Reviewerly/exHarmony huggingface dataset:
An Ensemble Approach for Automatic Artefact Detection on Gastroendoscopy Images

Endoscopy imaging is a clinical procedure for the early detection of numerous cancers as well as for therapeutic procedures and minimally invasive surgery. Using endoscopic examination data to detect diseases is very helpful for medical doctors and speeds up the diagnosis. Because of the very narrow area, captured frames during endoscopic examination include a variety of artefacts. Artefacts degrade diagnostic image quality, which in turn makes disease diagnosis difficult for both clinicians and computer aided disease detection algorithms. Therefore, it is very crucial to find and eliminate those artefacts from medical images. In this paper, a detection system which utilizes ensemble of deep learning models and data augmentation is proposed. A fast and accurate object detection model which is YOLOv5 (improved version of YOLOv4) is selected as a base model. The 3 separate models are trained with segregated and augmented data; then, the models are combined to make an ensemble. The EndoCV2020 dataset is utilized to benchmark the ensemble model. The model achieves state-of-the-art performance with 49.6 mAP. The final mAP is calculated averaging several APs for different IoU thresholds (starting from 0.25 IoU to 0.75 Iou with step size 0.05).
Distance and Edge Transform for Skeleton Extraction

The shape skeleton or medial axis, is a concise shape description, defined by the centers of the maximally inscribed circles. Skeletonization algorithms support many applications, including optical character recognition, object recognition, pose estimation, shape matching, biomedical image analysis, etc. Usually, classical algorithms tend to produce redundant skeleton branches at edge noise regions and require a branch pruning post process. Recently many CNN based algorithms achieved significant performance improvements compared with classical algorithms. Most deep learning algorithms directly used the shape of image as input data and its complex for end-to-end learning algorithms to fit the transformation from shape to skeleton. In this work, we proposed to use Smooth Distance Estimation (SDE) and Edge transformation to preprocess the input shape. Combined with a modified U-Net model and multimodel ensemble, the proposed method achieved 0.8129 F1 score in the Pixel SkelNetOn validation set, 1.5752 symmetric chamfer distance in the Point SkelNetOn validation set and 6407.4 squared distance score in the Parametric SkelNetOn validation set.
Implicit Feature Refinement for Instance Segmentation

We propose a novel implicit feature refinement module for high-quality instance segmentation. Existing image/video instance segmentation methods rely on explicitly stacked convolutions to refine instance features before the final prediction. In this paper, we first give an empirical comparison of different refinement strategies, which reveals that the widely-used four consecutive convolutions are not necessary. As an alternative, weight-sharing convolution blocks provides competitive performance. When such block is iterated for infinite times, the block output will eventually converge to an equilibrium state. Based on this observation, the implicit feature refinement (IFR) is developed by constructing an implicit function. The equilibrium state of instance features can be obtained by fixed-point iteration via a simulated infinite-depth network. Our IFR enjoys several advantages: 1) simulates an infinite-depth refinement network while only requiring parameters of single residual block; 2) produces high-level equilibrium instance features of global receptive field; 3) serves as a plug-and-play general module easily extended to most object recognition frameworks. Experiments on the COCO and YouTube-VIS benchmarks show that our IFR achieves improved performance on state-of-the-art image/video instance segmentation frameworks, while reducing the parameter burden (e.g. 1% AP improvement on Mask R-CNN with only 30.0% parameters in mask head). Code will be made available at \hrefhttps://github.com/lufanma/IFR.git https://github.com/lufanma/IFR.git .
AEMB: An Automated Exploit Mitigation Bypassing Solution

Modern operating systems set exploit mitigations to thwart the exploit, which has also become a barrier to automated exploit generation (AEG). Many current AEG solutions do not fully account for exploit mitigations, and as a result, they are unable to accurately assess the exploitability of vulnerabilities in such settings.This paper proposes AEMB, an automated solution for bypassing exploit mitigations and generating useable exploits (EXPs). Initially, AEMB identifies exploit mitigations in the system based on characteristics of the program execution environment. Then, AEMB implements exploit mitigations bypassing the payload generation by modeling expert experience and constructs the corresponding constraints. Next, during the program’s execution, AEMB uses symbol execution to collect symbol information and create exploit constraints. Finally, AEMB utilizes a solver to solve the constraints, including payload constraints and exploit constraints, to generate the EXP. In this paper, we evaluated a prototype of AEMB on six test programs and seven real-world applications. Furthermore, we conducted 54 sets of experiments on six different combinations of exploit mitigations. Experiment results indicate that AEMB can automatically overcome exploit mitigations and produce successful exploits for 11 out of 13 applications.
Research on Interactive Art Online Teaching System Based on BS Mode and Internet of Things

