Reviewerly/exHarmony huggingface dataset:
SpacePhaser: Phase Space Embedding Visual Analytics

In the paper, we focus on the statistical detection and understanding of signaling behavior via various media outlets that are associated with a violent hate crime. Following a body of theory that identifies the links between hate rhetoric with violence, we explore textual patterns of hate speech in traditional and non-traditional media outlets, including television news shows, social media, political blogs, and political forums, to detect an association with hate crime events per time and place. Applying the dynamical system theory, we can characterize the dynamic behaviors of essential keywords. Grouping keyword with similar temporal signatures enables us to highlight important information/topics corresponding to emerging events. We also introduce SpacePhaser, an interactive visual tool for textual pattern analysis. To show the usefulness of SpacePhaser, we demonstrate the results of its applications on various real-world datasets.
Reading door

With a growing research interest in experiential aspects of HCI, this study is aimed to explore how embodied thinking can guide designing meaningful interaction in the context of ambient media. Based on an interactive prototype Reading Door, experience tests are conducted. Thematic analysis indicates that bodily interactions with the prototype can activate embodied metaphorical mapping from the concrete action to the abstract meaning. The findings show that the embodied approach provides useful guidance for designing meaningful experience with ambient media.
An Improved Strategy for Machine Comprehension Based upon Dynamic Memory Networks

Recently many complicated reading comprehension models have been proposed. Most of these models are constantly improved from a basic model. However, most of the innovations are at the interaction layer. In this work, we analyse the shortcomings of Dynamic Memory Networks and its extended model. Then we decide to put forward some different opinions that making some improvements in the embedding layer based upon these models. First of all, this method uses pre-training to get character embedding. Our model uses CNN to train the character vector, and then we splicing the character vector and the word vector. Meanwhile, we validate the model with the bAbI dataset. Our results show that this method can improve the interaction between context and question words.
Reconstruction with Guided PatchMatch Stereo

Stereo matching is an operation that calculates a dense set of correspondences from a pair of images engineered to conform to canonical epipolar geometry. Stereo matching is a key component in many 3D reconstruction systems, thus it is a heavily studied area in the field of computer vision. Stereo matching, in the general case, is a difficult problem that remains unsolved to a satisfactory level for most scientists in the field. However, due to the large amount of attention given to this problem, there exists many algorithms, each with their own advantages and problems. This work explores the notion of combining the outputs of two well known stereo matching algorithms (symmetric dynamic programming stereo and patch match stereo) using a statistical framework, with the hope that the strategy will preserve the advantages of both algorithms while overcoming some of the weaknesses. Experiments were performed both on images from the Middlebury data set and ones captured for this study using a stereo camera. The results indicate that the proposed strategy is promising.
Finding Periods in Cartesian Tree Matching

In Cartesian tree matching, two strings match if the Cartesian trees of the strings are the same. In this paper we define full, initial, and general periods in Cartesian tree matching, and present an O(n) time algorithm for finding all full periods, an $$O(n \log \log n)$$ time algorithm for finding all initial periods, and an $$O(n \log n)$$ time algorithm for finding all general periods of a string of length n.
Multi-scale vector quantization with reconstruction trees

Abstract We propose and study a multi-scale approach to vector quantization (VQ). We develop an algorithm, dubbed reconstruction trees, inspired by decision trees. Here the objective is parsimonious reconstruction of unsupervised data, rather than classification. Contrasted to more standard VQ methods, such as $k$-means, the proposed approach leverages a family of given partitions, to quickly explore the data in a coarse-to-fine multi-scale fashion. Our main technical contribution is an analysis of the expected distortion achieved by the proposed algorithm, when the data are assumed to be sampled from a fixed unknown distribution. In this context, we derive both asymptotic and finite sample results under suitable regularity assumptions on the distribution. As a special case, we consider the setting where the data generating distribution is supported on a compact Riemannian submanifold. Tools from differential geometry and concentration of measure are useful in our analysis.
Analysis of transport workers’ postures in the loading process of manual material handling activities by using the photogrammetric method

Abstract This research was conducted on the loading process of manual material handling of cup mineral water activity at PT. PA, Aceh Besar, Indonesia. With samples of 12 transport workers, the observed loading activity includes the position of lifting, walking, and loading. The loading process is done manually from the warehouse into the truck. This work is done continuously by a transport worker with each transported load reaching 48 kilograms. In a one-time loading process, a transport worker performs as many as 33 trips and the timing for each transport loading process is 20 seconds. With the work done continuously and the loads transported are weighty; therefore there are risks of experiencing musculoskeletal disorders by the transport workers. Thus, questionnaires, interviews, and observations were used as instruments to obtain data. Calculations were further conducted to analyze the postures of transport workers by using the Photogrammetric Method. The results show that the moment force calculation at L5/S1 is greatest in the position of lifting the load at 5.756,81 N; meanwhile, the NIOSH provision is 3,400 N for a normal lift limit. Hence, the solutions proposed to this problem are to reduce the load to 24 kilograms/loading and the utilization of the trolley.
Dynamic defenses and the transferability of adversarial examples

