OpenRaiser/Intern-Atlas huggingface dataset:
Cross-Discourse and Multilingual Exploration of Textual Corpora with the DualNeighbors Algorithm
Venue: arXiv cs.CL 2018
Year: 2018

Word choice is dependent on the cultural context of writers and their subjects. Different words are used to describe similar actions, objects, and features based on factors such as class, race, gender, geography and political affinity. Exploratory techniques based on locating and counting words may, therefore, lead to conclusions that reinforce culturally inflected boundaries. We offer a new method, the DualNeighbors algorithm, for linking thematically similar documents both within and across discursive and linguistic barriers to reveal cross-cultural connections. Qualitative and quantitative evaluations of this technique are shown as applied to two cultural datasets of interest to researchers across the humanities and social sciences. An open-source implementation of the DualNeighbors algorithm is provided to assist in its application.
A Hierarchical Game with Strategy Evolution for Mobile Sponsored Content and Service Markets
Venue: arXiv cs.NI 2018
Year: 2018

In sponsored content and service markets, the content and service providers are able to subsidize their target mobile users through directly paying the mobile network operator, to lower the price of the data/service access charged by the network operator to the mobile users. The sponsoring mechanism leads to a surge in mobile data and service demand, which in return compensates for the sponsoring cost and benefits the content/service providers. In this paper, we study the interactions among the three parties in the market, namely, the mobile users, the content/service providers and the network operator, as a two-level game with multiple Stackelberg (i.e., leader) players. Our study is featured by the consideration of global network effects owning to consumers' grouping. Since the mobile users may have bounded rationality, we model the service-selection process among them as an evolutionary-population follower sub-game. Meanwhile, we model the pricing-then-sponsoring process between the content/service providers and the network operator as a non-cooperative equilibrium searching problem. By investigating the structure of the proposed game, we reveal a few important properties regarding the equilibrium existence, and propose a distributed, projection-based algorithm for iterative equilibrium searching. Simulation results validate the convergence of the proposed algorithm, and demonstrate how sponsoring helps improve both the providers' profits and the users' experience.
Generate the corresponding Image from Text Description using Modified GAN-CLS Algorithm
Venue: arXiv cs.LG 2018
Year: 2018

Synthesizing images or texts automatically is a useful research area in the artificial intelligence nowadays. Generative adversarial networks (GANs), which are proposed by Goodfellow in 2014, make this task to be done more efficiently by using deep neural networks. We consider generating corresponding images from an input text description using a GAN. In this paper, we analyze the GAN-CLS algorithm, which is a kind of advanced method of GAN proposed by Scott Reed in 2016. First, we find the problem with this algorithm through inference. Then we correct the GAN-CLS algorithm according to the inference by modifying the objective function of the model. Finally, we do the experiments on the Oxford-102 dataset and the CUB dataset. As a result, our modified algorithm can generate images which are more plausible than the GAN-CLS algorithm in some cases. Also, some of the generated images match the input texts better.
Posthoc Interpretability of Learning to Rank Models using Secondary Training Data
Venue: arXiv cs.IR 2018
Year: 2018

Predictive models are omnipresent in automated and assisted decision making scenarios. But for the most part they are used as black boxes which output a prediction without understanding partially or even completely how different features influence the model prediction avoiding algorithmic transparency. Rankings are ordering over items encoding implicit comparisons typically learned using a family of features using learning-to-rank models. In this paper we focus on how best we can understand the decisions made by a ranker in a post-hoc model agnostic manner. We operate on the notion of interpretability based on explainability of rankings over an interpretable feature space. Furthermore we train a tree based model (inherently interpretable) using labels from the ranker, called secondary training data to provide explanations. Consequently, we attempt to study how well does a subset of features, potentially interpretable, explain the full model under different training sizes and algorithms. We do experiments on the learning to rank datasets with 30k queries and report results that serve show in certain settings we can learn a faithful interpretable ranker.
Quantum aspects of high dimensional formal representation of conceptual spaces
Venue: arXiv cs.AI 2018
Year: 2018

Human cognition is a complex process facilitated by the intricate architecture of human brain. However, human cognition is often reduced to quantum theory based events in principle because of their correlative conjectures for the purpose of analysis for reciprocal understanding. In this paper, we begin our analysis of human cognition via formal methods and proceed towards quantum theories. Human cognition often violate classic probabilities on which formal representation of conceptual spaces are built. Further, geometric representation of conceptual spaces proposed by Gardenfors discusses the underlying content but lacks a systematic approach (Gardenfors, 2000; Kitto et. al, 2012). However, the aforementioned views are not contradictory but different perspective with a gap towards sufficient understanding of human cognitive process. A comprehensive and systematic approach to model a relatively complex scenario can be addressed by vector space approach of conceptual spaces as discussed in literature. In this research, we have proposed an approach that uses both formal representation and Gardenfors geometric approach. The proposed model of high dimensional formal representation of conceptual space is mathematically analysed and inferred to exhibit quantum aspects. Also, the proposed model achieves cognition, in particular, consciousness. We have demonstrated this process of achieving consciousness with a constructive learning scenario. We have also proposed an algorithm for conceptual scaling of a real world scenario under different quality dimensions to obtain a conceptual scale.
The Economics of Video Websites with Membership-Advertising Mode in Wireless Networks
Venue: arXiv cs.NI 2018
Year: 2018

