Reviewerly/exHarmony huggingface dataset:
Exploiting Knowledge Embedded Soft Labels for Image Recognition

Objects from correlated classes usually share highly similar appearance while objects from uncorrelated classes are very different. Most of current image recognition works treat each class independently, which ignores these class correlations and inevitably leads to sub-optimal performance in many cases. Fortunately, object classes inherently form a hierarchy with different levels of abstraction and this hierarchy encodes rich correlations among different classes. In this work, we utilize a soft label vector that encodes the prior knowledge of class correlations as extra regularization to train the image classifiers. Specifically, for each class, instead of simply using a one-hot vector, we assign a high value to its correlated classes and assign small values to those uncorrelated ones, thus generating knowledge embedded soft labels. We conduct experiments on both general and fine-grained image recognition benchmarks and demonstrate its superiority compared with existing methods.
An effective multi-objective hybrid local search method for the optimal web services composition

This paper proposes an effective hybrid local search method for solving the Web service composition problem. We propose to combine the variable neighborhood search (VNS) and the stochastic local search (SLS) in a new multiobjective local search method for solving effectively the web services composition problem. The proposed approach is a quality of service based model including non-functional properties. The aim is to minimize both cost and time and to maximize both availability and reputation. The proposed approach denoted MOVNS-SLS is evaluated on some datasets. The numerical results are encouraging and demonstrate the benefit of the proposed model.
MirrorNet: A Deep Reflective Approach to 2D Pose Estimation for Single-Person Images

This paper proposes a statistical approach to 2D pose estimation from human images. The main problems with the standard supervised approach, which is based on a deep recognition (image-to-pose) model, are that it often yields anatomically implausible poses, and its performance is limited by the amount of paired data. To solve these problems, we propose a semi-supervised method that can make effective use of images with and without pose annotations. Specifically, we formulate a hierarchical generative model of poses and images by integrating a deep generative model of poses from pose features with that of images from poses and image features. We then introduce a deep recognition model that infers poses from images. Given images as observed data, these models can be trained jointly in a hierarchical variational autoencoding (image-to-pose-to-feature-to-pose-to-image) manner. The results of experiments show that the proposed reflective architecture makes estimated poses anatomically plausible, and the pose estimation performance is improved by integrating the recognition and generative models and also by feeding non-annotated images.
PROBLEMS OF THE PROFESSIONAL STATUS OF THE TEACHER

The article analyzes the problems of the professional status of a teacher, that the work of a teacher in the context of global information technologies acquires an incomparably high importance, since the training and education of the younger generation determines the fate of the peoples of the world. The formation and development of the professionalism of teachers are interconnected with the socio-cultural traditions and national characteristics of the people, this ultimately contributes to the improvement of the quality and efficiency of the education system. Being a teacher our time is to engage in creative intellectual work on the introduction of the most advanced teaching methods, skills and abilities of educational work among students, innovative approaches to regulating moods and educational orientations of pupils / students. In pedagogical analyzes of the status of a teacher in the context of the entire educational policy of the government, the authors emphasize the need to improve personnel policy in the higher education system.
You Are (Not) The Robot: Variable Perspective Motion Control of a Social Telepresence Robot

COVID-19 has dramatically limited opportunities for in-person human-robot interaction research and shifted focus towards remote technologies such as telepresence robots. Telepresence robots enable rich communication and agency through their physical presence and controllability, but their screen-oriented designs and button-centric controls abstract users away from their own physicality. In this demonstration, we present a telepresence system for remotely controlling a social robot using a smartphone’s motion sensors. Users can select between a first-person perspective from the robot’s internal camera or a third-person perspective showing the robot’s whole body. Users can also record their movements for later playback. This system has applications as an embodied remote communication platform and for crowdsourcing demonstrations of user-crafted robot movements.
Extraction of Cooking Tips from a Recipe and Application to Another Recipe

There have been posted recipes to user-submitted recipe site, such as "Cookpad" or "Rakuten Recipe" and we can use them freely. Some recipes have tips of cooking and these are useful for better cooking. For example, it is written that if you sprinkle flour on meat, you can make it soft. However, tips of cooking are not always written on all recipes. We propose a method to extract tips of cooking from a recipe and apply them to another recipe. One recipe consists of in ingredients information and procedure of cooking. Tips of cooking is sometimes described in a part of procedure of cooking. We prepare several patterns to extract tips of cooking. In the evaluation of extracted tips of cooking, the accuracy was 79.4% in average. The result of apply tips of cooking to other recipes was 69.7% accuracy in average.
A Salient Object Detection Technique Based on Color Divergence

