Reviewerly/exHarmony huggingface dataset:
Label Representations in Modeling Classification as Text Generation.

Several recent state-of-the-art transfer learning methods model classification tasks as text generation, where labels are represented as strings for the model to generate. We investigate the effect that the choice of strings used to represent labels has on how effectively the model learns the task. For four standard text classification tasks, we design a diverse set of possible string representations for labels, ranging from canonical label definitions to random strings. We experiment with T5 on these tasks, varying the label representations as well as the amount of training data. We find that, in the low data setting, label representation impacts task performance on some tasks, with task-related labels being most effective, but fails to have an impact on others. In the full data setting, our results are largely negative: Different label representations do not affect overall task performance.
Sparse additive machine with pinball loss

Sparse additive models have shown promising performance for classification and variable selection in high-dimensional data analysis. However, existing methods are limited to the error metric associated with hinge loss, which are sensitive to noise around the decision boundary. In this paper, we propose a new sparse additive machine with the pinball loss, called as pin-SAM, to make the model more robust to noise around the decision boundary. Theoretical analysis on the excess misclassification error is established by integrating error decomposition and concentration estimation techniques, which shows our pin-SAM can achieve the fast learning rate under appropriate parameter conditions. The empirical studies confirm the effectiveness of the proposed approach on simulated, benchmark and coronal mass ejection data.
An Algorithm for Skew Angle Estimation and It’s Application Domain

Angle detection and estimation is important in various fields like line following mobile robots, document analysis, construction sites etc. There are several methods and algorithms proposed earlier to do that. This paper represents a new algorithm to estimate skew angle and its possible applications in different fields. In this algorithm, angle in the preprocessed black-white image is estimated using matrix traversal and slope equation from Euclidean geometry. This algorithm can measure the angle between −90 $$^{\circ }$$ and +90 $$^{\circ }$$ efficiently.
Circumstances and technology of implementation of the model of metadisciplinary methodical system on the problems of maintenance of language culture in public educational institutions

This article describes the model of metadisciplinary methodical system on the problems of maintenance of language culture, include speech culture, reading culture, and culture of communication on the Russian language. Such model serves as the foundation for reorganization of education for the purpose of increasing language culture of the students based in the use of metadisciplinary methodical system that allows translating the necessary content not as a set of information for memorization, but as a set of knowledge for sensible usage. The author underlines the need for creating a model based on the principle that reflect the mechanisms of maintenance of language culture of students at the stage of public education, as well as describes the technology and conditions for implementation of the model. This model is development in terms of the project Joint Venture "Concord" LLC supported by the Ministry of Education of the Russian Federation. The scientific novelty consists in structural-algorithmic representation of the sequence, nature, and specificity of the model of metadisciplinary methodical system on the problems of maintenance of language culture of the students. The main conclusion consists in the observation that the creation of such model would improve the quality of public education overall, and with regards to formation of language culture of the students in particular.
Solution for Corresponding Problem of Stereovision By Using AKAZE features

Stereo camera can measure the distance to the object using parallax information between left and right images. In order to obtain parallax information, it is necessary to correlate feature points between left and right images. However, there are many problems with mapping. In this paper, we proposes an algorithm carrying out matching using AKAZE features. The feature points of the image are extracted using the AKAZE features, and matching is performed. Some constraints ared used in the matching to improve accuracy. Distance measurement was performed from the coordinates of feature points obtained to get correspondence between left and right images on the AKAZE features.
A Center-to-Edge Progression for Equirectangular Projected 360° JPEG Images

Imaging devices capable of capturing 360° images were recently released into the market. The spherical images acquired by imaging devices, such as 360° cameras and multiple camera smartphones, are projected onto the 2D image plane to allow the use of the existing image coding standard. The 360° image market is currently dominated by the equirectangular projection format coded by the JPEG image coding standard. Spherical images are characterized by their large size, and they are typically viewed by displaying a default viewport of the entire image first, followed by navigation by the user. Therefore, progressive image coding capable of transmitting the default viewport first is in high demand. However, the progressive image coding methods proposed thus far, such as quality-based and resolution-based progressive coding, do not support the spatial region-based progressiveness necessary to first display the default viewport. This paper proposes a center-to-edge progressive mode for the JPEG image coding standard, which enables transmittance of the discrete cosine transform blocks including the default viewport first by reordering the blocks' scanning path. This progressive mode was designed to maintain all other encoding structures of the JPEG standard, such that use of the already established toolchain of the JPEG is maintained. Results of the test performed with our proposed mode using eleven 360° images in the equirectangular projection format and six objective image quality parameters show that transmission of default viewports is notably faster than existing JPEG modes and negligible effects on JPEG image coding efficiency.
Rendering videos from HTML elements and web animations

