Reviewerly/exHarmony huggingface dataset:
THE CONTEXT IMPORTANCE IN THE PROBLEM OF COOPERATIVE MANEUVERING OF UNMANNED SHIPS

The problem of collision avoidance of a group of ships offshore is investigated in the paper. To solve this problem it is necessary to ensure the cooperative maneuvering of several vessels from the group. The case of unmanned (autonomous) ships, when each vessel is operated automatically, is considered in the paper. The main attention is paid to the problem of equally understanding of the current navigation situation conditions by the various agents, jointly solving the problem of cooperative maneuvering in a given water area. To solve the problem of cooperative maneuvering, all agents operating unmanned ships must equally interpret the current navigational situation. Factors defining the current navigation situation are divided into two groups: the main factors necessary to solve the problem of collision avoidance under ideal conditions and additional factors that must be additionally taken into account when solving the problem of collision avoidance in the real navigation environment. Failure to take into account the additional factors can lead to inadequate decisions on the vessel management. The concept of context is introduced as a set of interpretations of the additional factors values. The importance of taking these factors into account when solving the problem of collision avoidance is shown. A list of factors, as well as an example of these factors interpretation for inclusion in the context, is given. Using the same context by all agents will allow them to equally interpret the current navigational situation in the navigation area and jointly solve the problem of cooperative maneuvering. The main properties of the context, such as universality and compactness, are defined. In the protocol of agents interaction, it is necessary to provide the special means for exchange and matching of the contexts between the agents operating unmanned ships.
Learning-Based Dark and Blurred Underwater Image Restoration

Underwater image processing is a difficult subtopic in the field of computer vision due to the complex underwater environment. Since the light is absorbed and scattered, underwater images have many distortions such as underexposure, blurriness, and color cast. The poor quality hinders subsequent processing such as image classification, object detection, or segmentation. In this paper, we propose a method to collect underwater image pairs by placing two tanks in front of the camera. Due to the high-quality training data, the proposed restoration algorithm based on deep learning achieves inspiring results for underwater images taken in a low-light environment. The proposed method solves two of the most challenging problems for underwater image: darkness and fuzziness. The experimental results show that the proposed method surpasses most other methods.
Impact of Geo-Distribution and Mining Pools on Blockchains: A Study of Ethereum - Practical Experience Report and Ongoing PhD Work

Given the large adoption and economical impact of permissionless blockchains, the complexity of the underlying systems and the adversarial environment in which they operate, it is fundamental to properly study and understand the emergent behavior and properties of these systems. We describe our experience on a detailed, one-month study of the Ethereum network from several geographically dispersed observation points. We leverage multiple geographic vantage points to assess the key pillars of Ethereum, namely geographical dispersion, network efficiency, blockchain efficiency and security, and the impact of mining pools. Among other new findings, we identify previously undocumented forms of selfish behavior and show that the prevalence of powerful mining pools exacerbates the geographical impact on block propagation delays. Furthermore, we provide a set of open measurement and processing tools, as well as the data set of the collected measurements, in order to promote further research on understanding permissionless blockchains.
Agile Team Work Quality in the Context of Agile Transformations – A Case Study in Large-Scaling Environments

The maturity of organizations is measured with process assessment models like the ISO/IEC 33001. The product quality is aligned with internal and external product quality characteristics based on models like the ISO/IEC 25010. With the shift from the Tailorism-driven process orientation to a more people centric organization, the two dimensions process and product quality have to be extended by the people or team quality dimension. The presented approach offers aspects for agile Team Work Quality (aTWQ), as well as related measurement indicators. The approach is evaluated in the large enterprise context of the Volkswagen AG. The indicators of aTWQ have been integrated and established in the agile project review for a sustainable agile transition of the company’s Group IT.
The Proposal on Electronic Evidence in the European Union

The news contain Internet links referring to more detailed information
Towards a Generative Theory of “Bugs”

During the past few years our group has been working on computer systems for diagnosing systematic student errors. These diagnostic systems, BUGGY and more recently DEBUGGY, have been used to analyze thousands of students (Brown & Burton, 1978; Burton, 1981) and have enabled us to construct an extensive catalogue of precisely defined bugs for place-value subtraction. These procedural definitions constitute an extensional, ad hoc theory of bugs. The theory is extensional in that it explains a student’s errors and misconceptions as a combination of some set of prespecified, primitive bugs. It is ad hoc (by design!) in that the language we used to describe the bugs was as open-ended and as powerful as we could find. Although we could have started out using a language restricted by psychological principles, we chose to use a powerful descriptive technique that facilitated our ability to express and experiment with any new bug that was conjectured to exist in the data.
A Hierarchical Attention Recommender System Based on Cross-Domain Social Networks

