Reviewerly/exHarmony huggingface dataset:
Mining Verb-Oriented Commonsense Knowledge

Commonsense knowledge acquisition is one of the fundamental issues in the implementation of human-level AI. However, commonsense is difficult to obtain, because it is a human consensus and rarely explicitly appears in texts or other data. In this paper, we focus on the automatic acquisition of a typical kind of implicit verb-oriented commonsense knowledge (e.g., "person eats food"), which is the concept level knowledge of verb phrases. For this purpose, we propose a knowledge-driven approach to mine verb-oriented commonsense knowledge from verb phrases with the help of taxonomy. First, we design an entropy-based filter to cope with noisy input verb phrases. Then, we propose a joint model based on minimum description length and a neural language model to generate verb-oriented common-sense knowledge. We conduct extensive experiments to show that our solution is more effective to mine verb-oriented commonsense knowledge than competitors, and finally, we harvest 18K verb-oriented commonsense knowledge.
Semantic Relevance Feedback on Queries and Search Results for Younger and Older Adults

In this chapter, we describe research in which the influence of providing feedback on the semantic relevance of queries and search results is examined with younger and older adults when they reformulate their search queries. Providing feedback on the semantic relevance of search queries and search results increased the semantic relevance of future search queries as they reformulated both for younger and older adults. This applies especially to more difficult search problems. For younger adults, in addition to the semantic relevance of search queries, also an improvement in their search performance (in terms of the amount of time and the number of clicks needed to solve the task) was observed. No such difference in search performance was found for older adults. A possible explanation could be that, older adults need more time to adjust to the new search interface in order to find effects not only in the semantic relevance of queries but also in their search performance.
Depth Estimation for Local Colon Structure in Monocular Capsule Endoscopy Based on Brightness and Camera Motion

SUMMARY We present a 3D reconstruction method using brightness and camera motion estimation for registering local colon structure in colonoscopy. The proposed method is based on reverse projection from 2D fold contours to 3D space, motion estimation from 3D reconstructed points between neighboring frames, and model registration to reconstruct the fold structure. On the synthetic colon, the average percentages of the reconstructed depth error and circumference error are about 14.2% and 15.2%, respectively. The accuracy is enough for the navigation and control in capsule robot. This work demonstrates that the proposed method is superior to the methods using single-frame-based brightness intensity.
Obstacle Avoidance Path Planning for Double Manipulators Based on Improved Artificial Potential Field Method

In this paper, an obstacle avoidance path planning algorithm based on improved artificial potential field method is proposed for a double six-degree-of-freedom manipulator. In the first, obstacle avoidance motion programming is carried out for the main arm in view of static obstacles, and path programming for the main arm is completed. Secondly, the main arm is regarded as the dynamic obstacle when the slave arm moves, and the path of obstacle avoidance is planned for the slave arm. Selecting new potential function by traditional artificial potential field method, and carrying out the obstacle avoidance path programming of the dual manipulator. The simulation results show that the method meets the requirements of double manipulator obstacle avoidance, and can plan collision free path for the manipulator.
State hacking at the edge of code, capitalism and culture

Hacking is a set of practices with code that provides the state an opportunity to defend and expand itself onto the internet. Bringing together science and technology studies and sociology scholarship on boundary objects and boundary work, we develop a theory of the practices of the hacker state. To do this, we investigate weaponized code, the state's boundary work at hacker conferences, and bug bounty programmes. In the process, we offer a depiction of the hacker state as aggressive, networked, and adaptive.
Puzzle Islamic Floral Patterns Product Tiles for Wall and Ceiling to Decorate of Al Huda Mosque Indonesia—Design, Manufacturing, and Fabrication

Ceramic wall ornaments that are commonly used as interior building and encountered in hotels, restaurants, residential elite, museums and places of worship. Ceramic is a decoration signifying strong characters or special characteristics of the building. This character is usually in the form of ornaments that adorn the walls and are designed according to the history of the development of the times. The basic characteristic of Islamic Ornament generally shaped floral such as leaves, stems, buds, flowers, and palms. Wall tiles with special needs with unique ornaments and precision are very unlikely produced by some industrial ceramics in Indonesia at this time which still depends on hand made. This study has introduced a range of novel applications that combine digital manufacturing technology and conventional fabrication in the ceramic industry. The input image with the nuances of Islamic motifs with the format. JPG successfully raised to 2.5 D CAD models using ArtCAM software. To get the master pattern prints that precision, accurate and appropriate use of technology additive manufacturing with 3D Objet 30Pro. Conventional ceramics Manufacturing process technology at PT. Nuanza Porcelain Indonesia used to get ceramic products with Islamic nuances of Pattern puzzle flora. The integration of two technologies of CAD and RP this managed to get the design ceramic wall ornamental Islamic precision and detail with the dimensional error of less than 2.00 mm. Ceramic design results in accordance with Islamic ornamentation and was already installed in the mosque of Al Huda in 2018.
Comparative Analysis Between State Estimation Algorithms under Static and Dynamic Scenarios

