bluuebunny/crossref_metadata_2025 huggingface dataset:
AntProphet: an Intention Mining System behind Alipay's Intelligent Customer Service Bot
Authors: Chen Cen , Zhang Xiaolu , Ju Sheng , Fu Chilin , Tang Caizhi , Zhou Jun , Li Xiaolong
Published: July 2019

We create an intention mining system, named AntProphet, for Alipay's intelligent customer service bot, to alleviate the burden of customer service. Whenever users have any questions, AntProphet is the first stop to help users to answer their questions. Our system gathers users' profile and their historical behavioral trajectories, together with contextual information to predict users' intention, i.e., the potential questions that users want to resolve. AntProphet takes care of more than 90% of the customer service demands in the Alipay APP and resolves most of the users' problems on the spot, thus significantly reduces the burden of manpower. With the help of it, the overall satisfaction rate of our customer service bot exceeds 85%.
Crowd View: Converting Investors' Opinions into Indicators
Authors: Chen Chung-Chi , Huang Hen-Hsen , Chen Hsin-Hsi
Published: July 2019

This paper demonstrates an opinion indicator (OI) generation system, named Crowd View, with which traders can refer to the fine-grained opinions, beyond the market sentiment (bullish/bearish), from crowd investors when trading financial instruments. We collect the real-time textual information from Twitter, and convert it into five kinds of OIs, including the support price level, resistance price level, price target, buy-side cost, and sell-side cost. The OIs for all component stocks in Dow Jones Industrial Average Index (DJI) are provided, and shown with the real-time stock price for comparison and analysis. The information embedding in the OIs and the application scenarios are introduced.
VEST: A System for Vulnerability Exploit Scoring & Timing
Authors: Chen Haipeng , Liu Jing , Liu Rui , Park Noseong , Subrahmanian V. S.
Published: July 2019

Knowing if/when a cyber-vulnerability will be exploited and how severe the vulnerability is can help enterprise security officers (ESOs) come up with appropriate patching schedules. Today, this ability is severely compromised: our study of data from Mitre and NIST shows that on average there is a 132 day gap between the announcement of a vulnerability by Mitre and the time NIST provides an analysis with severity score estimates and 8 important severity attributes. Many attacks happen during this very 132-day window. We present Vulnerability Exploit Scoring \& Timing (VEST), a system for (early) prediction and visualization of if/when a vulnerability will be exploited, and its estimated severity attributes and score.
Demonstration of PerformanceNet: A Convolutional Neural Network Model for Score-to-Audio Music Generation
Authors: Chen Yu-Hua , Wang Bryan , Yang Yi-Hsuan
Published: July 2019

We present in this paper PerformacnceNet, a neural network model we proposed recently to achieve
score-to-audio music generation. The model learns
to convert a music piece from the symbolic domain
to the audio domain, assigning performance-level
attributes such as changes in velocity automatically
to the music and then synthesizing the audio. The
model is therefore not just a neural audio synthesizer, but an AI performer that learns to interpret a
musical score in its own way. The code and sample outputs of the model can be found online at
https://github.com/bwang514/PerformanceNet.
InterSpot: Interactive Spammer Detection in Social Media
Authors: Ding Kaize , Li Jundong , Dhar Shivam , Devan Shreyash , Liu Huan
Published: July 2019

Spammer detection in social media has recently received increasing attention due to the rocketing growth of user-generated data. Despite the empirical success of existing systems, spammers may continuously evolve over time to impersonate normal users while new types of spammers may also emerge to combat with the current detection system, leading to the fact that a built system will gradually lose its efficacy in spotting spammers. To address this issue, grounded on the contextual bandit model, we present a novel system for conducting interactive spammer detection. We demonstrate our system by showcasing the interactive learning process, which allows the detection model to keep optimizing its detection strategy through incorporating the feedback information from human experts.
Explore Truthful Incentives for Tasks with Heterogenous Levels of Difficulty in the Sharing Economy
Authors: Zhou Pengzhan , Wei Xin , Wang Cong , Yang Yuanyuan
Published: July 2019

Incentives are explored in the sharing economy to inspire users for better resource allocation. Previous works build a budget-feasible incentive mechanism to learn users' cost distribution. However, they only consider a special case that all tasks are considered as the same. The general problem asks for finding a solution when the cost for different tasks varies. In this paper, we investigate this general problem by considering a system with k levels of difficulty. We present two incentivizing strategies for offline and online implementation, and formally derive the ratio of utility between them in different scenarios. We propose a regret-minimizing mechanism to decide incentives by dynamically adjusting budget assignment and learning from users' cost distributions. Our experiment demonstrates utility improvement about 7 times and time saving of 54% to meet a utility objective compared to the previous works.
Embodied Conversational AI Agents in a Multi-modal Multi-agent Competitive Dialogue
Authors: Divekar Rahul R. , Mou Xiangyang , Chen Lisha , Gatti de Bayser Maíra , Guerra Melina Alberio , Su Hui
Published: July 2019

In a setting where two AI agents embodied as animated humanoid avatars are engaged in a conversation with one human and each other, we see two challenges. One, determination by the AI agents about which one of them is being addressed. Two, determination by the AI agents if they may/could/should speak at the end of a turn. In this work we bring these two challenges together and explore the participation of AI agents in multi-party conversations. Particularly, we show two embodied AI shopkeeper agents who sell similar items aiming to get the business of a user by competing with each other on the price. In this scenario, we solve the first challenge by using headpose (estimated by deep learning techniques) to determine who the user is talking to. For the second challenge we use deontic logic to model rules of a negotiation conversation.
A Mobile Application for Sound Event Detection
Authors: Fu Yingwei , Xu Kele , Mi Haibo , Wang Huaimin , Wang Dezhi , Zhu Boqing
Published: July 2019