Artificial learners are generally open to adversarial attacks. The field of adversarial machine learning focuses on this study when a machine learning system is in an adversarial environment. In fact, machine learning systems can be trained to produce adversarial inputs against such a learner, which is frequently done. Although can take measures to protect a machine learning system, the protection is not complete and is not guaranteed to last. This is still an open issue due to the transferability of adversarial examples. The main goal of this study is to examine the effectiveness of black-box attacks on a dynamic model. This study investigates the currently intractable problem of transferable adversarial examples, as well as a little- explored approach that could provide a solution, implementing the Fast Model-based Online Manifold Regularization (FMOMR) algorithm which is a recent published algorithm that seemed to fit the needs of our experiment.
ANALISIS DAN PERANCANGAN SISTEM INFORMASI DALAM PEMBAYARAN ADMINISTRASI KEUANGAN SISWA DI SMA NOMMENSEN JAMBI

Nommensen High School is one of the Educational Institutions in Jambi City having its address at JalanBarau - Barau I Kel. Pakuan Baru Kec. South Jambi which was founded in 1988, which has a number of195 students with increasing numbers during the Nommensen High School in Jambi City experienced adevelopment. This development requires the existence of a means to accelerate performance andefficiency in School Administration especially School Financial Administration such as new studentregistration payment of tuition fees and committee fees, school financial expenditures and so ontherefore there is a need for a school financial administration management information system that canrecord, process and produce a fast school financial information system. In this system research systemdesign is used using UML mo deling which produces use case diagrams, activity diagrams and classdiagrams. The results of this study were in the form of a prototype. The purpose of this study is expectedto facilitate the management of school financial administration to be faster, more precise and accurate.
Development of Graph-Theoretical Model and Operations on Graph Representations of Ontologies as Applied to Information Retrieval Tasks

This paper presents a graph-theoretical model of ontologies of subject domains. An ontology represented as a weighted graph. At the vertices of the graph are concepts. The edges of the graph marked the relationship between concepts. In addition, the basic operations on the ontology graph representations introduced for ontology editing in the semantic search system.
원양참치 연승 및 선망어업의 생산성 비교연구

This study is to analyze the productivity of overseas tuna fisheries comparing longline to pursue seine based on various DEA models with respect to...
An Interactive Visual System for Generating Striking Pseudo Base Stations Decisions

Abstract The Pseudo base stations cause a great number of social and economic security problems with spreading spam messages. However, it is a serious challenge for security department to recognize the behavior patterns of pseudo base stations, then make use of limited police resources to prevent their activities and even to arrest related criminals. In this paper, a novel visual analytic approach to reveal the spatial-temporal behavior patterns of pseudo base stations is presented. In this approach, it takes advantage of density clustering over high frequency positions, and curve fitting on each cluster. This system supports decision makers to formulate patrol routes on the basis of above approximate trajectory. This approach has been demonstrated effective by means of case studies of authority data and has been evaluated novel and valuable in the view of domain experts.
Challenges in Evaluating Efficacy of Scientific Visualization for Usability and Aesthetics

This paper presents the results of a study to evaluate the efficacy of scientific visualization for multiple categories of users, including both domain experts as well as users from the general public. Efficacy was evaluated for understanding, usability, and aesthetic value. Results indicate that aesthetics play a critical, but complex role in enhancing both user understanding and usability.
Projection Mapping for Implementing Immersive User Scenarios in Autonomous Driving: Insights from Expert Interviews

We propose the use of projection mapping as a prototyping tool to create an experimentation environment to design, evaluate, and control immersion experience in future autonomous vehicles. As the first step, we conducted expert interviews with professionals in the automotive industry to understand the general implications of prototyping tools in future mobility solution development and their usefulness in concept test settings. The interview results reveal that projection mapping is one popular prototyping method for automotive professionals to demonstrate immersive future user scenarios. The paper includes additional insights from the interviews and current work-in-progress.
Revisiting Multivariate Lattices for Encrypted Signal Processing

