Miracle-dz/UniFAR-FTUs huggingface dataset:
The Consent of the Crowd Detected in an Open Forum

Within Reddit, Change My View is a specific debate forum where anyone can expose her or his view on a given topic and ask the crowd to provide counter-arguments with the aim of potentially changing this view. CMV uses a dual reward system according to which a) anyone, often the person who had asked the initial question, can highlight and acknowledge an argument (a post) by giving it a "delta" (Δ) and b) anyone can up- or down-vote one or several posts in a discussion. We take advantage of this dual reward system to investigate a phenomenon we call the Consent of the Crowd. Our results provide evidence on the use of the up-vote reward system by the crowd in order to express a dissent against the Δ reward. This phenomenon may be observed when not enough contributors got a chance to join the discussion before the Δ is granted. Our result highlights the necessity for forum moderators to foster discussion between as many participants as possible before closing discussions.
Understanding Crowdsourcing Systems from a Multiagent Perspective and Approach

Crowdsourcing has recently been significantly explored. Although related surveys have been conducted regarding this subject, each has mainly consisted of a review of a single aspect of crowdsourcing systems or on the application of crowdsourcing in a specific application domain. A crowdsourcing system is a comprehensive set of multiple entities, including various elements and processes. Multiagent computing has already been widely envisioned as a powerful paradigm for modeling autonomous multi-entity systems with adaptation to dynamic environments. Therefore, this article presents a novel multiagent perspective and approach to understanding crowdsourcing systems, which can be used to correlate the research on crowdsourcing and multiagent systems and inspire possible interdisciplinary research between the two areas. This article mainly discusses the following two aspects: (1) The multiagent perspective can be used for conducting a comprehensive survey on the state of the art of crowdsourcing, and (2) the multiagent approach can bring about concrete enhancements for crowdsourcing technology and inspire future research directions that enable crowdsourcing research to overcome the typical challenges in crowdsourcing technology. Finally, this article discusses the advantages and disadvantages of the multiagent perspective by comparing it with two other popular perspectives on crowdsourcing: the business perspective and the technical perspective.
Phrase2VecGLM: Neural generalized language model–based semantic tagging for complex query reformulation in medical IR

In this work, we develop a novel, completely unsupervised, neural language model-based document ranking approach to semantic tagging of documents, using the document to be tagged as a query into the GLM to retrieve candidate phrases from top-ranked related documents, thus associating every document with novel related concepts extracted from the text. For this we extend the word embedding-based general language model due to Ganguly et al 2015, to employ phrasal embeddings, and use the semantic tags thus obtained for downstream query expansion, both directly and in feedback loop settings. Our method, evaluated using the TREC 2016 clinical decision support challenge dataset, shows statistically significant improvement not only over various baselines that use standard MeSH terms and UMLS concepts for query expansion, but also over baselines using human expert–assigned concept tags for the queries, run on top of a standard Okapi BM25–based document retrieval system.
An Ensembled Scheme for QoS-Aware Traffic Flow Management in Software Defined Networks

In recent times, smart communities such as-smart grid, smart healthcare, and smart manufacturing units consists of large number of connected devices equipped with advanced processing and communication capabilities. The focus of these smart communities have shifted towards the use of intelligent processing and control for providing better quality of service (QoS) to the end user domain. To support this aspect, software defined networking (SDN) is being widely deployed in different domains such as-data center networks, fog/edge computing, smart grid, and vehicular networks. The variable requirements of different applications in smart communities make it necessary to deploy flexible and scalable SDN. The dynamic flow management capability of SDN has lots of potential that needs to be effectively explored in order to provide QoS guarantee for traffic generated from different smart applications. In this direction, in this paper, an ensembled scheme for QoS-aware traffic flow management in SDN is designed. The proposed scheme works in three phases: 1) a linear ordering scheme for dependency removal of the incoming packets is designed, 2) an application-specific traffic classification scheme is designed, and 3) a queue management scheme is designed for efficient scheduling of traffic flow. The proposed scheme is evaluated over an experimental setup. The results obtained shows that the proposed scheme behaves effectively with respect to different QoS parameters.
On the Profit Maximization of Spectrum Investment under Uncertainties in Cognitive Radio Networks