Sound event detection is intended to analyze and recognize the sound events in audio streams and it has widespread applications in real life. Recently, deep neural networks such as convolutional recurrent neural networks have shown state-of-the-art performance in this task. However, the previous methods were designed and implemented on devices with rich computing resources, and there are few applications on mobile devices. This paper focuses on the solution on the mobile platform for sound event detection. The architecture of the solution includes offline training and online detection. During offline training process, multi model-based distillation method is used to compress model to enable real-time detection. The online detection process includes acquisition of sensor data, processing of audio signals, and detecting and recording of sound events. Finally, we implement an application on the mobile device that can detect sound events in near real time.
The Open Vault Challenge - Learning How to Build Calibration-Free Interactive Systems by Cracking the Code of a Vault
Authors: Grizou Jonathan
Published: July 2019

This demo takes the form of a challenge to the IJCAI community. A physical vault, secured by a 4-digit code, will be placed in the demo area. The author will publicly open the vault by entering the code on a touch-based interface, and as many times as requested. The challenge to the IJCAI participants will be to crack the code, open the vault, and collect its content. The interface is based on previous work on calibration-free interactive systems that enables a user to start instructing a machine without the machine knowing how to interpret the user’s actions beforehand. The intent and the behavior of the human are simultaneously learned by the machine. An online demo and videos are available for readers to participate in the challenge. An additional interface using vocal commands will be revealed on the demo day, demonstrating the scalability of our approach to continuous input signals.
Agent-based Decision Support for Pain Management in Primary Care Settings
Authors: Guo Xu , Yu Han , Miao Chunyan , Chen Yiqiang
Published: July 2019

The lack of systematic pain management training and support among primary care physicians (PCPs) limits their ability to provide quality care for patients with pain. Here, we demonstrate an Agent-based Clinical Decision Support System to empower PCPs to leverage knowledge from pain specialists. The system learns a general-purpose
representation space on patients, automatically diagnoses pain, recommends therapy and medicine, and suggests a referral program to PCPs in their decision-making tasks.
DISPUTool -- A tool for the Argumentative Analysis of Political Debates
Authors: Haddadan Shohreh , Cabrio Elena , Villata Serena
Published: July 2019

Political debates are the means used by political
candidates to put forward and justify their positions
in front of the electors with respect to the issues at
stake. Argument mining is a novel research area
in Artificial Intelligence, aiming at analyzing discourse on the pragmatics level and applying a certain argumentation theory to model and automatically analyze textual data. In this paper, we present
DISPUTool, a tool designed to ease the work of historians and social science scholars in analyzing the
argumentative content of political speeches. More
precisely, DISPUTool allows to explore and automatically identify argumentative components over
the 39 political debates from the last 50 years of
US presidential campaigns (1960-2016).
Contextual Typeahead Sticker Suggestions on Hike Messenger
Authors: Hanoosh Mohamed , Laddha Abhishek , Mukherjee Debdoot
Published: July 2019

In this demonstration, we present Hike's sticker recommendation system, which helps users choose the right sticker to substitute the next message that they intend to send in a chat. We describe how the system addresses the issue of numerous orthographic variations for chat messages and operates under 20 milliseconds with low CPU and memory footprint on device.
The pywmi Framework and Toolbox for Probabilistic Inference using Weighted Model Integration
Authors: Kolb Samuel , Morettin Paolo , Zuidberg Dos Martires Pedro , Sommavilla Francesco , Passerini Andrea , Sebastiani Roberto , De Raedt Luc
Published: July 2019

Weighted Model Integration (WMI) is a popular technique for probabilistic inference that extends Weighted Model Counting (WMC) -- the standard inference technique for inference in discrete domains -- to domains with both discrete and continuous variables.  However, existing WMI solvers each have different interfaces and use different formats for representing WMI problems.  Therefore, we introduce pywmi (http://pywmi.org), an open source framework and toolbox for probabilistic inference using WMI, to address these shortcomings.  Crucially, pywmi fixes a common internal format for WMI problems and introduces a common interface for WMI solvers.  To assist users in modeling WMI problems, pywmi introduces modeling languages based on SMT-LIB.v2 or MiniZinc and parsers for both.  To assist users in comparing WMI solvers, pywmi includes implementations of several state-of-the-art solvers, a fast approximate WMI solver, and a command-line interface to solve WMI problems.  Finally, to assist developers in implementing new solvers, pywmi provides Python implementations of commonly used subroutines.
ERICA and WikiTalk
Authors: Lala Divesh , Wilcock Graham , Jokinen Kristiina , Kawahara Tatsuya
Published: July 2019

The demo shows ERICA, a highly realistic female android robot, and WikiTalk, an application that helps robots to talk about thousands of topics using information from Wikipedia. The combination of ERICA and WikiTalk results in more natural and engaging human-robot conversations.
Design and Implementation of a Disambiguity Framework for Smart Voice Controlled Devices
Authors: Lei Kehua , Ma Tianyi , Jia Jia , Zhang Cunjun , Yang Zhihan
Published: July 2019

With about 100 million people using it recently, SVCD(Smart Voice Controlled Device) are becoming demotic. Whether at home or in an office, usually, multiple appliances are under the control of a single SVCD and several people may manipulate an SVCD simultaneously. However, present SVCD fails to handle them appropriately. In this paper, we propose a novel framework for SVCD to eliminate orders’ ambiguity for single user or multi-user. We also design an algorithm combining Word2Vec and emotion detection for the device to wipe off ambiguity. Finally, we apply our framework into a virtual smart home scene and the performance of it indicates that our strategy resolves the problems commendably.
Neural Discourse Segmentation
Authors: Li Jing
Published: July 2019