Multimedia contents are inherently sensitive signals that must be protected when processed in untrusted environments. The field of Secure Signal Processing addresses this challenge by developing methods which enable operating with sensitive signals in a privacy-conscious way. Recently, we introduced a hard lattice problem called m-RLWE (multivariate Ring Learning with Errors) which gives support to efficient encrypted processing of multidimensional signals. Afterwards, Bootland et al. presented an attack to m-RLWE that reduces the security of the underlying scheme from a lattice with dimension \prod_in_i to \max\n_i\ _i . Our work introduces a new pre-/post-coding block that addresses this attack and achieves the efficient results of our initial approach while basing its security directly on RLWE with dimension \prod_in_i, hence preserving the security and efficiency originally claimed. Additionally, this work provides a detailed comparison between a conventional use of RLWE, m-RLWE and our new pre-/post-coding procedure, which we denote "packed''-RLWE. Finally, we discuss a set of encrypted signal processing applications which clearly benefit from the proposed framework, either alone or in a combination of baseline RLWE, m-RLWE and "packed''-RLWE.
Multi-camera Coordinate Calibration and Accuracy Evaluation for Robot Control

In this study, our goal is to create a surgical navigation system that takes stable measurements of a surgical area using RGB/depth cameras without obstructions, such as the surgeon’s head or hands. We mounted three cutting-edge D435 Intel RealSense Depth Cameras onto a ring and photographed the surgical area from three directions. We also installed a robotic mechanical system that can move the camera ring up and down so that the surgery can proceed smoothly. First, we calibrated the coordinate systems so that the coordinate systems of the three cameras (three-dimensional XYZ coordinate system) align and their Y-axis (vertical axis) aligns with the moving axis of the robot slider. Next, we captured an ArUco marker with each camera and visualized its position within the camera coordinate system. After verifying that the initial positions of the ArUco marker captured by the three cameras match, we moved the robot slider up by 50 mm thrice and investigated the degree of change in the measured position of the ArUco marker measured by the three cameras. The results show that as the ArUco marker moves farther away, the extent of error in the measured position of the ArUco marker increases. Additionally, the measured position of all the ArUco markers varied owing to the digital pixel error in the two-dimensional images (the pixel in which the ArUco marker is visible moves between neighboring pixels). Future work includes checking that the calibration pattern and ArUco marker are parallel to the camera ring and perpendicular to the robot slider using a level instrument; packing the calibration pattern and ArUco marker horizontally with vinyl; and implementing a moving average in the program to reduce pixel digital error in the calculations to sub-pixel level.
From typestate verification to interpretable deep models (invited talk abstract)

The paper ``Effective Typestate Verification in the Presence of Aliasing'' was published in the International Symposium on Software Testing and Analysis (ISSTA) 2006 Proceedings, and has now been selected to receive the ISSTA 2019 Retrospective Impact Paper Award. The paper described a scalable framework for verification of typestate properties in real-world Java programs. The paper introduced several techniques that have been used widely in the static analysis of real-world programs. Specifically, it introduced an abstract domain combining access-paths, aliasing information, and typestate that turned out to be simple, powerful, and useful. We review the original paper and show the evolution of the ideas over the years. We show how some of these ideas have evolved into work on machine learning for code completion, and discuss recent general results in machine learning for programming.
Hierarchical Meta-Learning Models with Deep Neural Networks for Spectrum Assignment

In this paper, we consider a data-driven approach and apply machine learning methods to facilitate frequency assignment. Specifically, an hierarchical meta-learning architecture, which harness the predictive capability of both statistical and deep learning approaches, is proposed to predict a diverse range of spectrum usage patterns. Using spectrum measurement, network simulations are conducted to evaluate the effectiveness of the proposed architecture. It is shown that the hierarchical meta-learning models with deep recurrent neural networks have great potential for predicting spectrum usage patterns to facilitate multi-tier spectrum assignment.
Some notes on the multiplicative order of $$\alpha + \alpha ^{-1}$$ in finite fields of characteristic two

In this paper we prove some results on the possible multiplicative orders of $\alpha + \alpha^{-1}$ when $\alpha$ is a non-zero element of a finite field of characteristic 2. The results of the paper rely on a previous investigation on the structure of the graphs associated with the map $x \mapsto x + x^{-1}$ in finite fields of characteristic 2.
Multi-joint Cooperative Control of Athletes under High Intensity Training