This paper explores the concept of web animations with the motive to generate exportable video files using a browser. We explain the process underlying animations such as tweening and controlling individual states of an object's animation cycle. We can composite many animations to follow a single timeline and capture individual frames at periodic intervals. Such frames, when stitched together, form a video. We will also explain the integration of an audio layer than runs in synchronization with the animations for better perception.
A Weighted Frequent Itemsets Mining Algorithm for Intelligent Decision in Smart Systems

The key innovation is the brilliant buyer guidance mining frameworks in data innovation which plays an undeniably a significant job in the activities and dynamics. Likewise normal mining is a significant advance research property of affiliation rules. It is one of the most significant zones of research in information mining. Database to learn by visit employments in issues of uncertainty, the weighted are so liable to be significant, and the components into regularly discovered extraordinary itemsets clients. In any case, the presentation of weight makes visit itemsets may fall back to the walled in area to be an issue longer. Consequently, every now and again utilized things out there, not to look through they are what are alluded to the poor proficiency. Said weight preliminary recurrence bolted to its own gravity and thing set sit is a genuine measure of material presented and tried. (possess weight and the finish of the preliminary Evaluation of the calculation is set before us by visit, rehashed the space of the substance of the successive itemsets, and improves the obstacle weighted with stones then proficiency. We proposed the elective strategy for E-FWARM (ENHANCED FUZZY-BASED WEIGHTED ASSOCIATION RULE MINING ALGORITHM) calculation yields greatest continuous things, affiliation rules, exactness and least execution time.
Joint Optimization of the Number of Clusters and Their Parameters in Acoustic Emission Clustering

Acoustic Emission (AE) is a non-destructive technique suitable for monitoring structural damages in complex systems. Interpretation of AE data is generally made by an unsupervised learning approach called clustering. A clustering method divides a dataset into different groups called clusters, which are expected to have a physical interpretation in terms of damages. The set of groups, called partition, is then evaluated through an external criterion. Since the number of clusters is a priori unknown, the standard approach for AE clustering consists in running a clustering method for different number of clusters and then to select the partition with the best value of the external criterion. The external criterion can vary across publications and aims at evidencing “natural” clusters. According to the criterion, the selected partition and its interpretation can be subjected to variability. In this publication, we explore a clustering method in which the parameters of clusters are optimised jointly with the number of clusters through an objective function and an optimisation procedure designed for that purpose. To our knowledge, it is the first method of this class in AE literature. Experimental results concern real data from tightening test.
Format Matters! And People do, too! Tools and Insights from an Innovation Network

Communication is one important key to successful innovation culture within companies. We developed three lightweight formats to support innovation communication – future personas, describing the needs of future customers, anchorvideos, for giving technological trends “a face” and anchoring them within a company, and the InnoDeck for efficiently spreading innovative methods and inspiration in practice. In this work, we extend previous work on the three topics by describing how the three artifacts work together and can be combined within a company or in innovation networks. We show this in three case studies. Two describing how companies use the artifacts internally. A third case study from an innovation network shows concrete visualization of the combination of the three concepts. Additionally, we provide new evaluation results for the innovation communication within the network based on a questionnaire. Concluding, we illustrate how we will extend and interleave the concepts for supporting communities of practice in the future.
Common-Knowledge-Based Pragmatics