In this paper, we investigate the profit maximization problem for the mobile virtual network operator in cognitive radio networks considering the uncertain property of users' spectrum demand. In order to achieve more revenues while simultaneously satisfying the needs of users, the cognitive mobile virtual network operator chooses to dynamically sense the idle spectrum in the licensed band which is more economic, and at the same time leases the spectrum from the spectrum owner which guarantees more stable spectrum resources. However, the fluctuant spectrum demand of users imposes unprecedented challenges on the decision making process. To deal with the uncertain features of the users' demand, a flexible distribution uncertainty model is developed. Particularly, a reference distribution is introduced based on historical data and then a uncertainty set is defined to confine the spectrum demand. The uncertainty model developed allows the actual users' spectrum requirement to fluctuate around the reference distribution. Chance constraint approximations and robust optimization approaches are developed to transform and then solve the optimization problem. Simulation results based on the real-world traces evaluate the performance of the proposed scheme and investigate the parameter impacts on the system utilities. Our research may also help shed some insights on the investment policy making for the mobile virtual network operator.
An Improved Algorithm for Road Markings Detection with SVM and ROI Restriction: Comparison with a Rule-Based Model

Machine learning method have increased in popularity in Advanced Driving Assistant System (ADAS) field recently. Although most of the best performances are achieved by machine learning and deep learning methods in benchmarks, a matured database of road markings with labels has not yet been built. This paper analyzes the road markings detection using Histogram of Oriented Gradient (HOG) with Support Vector Machine (SVM) on our local database. Compared with the results of the rulebased detection method, our exploration provides a possible solution to improve the machine learning method in order to get a balance on speed and accuracy.
Indoor Map Generation from Multiple LIDAR Point Clouds

This paper presents a new algorithm for building an indoor map by integrating point clouds of 2D light detection and ranging (LIDAR) scanners in indoor environments. Iterative closest point (ICP) algorithm is one of the well-known methods for such purpose and often used for mobile robot SLAM. However, the algorithm is designed based on "dense" (or "continuous") measurement of the same space with known relative positions of measurement points and angles, and it does not often work efficiently if the measurement is sparse and/or LIDAR locations are unknown. Such situations are seen when some installed LIDARs in a room are used to build a background indoor map for object tracking, or mobile LIDARs are used by technicians to build a digital indoor map, where each space is captured only at a few locations with different angles. To tackle this issue, our method extracts line segments and edge points as features from given LIDAR point clouds and finds shape coincidences commonly contained in a pair of given point clouds to identify positional relationships between LIDARs. By this information, these point clouds can be integrated into common 2D coordinates. Indoor map generation is realized by sequentially applying this integration procedure to every pair of point clouds. Also, the experiments on real data show that our method can identify the relative positions of LIDARs with 10cm-order errors in average, and by sequentially applying the point-cloud integration, the generated maps have only 3% errors.
An Adaptive Backoff Mechanism for IEEE 802.15.4 Beacon-Enabled Wireless Body Area Networks

Carrier sense multiple access mechanism with collision avoidance (CSMA/CA) in IEEE 802.15. 4-based wireless body area networks (WBANs) may impair the transmission reliability of emergency traffic under high traffic loads, which may result in loss of high valued medical information. Majority of the recent proposals recommend an early retransmission of failed frame while ignoring the history of past failed transmissions. More importantly, these proposals do not consider the number of failed transmissions experienced by each sensor node, thereby affecting the reliability of retransmissions. In this paper, we propose a dynamic retransmission adaptive intelligent MAC (RAI-MAC) scheme. In our proposed scheme retransmission class of each sensor node is decided by the coordinator according to the number of failed transmissions of each node as observed by the coordinator during the last superframe. Based on the retransmission class received from the coordinator, each node adjusts its next backoff value. The proposed scheme increases the probability of successful frame retransmissions without incurring extra overhead. The simulation results prove that the proposed scheme based on its adaptive retransmission mechanism achieves higher average throughput and average end-to-end delay, while not compromising on energy efficiency as compared to the IEEE 802.15. 4 and Block Acknowledgment (Block Ack). Moreover, our scheme appears more stable in terms of average throughput, end-to-end delay, and energy efficiency under different values of beacon order (BO) and superframe order (SO).
A Coefficient Test for Quintic Permutation Polynomials Over Integer Rings

For selecting appropriate permutation polynomials (PPs) in practical applications, it is necessary to know the coefficients of the polynomial since a brute-force exhaustive search is impractical when the number of PPs is large. Previous results give the conditions on the coefficients of a polynomial of degree up to four so that it is a PP modulo a given positive integer. For polynomials of degree higher than four, we only know the conditions so that they are PPs modulo a power of two. In [13] all PPs of degree no more than six are generated using an algorithm based on normalized PPs, two previous important theorems about PPs and the Chinese remainder theorem. In this paper, we propose a coefficient test for quintic permutation polynomials (5-PPs) over integer rings which, unlike the algorithm from [13], allows to decide directly whether a polynomial of degree five or less is PP. Using the proposed coefficient test, the coefficients of PPs modulo a given positive integer can be obtained in a desired order, which is tractable in computer processing.
A Hybrid Learning Scheme for Chinese Word Embedding