Identifying discourse structures and coherence relations in a piece of text is a fundamental task in natural language processing. The first step of this process is segmenting sentences into clause-like units called elementary discourse units (EDUs). Traditional solutions to discourse segmentation heavily rely on carefully designed features. In this demonstration, we present SegBot, a system to split a given piece of text into sequence of EDUs by using an end-to-end neural segmentation model. Our model does not require hand-crafted features or external knowledge except word embeddings, yet it outperforms state-of-the-art solutions to discourse segmentation.
CoSegNet: Image Co-segmentation using a Conditional Siamese Convolutional Network
Authors: Banerjee Sayan , Hati Avik , Chaudhuri Subhasis , Velmurugan Rajbabu
Published: July 2019

The objective in image co-segmentation is to jointly
segment unknown common objects from a given
set of images. In this paper, we propose a novel
deep convolution neural network based end-to-end
co-segmentation model. It is composed of a metric
learning and decision network leading to a novel
conditional siamese encoder-decoder network for
estimating a co-segmentation mask. The role of the
metric learning network is to find an optimum latent
feature space where objects of the same class
are closer and that of different classes are separated
by a certain margin. Depending on the
extracted features, the decision network decides
whether input images have common objects or not
and the encoder-decoder network produces a cosegmentation
mask accordingly. Key aspects of the
architecture are as follows. First, it is completely
class agnostic and does not require any semantic
information. Second, in addition to producing
masks, the decoder network also learns similarity
across image pairs that improves co-segmentation
significantly. Experimental results reflect an excellent
performance of our method compared to state of-the-art
methods on challenging co-segmentation
datasets.
CoTrRank: Trust Evaluation of Users and Tweets
Authors: Li Peiyao , Zhao Weiliang , Yang Jian , Wu Jia
Published: July 2019

Trust evaluation of people and information on Twitter is critical for maintaining a healthy online social environment. How to evaluate the trustworthiness of users and tweets becomes a challenging question. In this demo, we show how our proposed CoTrRank approach deal with this problem. This approach models users and tweets in two coupled networks and calculate their trust values in different trust spaces. In particular, our solution provides a configurable way when mapping the calculated raw evidences to the trust values. The CoTrRank demo system has an interactive interface to show how our proposed approach produces more effective and adaptive trust evaluation results comparing with baseline methods.
Mappa Mundi: An Interactive Artistic Mind Map Generator with Artificial Imagination
Authors: Liu Ruixue , Chen Baoyang , Chen Meng , Wu Youzheng , Qiu Zhijie , He Xiaodong
Published: July 2019

We present a novel real-time, collaborative, and interactive AI painting system, Mappa Mundi, for artistic Mind Map creation. The system consists  of a voice-based input interface, an automatic topic expansion module, and an image projection module. The key innovation is to inject Artificial Imagination into painting creation by considering lexical and phonological similarities of language, learning and inheriting
artist’s original painting style, and applying the principles of Dadaism and impossibility of improvisation. Our system indicates that AI and artist can collaborate seamlessly to create imaginative artistic painting and Mappa Mundi has been applied in art exhibition in UCCA, Beijing.
CRSRL: Customer Routing System Using Reinforcement Learning
Authors: Long Chong , Liu Zining , Lu Xiaolu , Hu Zehong , Wang Yafang
Published: July 2019

Allocating resources to customers in the customer service is a difficult problem, because designing an optimal strategy to achieve an optimal trade-off between available resources and customers' satisfaction is non-trivial.
In this paper, we formalize the customer routing problem, and propose a novel framework based on deep reinforcement learning (RL) to address this problem. To make it more practical, a demo is provided to show and compare different models, which visualizes all decision process, and in particular, the system shows how the optimal strategy is reached. Besides, our demo system also ships with a variety of models that users can choose based on their needs.
ACTA A Tool for Argumentative Clinical Trial Analysis
Authors: Mayer Tobias , Cabrio Elena , Villata Serena
Published: July 2019

Argumentative analysis of textual documents of various nature (e.g., persuasive essays, online discussion blogs, scientific articles) allows to detect the main argumentative components (i.e., premises and claims) present in the text and to predict whether these components are connected to each other by argumentative relations (e.g., support and attack), leading to the identification of (possibly complex) argumentative structures. Given the importance of argument-based decision making in medicine, in this demo paper we introduce ACTA, a tool for automating the argumentative analysis of clinical trials. The tool is designed to support doctors and clinicians in identifying the document(s) of interest about a certain disease, and in analyzing the main argumentative content and PICO elements.
A Quantitative Analysis Platform for PD-L1 Immunohistochemistry based on Point-level Supervision Model
Authors: Mi Haibo , Xu Kele , Xiang Yang , He Yulin , Feng Dawei , Wang Huaimin , Wu Chun , Song Yanming , Sun Xiaolei
Published: July 2019

Recently, deep learning has witnessed dramatic progress in the medical image analysis field. In the precise treatment of cancer immunotherapy, the quantitative analysis of PD-L1 immunohistochemistry is of great importance. It is quite common that pathologists manually quantify the cell nuclei. This process is very time-consuming and error-prone. In this paper, we describe the development of a platform for PD-L1 pathological image quantitative analysis using deep learning approaches. As point-level annotations can provide a rough estimate of the object locations and classifications, this platform adopts a point-level supervision model to classify, localize, and count the PD-L1 cells nuclei. Presently, this platform has achieved an accurate quantitative analysis of PD-L1 for two types of carcinoma, and it is deployed in one of the first-class hospitals in China.
SAGE: A Hybrid Geopolitical Event Forecasting System
Authors: Morstatter Fred , Galstyan Aram , Satyukov Gleb , Benjamin Daniel , Abeliuk Andres , Mirtaheri Mehrnoosh , Hossain KSM Tozammel , Szekely Pedro , Ferrara Emilio , Matsui Akira , Steyvers Mark , Bennet Stephen , Budescu David , Himmelstein Mark , Ward Michael , Beger Andreas , Catasta Michele , Sosic Rok , Leskovec Jure , Atanasov Pavel , Joseph Regina , Sethi Rajiv , Abbas Ali
Published: July 2019