Search engines and recommendation systems are an essential means of solving information overload, and recommendation algorithms are the core of recommendation systems. Recently, the recommendation algorithm of graph neural network based on social network has greatly improved the quality of the recommendation system. However, these methods paid far too little attention to the heterogeneity of social networks. Indeed, ignoring the heterogeneity of connections between users and interactions between users and items may seriously affect user representation. In this paper, we propose a hierarchical attention recommendation system (HA-RS) based on mask social network, combining social network information and user behavior information, which improves not only the accuracy of recommendation but also the flexibility of the network. First, learning the node representation in the item domain through the proposed Context-NE model and then the feature information of neighbor nodes in social domain is aggregated through the hierarchical attention network. It can fuse the information in the heterogeneous network (social domain and item domain) through the above two steps. We propose the mask mechanism to solve the cold-start issues for users and items by randomly masking some nodes in the item domain and in the social domain during the training process. Comprehensive experiments on four real-world datasets show the effectiveness of the proposed method.
Robotic Arm Controlling Based on Fingers and Hand Gesture

Nowadays, robots are a rapidly developing field. A robot is a mechanical device that can perform physical tasks, using human supervision and control. Several robots have been built to do dangerous work that is impossible done by humans directly. One type of them that is very popular is the robot arm. In this paper, it is proposed controlling robot arms based on finger and hand movements. This research is aimed at describing simple robot arms that must be controlled by human fingers and human hand movements. We have designed a robot arm with a Fuzzy logic method consisting of flexible sensors on the fingers, and human hand movements to control the robot arm's movement system. The movement of the robot arm is controlled by using a flexible sensor, the Arduino microcontroller, which is connected to several servo motors. The control angle of the robot arm, controlled using human hand movements and gyroscopes. All input and output processes on sensors, hand movements, and microcontrollers used are processed by fuzzy logic. From the experimental result, it was found that Fuzzy Logic was successfully implemented on the robot arm to control objects using Flex sensor, and hand gestures.
Conditional Domain Adversarial Adaptation for Heterogeneous Defect Prediction

Heterogeneous defect prediction (HDP) has become a very active research field in software engineering, which predicts the maximum number of bug-suspiciousness modules of a target project by prediction models built on source project with heterogeneous metric set. At present, some researchers have proposed some HDP models with a promising performance. Most of existing HDP models adopted unsupervised transfer learning to map source project and target project into the same feature space, which only considered the metrics space, not the label information from source project and few part of target project. Meanwhile, the predictive ability of these HDP models in effort-aware context have not been compared. Therefore, we set up to investigate the effectiveness of label information on HDP, and to propose a HDP model for improving the predicting performance in classification and effort-aware contexts. In order to use these label information, we propose a novel conditional domain adversarial adaptation (CDAA) approach to tackle heterogeneous problem in SDP, which is motivated by generative adversarial networks (GANs). There are three networks in architecture of our CDAA, including one generator, one discriminator and one classifier. The generator learns how to transfer source instance space to target instance space. The discriminator learns how to identify the fake instances generated by generator. The classifier learns how to correctly classify the label of instances. In our CDAA, the loss function of classifier and discriminator are both back propagate to generator. Then, to ensure a fair comparison between state-of-the art methods and CDAA, we take AUC, MCC and $P_{opt}$ as measures to evaluate 28 open-source projects. Experimental results demonstrate that CDAA method could take advantage of label information to effectively map source project to target project and improve the predictive performance. Also, experimental results demonstrate that our CDAA method is not affected by the number of same metrics between source project and target project.
Pemanfaatan E-Learning Pada Pembelajaran Jarak Jauh di Masa Pandemi Covid-19

This research is a qualitative descriptive study that examines the use of e-learning in accounting students in the morning 8A accounting study program in the distance learning conditions due to the impact of the co-19 pandemic. Data collection uses data reduction steps, data display and conclusion making using interview techniques through telephone connections and deepening through WhatsApp chat. The results obtained are the level of respondents' understanding of the use of e-learning as a distance learning media as a whole at a rate of 70%, the level of willingness to continue the use of e-learning as a whole is at 25% while the level of need for the presence of lecturers in real is as much as 100%
Virtual and Real-Time Synchronous Interaction for Playing Table Tennis with Holograms in Mixed Reality