Traditional online art teaching system has problems such as poor score improvement and low system throughput. Therefore, this paper designs an interactive online art teaching system based on BS mode and IoT. Design the overall structure of the art teaching system according to THE B/S structure, build the interactive art online teaching model according to the system role use cases, introduce the RFID technology in the Internet of Things to control the information transmission of the interactive art online teaching system, and complete the code development of interactive art online teaching function. Complete the interactive art online teaching system based on BS mode and the Internet of Things. The experimental results show that the designed system can improve the scores of students in art colleges and improve the throughput of the system.
ShiftTouch: Sheet-type Interface Extending Capacitive Touch Inputs with Minimal Screen Occlusion

We present ShiftTouch, a sheet-type passive interface that provides multiple inputs for capacitive touch surfaces with minimal screen occlusion. It consists of a conductive layer and a masking layer that partially insulates the conductive one. ShiftTouch uses multiple linear electrodes to control the fine touch position. The touch input is triggered under the electrodes when several adjacent electrodes are grounded simultaneously. Although each input area shares some electrodes with neighboring input areas, the touch surface can identify the inputs from each input area by detecting the slight displacement of the touch position. Our interface is simple yet effective in implementing multiple input areas while reducing screen occlusion compared to existing approaches using finger-sized electrodes.
Evaluating the Effects of Saccade Types and Directions on Eye Pointing Tasks

With the portable and affordable gaze input devices being marketed for end users, gaze-based interactions were getting increasingly popular. Unfortunately, the understanding about the dominant task of gaze input, i.e. eye pointing task, was still not sufficient although a performance model had been specifically proposed in previous study because of that 1) the original model was based on a specific circular target condition, without the ability to predict the performance of acquiring conventional rectangular targets and that 2) there was a lack of explanation from the perspective of the anatomical structure of the eyes. In this paper, we proposed a 2D extension to take account of more general target conditions. Carrying out two experiments, we evaluated the effectiveness of the new model and furthermore we found that the index of difficulty that we redefined for 2D eye pointing (IDeye) was able to properly reflect the asymmetrical impacts of target width and height, and consequently the IDeye model could more accurately and properly predict the performance when acquiring 2D targets than Fitts’ law, no matter what kind of saccades or eye orientations (i.e. saccadic eye movement directions) was employed to acquire the desired targets. According to the results, we provided more useful implications and recommendations for gaze-based applications.
Mineralogy and geochemistry of tellurides and pyrite from the Laowan Au−Ag−Te deposit, Qinling−Dabie orogenic belt, central China: Implications for ore genesis

The Laowan Au − Ag − Te deposit (containing greater than 100 t Au), located at the center of the Qinling − Dabie orogenic belt in central China, represents a breakthrough in gold prospecting in the Tongbai district. A tunnel geological survey and microscope-based mineral identification, combined with EMPA/EDS mapping and LA-ICP-MS analysis, were used in this study to determine the mineral types present. The sequence of formation and symbiosis were determined using the Au-hosting mineral geochemical element typomorphism to provide a basis for Au enrichment and the sources of material and ore − forming fluids. The results indicate that: (1) Tellurides were widespread in the main metallogenic auriferous pyrites, consisting of petzite, calaverite, tellurobismuthite, tetradymite, volynskite, and hessite. The vertical zonation of different telluride combinations indicates that mineral distribution shows the vertical zoning characteristics of upper Ag and lower Bi. (2) Petrographic analyses indicate that 5 generations of pyrite formed during the ore-forming stage (Py1, Py2, Py3, Py4, and Py5). LA-ICP-MS analytical data showed that Py3 and Py4 were rich in Au − Ag − Bi − Te, with a correlation between gold and tellurium. Auriferous pyrites were rich in both As and Te, and the enrichment of Au was unrelated to As. Te is an important scavenger of gold, the effect of As is not obvious. (3) From 250 °C to 200 °C, logfTe2 and logfS2 decreased from − 13.0 to − 8.3 and − 14.7 to − 8.6 to − 15.2 to − 11.2 and − 16.7 to − 10.5, respectively. fTe2, fS2 and temperature were the conditions controlling variations in the telluride combination. (4) The occurrence of tellurides, the correlation of Au − Ag − Bi − Te elements and enrichment of Te and Bi, combined with previous isotopic geochemical data, indicate that the ore-forming fluids and metals might be stemmed from the mantle-derived magmatic system, which could have provided materials, sufficient heat energy, volatiles, and components for the gold mineralisation.
Optimal Searching Time Allocation for Information Collection Under Cooperative Path Planning of Multiple UAVs