This paper analyzes the main advantages and disadvantages of three algorithms applied to the state estimation of a power system under different static and dynamic scenarios. The analyzed algorithms are: Non-Linear Weighted Least Square (NLWLS), Extended Kalman Filter (EKF) and Square-Root Unscented Kalman Filter (SRUKF). The studied scenarios include noise, wrong measurements and severe incidents on the power system such as the trip of a generator and a short-circuit at the transmission level. The performance of the estimators is discussed in terms of accuracy, computational time and robustness.
FBDL: A Declarative Language for Interpolative Fuzzy Behavior Modeling

Behavior-based system (BBS) is a structure built upon behavior components, behavior coordination and behavior fusion. The goal of this paper is to introduce a declarative language especially designed for supporting embedded behavior- based applications. The suggested language adapts the BBS implementation based on fuzzy rule-based systems, fuzzy rule interpolation (FRI) and fuzzy state machines. This case the description of the BBS as a network of behaviors turns to a network of fuzzy systems. The suggested Fuzzy Behavior Description Language (FBDL), is a declarative language for defining FRI rule-bases and a network of fuzzy systems in a unified framework. According to the suggested concept, the FBDL code is "running", as a parameter configuration, directly on a FRI state machine called "FRI Behavior Engine". In an embedded behavior-based application, the FRI Behavior Engine with the parameter configuration fetched from the FBDL code can directly serve the actions according to the observations and the state of the system.
Geometric Constraints Based Selective Segmentation for Vector Valued Images

Selective image segmentation extracts object of interest from an image based on user input. There are many variational methods which are effective in segmenting object which has uniform intensity. But many times object with multiple intensities needs to extracted. In this paper, we propose an active contour based algorithm for selective segmentation of objects in vector valued image. Here, we introduced new energy function by adding new geometric constraints based fidelity terms from local region of the object for vector valued images. The model minimizes new functional over the length of the contour along various parts of the object in different component images. Experimental results shows that the proposed method is effective in segmenting object having multi intensities. Performance of the proposed method is determined using Jaccard's similarity index for all methods.
Towards an ILP Application in Machine Ethics

Machine Ethics is a newly emerging interdisciplinary field which is concerned with adding an ethical dimension to Artificial Intelligent (AI) agents. In this paper we address the problem of representing and acquiring rules of codes of ethics in the online customer service domain. The proposed solution approach relies on the non-monotonic features of Answer Set Programming (ASP) and applies ILP. The approach is illustrated by means of examples taken from the preliminary tests conducted with a couple of state-of-the-art ILP algorithms for learning ASP rules.
Heterogeneous Non-Local Fusion for Multimodal Activity Recognition

In this work, we investigate activity recognition using multimodal inputs from heterogeneous sensors. Activity recognition is commonly tackled from a single-modal perspective using videos. In case multiple signals are used, they come from the same homogeneous modality, e.g. in the case of color and optical flow. Here, we propose an activity network that fuses multimodal inputs coming from completely different and heterogeneous sensors. We frame such a heterogeneous fusion as a non-local operation. The observation is that in a non-local operation, only the channel dimensions need to match. In the network, heterogeneous inputs are fused, while maintaining the shapes and dimensionalities that fit each input. We outline both asymmetric fusion, where one modality serves to enforce the other, and symmetric fusion variants. To further promote research into multimodal activity recognition, we introduce GloVid, a first-person activity dataset captured with video recordings and smart glove sensor readings. Experiments on GloVid show the potential of heterogeneous non-local fusion for activity recognition, outperforming individual modalities and standard fusion techniques.
Recovering and rendering silenced experiences of genocides: Testimonial fragments of the Holocaust