Real-time and accurate interaction technology is required to realize new wearable Mixed Reality (MR) solutions. At present, the mainstream interaction method relies on gesture detection technology, which has two shortcomings: 1. the hand feature points may easily be obstructed by obstacles and cannot be detected and 2. the kinds of gesture that can be recognized are limited. Hence, it cannot support complex interactions well. Moreover, the traditional collision detection algorithm has difficulty detecting the collision between real and virtual objects under motion. Because location information of real objects needs updating in real time, it is easy to lose collision detection under high speeds. In the implementation of our system, Mixed Reality Table Tennis System, we propose novel methods which overcome these shortcomings. Instead of using gesture detection technology, we use a locator as the main input device and build a data exchange channel for the devices, so that the system can update the motion state of the racket in real time. Besides, we adjust the thickness of the collider dynamically to solve the collision detection problem and calculate rebound results responding to the motion state of the racket and the ball. Experimental results show that our method avoids losing collision detection and improves the authenticity of simulation. It keeps good interaction in real time.
Fractal Analysis Method for the Complexity of Cell Cluster Staining on Breast FNAB

&lt;b&gt;&lt;i&gt;Objective:&lt;/i&gt;&lt;/b&gt; Because of the increased precision of ultrasound breast cancer screening, early cancer cases with no clear mass or extraction of microcysts on imaging have recently increased, and improvement of the accuracy of breast fine-needle aspiration biopsy (FNAB) cytology is needed. The objective of this study was to investigate the usefulness of cluster gray image-fractal analysis evaluating the darkness of clusters, cluster unevenness, and complexity of hyperchromicity (cluster density) of deep-stained cell clusters, known as hyperchromatic crowded cell groups (HCG), on FNAB as a cytology assistance system for breast FNAB. &lt;b&gt;&lt;i&gt;Study Design:&lt;/i&gt;&lt;/b&gt; One hundred clusters collected from 10 patients with fibroadenoma (FA), 90 clusters from 9 patients with ductal carcinoma in situ (DCIS), and 122 clusters from 11 patients with invasive breast carcinoma of no special type (IBC-NST) were used. (1) Cluster size classification: clusters were classified into small, middle, and large clusters (small cluster: smaller than 40 × 10&lt;sup&gt;2&lt;/sup&gt; μm&lt;sup&gt;2&lt;/sup&gt;; large cluster: 100 × 10&lt;sup&gt;2&lt;/sup&gt; μm&lt;sup&gt;2&lt;/sup&gt; or larger; middle cluster: intermediate), and their frequency was calculated. (2) Cluster gray image-fractal analysis: (a) the darkness of clusters (luminance), (b) cluster unevenness (complexity), and (c) complexity of cluster density (roundness-corrected fractal value) were assessed. For statistical analysis, the multiple comparison Steel-Dwass test was used, with a significance level of &lt;i&gt;p&lt;/i&gt; &amp;#x3c; 0.05. &lt;b&gt;&lt;i&gt;Results:&lt;/i&gt;&lt;/b&gt; (1) Cluster size classification: in FA, small, middle, and large clusters appeared at a similar frequency, and the frequency (30%) of large clusters was significantly higher than that in other diseases. In IBC-NST, many small clusters (61%) appeared and their frequency was significantly higher than that in other diseases, whereas the frequency of large clusters was significantly lower. (2) Cluster gray image-fractal analysis: in IBC-NST, the luminance of small clusters was low (dark), the cluster unevenness was high, and the complexity of cluster density was high, whereas the luminance of large clusters was high (bright), the cluster unevenness was high, and complexity of cluster density was high compared with those in FA. &lt;b&gt;&lt;i&gt;Conclusion:&lt;/i&gt;&lt;/b&gt; Cluster gray image-fractal analysis evaluating the darkness of clusters, cluster unevenness, and complexity of cluster density in breast FNAB HCG is a useful cytology assistance system for breast FNA.
Efficient Ego-Motion Estimation for Multi-Camera Systems With Decoupled Rotation and Translation

In this article, we present novel solutions to estimate the ego-motion of a multi-camera system with a known vertical direction (e.g., from the inertial measurement unit). By assuming small camera motion between successive video frames, we demonstrate that rotation and translation estimation can be decoupled. This makes our methods require fewer correspondences to estimate the ego-motion and have a good accuracy. Accordingly, we estimate the ego-motion with two steps. First, we propose a 1-point method to estimate rotation with only a single correspondence which produces up to two solutions. Then, we adopt a 3-point linear method and a 2-point sampling method to solve translation which produce a single solution. We compared our algorithms with state-of-the-art algorithms on synthetic and real datasets. The experiments demonstrate that our algorithms are accurate and efficient in road driving scenarios. We also demonstrate that our proposed methods can efficiently find an optimal inlier set using histogram voting or exhaustive search instead of RANSAC.
Comprehensive Anonymity Trilemma: User Coordination is not enough