Nowadays, multimedia and visual computing advances in digital technology make a potential change in human life. Many applications exploit the captured images from autonomous entities as data sources for several goals. In fact, these captured images need to be interpreted in order to extract their external environment. The researchers of this domain will meet some challenges such as how to detect and interpret the images’ context. This paper is to propose an efficient technique that detects objects of a given image based on the color divergence. The results clearly show the accuracy and the computation speed of the proposed technique compared with other methods.
Robots Entering the Care Sector. The Case of a New Curriculum for the Education of Assistant Nurses in Sweden

Robots are entering care work but the development remains slow. One key issue is how to educate healthcare professionals in the use of robots. This paper explores robots in relation to the education of assistant nurses working in care for the elderly. How can we make robots part of the curriculum and what is it that future care workers really need to know? A collaboration between the Royal Institute of Technology, KTH, and 49 teachers at the College of Care and Welfare, VO-COLLEGE, in Sweden that includes teachers, two seminars, two course development meetings and one design workshop aims to explore teachers’ experiences of technology in care for the elderly work and in teaching technological matters. The results show that design workshops seem to be a more useful method to generate suggestions on how robots can be better adapted to care work, compared to seminars that brought up broader, though important, questions about care work and the role of assistant nurses and care receivers. The conclusion is that a greater awareness of technology used in care for the elderly is important to contribute to a relevant adaptation of robots and to be able to meet the challenges of an increased demand for care and care professionals. Increased responsibility for this development conveyed in education can help to broaden the view of technology, from being passive recipients of technology to being active co-creators.
Integrity Measurement Based on TEE Virtualization Architecture

The Trusted Execution Environment (TEE) is a safe area on the processor of all kinds of mobile smart devices. With the continuous development of TEE, attack methods against it have gradually appeared. Existing research has proposed existing attack methods that are solved by the security enhancement solution of the TEEv virtualization architecture, but it still cannot solve the problems that will affect other TEEs after being attacked, and the monitor cannot understand the overall security status of the equipment, which makes it impossible to completely solve the hidden dangers. In order to solve these problems in the TEEv virtualization architecture, a dynamic integrity measurement system is introduced to monitor the operating status of the system in real time, and through experiments, the accuracy of kernel attack detection and the impact of the dynamic measurement system on system performance are obtained. Experimental results show that the introduced dynamic integrity measurement system can effectively detect kernel attacks and feed back to the monitor, making the TEEv virtualization architecture more secure.
[DC] Towards Universal VR Sickness Mitigation Strategies

Although various issues related to VR hardware and virtual content have been identified as likely causes of VR sickness, human factors, such as age, gender and personal susceptibility to VR sickness, determine whether a user will experience VR sickness or how severe their symptoms will be. However, current VR sickness mitigation or measurement techniques do not take these human factors into consideration. This doctoral thesis aims to identify and quantify the human factors that cause difference in susceptibility to VR sickness among different demographic user groups.
Mecanum Wheeled Mobile Platform Odometry Error Analysis and Correction

Autonomous navigation is one of the key development directions of mobile robots. The localization in robotic motion is a fundamental problem. The Mecanum wheeled robot which has strong load capacity, high flexibility and efficiency, is suitable for logistic environments with narrow channels and dense goods such as warehouses and production lines. The wheeled odometry model of the Mecanum wheeled mobile platform and the error source of the odometry are described. Then an industrial-grade Mecanum wheeled mobile platform is built, and a series of longitudinal and lateral linear motion and yaw rotation experiments are performed based on ROS and the odometry values are analyzed. It is found that the main direction component of odometry data is always larger than the actual movement distance of the platform whether it moves longitudinally or laterally, and the error in lateral motion is much larger than in longitudinal motion; in the yaw rotation, the odometry angle displacement is smaller than the actual value; the odometry error accumulates along with the motion distance increment. Taking the AGV trajectory detected by LiDAR as a reference, the odometry correction coefficients in the longitudinal, lateral and yaw rotation of the platform are obtained by the iterative approximation of experimental data, and the effectiveness are verified by experiments.
Nonlinear State-Space Generalizations of Graph Convolutional Neural Networks