Forecasting of geopolitical events is a notoriously difficult task, with experts failing to significantly outperform a random baseline across many types of forecasting events. One successful way to increase the performance of forecasting tasks is to turn to crowdsourcing: leveraging many forecasts from non-expert users. Simultaneously, advances in machine learning have led to models that can produce reasonable, although not perfect, forecasts for many tasks. Recent efforts have shown that forecasts can be further improved by ``hybridizing'' human forecasters: pairing them with the machine models in an effort to combine the unique advantages of both. In this demonstration, we present Synergistic Anticipation of Geopolitical Events (SAGE), a platform for human/computer interaction that facilitates human reasoning with machine models.
Reagent: Converting Ordinary Webpages into Interactive Software Agents
Authors: Peveler Matthew , Kephart Jeffrey O. , Su Hui
Published: July 2019

We introduce Reagent, a technology that can be used in conjunction with automated speech recognition to allow users to query and manipulate ordinary webpages via speech and pointing. Reagent can be used out-of-the-box with third-party websites, as it requires neither special instrumentation from website developers nor special domain knowledge to capture semantically-meaningful mouse interactions with structured elements such as tables and plots. When it is unable to infer mappings between domain vocabulary and visible webpage content on its own, Reagent proactively seeks help by engaging in a voice-based interaction with the user.
AiD-EM: Adaptive Decision Support for Electricity Markets Negotiations
Authors: Pinto Tiago , Vale Zita
Published: July 2019

This paper presents the Adaptive Decision Support for Electricity Markets Negotiations (AiD-EM) system. AiD-EM is a multi-agent system that provides decision support to market players by incorporating multiple sub-(agent-based) systems, directed to the decision support of specific problems. These sub-systems make use of different artificial intelligence methodologies, such as machine learning and evolutionary computing, to enable players adaptation in the planning phase and in actual negotiations in auction-based markets and bilateral negotiations. AiD-EM demonstration is enabled by its connection to MASCEM (Multi-Agent Simulator of Competitive Electricity Markets).
Deep Reinforcement Learning for Ride-sharing Dispatching and Repositioning
Authors: Qin Zhiwei (Tony) , Tang Xiaocheng , Jiao Yan , Zhang Fan , Wang Chenxi , Li Qun (Tracy)
Published: July 2019

In this demo, we will present a simulation-based human-computer interaction of deep reinforcement learning in action on order dispatching and driver repositioning for ride-sharing.  Specifically, we will demonstrate through several specially designed domains how we use deep reinforcement learning to train agents (drivers) to have longer optimization horizon and to cooperate to achieve higher objective values collectively.
GraspSnooker: Automatic Chinese Commentary Generation for Snooker Videos
Authors: Sun Zhaoyue , Chen Jiaze , Zhou Hao , Zhou Deyu , Li Lei , Jiang Mingmin
Published: July 2019

We demonstrate a web-based software system, GraspSnooker, which is able to automatically generate Chinese text commentaries for snooker game videos. It consists of a video analyzer, a strategy predictor and a commentary generator. As far as we know, it is the first attempt on snooker commentary generation, which might be helpful for snooker learners to understand the game.
Multi-Margin based Decorrelation Learning for Heterogeneous Face Recognition
Authors: Cao Bing , Wang Nannan , Gao Xinbo , Li Jie , Li Zhifeng
Published: July 2019

Heterogeneous face recognition (HFR) refers to matching face images acquired from different domains with wide applications in security scenarios. However, HFR is still a challenging problem due to the significant cross-domain discrepancy and the lacking of sufficient training data in different domains. This paper presents a deep neural network approach namely Multi-Margin based Decorrelation Learning (MMDL) to extract decorrelation representations in a hyperspherical space for cross-domain face images. The proposed framework can be divided into two components: heterogeneous representation network and decorrelation representation learning. First, we employ a large scale of accessible visual face images to train heterogeneous representation network. The decorrelation layer projects the output of the first component into decorrelation latent subspace and obtain decorrelation representation. In addition, we design a multi-margin loss (MML), which consists of tetradmargin loss (TML) and heterogeneous angular margin loss (HAML), to constrain the proposed framework. Experimental results on two challenging heterogeneous face databases show that our approach achieves superior performance on both verification and recognition tasks, comparing with state-of-the-art methods.
Multi-Agent Visualization for Explaining Federated Learning
Authors: Wei Xiguang , Li Quan , Liu Yang , Yu Han , Chen Tianjian , Yang Qiang
Published: July 2019

As an alternative decentralized training approach, Federated Learning enables distributed agents to collaboratively learn a machine learning model while keeping personal/private information on local devices. However, one significant issue of this framework is the lack of transparency, thus obscuring understanding of the working mechanism of Federated Learning systems. This paper proposes a multi-agent visualization system that illustrates what is Federated Learning and how it supports multi-agents coordination. To be specific, it allows users to participate in the Federated Learning empowered multi-agent coordination. The input and output of Federated Learning are visualized simultaneously, which provides an intuitive explanation of Federated Learning for users in order to help them gain deeper understanding of the technology.
Fair and Explainable Dynamic Engagement of Crowd Workers
Authors: Yu Han , Liu Yang , Wei Xiguang , Zheng Chuyu , Chen Tianjian , Yang Qiang , Peng Xiong
Published: July 2019