Unmanned Aerial Vehicles (UAVs) play an essential role in information collection where routing and time scheduling are two critical factors and are considered sequently. Given the paths of UAVs, metaheuristic algorithms are explored to allocate searching time of UAVs, which are hard to guarantee the optimal solution for time allocation. In this paper, a novel searchinG timE allocatioN undEr coopeRative pAth pLaning (GENERAL) is proposed to solve the optimal solution of the time allocation given paths of UAVs. GENERAL adopts a semi-greedy construction and a repair procedure to initialize and amend the routing solutions during iterations. Motivated by the Newton’s method from convex optimization domains, we introduce a new Perturbed Parametric Nonlinear Complementarity Problem function (PPNCP-function), which reformulates the time allocation problem as a smoothing system of equations according to Karush-Kuhn-Tucker (KKT) theorem. Then a smoothing Newton method is introduced to obtain the optimal time allocation solution with superlinear convergence. Experimental results empirically indicate the GENERAL’s effectiveness compared to Ant Colony Optimization with Simulated Annealing (ACO-SA) and Genetic Algorithm (GA). Besides, some numerical results indicate that the smoothing Newton method with PPNCP function is promising. The emerging aspects of this paper include: 1) it integrates the path planning with time allocation problem for maximizing the information collection reward; 2) a new PPNCP-function and a smoothing Newton method have been proposed to solve the optimal time allocation under path planning of UAVs; 3) the theoretical convergence analysis of the smoothing Newton method has been provided in this paper.
Precise calibration of dynamic geometric parameters cameras for aerial mapping

Calibration is crucial for mapping cameras. Traditional calibration methods assume the geometric parameters are unchanged during the imaging process. However, aerial mapping cameras perform earth observation during a moving process. Owing to the forward flying of airplane, the exterior orientation parameters are changed during the exposure process. This leads to problems if directly applying the traditional methods to aerial mapping cameras. This paper presents a set of working time sequence and a new high-precision calibration method for aerial mapping cameras. We prove that the aerial mapping on a moving platform can be transformed into a mapping on a stationary platform by using the proposed method. Specifically, three steps are given to perform as follows. First, a proper distance is selected to decide a spot for the detector. We fix the detector at the selected spot and calibrate intrinsic parameters by exploiting traditional calibration methods. Second, the working time sequence of camera imaging is designed. The camera starts to expose an image and the exterior orientation parameters are obtained when the detector is driven to move to the calibrated spot. Third, a distortion model is proposed to correct distortions. We show that the dynamic measurement is identical to static measurement by using the proposed method. Experiments based on both simulated and real data are provided to show the effectiveness of the method.
Named Entity Disambiguation for Archival Collections: Metadata, Wikidata, and Linked Data

Abstract Representing archival metadata as linked data can increase the findability and usability of items, and linked data sources such as Wikidata can be used to further enrich existing collection metadata. However, a central challenge to this process is the named entity disambiguation or entity linking that is required to ensure that the named entities in a collection are being properly matched to Wikidata entities so that any additional metadata is applied correctly. This paper details our experimentation with one entity linking system called OpenTapioca, which was chosen for its use of Wikidata and its accessibility to librarians and archivists with minimal technical intervention. We discuss the results of using OpenTapioca for named entity disambiguation on the Belfer Cylinders Collection from the Special Collections Research Center at Syracuse University, highlighting the successes and limitations of the system and of using Wikidata as a knowledge base.
Maximum Entropy Differential Dynamic Programming

In this paper, we present a novel maximum entropy formulation of the Differential Dynamic Programming algorithm and derive two variants using unimodal and multimodal value functions parameterizations. By combining the maximum entropy Bellman equations with a particular approximation of the cost function, we are able to obtain a new formulation of Differential Dynamic Programming which is able to escape from local minima via exploration with a multimodal policy. To demonstrate the efficacy of the proposed algorithm, we provide experimental results using four systems on tasks that are represented by cost functions with multiple local minima and compare them against vanilla Differential Dynamic Programming. Furthermore, we discuss connections with previous work on the linearly solvable stochastic control framework and its extensions in relation to compositionality. Link to Video.
Impact of 5G Network Performance on Augmented Reality Application QoE

Deployment of 5G mobile communication networks has released the full potential of Internet of Things, enabling emerging interactive technologies such as Augmented Reality and Virtual Reality, as well as their merge. In this paper, we consider the impact of introducing 5G network on the perceptual user experience for an Augmented Reality application – specifically, the educational mobile web-based one as the most promising with this regard. However, it is an extremely demanding application in which various multimedia processing tasks are performed. Therefore, we used appropriate simulations to validate the expectations that 5G network enabling higher data rates and low latency, would significantly improve the application-layer quality of experience, with a mobile web access in particular. These expectations were found to be fulfilled, as, specifically, the required time for the end user to load the tested web-based AR application and required Script, has been drastically reduced with respect to the previous generations of mobile networks.
Emergent Heterogeneous Strategies from Homogeneous Capabilities in Multi-Agent Systems