Suppose a speaker S and an audience A are in a communication coordination problem. That is, for some proposition p, they each prefer that S mean that p and that A believe p in response. How do they coordinate their thought and action to solve the problem? The Gricean answer is that they reason their way to the solution. Pragmatics makes a similar assumption. “Pragmatics involves perception augmented by some species of ‘ampliative’ inference … a sort of reasoning” (Kepa Korta and John Perry, “Pragmatics”). There are two objections. The first is that it is not plausible to attribute such reasoning to speakers and audiences. The second objection grants, for the sake of argument, that speakers and audiences reason in the required ways. The objection is that this is not sufficient to solve coordination problems since a speaker and an audience may not know how each has reasoned. The problem is well-known in game theory, which typically solves it by assuming the parties’ preferences are common knowledge. Common knowledge is the recursive belief state in which people are know something, know they know it, know they know they know it, ad infinitum. I propose a similar solution. When people interact as speaker and audience, common knowledge arises from their perceptions of each other as fulfilling the roles of speaker and audience. The account of how common knowledge arises entails Grice’s general characterization of conversational implicatures. More precisely, it entails speakers and audiences involved in communication coordination problems typically commonly know that a relevant instance of the conditions holds. The common knowledge approach requires attributing reasoning to speakers and audiences, but it keeps those attributions to a plausible minimum. Thus the common knowledge approach is a viable alternative to current approaches that attribute extensive reasoning to speakers and audiences.A common knowledge-based approach to pragmatics would answer at least the following question: What informative generalizations are possible about the role of contexts in generating common knowledge relevant to conversational implicatures? The question is important. “Much has been learned about these domains of psychology from a focus on the problem of altruistic cooperation and the mechanisms of reciprocity. We hope that comparable insights are waiting to be discovered by psychologists as they investigate the problem of mutualistic cooperation, and the mechanisms of common knowledge are—as we might say—put out there” (Thomas et al., “The Psychology of Coordination and Common Knowledge”).
SurfaceNet: A Surface Focused Network for Pedestrian Detection and Segmentation in 3D Point Clouds

Pedestrian detection is an important problem for autonomous driving. It is still chanllenging to detect and segment pedestrians from point clouds. In this paper, we propose a method named SurfaceNet to detect and segment pedestrians from point clouds. Specifically, we propose a novel representation, named surface map, to represent a point cloud as a 2D pseudo-image. For pedestrian detection, the proposed method comprises of four modules: 1) a grid feature encoder that can processes arbitrary number of points within each grid; 2) a surface feature convolutional module that employs a set of 2D convolutional layers to extract high level features; 3) a view transform module that transforms features from front view to bird's eye view; and 4) an anchor-free 3D object detection head that produces rotated 3D bounding box predictions. For semantic segmentation, the 2D pseudo-image is used for semantic segmentation and the segmentation results are re-projected to the original point cloud to achieve point cloud segmentation. Experimental results on the KITTI dataset show that our method achieves promising performance on pedestrian detection and segmentation in point clouds.
Organisational Cyber Resilience and its Influence on Cyber Attack Outcomes: An Exploratory Study of 1,145 Publicised Attacks

Evidence shows that it is paramount for stakeholders to understand the cybersecurity of relevant organizations. However, the secrecy surrounding cyber attacks and how organizations manage their cyber resilience make it impossible for stakeholders to develop this understanding. This paper analyses organizational cyber resilience, its contextual factors and its impact on the outcomes of cyber attacks based on publicly available data. Using the PRISMA methodology, we collated and analyzed a dataset of 1,145 publicly-known cyber attacks. We conceptualize and operationalize cyber resilience from a governance perspective. Our findings indicate that organizations that suffered cyber attacks had the following cyber resilience characteristics: a relatively low level of cyber resilience reflected in the low frequency of cybersecurity roles, low reliance on cybersecurity frameworks, and relatively low strength of prevention, detection, and recovery controls. Cyber resilience is found to be associated with the sector, size, and digital intensity. Linear regression indicates that, expectedly, stronger prevention, detection and recovery processes are related to lower breach severity and occurrence of investigations or penalties, but contrary to expectations, cybersecurity roles and frameworks are not. Furthermore, better organizational responses are associated with higher breach severity but they are not found to have an impact on the level of investigations, fines and penalties imposed. We discuss our findings and their implications for cyber resilience regulation, future research and sector cooperation.
ConstBin: A Tool for Automatic Fixing of Unreproducible Builds

Compiling a software package multiple times may result in different artifacts. This problem is called unreproducible builds, which affect the credibility of the software. Researchers mainly focus on how to automatically locate the causes for unre-producible builds. However, automatic fixing is rarely studied. In this paper, we propose ConstBin, an automated tool that helps to generate consistent artifacts. It captures unreproducible commands in a build process and automatically replace them with their fixing operations based on an extensible rules set. As far as we know, ConstBin is the first tool that fixes inconsistencies during build processes. We have evaluated this tool on 348 open source packages whose original builds are unreproducible. The results of the evaluation show that 83.91% of these builds have successfully generated consistent artifacts with the application of ConstBin.
Geometric Path Following of Underwater Vehicles