Abstract For anonymous communication networks (ACNs), Das et al. recently confirmed a long-suspected trilemma result that ACNs cannot achieve strong anonymity, low latency overhead and low bandwidth overhead at the same time. Our paper emanates from the careful observation that their analysis does not include a relevant class of ACNs with what we call user coordination where users proactively work together towards improving their anonymity. We show that such protocols can achieve better anonymity than predicted by the above trilemma result. As the main contribution, we present a stronger impossibility result that includes all ACNs we are aware of. Along with our formal analysis, we provide intuitive interpretations and lessons learned. Finally, we demonstrate qualitatively stricter requirements for the Anytrust assumption (all but one protocol party is compromised) prevalent across ACNs.
Densely connected GCN model for motion prediction

Abstract Human motion prediction is a fundamental problem in understanding human natural movements. This task is very challenging due to the complex human body constraints and diversity of action types. Due to the human body being a natural graph, graph convolutional network (GCN)‐based models perform better than the traditional recurrent neural network (RNN)‐based models on modeling the natural spatial and temporal dependencies lying in the motion data. In this paper, we develop the GCN‐based models further by adding densely connected links to increase their feature utilizations and address oversmoothing problem. More specifically, the GCN block is used to learn the spatial relationships between the nodes and each feature map of the GCN block propagates directly to every following block as input rather than residual linked. In this way, the spatial dependency of human motion data is exploited more sufficiently and the features of different level of scale are fused more efficiently. Extensive experiments demonstrate our model achieving the state‐of‐the‐art results on CMU dataset.
Correct Me If You Can: Learning from Error Corrections and Markings

Sequence-to-sequence learning involves a trade-off between signal strength and annotation cost of training data. For example, machine translation data range from costly expert-generated translations that enable supervised learning, to weak quality-judgment feedback that facilitate reinforcement learning. We present the first user study on annotation cost and machine learnability for the less popular annotation mode of error markings. We show that error markings for translations of TED talks from English to German allow precise credit assignment while requiring significantly less human effort than correcting/post-editing, and that error-marked data can be used successfully to fine-tune neural machine translation models.
A Survey for Comparative Analysis of various Cryptographic Algorithms used to Secure Data on Cloud

Cloud computing is the recent trend for the growth in IT industry.We are able to store any amount of data on cloud today whether it is text, image, audio, and video and many more.Storing data on cloud is easy but it is very important that the data which we store on store is also secure.Many cryptographic algorithms have been implemented to maintain the privacy of data over cloud.In this paper, we will make a comparative analysis of various cryptographic algorithms used over cloud to secure data.This analysis will be made using various performance metrics.
Educational modules and research surveys on critical cybersecurity topics

Cybersecurity comprised all the technologies and practices that protect data as well as computer and network systems. In this article, we develop four course modules on critical cybersecurity topics that can be adopted in college-level cybersecurity courses in which these topics are covered. Our goal for developing these course modules with the hands-on labs is to increase students’ understanding and hands-on experiences on these critical topics that support cyber skills development for college students. The hands-on labs are designed to enhance students’ engagement and provide them hands-on experiences with real-world cyber activities to augment their cyber education of both foundational and advanced skills. We also conduct research surveys on the most-recent significant research in these critical cybersecurity fields. These cybersecurity course modules with the labs are also designed to help college/university professors enhance and update their cybersecurity course content, activities, hands-on lab exercises, and pedagogical methods, as well as emphasize the cyber skills to meet today’s pressing cybersecurity education needs for college students. Our proposed cybersecurity modules with hands-on labs will also help building the nation’s cybersecurity workforce.
Detection and tracking of moving object using modified background subtraction and Kalman filter

Moving object detection and tracking (MODT) is the major challenging issue in computer vision, which plays a vital role in many applications like robotics, surveillance, navigation systems, militaries, environmental monitoring etc. There are several existing techniques, which has been used to detect and track the moving object in Surveillance system. Therefore it is necessary to develop new algorithm or modified algorithm which is robust to work in both day and night time. In this paper, modified BGS technique is proposed. The video is first converted to number of frames, then these frame are applied to modified background subtraction technique with adaptive threshold which gives detected object. Kalman filter technique is used for tracking the detected object. The experimental results shows this proposed method can efficiently and correctly detect and track the moving objects with less processing time which is compared with existing techniques.