In multi-agent systems, agents’ abilities are often used to classify a system as either homogeneous or heterogeneous. In the context of multi-agent reinforcement learning (MARL) systems, the agents can also be homogeneous or heterogeneous in their strategies. In this work, we explore instances where agents with homogeneous capabilities must collaborate to achieve a common goal in a predator–prey pursuit task. We show that results from homogeneous and heterogeneous strategies associated with learning differ substantially from agents with fixed strategies that are analytically defined. Given that our agents are homogeneous in capability, here we explore the impact of homogeneous and heterogeneous strategies in a MARL paradigm.
Update the power project knowledge graph of power project using Neo4j Graph Database

The power knowledge graph is able to realize semantic-based power knowledge searching. After the constructing of the current power knowledge graph, the power data in graphs are not often updated. With the increasing of power project data and the change of existing power project progress, it is necessary to update the power project knowledge graph. In order to ensure the effective of the power project knowledge graph, the old power knowledge graph should be updated using the new power data. The method, which is used for updating the current power knowledge graph using Neo4j graph database through Cypher, was proposed in this paper. The proposed power project knowledge graph updating method is not only for per-data, but also effective for batch data. This proposed method can deal with different demand scenarios, expand the knowledge graph of power technology and ensure the accuracy of the content of the knowledge graph.
Student Intrinsic-Motivational Perceptions on a 3D Immersive Learning Environment

This paper introduces an immersive learning platform called Digital Exhibition Space (DES). It utilizes 3D technology to create a communal learning environment, in which student participation and contribution in online activities are positively driven and motivated via explorative, visualized and socialized learning experiences. To investigate the intrinsic-motivational perspectives of student learning experiences with DES, data was collected from semi-structured interviews with 13 students and 36 survey replies of student teaching and learning evaluations. Deductive content analysis, based on the Tripartite Model of Intrinsic Motivation, was carried out to interpret data. Results show that three types of intrinsic motivation, and associated stimulators and consequent behaviours are identified from the interview data, which suggests students perceive being driven by intrinsic motivations when learning with DES and validates the pedagogical design of DES.
Semantic Scene Segmentation for Robotics Applications

Semantic scene segmentation plays a critical role in a wide range of robotics applications, e.g., autonomous navigation. These applications are accompanied by specific computational restrictions, e.g., operation on low-power GPUs, at sufficient speed, and also for high-resolution input. Existing state-of-the-art segmentation models provide evaluation results under different setups and mainly considering high-power GPUs. In this paper, we investigate the behavior of the most successful semantic scene segmentation models, in terms of deployment (inference) speed, under various setups (GPUs, input sizes, etc.) in the context of robotics applications. The target of this work is to provide a comparative study of current state-of-the-art segmentation models so as to select the most compliant with the robotics applications requirements.
Is “periodization programming” periodization or programming?

Within the literature examining the periodization of resistance training, "periodization" and "programming" are often confused and used interchangeably.This has resulted in the drawing of inferences regarding the efficacy of periodization from training studies comparing different programming models over short periods of time.As this conflation has become an area of scientific discussion, what was once referred to as "periodization" is now often referred to as "periodization programming" or "periodized programming."Presumably, the use of the term "periodization programming" acknowledges the shortterm nature of a given research intervention.However, this term has never been explicitly defined in the scientific literature.Furthermore, it is unclear if "periodization programming" is actually a form of periodization.Objectives: To define "periodization programming" and to discuss its use within the literature.Design & Methods: Literature including the terms "periodization programming" and "periodized programming" were reviewed.Results: Deliberate manipulations in the volume and training load over the short-term seem to be defining characteristics of "periodization programming."The resistance training methods employed by "periodization programming" studies share common ground such that they are structured in a manner resembling one stage of a block periodization model and are carried out alongside other stressors.Conclusions: "Periodization programming" studies are similar to previous studies employed to examine the concept of periodization and thus, may more accurately be investigating resistance training at the programming level.Without a formal definition, these studies may add to the confusion within the literature and further challenge the ability to draw inferences surrounding the efficacy of periodization.
<i>Animal Crossing: New Horizons</i> meets “Maslow's pyramid”

After its release in 2020, the video game Animal Crossing: New Horizons (ACNH) has recorded high sales figures and attracted a large number of game players. In order to analyze this success, we rely on Maslow's needs-based theory of motivation and analyze the role of the video game ACNH in satisfying needs. By using an interdisciplinary framework involving both psychology and gaming, we investigate ACNH's role in satisfying real-world needs from the players' perspective and virtual-world needs from avatars' and villagers' perspective. We find that deficiency-motivated needs, with the exception of players' physical needs in the real world, are satisfied in both the real world and the virtual world. Moreover, while growth-motivated needs are satisfied in the real world, such needs of solely virtual elements are partially satisfied. Generally, we highlight the role of the game in satisfying the needs of the real person and of virtual elements while also reflecting the interdependence between the real world and the virtual one.