Abstract The experiences of murdered victims of Nazi persecutions perished with them. This article discusses how text and data mining technology has helped to recover fragments of these lost experiences out of 2,500 oral history interviews with survivors. This gave rise to Let them Speak, a data edition of Holocaust testimonies. The first part situates the challenge of revealing lost experiences in historiography, and argues that the experience of murdered victims can be reconstructed through the collective experience. The second part shows how text and data mining techniques assisted the author to identify some pieces of the collective experience. The third part presents how web technology and visualization are used to render pieces of the collective experience as testimonial fragments of the Holocaust.
Evaluation of Encoding Schemas for Optimization of Bit-Level Run-Length Encoding Within Lossless Compression of Binary Images

The paper deals with the problematics of lossless data compression and is focused on the use of Run-Length Encoding (RLE) principle of lossless compression of binary images, especially binarized volume datasets that can be considered as the 3D binary images. Different alternative encoding schemas are proposed in the first part of this work for bit-level run-length encoding and capacities of their encoding words are discussed also with the focus on the encoding of long symbol runs. Test results of proposed encoding schemas using binarized volume datasets obtained by the medical imaging techniques including Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) and the influence of the length of the encoding word to the compression rate of proposed encoding schemas are discussed in the second part of the paper.
Long Term Retention of Programming Concepts Learned Using a Software Tutor

Do students retain the programming concepts they have learned using software tutors over the long term? In order to answer this question, we analyzed the data collected by a software tutor on selection statements. We used the data of the students who used the tutor more than once to see whether they had retained for the second session what they had learned during the first session. We found that students retained over 71% of selection concepts that they had learned during the first session. The more problems students solved during the first session, the greater the percentage of retention. Even when students already knew a concept and did not benefit from using the tutor, a small percentage of concepts were forgotten from the first session to the next, corresponding to transience of learning. Transience of learning varied with concepts. We list confounding factors of the study.
TextCNN-based Text Classification for E-government

The proposals of CPPCC members are divided into seven areas and more than 74 sub-directions according to their themes. To classify the proposals, that is to say, to determine the subject words is the primary task of handling the proposals. In this paper, TextCNN is used to pre-classify the subject words of proposals in order to assist in the final determination of the subject words manually. According to the text characteristics of the proposal, a professional dictionary and a special stop dictionary for uncommon words are first established. The text is trained as vectors through Word2dev and used as input of TextCNN neural network. High-level features are extracted by convolution layer, dimensionality reduction by pooling layer, feature integration by full connection layer and multiclassification by softmax layer. The open source deep learning framework TensorFlow is used to realize the base. The training and design of E-government text categorization algorithm based on in-depth learning, the structure of deep convolution neural network model and the optimization of model parameters are designed and saved. The experimental results show that the accuracy is improved by 3% compared with the relative entropy classification algorithm used in the previous system. At the same time, the training model is used to restore and predict the classification in the actual system, which reduces the computational pressure of the server.
EyeLinks: Methods to compute reliable stereo mappings used for eye gaze tracking

We present methods for extracting corneal images and estimating pupil centers continuously and reliably using head worn glasses that consists of two eye cameras. An existing CNN was modified for detecting pupils in IR and RGB images, and stereo vision together with 2D and 3D models are used. We confirm the feasibility of the proposed methods through user study results, which show that the methods can be used in future real gaze estimation systems.
Regional income management information system with Enterprise Architecture Planning Approach (EAP): a case study in BPKPD Klungkung Regency

Abstract Financial and Revenue Management Agency (BPKPD) Klungkung is a type of agency that carries out financial support functions to assist the Regent of Klungkung in administering regional autonomy. Although the regional revenue is in considerable amount, BPKPD Klungkung has no guidance in developing a regional revenue management information system in Klungkung. Thus, a good, efficient, effective, and accountable local revenue management procedure becomes essential. One of the options is to employ information systems and information technology (SI/IT) through the implementation of applications in various organizational functions. Enterprise Architecture Planning (EAP) is a method or frame of reference in building an information architecture that focuses on business needs that consist of data architecture, applications, and technology as well as the implementation plan of architecture created to support business activities to achieve the organization objectives. The result of the research using EAP showed that data architecture methodology was first to be defined so as to produce 19 data entities, defining the application architecture and technology. Based on application portfolio, there were 6 key operational applications 2 strategic applications, 2 high potential applications, and 1 supporting application displayed. It was expected that the regional revenue of Klungkung will be optimized through the strategic information system planning using EAP.
Google Helps YouTube: Learning Few-Shot Video Classification from Historic Tasks and Cross-Domain Sample Transfer

