Reviewerly/exHarmony huggingface dataset:
Analysis Of Functional Mechanism of Economic Development in Yichang Region Driven by Vocational Education Considering the Structural Time Series Model

In the mechanism of economic development in Yichang region driven by the traditional vocational education, one or two models are often used to addresses certain specific objectives, which are subject to the system structure limit and fail to take the multi-domain problems into full consideration. A kind of mechanism of economic development in Yichang region driven by vocational education based on the structural time series model is put forward in this paper, which has achieved the multi-disciplinary integration of multi-disciplinary model in a system and provided comprehensive scientific support for the regional economic development. The structural time series model management method is applied in the system, and its script extension mechanism allows the system to accommodate almost all models. The model factor method enables the various forms of scientific model simulation results to be applied to the decision method so that the system can provide a scientific and reasonable decision scheme under the support of the structural time series model. The decision process of the structural time series model, the guided interactive interface and the support of GIS and other graphical tools make the system user interface friendly and easy to use.
Mining Frequent Weighted Itemsets Using Extended N-List and Subsume

Discovering frequent weighted itemsets(FWIs) is an important research in practical applications of field of data mining. Recently the PrePost algorithm based on the idea of N-lists has been presented. In this paper, we propose an improved version method ENSFWI(extended N-list subsume-based algorithm for finding FWIs). The subsume concept and related theorems are proposed to calculate the weighted supports of itemsets fast and generate directly FWIs without extended N-list intersection, and then an algorithm is built based on these concept for efficiently mining FWIs. It is shown by experimental results that our approach not only results in shorter execution times, but also reduces the memory usage when run on very large and dense database.
Dynamic Time Warping Based Sign Retrieval

Sign language, a visual language, is the main mode of communication for the Deaf. A large body of audio-visual material are annotated with sign language for the utilization of the Deaf. Enabling the sign based retrieval of these sources will serve two purposes: (i) Deaf people will be able to make searches using sign language; (ii) People who do not know the meaning of a sign will be able to search that sign in its source, and infer the meaning from accompanying subtitles and speech. In this work, we have developed a baseline system based on dynamic time warping for the sign sample based retrieval in videos. We analyze the effects of using different features, distance measures and temporal reduction techniques on the query performance.
Zero-Shot Slot Filling via Latent Question Representation and Reading Comprehension

Slot filling is a demanding task of knowledge base population, which aims to extract facts about particular entities from unstructured text automatically. Most of the existing approaches rely on pre-trained extraction models which may suffer from robustness caused by unseen slots, or the so-called zero-shot slot filling problem. Recent studies try to reduce the slot filling to a machine reading comprehension task and achieve certain improvements on unseen slots, but they still face challenges to generate appropriate questions for models and find the right answers. In this paper, we propose a novel end-to-end approach to address the zero-shot slot filling by unifying the natural language question generation and machine reading comprehension. Especially, we explore how to learn a well-organized latent question representation by incorporating external knowledge. We conduct extensive experiments to validate the effectiveness of our model. Experimental results show that the proposed approach outperforms the state-of-the-art baseline methods in zero-shot scenarios.
Topic Change Detection on Dialog Based Text

It has become easier to reach and share data thanks to the communication methods (internet, social media, smartphone, etc.) which have been advancing in the last years. Especially in recent years, oral and written sharing channels have developed rapidly. Social media sites and forums are among the areas where written sharing is the fastest. Unlike social media, in forums it is expected that people will discuss specific issues under corresponding topics. Call centers are also among rapidly developing oral communication channels in the recent years where the scope of the conversation is restricted. The automatic determination of digressing from certain subjects or changing the main subject is especially important for the evaluation of the communication performance and automatic management of the media such as call centers and technical forums. With this study, classifiers that can automatically detect the subject change within the dialogue-based Turkish texts have been developed. In order to develop these classifiers, first of all, the subject-based conversation data from Turkish forums were compiled and a raw data set was obtained. In this study, a classical method (TF-IDF) and a deep learning model (LSTMs) have been applied to the generated dataset for topic change detection task. Results show that 80% accuracy can be achieved by the classical method while deep learning model achieves 76%.
Toward Robust Image Classification