Years of rural-urban migration has resulted in a significant population in China seeking ad-hoc work in large urban centres. At the same time, many businesses face large fluctuations in demand for manpower and require more efficient ways to satisfy such demands. This paper outlines AlgoCrowd, an artificial intelligence (AI)-empowered algorithmic crowdsourcing platform. Equipped with an efficient explainable task-worker matching optimization approach designed to focus on fair treatment of workers while maximizing collective utility, the platform provides explainable task recommendations to workers' personal work management mobile apps which are becoming popular, with the aim to address the above societal challenge.
An Online Intelligent Visual Interaction System
Authors: Zeng Anxiang , Yu Han , Gao Xin , Ou Kairi , Huang Zhenchuan , Hou Peng , Song Mingli , Zhang Jingshu , Miao Chunyan
Published: July 2019

This paper proposes an Online Intelligent Visual Interactive System (OIVIS), which can be applied to various live video broadcast and short video scenes to provide an interactive user experience. In the live video broadcast, the anchor can issue various commands by using pre-defined gestures, and can trigger real-time background replacement to create an immersive atmosphere. To support such dynamic interactivity, we implemented algorithms including real-time gesture recognition and real-time video portrait segmentation, developed a deep network inference framework, and a real-time rendering framework AI Gender at the front end to create a complete set of visual interaction solutions for use in resource constrained mobile.
DeepRec: An Open-source Toolkit for Deep Learning based Recommendation
Authors: Zhang Shuai , Tay Yi , Yao Lina , Wu Bin , Sun Aixin
Published: July 2019

Deep learning based recommender systems have been extensively explored in recent years. However, the large number of models proposed each year poses a big challenge for both researchers and practitioners in reproducing the results for further comparisons. Although a portion of papers provides source code, they adopted different programming languages or different deep learning packages, which also raises the bar in grasping the ideas. To alleviate this problem, we released the open source project: \textbf{DeepRec}. In this toolkit, we have implemented a number of deep learning based recommendation algorithms using Python and the widely used deep learning package - Tensorflow. Three major recommendation scenarios: rating prediction, top-N recommendation (item ranking) and sequential recommendation, were considered. Meanwhile, DeepRec maintains good modularity and extensibility to easily incorporate new models into the framework. It is distributed under the terms of the GNU General Public License. The source code is available at github: https://github.com/cheungdaven/DeepRec
ATTENet: Detecting and Explaining Suspicious Tax Evasion Groups
Authors: Zheng Qinghua , Lin Yating , He Huan , Ruan Jianfei , Dong Bo
Published: July 2019

In this demonstration, we present ATTENet, a novel visual analytic system for detecting and explaining suspicious affiliated-transaction-based tax evasion (ATTE) groups. First, the system constructs a taxpayer interest interacted network, which contains economic behaviors and social relationships between taxpayers. Then, the system combines basic features and structure features of each group in the network with network embedding method structure2Vec, and then detects suspicious ATTE groups with random forest algorithm. Last, to explore and explain the detection results, the system provides an ATTENet visualization with three coordinated views and interactive tools. We demonstrate ATTENet on a non-confidential dataset which contains two years of real tax data obtained by our cooperative tax authorities to verify the usefulness of our system.
Intelligent Decision Support for Improving Power Management
Authors: Zheng Yongqing , Yu Han , Zhang Kun , Shi Yuliang , Leung Cyril , Miao Chunyan
Published: July 2019

With the development and adoption of the electricity information tracking system in China, real-time electricity consumption big data have become available to enable artificial intelligence (AI) to help power companies and the urban management departments to make demand side management decisions. We demonstrate the Power Intelligent Decision Support (PIDS) platform, which can generate Orderly Power Utilization (OPU) decision recommendations and perform Demand Response (DR) implementation management based on a short-term load forecasting model. It can also provide different users with query and application functions to facilitate explainable decision support.
Generalized Zero-Shot Vehicle Detection in Remote Sensing Imagery via Coarse-to-Fine Framework
Authors: Chen Hong , Luo Yongtan , Cao Liujuan , Zhang Baochang , Guo Guodong , Wang Cheng , Li Jonathan , Ji Rongrong
Published: July 2019

Vehicle detection and recognition in remote sensing images are challenging, especially when only limited training data are available to accommodate various target categories. In this paper, we introduce a novel coarse-to-fine framework, which decomposes vehicle detection into segmentation-based vehicle localization and generalized zero-shot vehicle classification. Particularly, the proposed framework can well handle the problem of generalized zero-shot vehicle detection, which is challenging due to the requirement of recognizing vehicles that are even unseen during training. Specifically, a hierarchical DeepLab v3 model is proposed in the framework, which fully exploits fine-grained features to locate the target on a pixel-wise level, then recognizes vehicles in a coarse-grained manner. Additionally, the hierarchical DeepLab v3 model is beneficially compatible to combine the generalized zero-shot recognition. To the best of our knowledge, there is no publically available dataset to test comparative methods, we therefore construct a new dataset to fill this gap of evaluation. The experimental results show that the proposed framework yields promising results on the imperative yet difficult task of zero-shot vehicle detection and recognition.
Structure-Aware Residual Pyramid Network for Monocular Depth Estimation
Authors: Chen Xiaotian , Chen Xuejin , Zha Zheng-Jun
Published: July 2019