Graph convolutional neural networks (GCNNs) learn compositional representations from network data by nesting linear graph convolutions into nonlinearities. In this work, we approach GCNNs from a state-space perspective revealing that the graph convolutional module is a minimalistic linear state-space model, in which the state update matrix is the graph shift operator. We show that this state update may be problematic because it is nonparametric, and depending on the graph spectrum it may explode or vanish. Therefore, the GCNN has to trade its degrees of freedom between extracting features from data and handling these instabilities. To improve such trade-off, we propose a novel family of nodal aggregation rules that aggregate node features within a layer in a nonlinear state-space parametric fashion allowing for a better trade-off. We develop two architectures within this family inspired by the recurrence with and without nodal gating mechanisms. The proposed solutions generalize the GCNN and provide an additional handle to control the state update and learn from the data. Numerical results on source localization and authorship attribution show the superiority of the nonlinear state-space generalization models over the baseline GCNN.
Context for Object Detection via Lightweight Global and Mid-level Representations

We propose an approach for explicitly capturing context in object detection. We model visual and geometric relationships between object regions, but also model the global scene as a first-class participant. In contrast to prior approaches, both the context we rely on, as well as our proposed mechanism for belief propagation over regions, is lightweight. We also experiment with capturing similarities between regions at a semantic level, by modeling class co-occurrence and linguistic similarity between class names. We show that our approach significantly outperforms Faster R-CNN, and performs competitively with a much more costly approach that also models context.
Text Synopsis Generation for Egocentric Videos

Mass utilization of body-worn cameras has led to a huge corpus of available egocentric video. Existing video summarization algorithms can accelerate browsing such videos by selecting (visually) interesting shots from them. Nonetheless, since the system user still has to watch the summary videos, browsing large video databases remain a challenge. Hence, in this work, we propose to generate a textual synopsis, consisting of a few sentences describing the most important events in a long egocentric videos. Users can read the short text to gain insight about the video, and more importantly, efficiently search through the content of a large video database using text queries. Since egocentric videos are long and contain many activities and events, using video-to-text algorithms results in thousands of descriptions, many of which are incorrect. Therefore, we propose a multi-task learning scheme to simultaneously generate descriptions for video segments and summarize the resulting descriptions in an end-to-end fashion. We Input a set of video shots and the network generates a text description for each shot. Next, visual-language content matching unit that is trained with a weakly supervised objective, identifies the correct descriptions. Finally, the last component of our network, called purport network, evaluates the descriptions all together to select the ones containing crucial information. Out of thousands of descriptions generated for the video, a few informative sentences are returned to the user. We validate our framework on the challenging UT Egocentric video dataset, where each video is between 3 to 5 hours long, associated with over 3000 textual descriptions on average. The generated textual summaries, including only 5 percent (or less) of the generated descriptions, are compared to groundtruth summaries in text domain using well-established metrics in natural language processing. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Design and Research of Control System of Gesture Manipulator

A manipulator is a humanoid robot that is used to assist human production and life, and can perform tasks such as grasping and operating tools. The robot has better flexibility and endurance than a human arm, and is suitable for scenes where the same action is repeated. Gesture control, as a human-computer interaction method, has more flexible control. The control system of the gesture control manipulator studied in this paper is divided into upper computer part and lower computer part. The upper computer uses opencv open-source library for gesture recognition, and the lower computer uses ESP32 as the control board. The manipulator uses a steering gear as a power device. The upper computer reads the gesture control instruction through the camera and sends it to the lower computer to drive the servo after processing to realize the gesture control manipulator.
Total Whitening for Online Signature Verification Based on Deep Representation

In deep metric learning targeted at time series, the correlation between feature activations may be easily enlarged through highly nonlinear neural networks, leading to suboptimal embedding effectiveness. An effective solution to this problem is whitening. For example, in online signature verification, whitening can be derived for three individual Gaussian distributions, namely the distributions of local features at all temporal positions 1) for all signatures of all subjects, 2) for all signatures of each particular subject, and 3) for each particular signature of each particular subject. This study proposes a unified method called total whitening that integrates these individual Gaussians. Total whitening rectifies the layout of multiple individual Gaussians to resemble a standard normal distribution, improving the balance between intraclass invariance and interclass discriminative power. Experimental results demonstrate that total whitening achieves state-of-the-art accuracy when tested on online signature verification benchmarks.
A New Trend in Securities Fraud: Punishing People Who Do Bad Things