Neural networks are frequently used for image classification, but can be vulnerable to misclassification caused by adversarial images. Attempts to make neural network image classification more robust have included variations on preprocessing (cropping, applying noise, blurring), adversarial training, and dropout randomization. In this paper, we implemented a model for adversarial detection based on a combination of two of these techniques: dropout randomization with preprocessing applied to images within a given Bayesian uncertainty. We evaluated our model on the MNIST dataset, using adversarial images generated using Fast Gradient Sign Method (FGSM), Jacobian-based Saliency Map Attack (JSMA) and Basic Iterative Method (BIM) attacks. Our model achieved an average adversarial image detection accuracy of 97%, with an average image classification accuracy, after discarding images flagged as adversarial of 99%. Our average detection accuracy exceeded that of recent papers using similar techniques.
Penerapan Decision Tree Menggunakan Algoritma C4.5 Untuk Deteksi Demam Berdarah Pada RS. IMC Bintaro

Abstract: According to the WHO report in 2004, Indonesia is the largest country with the highest number of sufferers and death rates due to dengue fever. If it is not handled properly, the postponed treatment can be fatal. In this study, the authors used the kepuutsan tree method with C4.5 algorithm to process patient data to predict whether patients experienced bloody help regarding existing indications with the help of Rapidminer software. The results of data processing using Rapidminer were evaluated and validated with a confussion matrix and AUC curve, the results of data processing using the C4.5 algorithm had an accuracy of 72% and AUC had a value of 0.758 with a fair classification category.&#x0D; &#x0D; Keywords: Algorithm C4.5, Decision Tree, Data Mining
COMPLAINT HANDLING THROUGH SOCIAL MEDIA: PERCEIVED JUSTICE AND CUSTOMER SATISFACTION

Nowadays, it is common for us to complaint about the service or product by using social media platform.Organizations are usually having their customer service or public relation to maintain their social media account such as Twitter, Instagram, Facebook or other platforms.This study aims to analyze perceived justice in complaint handling process through social media and its impact towards customer satisfaction.Customer satisfaction is evaluated together with trust and perceived product quality for their influence in e-WOM and continues usage.The sample of the study is customer of a telecommunication company who is the follower of its customer care account in Twitter.The questionnaires were distributed online, and 238 valid response were analyzed using CB-SEM.The results show that, customers would be satisfied if the complaint handling process is accurate and quick (procedural justice) and supported by compensation (distributive justice).The satisfaction would increase customer trust and perceived product quality and eventually increase their intention to continue using the service and spread positive e-WOM.
Developing Interactive E-book as Material Technology Coursebook by Flipbook Maker Software

Material technology course was one of the compulsory courses for Bachelor of Architecture Program in STT Stikma Internasional Malang.This course was offered in semester two (even semester) in the new academic year for 3 credits.This course aimed to make the students able to explain type, characteristic, and technical specification of some basic materials for construction, and its application in construction and some finishing material for construction.The insufficient laboratory facilities and infrastructure made the teaching material which were supposed to be done in laboratory must be done theoretically in classroom.The limited collection of books in the library also caused students difficulty in finding appropriate learning resources according to the course material.Therefore, this study aimed to produce interactive digital book products (e-books) on Material Technology courses which were appropriate and attractive to students, especially for Bachelor of Architecture in STT Stikma Internasional Malang.This research was a research and development model which main purpose was to produce products in the form of interactive digital books, especially for Material Technology courses.The development model used was the 4D model which consisted of 4 main steps: define, design, develop, and disseminate.Data collection techniques were carried out through observation, literature review and questionnaires.The questionnaire was used to carry out validation tests by material and media experts, as well as user development tests.The results of the material expert validation showed that this interactive e-book was very appropriate with an average percentage of 88.3%.The validation results of the media experts indicated that this interactive e-book was very appropriate with an average percentage of 89.6%.The results of the assessment involving 3 students as users also showed that this interactive e-book was very appropriate with a percentage of 88.8%.
The Implicit Metropolis-Hastings Algorithm

Recent works propose using the discriminator of a GAN to filter out unrealistic samples of the generator. We generalize these ideas by introducing the implicit Metropolis-Hastings algorithm. For any implicit probabilistic model and a target distribution represented by a set of samples, implicit Metropolis-Hastings operates by learning a discriminator to estimate the density-ratio and then generating a chain of samples. Since the approximation of density ratio introduces an error on every step of the chain, it is crucial to analyze the stationary distribution of such chain. For that purpose, we present a theoretical result stating that the discriminator loss upper bounds the total variation distance between the target distribution and the stationary distribution. Finally, we validate the proposed algorithm both for independent and Markov proposals on CIFAR-10, CelebA, ImageNet datasets.
Application of Fuzzy Clustering for Text Data Dimensionality Reduction