Monocular depth estimation is an essential task for scene understanding. The underlying structure of objects and stuff in a complex scene is critical to recovering accurate and visually-pleasing depth maps. Global structure conveys scene layouts, while local structure reflects shape details. Recently developed approaches based on convolutional neural networks (CNNs) significantly improve the performance of depth estimation. However, few of them take into account multi-scale structures in complex scenes. In this paper, we propose a Structure-Aware Residual Pyramid Network (SARPN) to exploit multi-scale structures for accurate depth prediction. We propose a Residual Pyramid Decoder (RPD) which expresses global scene structure in upper levels to represent layouts, and local structure in lower levels to present shape details. At each level, we propose Residual Refinement Modules (RRM) that predict residual maps to progressively add finer structures on the coarser structure predicted at the upper level. In order to fully exploit multi-scale image features, an Adaptive Dense Feature Fusion (ADFF) module, which adaptively fuses effective features from all scales for inferring structures of each scale, is introduced. Experiment results on the challenging NYU-Depth v2 dataset demonstrate that our proposed approach achieves state-of-the-art performance in both qualitative and quantitative evaluation. The code is available at https://github.com/Xt-Chen/SARPN.
A Deep Bi-directional Attention Network for Human Motion Recovery
Authors: Cui Qiongjie , Sun Huaijiang , Li Yupeng , Kong Yue
Published: July 2019

Human motion capture (mocap) data, recording the movement of markers attached to specific joints, has gradually become the most popular solution of animation production. However, the raw motion data are often corrupted due to joint occlusion, marker shedding and the lack of equipment precision, which severely limits the performance in real-world applications. Since human motion is essentially a sequential data, the latest methods resort to variants of long short-time memory network (LSTM) to solve related problems, but most of them tend to obtain visually unreasonable results. This is mainly because these methods hardly capture long-term dependencies and cannot explicitly utilize relevant context, especially in long sequences. To address these issues, we propose a deep bi-directional attention network (BAN) which can not only capture the long-term dependencies but also adaptively extract relevant information at each time step. Moreover, the proposed model, embedded attention mechanism in the bi-directional LSTM (BLSTM) structure at the encoding and decoding stages, can decide where to borrow information and use it to recover corrupted frame effectively. Extensive experiments on CMU database demonstrate that the proposed model consistently outperforms other state-of-the-art methods in terms of recovery accuracy and visualization.
دور السلطة المالية في الحد من التهرب الضريبي بحث استطلاعي لاراء عينة من المختصين في دوائر ضريبة الدخل في محافظتي أربيل ودهوك
Authors: Mohammed Salih Ahmed I.
Published: July 2019

يعد فرض الضرائب في الدول المتقدمة والنامية من الأداوات التي تستخدمها الدولة في الحصول على الايرادات، وتوجيه الاقتصاد نحو تحقيق العدالة الاجتماعية، وتوفير االموارد لتأمين الخدمات العامة, وللأهمية التي تتصف بها ضريبة الدخل هناك حاجة الى معالجة كافة المشاكل التي تواجه ضريبة الدخل في مختلف الجوانب، ومنها مشكلة التهرب الضريبي.

يهدف البحث إلى تقييم الواقع الحالي لضريبة الدخل في إقليم كوردستان العراق، تقييم الواقع الحالي لقانون ضريبة الدخل في الإقليم، بيان مدى التزام مكلفي ضريبة الدخل بالسياسات والإجراءات الواردة في قانون ضريبة الدخل في الإقليم ، بيان مدى تطبيق العقوبات الواردة في قانون ضريبة الدخل من قبل السلطات المالية لمنع التهرب الضريبي في إقليم كوردستان.

ولتحقيق أهداف البحث اعتمد على فرضية رئيسة وفرضيتين فرعيتين لاختبار العلاقة والتأثير بين السلطة المالية والتهرب الضريبي في محافظتي أربيل ودهوك، ولإختبار فرضيات البحث فقد أعتمد على المنهج النظري التحليلي في إطاره النظري، والمنهج الوصفي التحليلي من خلال إعداد إستبانة خاصة لإستقصاء أراء عينة عشوائية تناسبية من مجتمع البحث المتمثلة بدوائر ضريبة الدخل في محافظتي أربيل ودهوك في الإقليم.

توصل البحث إلى مجموعة من الإستنتاجات أبرزها، هناك تطبيق غير كافي لإجراءات قانون ضريبة الدخل من قبل مكلفي ضريبة الدخل في محافظتي أربيل ودهوك، خصوصاً فيما يتعلق بتقديم المكلف تقريراً عن دخله خلال المدة التي حددها قانون ضريبة الدخل، وتقديم معلومات حقيقية عن دخولهم، والالتزام بمسك الدفاتر والسجلات التجارية لأغراض ضريبة الدخل، تطبيق غير مقبول للعقوبات القانونية من قبل دوائر ضريبة الدخل في محافظتي أربيل ودهوك بحق المكلفين الذين يخالفون إجراءات قانون ضريبة الدخل ، دورالسلطة المالية في منع التهرب الضريبي في محافظتي أربيل ودهوك ليس بالمستوى المطلوب. قدم البحث مجموعة من التوصيات أهمها التزام أكثر لمكلفي ضريبة الدخل بإجراءات قانون ضريبة الدخل، قيام دوائر الضريبة بدورها في منع التهرب الضريبي من خلال تطبيق العقوبات القانونية بحق الاشخاص الذين يخالفون إجراءات قانون ضريبة الدخل، متابعة دوائر ضريبة الدخل من قبل الجهات القانونية المعنية حولة مدى التزامهم بتنفيذ القوانين وتعليمات الضريبة.
الدور الوظيفي للأسعرديين في بلاد الشام و مصر في العهدين الأيوبي والمماليك البحرية (570-784 هـ/1175 – 1382 م)
Authors: Zubair Al-Babiri Hakim A. , Ahmed Karavan M.
Published: July 2019