This paper addresses the problem of guidance and control of underwater vehicles. The overall control architecture is broken into three different functions: path generation, path following, and vehicle autopilot. The path-generation function creates a 3D path based on mission objectives specified by an operator. A point along the path is tracked by the path-following algorithm, which passes pitch and yaw rate commands to the autopilot. The latter adjusts the vehicle's control surfaces to track the angular rate commands. The introduction of a variable called virtual time allows the target point to be adjusted along the path according to a specified control law. The framework for the path-following algorithm is built upon the Special Orthogonal Group SO(3) with several reference frames relating the vehicle, the virtual target, and the global (inertial) coordinate system. A critical component of the path-following algorithm is the construction of a desired reference frame that specifies the ideal orientation of the vehicle. The desired frame features an adjustable parameter that affects the aggressiveness of the controller, offering the ability to easily adjust its performance. The paper provides guaranteed performance bounds of the path-following algorithm and considers actuation limits on the vehicle's angular rates, as well as limited performance of the on-board autopilot.
Similarity Analysis of Legal Documents: A Survey

A non-trivial task in the legal domain is grouping together similar legal case documents. This becomes particularly necessary in judiciaries following a Common Law System, as the judgements of previous similar cases prominently affect the outcome of a present case. A common approach of analysing the similarity of documents is by considering the documents as a whole. But, because legal case documents are extremely verbose, it may be beneficial to analyse the similarities of only the important concepts, or the summary, of the documents at hand. The primary focus of this survey is to explore the different methods used to analyse the similarity between legal case documents. This paper categorizes these efforts into citation-based, content-based or summary-based methods. A thorough survey of the existing methods of summarizing legal documents has also been explored.
Development of engineering educational support system for manufacturing using Augmented Reality

In recent years, technologies such as AR and VR have rapidly evolved and become widely used. One of its most important areas of use is the use of VR and AR in education. By visualizing invisible phenomena, AR may be more likely to promote students' understanding than conventional methods. Therefore, in this report, we newly develop a lesson support system using AR in engineering manufacturing. The reason for this is that in engineering manufacturing, a large amount of data can be managed collectively by associating the data saved in the e-portfolio with the AR. In addition, when working as an engineer, the work and the learning records such as memos are stored separately, so there is a problem that they will be forgotten or lost during the next week's class. Therefore, by developing an AR education support system, by putting digital data in the work, it is possible to unify management and solve the problem. Moreover, the burden on the teacher can be reduced, and the contribution to the field of education can be further enhanced. The effectiveness of the developed AR education support system will be verified by conducting an evaluation experiment.
Trajectory planning for dual arm robot system in consideration of the system controller

The paper has developed optimal trajectory planning for the object manipulated by two robot arms collaborate with each other. The object's trajectory that includes motions along X and Y axes, and object rotation are chosen to be fifth-order polynomial type. ln order to determine the polynomial coefficients, Genetic Algorithm (GA) based optimization is done with robot kinematic and dynamic constraints are considered. Difference from other previous research that use inverse dynamics to calculate input torque, in this paper, the input torque is calculated as controller output that is the same as practical robot system. Finally, simulation works on Matlab-Simulink have been carried out to reconfirm the accuracy and the effectiveness of the designed trajectory.
Hand Recognition System Based on Invariant Moments Features

Invariant moments are most used in the pattern recognition system because it has inclusive properties that do not change in an image scaling, rotates, and translates. For hand shape recognition, these moments are normalized, so the size of that moments is invariant. This work uses an image feature extraction with invariant moments in which it uses invariant moments as the shape feature vectors in the hand recognition system that includes two phases: the first is the features extraction phase based on invariant moments and some preprocessing techniques used together for extracting the hand shape feature; the second phase for recognition of the hand palm image. It is compared between the shape features for entered test hand images with the hand’s shape features which are stored in the shape hand features database. The comparison is computed by suggested rate function. We recorded false acceptance rate, false rejection rate, and accuracy, where the accuracy of the system is 89%.
MRC Examples Answerable by BERT without a Question Are Less Effective in MRC Model Training.

Models developed for Machine Reading Comprehension (MRC) are asked to predict an answer from a question and its related context. However, there exist cases that can be correctly answered by an MRC model using BERT, where only the context is provided without including the question. In this paper, these types of examples are referred to as “easy to answer”, while others are as “hard to answer”, i.e., unanswerable by an MRC model using BERT without being provided the question. Based on classifying examples as answerable or unanswerable by BERT without the given question, we propose a method based on BERT that splits the training examples from the MRC dataset SQuAD1.1 into those that are “easy to answer” or “hard to answer”. Experimental evaluation from a comparison of two models, one trained only with “easy to answer” examples and the other with “hard to answer” examples demonstrates that the latter outperforms the former.