The fact that video annotation is labor-intensive inspires recent research to endeavor on few-shot video classification. The core motivation of our work is to mitigate the supervision scarcity issue in this few-shot setting via cross-domain meta-learning. Particularly, we aim to harness large-scale richly-annotated image data (i.e., source domain) for few-shot video classification (i.e., target domain). The source data is heterogeneous (image v.s. video) and has noisy labels, not directly usable in the target domain. This work proposes meta-learning input-transformer (MLIT), a novel deep network that tames the noisy source data such that they are more amenable for being used in the target domain. It has two key traits. First, to bridge the data distribution gap between source / target domains, MLIT includes learnable neural layers to reweigh and transform the source data, effectively suppressing corrupted or noisy source data. Secondly, MLIT is designed to learn from historic video classification tasks in the target domain, which significantly elevates the accuracy of the unseen video category. Comprehensive empirical evaluations on two large-scale video datasets, ActivityNet and Kinetics-400, have strongly shown the superiority of our proposed method.
Content design: e-learning module for study on cultural tourism information systems based on local wisdom

Abstract Facing the 4.0 industrial revolution, it is necessary to carry out reforms and innovations related to national education. Reforms and innovations in the implementation of components of the education system involve changes in curriculum, graduate competencies and assessments, teacher qualifications, funding, facilities and infrastructure, decentralization and educational autonomy. Bali as a world tourist destination must be maintained and passed on to the younger generation. The younger generation can use information technology to help promote Balinese culture. Bali has a lot of local wisdom that can be implemented in the tourism sector. Therefore to promote those local wisdoms, a module that can be accessed through e-learning is developed to help students develop a cultural tourism package based on local wisdom to be promoted online. The modules developed have been tested by content and media experts, which can be used independently by students. Modules can be accessed online at e-learning and they are ready to use instructions for students so they can work on assignments and projects independently. Modules containing this theory and practice are for making brochures, tour packages, videos, and websites, all of which are connected to promote tourism based on local Balinese wisdom.
Intelligent Collaborative Authoring of Place-Based, Cross-Cultural and Media-Rich Experiences

In this paper, we present a framework that aims to support the active participation and collaboration of knowledge workers and engineers in the co-authoring of place-based, cross-cultural and media-rich memories, experiences, stories and narration. To achieve this, the framework proposes a novel approach for facilitating such a participation and collaboration through the semantic integration of data/information and integrated tools that will be both accessible via an open, user-friendly, mobile and knowledge-based platform, emphasizing a low-effort participative and guided co-authoring approach. The presented collaborative and participative approach is expected to foster social cohesion in heterogeneous communities of interest and practice. For the realization of the framework, we propose the implementation of a proof-of-concept system and its evaluation in the socio-cultural group of immigrants and refugees within the context of creating and sharing knowledge related to the physical and digital artifacts of a modern art museum. Our vision for the proposed framework is to introduce new technology for the collaborative authoring of cultural experiences with low effort using an intelligent assistant. Additionally, we envision a Shared Experiences Ecosystem (SEE) that aims to provide media-rich content and tools that will eventually foster the inclusive access of heterogeneous socio-cultural groups to shared experiences, increasing social cohesion in resilient local environments.
Pedestrians Detection of Offline Retail Stores Based on Computer Vision

Abstract Pedestrian Detection is a sub-task in the object detection due to it plays an important role in video surveillance. This project train model algorithm by using offline retail data and intelligent identification based on the offline retail brand store scene and focuses on the two core personnel of the retail scene-shopping guides and customers. This project detects pedestrian’s localization and their key points by using deep learning and computer vision technologies.
Customized Particle Swarm Optimization Algorithm for Frequent Itemset Mining

In data analytics, frequent itemset mining is the key process for decision making. Frequent patterns are analyzed to increase the benefits, reduce the costs, maximize the income and to develop the overall resources. Every day there is an increase in data, which leads to a massive dataset. Mining the frequent itemset is a time and memory consuming task. Handling a massive transactional database is a challenging issue. There is a need for an efficient technique to retrieve the frequent patterns by reducing the computational time and memory utilization. In this paper, evolutionary computing technique is implemented to retrieve the frequent itemsets. Customized PSO is applied to mine the frequent patterns by maximizing the number frequent itemsets identified in the transactional database. First, the standard Particle Swarm Optimization (SPSO), second, Constriction Factor Particle Swarm Optimization (CFPSO), third, Customized PSO with Local Search (CPSO_LS) technique are applied to deal with the retrieval of frequent itemsets efficiently. Finally, the proposed techniques are investigated based on the number of frequent itemsets retrieved, and computational time. Experimental results illustrates that the customized PSO with local search outperforms the other two variants of the PSO algorithm.