ساهم العديد من الأشخاص المنحدرين من مدينة أسعرد (سيرت) مساهمة فعالة في مختلف الميادين الحضارية في مصر وبلاد الشام خلال العهدين الأيوبي والمماليك البحرية (570-784 هـ / 1175 – 1382م)، ففضلا عن دورهم البارز في الحركة العلمية في مختلف مجالاتها،فكان لهم دور مميز في العديد من الوظائف الدينية والديوانية والإدارية مثل القضاء والحسبة والوزارة والنيابة والكتابة، ووظائف اخرى عديدة، وذلك عن طريق تولي العديد منهم لتلك الوظائف وأدائهم المتميز فيها، حيث قدموا من خلال جميع تلك الوظائف خدمات جليلة للمجتمع المصري والشامي في العهدين المذكورين، اسهبت كتب التراجم والسير والطبقات –المعاصرة لتلك الفترة – في ذكر العديد من الشخصيات الأسعردية الذين تولوا وظائف الدولة بمختلف المجالات.

تستهدف هذه الدراسة بيان الخدمات التي قدمها اولئك الاعلام الأسعردية للحضارة الاسلامية من خلال تلك الوظائف أبان الإطار الزماني والمكاني للدراسة، والوقوف على نهجهم وسلوكهم وصفاتهم وأدائهم الوظيفي من خلال تتبع اخبارهم في المصادر المختصة وبيان مكانتهم ومنزلتهم عند السلطات الحاكمة وجمهور الناس.
Analisis Good Corporate Governance (GCG) pada Badan Usaha Milik Daerah (BUMD) Provinsi Sumatera Barat(Studi Kasus PT Grafika Jaya Sumbar)
Authors: Azre Ilham Aldelano
Published: July 2019

BUMD memiliki peran dalam mewujudkan kemakmuran daerah dengan memberikan kontribusi terhadap Penerimaan PAD baik dalam bentuk deviden atau pajak. Tantangan meningkatkan PAD salah satunya dapat dijawab dengan meningkatkan peran/kontribusi BUMD. BUMD banyak dijalankan tidak secara efisien,inefisiensi yang dialami tersebut disebabkan adanya intervensi politik, sentralisasi dan manajemen yang buruk, perilaku perburuan rente dalam kebijakan, serta ketidak efisienan dalam menjalankan perusahaan, serta tidak adanya tata kelola perusahaan (Good Corporate Governance) yang baik sehingga prinsip-prinsip tranparansi, akuntabilitas serta keadilan menjadi pemasalahan yang mendasar dalam pengelolaan BUMD. Kebanyakan BUMD di Indonesia beroperasi di bawah kondisi yang sangat tidak efisien. Terjadi pemborosan dana di sana-sini karena para pengelolanya tidak memiliki keahlian yang cukup. Terkadang keputusan-keputusan manajerial berkaitan dengan investasi baru, penentuan tarif atau keputusan lain diambil secara tidak profesional. Pekatnya nuansa kolusi, korupsi dan nepotisme menandakan ketidakprofesionalan para pengelola BUMD tersebut. Bila saat ini banyak BUMD yang kalah bersaing dengan sektor swasta dan akhirnya tumbang di tengah jalan, salah satu penyebabnya adalah besarnya campur tangan dan lambannya pemerintah daerah dalam mengantisipasi perubahan situasi dan kondisi bisnis. Selama ini semua keputusan bisnis baik yang bersifat strategis maupun keputusan-keputusan konvensional lainnya harus selalu izin kepada pemerintah.
Pendidikan Pelatihan dan Bimbingan Teknis Bagi Aparatur Pemerintahan: Sebuah Analisis Kebutuhan di Pemerintah Kota Dumai
Authors: Mulianto Budi
Published: July 2019

Penelitian ini menjelaskan mengenai analisis kebutuhan pendidikan pelatihan dan bimbingan teknis bagi aparatur pemerintahan di pemerintah kota Dumai. Penelitian ini menemukan bahwa  ada 12 program prioritas yang harus segera dilaksanakan  oleh pemerintah kota Dumai kepada para aparatur pemerintahannya. Beberapa diantaranya adalah pelatihan penyusunan laporan keuangan yang tepat dan pelatihan pengunaan media sosial untuk instansi pemerintahan. Ini semata-mata untuk meningkatkan kualitas aparatur negara yang kemudian berujung pada pelayanan publik yang memuaskan. Penelitian ini menggunakan konsep manajemen sumber daya manusia dan pendidikan dan pelatihan bagi aparatur pemerintahan. Metode penelitian kualitatif adalah metode yang digunakan pada penelitian ini.
Akuntabilitas Pengelolaan Keuangan Nagari di Nagari Cubadak Kecamatan Lima Kaum Kabupaten Tanah Datar Tahun 2016
Authors: Sari Yusmalinda
Published: July 2019