Large textual corpora are often represented by the document-term frequency matrix whose elements are the frequency of terms; however, this matrix has two problems: sparsity and high dimensionality. Four dimension reduction strategies are used to address these problems. Of the four strategies, unsupervised-feature transformation (UFT) is a popular and efficient strategy to map the terms to a new basis in the document-term frequency matrix. Although several UFT-based methods have been developed, fuzzy clustering has not been considered for dimensionality reduction. This research explores fuzzy clustering as a new UFT-based approach to create a lower-dimensional representation of documents. Performance of fuzzy clustering with and without using global term weighting methods is shown to exceed principal component analysis and singular value decomposition. This study also explores the effect of applying different fuzzifier values on fuzzy clustering for dimensionality reduction purpose.
COMMONS: A Board Game for Enhancing Interdisciplinary Collaboration When Developing Health and Activity-Related Wearable Devices

Physical activity provides substantial health benefits yet physical inactivity is one of the most challenging problems of these days. Development in mHealth and eHealth applications, such as wearable technology, create vast opportunities but are still insufficiently used to promote sustainable physical activity. To enhance the development of such products, participation and cooperation of professionals with different knowledge and expertise is required. We developed a board game called COMMONS to enhance interdisciplinary collaboration in the design of health-related wearable technologies. In this paper we present the design process of COMMONS, results of the play sessions and discuss the future development of COMMONS and the possible implications within the field of Human-Computer Interaction.
A Summary of Hamiltonian Based on Data Center Network

Cloud computing has entered our life, data center network as the infrastructure of cloud computing is getting more and more attention, Hamiltonian is one of the important indicators to measure the performance of network structure. This paper makes a simple analysis of some classical structures by consulting data. This paper classified the Hamiltonian of data center network structure, and summarizes the method of network structure Hamiltonian proof.
Kalman Filter, Sensor Fusion, and Constrained Regression: Equivalences and Insights

The Kalman filter (KF) is one of the most widely used tools for data assimilation and sequential estimation. In this work, we show that the state estimates from the KF in a standard linear dynamical system setting are equivalent to those given by the KF in a transformed system, with infinite process noise (i.e., a ``flat prior'') and an augmented measurement space. This reformulation---which we refer to as augmented measurement sensor fusion (SF)---is conceptually interesting, because the transformed system here is seemingly static (as there is effectively no process model), but we can still capture the state dynamics inherent to the KF by folding the process model into the measurement space. Further, this reformulation of the KF turns out to be useful in settings in which past states are observed eventually (at some lag). Here, when the measurement noise covariance is estimated by the empirical covariance, we show that the state predictions from SF are equivalent to those from a regression of past states on past measurements, subject to particular linear constraints (reflecting the relationships encoded in the measurement map). This allows us to port standard ideas (say, regularization methods) in regression over to dynamical systems. For example, we can posit multiple candidate process models, fold all of them into the measurement model, transform to the regression perspective, and apply $\ell_1$ penalization to perform process model selection. We give various empirical demonstrations, and focus on an application to nowcasting the weekly incidence of influenza in the US.
UDS–DFKI Submission to the WMT2019 Czech–Polish Similar Language Translation Shared Task

In this paper we present the UDS-DFKI system submitted to the Similar Language Translation shared task at WMT 2019.The first edition of this shared task featured data from three pairs of similar languages: Czech and Polish, Hindi and Nepali, and Portuguese and Spanish.Participants could choose to participate in any of these three tracks and submit system outputs in any translation direction.We report the results obtained by our system in translating from Czech to Polish and comment on the impact of out-of-domain test data in the performance of our system.UDS-DFKI achieved competitive performance ranking second among ten teams in Czech to Polish translation.
Visualizations of User’s Paths to Discover Usability Problems

This paper reports on an on-going work that investigates the use of visualization techniques to help evaluators discovering usability problems by visualizing data collected during usability tests of web sites. Two visualization techniques are described and some results of the evaluation study that compared the two techniques are provided.
Extended Vehicle Tracking with Probabilistic Spatial Relation Projection and Consideration of Shape Feature Uncertainties

This work focuses on a novel probabilistic approach for extended vehicle tracking, where multiple spatially distributed measurements can originate from the target, and kinematic state and geometry variables are estimated jointly. Prominent shape features extracted from raw measurement points contain spatial uncertainties due to noise in sensor measurements, the feature extraction process, approximation error of shape hypothesis, partial vision occlusion, to name a few. This work proposes a novel tracking paradigm that respects the variant spatial measurement model subject to changes in target pose and sensor viewpoint. This is achieved through probabilistic projection of the spatial measurement points to the predicted measurement sources on the visible side(s) of the target shape. The spatial uncertainties in the shape features are probabilistically modelled and incorporated in the unscented Kalman filter based estimation. The proposed approach is validated with field experiment results using cameras and a laser range scanner.
Indexing Graph Search Trees and Applications.