Abstract The control structure and performance of high intensity sports training on the trunk joint of athletes is analyzed to improve the quality of sports training, a multi-joint cooperative control model for athletes under high intensity sports training is proposed based on attitude sensor fusion tracking compensation. The multi-joint kinematics model of the athletes’ torso under high intensity sports training is constructed, and the constraint parameters of the multi-joint linkage and cooperative control of the athletes are analyzed combined with the identification method of the parameters of the state space model. The mechanical control model of the action of high intensity sports training on athletes’ torso is established by using the coupling method of driving branch chain inertia, and the characteristics of multi-joint linkage coupling of athletes are analyzed. The collaborative filtering Kalman fusion method is used to realize the parameter fusion and adaptive parameter identification of athletes’ multi-joint linkage. According to the results of parameter recognition, athletes’ multi-joint linkage collaborative control is realized. The simulation results show that the high intensity sports training method has good stability and low error of attitude parameter adjustment for athletes’ multi-joint linkage and coordination control, which improves the effect of sports training.
Guidelines on Context Integration: Developing Technological Solutions Communication for Education Professionals

This article presents a project that arose from the need to design applications with natural interactions for professionals in the field of education, who have long working days and who mix personal and social activities together with professional activities. The project is based on the Design Process Model for Health Applications: Integrating Contexts and Adding Abilities (ICAH, in Portuguese), developed to guide and support the work of application developers for health professionals who care for patients in need of long-term care [1–3]. Its objective is to validate the guidelines of integration of contexts proposed in the ICAH Model applied to education professionals. To fulfill the proposed objective, the ICAH Model was used for the definition and collection of requirements for an application of communication and dissemination of information in the educational environment. The application was developed from research and observations of professionals who worked in a professional education institution during the year 2018. The application provides the disclosure of information related to extracurricular events and activities and official communications that are part of the Institution. The observations and feedbacks collected by the application gave us indications that the application was effectively adopted by education professionals. The content of the information that had favorable feedback was, in its majority, information that added extracurricular contents and social events of the Institution, evidencing the mix of contexts in the educational environment.
Margin-based Sampling in Deep Metric Learning

Deep metric learning is routinely trained with a pair or a triplet of data samples, either of which converges slowly. It is usually to adopt hard negative mining for a fast convergence. Many existing methods train with the hardest examples, which are selected by ranking or scoring the hardness of examples, and are computationally expensive. In this paper, we propose a sampling strategy for hard negative mining, in which sampling directions are determined by the similarity between data examples and the margin. Our proposed method reduces the impact of noise by amplifying the dissimilarity of Softmax loss between hard examples for a more accurate model. The experimental results of image retrieval on CARS196 and CUB-200-2011 dataset demonstrate the effectiveness and superiority of our proposed method, compared to several existing state-of-the-art techniques.
A Pattern Methodology to Specify Usable Design and Security in Websites

Over the past decade, with the progress in technology, there has emerged various needs for users to get more interactive with Web applications. Commercial web services have spread the use of rich interfaces to provide users with a meaningful interaction with these applications. Nevertheless, the dynamic nature of the context of interaction imposes practitioners to extract various requirements such as user needs and choose the appropriate actions to perform them. However, in real practice, practitioners hardly combine between exploring the space problem to extract users’ goals and creating a high quality user interface. To overcome this challenge, we propose a set of measurements criteria basically integrated into specific comprehensive indicators. Our goal is to evaluate the main aspects of quality requirements (Design, security). Thus, practitioners will have a flexible support based on a high quality model to improve the extracted services and the usability of commercial Web-based application in a dynamic context.
Analysis of the Passion Criminal Tendencies Based on the Topic-Emotion Model

Abstract With the penetration of the Internet into daily life, more and more crime clues could be discovered on the Internet. Therefore, this paper uses a new method to predict whether the instant message on the internet has the tendency of emotional crime. It is because that the single theme-based analysis of criminal tendency, which ignores the impact of emotions, may confound the declarative sentences and normal expression with statements that contain criminal tendencies. The method used in this study introduces the analysis of emotional fluctuations while analysing themes. In order to solve the problems above, the study adopts seq2seq model to analyse emotional fluctuation. Themes and the values of emotional fluctuation are put into linear SVM as input vector to judge whether the instant message has an emotional crime tendency. The average predication accuracy of the model reached 86%, which basically accords with the assumption made before the experiment. This model can not only be used to analyse emotional crime, but also to screen the radical opinions on the internet.
Design for Bilingual Information in a Cross-Cultural Context—Design Strategy and Conceptualization in Branding Tongji Design Week

The Tongji Design Week held since 2012 in Shanghai by Tongji University College of Design and Media, presents a classic case study of cross-cultural design. By analyzing the problems and challenges faced by this recurring design project, this paper examines the strategy and approaches taken by the design team of Tongji Design Week, and the specific design solutions, guidelines and rules in relation to the design strategy and concept set forth by the designer. This paper also examines how innovative contemporary communication design concepts and approaches could be combined with the needs of cross-cultural design to shape an up-to-date design language in a bilingual and cross-cultural context.