In the last few years, data privacy issues have taken center stage. The responsibility of corporations to safeguard personal user data has become a mainstream political issue in the wake of the 2016 presidential election. Social media giants have been repeatedly hauled in front of Congress to explain how the consumer data they have been charged with protecting has ended up in the public domain. But absent a regulatory scheme to punish irresponsibility and encourage vigilance, public floggings in various congressional hearings is the only major consequence for this conduct. However, because many of these companies are public, the Securities and Exchange Commission, the Department of Justice, and private shareholders all have used federal securities law as a means of recourse when these companies have attempted to hide data breaches or other misconduct. But, as the author notes, this strategy is not limited to data privacy issues, and although the author devotes most of this article to enforcement actions against Facebook, the parallel to many other industries is obvious. This article seeks to articulate a distinct view of federal securities law as it is increasingly used in non-traditional enforcement actions commenced to punish corporate bad behavior. This paper argues that these non-traditional enforcement mechanisms should be viewed with skepticism. This skepticism should not be misinterpreted as cynicism, as the author believes that these non-traditional enforcement actions are beneficial vehicles to accomplish the admirable governmental objective of “punishing people who do bad things.” However, the author recognizes that such use of securities law does not fall into a category of clearly defined criminal law and carries a significant risk of abuse. The author also recognizes the “admirable governmental objective” may be thwarted when it comes to private companies. Finally, the author is uneasy with the societal values conveyed when the government sanctions corporate misbehavior in the name of protecting shareholders from deception.
Tsunami Evacuation System with Cluster Method And Dijkstra Algorithm Based Mobile Application In Palabuhanratu, West Java, Indonesia

Tsunami occurs quite often in Indonesia. It is inseparable from the geographical facts of Indonesia located on three main tectonic plates. Palabuhanratu is one of the regions that have tsunami potential. Considering these matters, the existence of the best evacuation path to evacuate citizens becomes so essential. This research aims to find the best evacuation path in the district. The best evacuation path is searched using the Clustering Method and the Dijkstra algorithm. In this article, the best evacuation path results are grouped based on evacuation and safe zone locations. Further, a comparison of this research results and another method from reference is in a good agreement.
Dynamic Stacked Ensemble with Entropy based Undersampling for the Detection of Fraudulent Transactions

Fraud detection in finance such as finding fraudulent credit card or debit card transactions is still an open research problem and the solutions are evolving. In a financial transactions database, the number of fraudulent transactions will be very less which creates the class imbalance. Imbalanced data always causes serious challenges to a fraud detection model. Existing solutions such as undersampling and oversampling alleviate class imbalance problem but still they have lot of limitations. For example, oversampling demands significant computational time and undersampling suffer from loss of samples containing critical information related to majority class, in turn leads to poor generalization of the fraud detection model. We developed an entropy-based undersampling with dynamic stacked ensemble model for fraud detection, which we named as EUStack. To achieve undersampling, it evaluates the information content from each sample using Shannon entropy and selects the most informative subset of samples from the majority class. A two-level stacked ensemble is combined with this new undersampling method to improve the generalization performance of the fraud detection model. The credit card transactions dataset by “Worldline and the Machine Learning Group of ULB, hosted at Kaggle” was used to verify the robustness of EUStack. The dataset is highly imbalanced with 492 fraudulent transactions (0.172%) out of 284,807 transactions. EUStack was evaluated using F1 score and Matthews Correlation Coefficient (MCC). Experimental results demonstrate that it achieved high F1 (0.88) and MCC (0.88) scores when compared to the conventional undersampling based fraud detection methods.
Robust and Accurate Method for Textual Information Extraction Over Video Frames

This paper presents a robust and accurate method for textual information extraction over video frames. Text has to be located and successfully discriminated from his background and other scene objects. Extraction is a challenging task due to varieties degradations related to environmental changes, acquisition conditions in addition to characteristics related to video content. Method consists of a three-step process: Slide region detection, text detection, and non-text filtering; text detection use region invariants features extraction based on moment functions to label frame pixels for text/non-text. Experimental results show high accuracy for text/non-text discrimination, and this is mainly related to orthogonal moment functions expressivity and invariants properties.