We consider the problem of compactly representing the Depth First Search (DFS) tree of a given undirected or directed graph having $n$ vertices and $m$ edges while supporting various DFS related queries efficiently in the RAM with logarithmic word size. We study this problem in two well-known models: {\it indexing} and {\it encoding} models. While most of these queries can be supported easily in constant time using $O(n \lg n)$ bits\footnote{We use $\lg$ to denote logarithm to the base $2$.} of extra space, our goal here is, more specifically, to beat this trivial $O(n \lg n)$ bit space bound, yet not compromise too much on the running time of these queries. In the {\it indexing} model, the space bound of our solution involves the quantity $m$, hence, we obtain different bounds for sparse and dense graphs respectively. In the {\it encoding} model, we first give a space lower bound, followed by an almost optimal data structure with extremely fast query time. Central to our algorithm is a partitioning of the DFS tree into connected subtrees, and a compact way to store these connections. Finally, we also apply these techniques to compactly index the shortest path structure, biconnectivity structures among others.
Identifying and Explaining Discriminative Attributes

Identifying what is at the center of the meaning of a word and what discriminates it from other words is a fundamental natural language inference task.This paper describes an explicit word vector representation model (WVM) to support the identification of discriminative attributes.A core contribution of the paper is a quantitative and qualitative comparative analysis of different types of data sources and Knowledge Bases in the construction of explainable and explicit WVMs: (i) knowledge graphs built from dictionary definitions, (ii) entity-attribute-relationships graphs derived from images and (iii) commonsense knowledge graphs.Using a detailed quantitative and qualitative analysis, we demonstrate that these data sources have complementary semantic aspects, supporting the creation of explicit semantic vector spaces.The explicit vector spaces are evaluated using the task of discriminative attribute identification, showing comparable performance to the state-ofthe-art systems in the task (F1-score = 0.69), while delivering full model transparency and explainability.
Training-Free Method for Generating Motion Video Clones From A Still Image Considering Self-Occlusion of Human Body

In this paper, we propose a method for generating photo-realistic video in which a person virtually performs a motion that is not performed in the real world. we refer to such video as motion video clones (MVCs). The proposed method requires only two kinds of source information as input data: a reference video, in which a person A performs some motion, and a target image, which includes the whole body of another person B. Using these data, our method generates a MVC in which the person A’s motion is re-enacted by the person B’s body. Since our method does not need 3D human body model nor any training phase, it is suitable to MVC-based entertainment systems. To handle the self-occlusion of the human body in the reference video, we employ a part-based approach. For each part such as the right arm and the left leg, we first extract its skeleton from the target image and move it so that the motion represented by the reference video is reenacted. Next, we compute a 2D affine transform between the original and moved positions of the skeleton. This transform is used to map the texture of the target image onto each frame of a resultant MVC. Finally, we extend the part-wise affine transforms to pixel-wise ones by computing their linear combination for each pixel, whose combination weights are computed according to the geodesic distance between the pixel and the center of each part. This allows us to avoid unnatural appearance around the joints of the body parts. In our experiments, the proposed method generated much visually-natural MVCs than existing methods.
Multi-Similarity Re-Ranking for Person Re-Identification

Re-ranking has been proved an effective method to boost the performance of person re-identification. Existing works focus on contextual or graph-based similarity to improve the initial ranking result. The former mainly concentrates on more accurate similarity description but neglects the manifold constraint. While, the later centers on solving similarities with manifold constraint, which acquires several accurate top ranks. In this paper, we propose a novel method which not only takes contextual similarity to generate top ranks accurately but also refines the ranks based on graph-based similarity. Specifically, given initial Euclidean distances between a probe and galleries, we mine contextual and graph-based similarities respectively and then re-rank all galleries with a diffusion procedure under constraints of both similarities. Experiments on two person re-ID datasets demonstrate that our method outperforms state-of-the-art re-ranking approaches in person re-identification.
VibroSquare: Vibro-Tactile Display for Body-Centric Implicit Interactions

Importance of touch cannot be underestimated. Touch is the first sense that a human develops already in a womb and the last sense that one loses with age. Touch can serve as a powerful modality for developing novel human-computer interfaces. We present VibroSquare - our first vibro-tactile display we designed to enable first set of experiments to test our research assumptions. During the experiments we will explore how vibro-tactile feedback can be communicated on an unconscious (implicit) level of perception. The objective of the demonstration is to offer first hand experience of the interaction with VibroSquare and create conversations around designing technologies for supporting body-centric interactions using mediated touch.