Dalam mewujudkan akuntabilitas pengelolaan keuangan nagari, Pemerintah Nagari Cubadak melakukan inovasi yang baru ada di Kabupaten Tanah Datar pada tahun 2016 yaitu pembuatan buku profil Nagari Cubadak, kebijakan manajemen Masjid, dan menampilkan informasi rincian penggunaan Anggaran Pendapatan Belanja Nagari (APBNag) kepada masyarakat dengan media papan informasi. Dengan adanya sikap akuntabel, diharapkan dapat mewujudkan penerapan prinsip good governance, dan dapat menyediakan informasi kepada publik secara terbuka dalam pengelolaan keuangan nagari. Namun nyatanya masih terdapat “temuan” dalam pertanggungjawaban keuangan di Nagari Cubadak, sehingga penelitian ini bertujuan untuk mendeskripsikan bagaimana akuntabilitas pengelolaan keuangan nagari di Nagari Cubadak, Kecamatan Lima Kaum Kabupaten Tanah Datar Tahun 2016. Metode penelitian yang digunakan adalah kualitatif dengan tipe penelitian analisis deskriptif. Teknik pengumpulan data melalui wawancara dengan perangkat nagari, observasi, dokumentasi, dan studi kepustakaan. Triangulasi data melalui informan triangulasi yaitu masyarakat, Camat Lima Kaum, Kasi. Pembinaan Desa dan Aset Dinas Pemberdayaan Masyarakat Desa, Pengendalian Penduduk, dan Keluarga Berencana (Dinas PMDPPKB) Kabupaten Tanah Datar dan Auditor Inspektorat Kabupaten Tanah Datar. Hasil penelitian menunjukkan bahwa akuntabilitas pengelolaan keuangan nagari di Nagari Cubadak tahun 2016 dianggap sudah mempertanggungjawabkan keuangannya dengan baik karena adanya beberapa program yang mengarah kepada keterbukaan informasi publik. Namun masih terdapat beberapa temuan sehingga perlunya peningkatan kualitas kinerja Perangkat Nagari Cubadak dalam meloporkan keuangannya secara akuntabel yang tidak hanya kepada masyarakat, melainkan juga kepada Pemerintah Daerah Kabupaten Tanah Datar yang berhubungan dengan pengelolaan keuangan nagari.
Pelayanan Adsministrasi Kependudukan Kota Surabaya Melalui Program E-LAMPID Ditinjau Dari Perspektif Electrnic Government
Authors: Rozani Ufi Rahmi , Mutiasari H Aprillia , Rachmania A Nurin Rachmania A
Published: July 2019

Electronic Government merupakan penggunaan teknologi dan informasi untuk administrasi pemerintahan yang efisien dan efektif serta memberikan pelayanan yang transparan dan memuaskan kepada masyarakat. Program E-lampid adalah program yang digagas oleh Disdukcapil Kota Surabaya sebagai wujud dari penerapan electronic government Kota Surabaya yang menjadi percontohan bagi 40 kepala daerah di Indonesia seperti Pemerintah Provinsi Sumatera Utara, Pemerintah Kota Medan, Pemerintah Provinsi Riau dan lain sebagainya. Latar belakang pembuatan program E-lampid adalah kepadatan volume antrean dalam pengurusan administrasi kependudukan seperti akta kelahiran, kematian dan surat pindah datang yang selalu padat setiap hari kerja. Hal ini menyebabkan penumpukan arsip dan banyaknya arsip yang terselip sehingga pengurusan administrasi kependudukan tidak sesuai dengan prosedur yang ada. Program yang digagas pada tahun 2013 ini bertujuan untuk memudahkan masyarakat Kota Surabaya dalam pelayanan administrasi kependudukan, meminimalisir penumpukan arsip, meningkatkan kualitas pelayanan kependudukan dan mempermudah Pemerintah Kota Surabaya dalam melakukan update data. Harapannya, ketika tujuan E-lampid tercapai Pemerintah Kota Surabaya mampu memanfaatkan informasi kependudukan sebagai bahan pembuat kebijakan untuk kesejahteraan masyarakat Kota Surabaya. Berdasarkan hasil observasi, E-lampid ini telah sampai pada 2 tipe relasi yaitu Government to Government dan Government to Citizens dimana pihak yang dapat merasakan manfaat dari pelaksanaannya adalah masyarakat Kota Surabaya dan juga pejabat pemerintah ditingkat kelurahan, kecamatan maupun Dinas Kependudukan dan Catatan Sipil Kota Surabaya. Diharapkan program E-lampid kedepannya dapat bekerjasama dengan rumah sakit swasta maupun pemerintah dalam pelaporan surat keterangan lahir sehingga dapat mewujudkan tipe relasi Government to Business. Pada pelaksanaannya program E-lampid terdapat berbagai tantangan dan hambatan yang dihadapi oleh Pemerintah Kota Surabaya dan Disdukcapil Kota Surabaya sehingga 2 instansi ini terus berusaha untuk mengatasi hambatan dan tantangan yang ada agar pelayanan program E-lampid dapat berjalan sesuai target yang sudah ditentukan
Tanah untuk Rakyat : Studi Fenomenologi Makna Penggusuran Penghuni Gumuk Pasir, Parangtritis
Authors: Widianto Hardian Wahyu
Published: July 2019

Upaya restorasi Gumuk Pasir, di pantai Parangtritis, Bantul, menimbulkan efek samping berupa penggusuran bagi warga yang tinggal disana. Penelitian ini disusun untuk meneliti makna penggusuran yang dilakukan oleh Pemerintah Daerah (PEMDA) Kabupaten Bantul terhadap warga penghuni Gumuk Pasir. Peneliti menggunakan pendekatan hermeneutik fenomenologi guna menafsirkan makna yang terbentuk pada penghuni Gumuk Pasir berdasarkan pengalaman hidup mereka.  Makna penggusuran bagi warga penghuni gumuk pasir jelas sebagai bentuk penguasaan tanah negara. Gumuk Pasir telah menjadi tanah negara dan dimanfaatkan mereka, dikuasai paksa menggunakan dasar Sultan Ground. Padahal Sultan Ground jelas – jelas sebagai warisan kolonial yang ingin diganti dengan hukum agraria nasional. Agar tercapai tujuan konstitusi Indonesia untuk menciptakan keadilan sosial bagi seluruh rakyat Indonesia melalui akes terhadap sumber daya (tanah).