To improve word embedding, subword information has been widely employed in state-of-the-art methods. These methods can be classified to either compositional or predictive models. In this paper, we propose a hybrid learning scheme, which integrates compositional and predictive model for word embedding. Such a scheme can take advantage of both models, thus effectively learning word embedding. The proposed scheme has been applied to learn word representation on Chinese. Our results show that the proposed scheme can significantly improve the performance of word embedding in terms of analogical reasoning and is robust to the size of training data.
Context-Oriented Algorithmic Design

Currently, algorithmic approaches are being introduced in several areas of expertise, namely Architecture. Algorithmic Design (AD) is an approach for architecture that allows architects to take advantage of algorithms to produce complex forms for their projects, to simplify the exploration of variations, or to mechanize tasks, including those related to analysis and optimization of designs. However, architects might need different models of the same project for different kinds of analysis, which tempts them to extend the same code base for different purposes, typically making the code brittle and hard to understand. In this paper, we propose to extend AD with Context-Oriented Programming (COP), a programming paradigm based on context that dynamically changes the behavior of the code. To this end, we propose a COP library and we explore its combination with an AD tool. Finally, we implement a case study with our approach, and discuss the advantages and disadvantages.
Skin Lesion Image Segmentation Based on Adversarial Networks

Traditional methods based active contours or region merging are powerless in processing images with blurring border or hair occlusion. In this paper, a structure based convolutional neural networks is proposed to solve segmentation of skin lesion image. The structure mainly consists of two networks which are segmentation net and discrimination net. The segmentation net is designed based U-net that used to generate the mask of lesion, while the discrimination net is designed with only convolutional layers that used to determine whether input image is from ground truth labels or generated images. Images were obtained from “Skin Lesion Analysis Toward Melanoma Detection” challenge which was hosted by ISBI 2016 conference. We achieved segmentation average accuracy of 0.97, dice coefficient of 0.94 and Jaccard index of 0.89 which outperform the other existed state-of-the-art segmentation networks, including winner of ISBI 2016 challenge for skin melanoma segmentation.
Comparison of immediate-load mini dental implants and conventional-size dental implants to retain mandibular Kennedy class I removable partial dentures: A randomized clinical trial.

BACKGROUND Complications of distal extension mandibular removable partial dentures are: loss of retention, irritation, and so forth. Dental implants have been used to support distal extension removable partial dentures. However, many patients have limited bone support in which to place conventional-size dental implants. PURPOSE To compare the clinical outcomes of using immediate-loaded mini dental implants and immediate-loaded conventional-size dental implants, when used to retain mandibular Kennedy class I removable partial dentures. MATERIALS AND METHODS Thirty patients were randomly divided into two groups. Mini dental implants and conventional-size dental implants were placed in participants in the first molar region on both sides. The dentures were connected immediately. Patients were recalled on 1, 3, 6, and 12 months after surgery. Digital periapical radiographs were made, and patient satisfaction was recorded. Data were analyzed by independent samples t-test and paired samples t-test (P = .05). RESULTS Twenty eight of the implants survived (survival rate = 93.3%) in each group. Mean radiographic bone loss was 0.47 ± 0.42 and 1.03 ± 1.07 mm in groups 1 and 2, respectively. Conventional-size implants revealed significantly greater marginal bone loss than mini implants (P = .01). Patient satisfaction showed significant improvement after treatment in both groups. CONCLUSIONS Immediate-loaded mini dental implants can be applied for retaining mandibular Kennedy class I removable partial dentures with very favorable results.
Leveraging Transitive Trust Relations to Improve Cross-Domain Recommendation

Cross-domain recommendation has become increasingly popular because it can determine dependencies and correlations among different domains. However, this type of recommendation still suffers from data sparsity limitations. Social trust relationship helps alleviate this problem. Existing cross-domain recommendation algorithms focus on modeling user behavior in different domains but disregard the social trust relationship among users. In this paper, we propose a transitive trust-aware cross-domain recommendation model that incorporates the context dependence and transitivity of social trust relations. First, we construct a different transitive trust network for each single domain. Then, we develop a novel probabilistic matrix factorization model for each domain that utilizes the transitive trust-aware model to mine social trust. Finally, we present a nonlinear user–vector mapping algorithm to bridge the feedback of different domains. Experimental results indicate that our method significantly outperforms several state-of-the-art methods, produces higher rating prediction accuracy, and exhibits better recommendation performance on several real-world recommendation tasks.
SCC-rFMQ Learning in Cooperative Markov Games with Continuous Actions

