
A Novel High Efficiency Synthetic Fire Strike System

Abstract A new type of multi-artillery, multi-functional, multi-purpose synthetically-arranged battle group with high-efficiency fire strike system is constructed. The hierarchical structure and weight relationship of the system are determined by the AHP analytic hierarchy process, and then the inherent ability of each performance index is calculated by the fuzzy membership degree theory. These methods are used to establish the combat effectiveness evaluation model of high-efficiency fire strike system. The analysis shows that, for different damage targets, the deployment method of the efficient fire strike battalion has significantly improved the combat effectiveness of the traditional howitzer battalion. The research results have certain reference value for other arms to improve their weapon combat capabilities, conduct joint weapon operations and evaluate system combat effectiveness.
Advancements in Named Entity Recognition using Deep Learning Techniques: A Comprehensive Study on Emerging Trends

Because of the amount of computer-processable text material and processing hardware, deep learning architecture has been the focus of knowledge projects. The task of named entity recognition in natural language processing is no exception. Because of the volume of text material that computers can process and their processing capability, knowledge projects are now focusing on deep learning architecture. Named entity recognition, is a frequently occurring task in natural language processing. Deep learning models, particularly recurrent neural net- works (RNNs) and their variations such as gated recurrent units (GRUs) and long short-term memory (LSTM) , have revolutionised natural language pro- cessing (NER) by capturing intricate contextual connections. These models can accurately forecast the borders and types of named entities because they can effectively capture the sequential and hierarchical patterns inside text. However, using deep learning in NER is a challenging task in itself. Annotated training data is critical for building reliable models, but getting labelled data for all item kinds and domains can be time-consuming. To solve this difficulty, transfer learn- ing and domain adaptation strategies have arisen, using pre-trained models and adapting them to various domains or target tasks. This paper looks at recent deep learning methods for NER and how they evolved from older linear learning approaches. It also analyses the status of activities that are either upstream or downstream of NER, such as sequence tagging and entity linking, among other things
Simulation Environment of Unmanned Group Reconnaissance System Based on RVIZ and GYM

Abstract This paper studies the simulation environment of the current cluster unmanned system, analyzes the shortcomings of the cluster unmanned system, and designs a cluster robot simulation system based on RVIZ and GYM, which has both the fast training algorithm and the physical attributes of the simulation environment, and uses DDS to transmit robot control instructions and state information. After the GYM fast training algorithm, Control the cluster robots to verify the algorithm and collect the state information of the robots in the physical simulation environment of RVIZ. After the whole simulation is passed, the algorithm can be directly used for the path planning of cluster robots. The results show that the simulation environment can effectively simulate the unmanned group reconnaissance system.
RealityMedia: immersive technology and narrative space

In this paper, we treat VR as a new writing space in the long tradition of inscription. Constructing Virtual Reality (VR) narratives can then be understood as a process of inscribing text in space, and consuming them as a process of “reading” the space. Our research objective is to explore the meaning-making process afforded by spatial narratives—to test whether VR facilitates traditional ways of weaving complex, multiple narrative strands and provides new opportunities for leveraging space. We argue that, as opposed to the linear space of a printed book, a VR narrative space is similar to the physical space of a museum and can be analyzed on three distinct levels: (1) the architecture of the space itself, (2) the collection, and (3) the individual artifacts. To provide a deeper context for designing VR narratives, we designed and implemented a testbed called RealityMedia to explore digital remediations of traditional narrative devices and the spatial, immersive, and interactive affordances of VR. We conducted task-based user study using a VR headset and follow-up qualitative interviews with 20 participants. Our results highlight how the three semantic levels (space, collection, and artifacts) can work together to constitute meaningful narrative experiences in VR.
Incorporating mobile learning and the CAP(E) framework in improving the receptive skills of students

Purpose Given the persistent receptive skills challenges faced by language learners and the numerous merits of using technologically-enhanced learning in the form of mobile-assisted language learning in language teaching, this study decides to incorporate a context-analysis-practice-exploration lesson-planning framework with mobile learning to improve the receptive skills of learners. This step is necessary on the recommendation by scholarly research that while implementing technology, teacher training, digital literacy and expertise, incorporating good teaching practices and lesson planning frameworks may assist in yielding positive results. Design/methodology/approach This study decides to utilize a mixed-method research design through an experimental approach to elicit information from 90 EFL students concerning the effectiveness of incorporating the context-analysis-practice-exploration framework in a mobile-leaning mode instructional delivery to improve their reading and listening skills. Findings After collecting and analyzing data, the study found that while the participants perceived enhanced-mobile learning to make lessons enjoyable, integrating this framework and m-learning improved their reading skills. This study used notable findings to make pedagogical conclusions on the objective of this study. Originality/value This study contributes through empirical evidence on the effectiveness of incorporating mobile learning and a responsive lesson planning framework towards improving English as a foreign language learners' learning outcome. Besides, this study is the first to probe and unpack the effectiveness of a responsive lesson-planning framework in language education.
Visual-admittance-based model predictive control for nuclear collaborative robots

This paper presents a novel visual-admittance-based model predictive control scheme to cope with the problem of vision/force control and several constraints of a nuclear collaborative robotic visual servoing system. A visual-admittance model considering the desired image feature and force command in the image feature space is proposed. Moreover, a novel constraint scheme of the model predictive control (MPC) is proposed to cancel the overshoot in the interaction force control for most cases by taking the desired force command as the constraint of the proposed MPC. Via applying the robotic dynamic image-based visual servoing (IBVS) model, some other constraints, such as the actuator saturation, joint angle, and visual limits, can be satisfied simultaneously. The simulation results for the two-degrees-of-freedom (DOF) robot manipulator with an eye-to-hand camera are present to demonstrate the effectiveness of the proposed controller.
A Study of Chinese News Headline Classification Based on Keyword Feature Expansion

Abstract Existing work generally classifies news headlines as a matter of short text classification. However, due to the strong domain nature and limited text length of news headlines, their classification results are usually determined by several specific keywords, which makes the traditional short text classification method ineffective. In this paper, we propose a new method to identify keywords in news headlines and expand their features from sentence level and word level respectively, and finally use convolutional neural networks (CNN) to extract and classify their features. The proposed model was tested on the Sogou News Corpus dataset and achieved 93.42 $$\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>%</mml:mo> </mml:math> accuracy.