Although many reinforcement learning methods have been proposed for learning the optimal solutions in single-agent continuous action domains, multiagent coordination domains with continuous action have received relatively few investigations. In this paper, we propose an independent learner hierarchical method, named Sample Continuous Coordination with recursive Frequency Maximum Q-Value (SCC-rFMQ), which divides the coordination problem into two layers. The first layer samples a finite set of actions from the continuous action spaces by a sampling mechanism with variable exploratory rates, and the second layer evaluates the actions in the sampled action set and updates the policy using a multiagent reinforcement learning coordination method. By constructing coordination mechanisms at both levels, SCC-rFMQ can handle coordination problems in continuous action cooperative Markov games effectively. Experimental results show that SCC-rFMQ outperforms other reinforcement learning algorithms.
Balancing the Pain and Gain of Hobnobbing: Utility-Based Network Building over Atributed Social Networks

The establishment of interpersonal ties is a pivotal problem in the structural analysis of social networks. In particular, link recommendation problem asks for valuable future links to establish by an individual. Existing methods for this problem rely on link prediction that evaluates the likelihood of successful tie creation between two individuals. Such methods do not consider the social capital gained by agents, nor do they concern with the required cost of this process. In light of this limitation, we propose a utility-based network building problem, with an aim to strike a balance between the gained social capital - in the form of closeness centrality - and the cost of establishing ties. We propose algorithms to solve this problem over networks whose nodes may or may not be labelled with attributes, and test their performance on a range of synthesized and real-world social networks. By having multiple agents adopting utility-based network building strategies, we propose a suite of models of network formation and demonstrate empirically that the they capture important structural properties. In particular, we investigate the emergence of a core/periphery structure as a joint result of preferential attachment and network building strategies.
ChOWDER: Dynamic Contents Sharing through Remote Tiled Display System

Due to the continuous increase in the scale of numerical simulations, research on visualization has shifted to in-situ/in-transit approaches. The interactivity of largescale visualization has also become increasingly important. In order to observe largescale visualization data in detail, high-resolution displays, such as those with 8K or 16K resolutions, give an opportunity to inspire new discovery. With the commoditization of high-resolution displays, tiled display walls (TDWs) have facilitated their use for the collaborative research, where a large screen size is required for sharing the content among multiple sites. In this paper, we propose a remote collaboration method that utilizes a TDW driver (ChOWDER), which enables content sharing among multiple sites even with different display configurations, and a visualization application (HIVE) for dynamic content sharing of interactive visualization results.
A MATLAB SMO Implementation to Train a SVM Classifier: Application to Multi-Style License Plate Numbers Recognition

This paper implements the Support Vector Machine (SVM) training procedure proposed by John Platt denominated Sequential Minimimal Optimization (SMO). The application of this system involves a multi-style license plate characters recognition identifying numbers from “0” to “9”. In order to be robust against license plates with different character/background colors, the characters (numbers) visual information is encoded using Histograms of Oriented Gradients (HOG). A reliability measure to validate the system outputs is also proposed. Several tests are performed to evaluate the sensitivity of the algorithm to different parameters and kernel functions. Source Code The source code is written in MATLAB and it is available at the IPOL web page of this article1. It implements Platt’s SMO algorithm to train and test a SVM classifier on a multi-style license plate character dataset. Compilation and usage instruction are included in the README.txt file of the archive. The online demo allows to test the character (numbers) recognition system, from license plate images. Supplementary Material The supplementary files of the work include a dataset of license plate numbers from four countries having different fonts, and character/background colors.
Optimizing error of high-dimensional statistical queries under differential privacy

Differentially private algorithms for answering sets of predicate counting queries on a sensitive database have many applications. Organizations that collect individual-level data, such as statistical agencies and medical institutions, use them to safely release summary tabulations. However, existing techniques are accurate only on a narrow class of query workloads, or are extremely slow, especially when analyzing more than one or two dimensions of the data. In this work we propose HDMM, a new differentially private algorithm for answering a workload of predicate counting queries, that is especially effective for higher-dimensional datasets. HDMM represents query workloads using an implicit matrix representation and exploits this compact representation to efficiently search (a subset of) the space of differentially private algorithms for one that answers the input query workload with high accuracy. We empirically show that HDMM can efficiently answer queries with lower error than state-of-the-art techniques on a variety of low and high dimensional datasets.