In this paper, we consider a novel business model of video websites via Membership-Advertising Mode in wireless network, where the video websites provide three video services for mobile users: \textit{VIP-Member} service, Regular-Member service and Non-Member service. The VIP-Member (Regular-Member) service provides the highest level (middle level) quality and non-advertising video service with high (low) price, while the Non-Member service provides the lowest level quality and advertising-containing video service for free. Meanwhile, the video websites sell their advertising spaces to the advertiser to create extra revenues. We formulate the interactions among the advertiser, video websites and mobile users as a three-stage Stackelberg game. Specifically, in Stage I, the advertiser decides the advertising budget; in Stage II, the video websites determine their advertising spaces selling strategies for advertiser and the membership pricing strategies for mobile users; in Stage III, the mobile users make their own decisions on video watching strategies for each video website. We analyze the equilibrium of each sub-game. Particularly, we derive the closed-form solutions of each mobile user's optimal video watching strategies, each video website's optimal membership price and the optimal advertising spaces selling number. In addition, we also investigate the piece-wise structure of the advertiser's utility function, and further propose an efficient algorithm to obtain the optimal advertising budget. Finally, numerical results show the impacts of different parameters' values on each entity's utility as well as the key indicators.
Personalizing Similar Product Recommendations in Fashion E-commerce
Venue: arXiv cs.IR 2018
Year: 2018

In fashion e-commerce platforms, product discovery is one of the key components of a good user experience. There are numerous ways using which people find the products they desire. Similar product recommendations is one of the popular modes using which users find products that resonate with their intent. Generally these recommendations are not personalized to a specific user. Traditionally, collaborative filtering based approaches have been popular in the literature for recommending non-personalized products given a query product. Also, there has been focus on personalizing the product listing for a given user. In this paper, we marry these approaches so that users will be recommended with personalized similar products. Our experimental results on a large fashion e-commerce platform (Myntra) show that we can improve the key metrics by applying personalization on similar product recommendations.
Ethical Aspects of Internet of Things from Islamic Perspective
Venue: arXiv cs.CY 2018
Year: 2018

The Internet of Things (IoTs) is an evolving new face of technology that provides state of the art services using ubiquitously connected smart objects. These smart objects are capable of sensing, processing, collaborating, communicating the events and provide services. The IoT is a collection of heterogeneous technologies like Sensor, RFID, Communication and nanotechnology. These technologies enable smart objects to identify objects, collect information about their status,communicating the collected information for taking some desired actions. Widespread adaptations of IoT based devices and services raised the ethical challenges for their users. In this paper we highlight ethical challenges raised by IoT and discuss the solutions and methods for encouraging people to properly use these technologies according to Islamic teachings.
A Rolling Blockchain for a Dynamic WSNs in a Smart City
Venue: arXiv cs.CR 2018
Year: 2018

Blockchain is one of the most popular topics for discussion now. However, most experts still see this technology as only part of Bitcoin, other crypto-currencies or money transfer systems. Often, new solutions, proposed by young researchers, are blocked by reviewers, only because these solutions can not be used for Bitcoins. However, Blockchain technology is more universal and can be used also in other areas, for example, in IoT, WSN and mobile devices. This paper considers the implementation of Blockchain technology in sensor networks as an element of IoT. The concept of "Rolling Blockchain" was proposed, which can be used to build WSN with the participation of Smart Cars, as nodes of the network. The order of block formation and structure in the chain is proposed and a mathematical model is created for it. We estimate the optimal number of WSN nodes, the number of connections between nodes, for specified network reliability values, was performed.
Discourse-Wizard: Discovering Deep Discourse Structure in your Conversation with RNNs
Venue: arXiv cs.CL 2018
Year: 2018

Spoken language understanding is one of the key factors in a dialogue system, and a context in a conversation plays an important role to understand the current utterance. In this work, we demonstrate the importance of context within the dialogue for neural network models through an online web interface live demo. We developed two different neural models: a model that does not use context and a context-based model. The no-context model classifies dialogue acts at an utterance-level whereas the context-based model takes some preceding utterances into account. We make these trained neural models available as a live demo called Discourse-Wizard using a modular server architecture. The live demo provides an easy to use interface for conversational analysis and for discovering deep discourse structures in a conversation.
Integrating Proactive Mode Changes in Mixed Criticality Systems
Venue: arXiv cs.OS 2018
Year: 2018

In this work, we propose to integrate prediction algorithms to the scheduling of mode changes under the Earliest-Deadline-First and Fixed-priority scheduling in mixed-criticality real-time systems. The method proactively schedules a mode change in the system based on state variables such as laxity, to the percentage difference in the temporal distance between the completion time of the instance of a task and its respective deadline, by the deadline (D) stipulated for the task, in order to minimize deadline misses. The simulation model was validated against an analytical model prior to the logical integration of the Kalman-based prediction algorithm. Two study cases were presented, one covering earliest-deadline first and the other the fixed-priority scheduling approach. The results showed the gains in the adoption of the prediction approach for both scheduling paradigms by presenting a significant reduction of the number of missed deadlines for low-criticality tasks.
Recognition of Offline Handwritten Devanagari Numerals using Regional Weighted Run Length Features
Venue: arXiv cs.CV 2018
Year: 2018

Recognition of handwritten Roman characters and numerals has been extensively studied in the last few decades and its accuracy reached to a satisfactory state. But the same cannot be said while talking about the Devanagari script which is one of most popular script in India. This paper proposes an efficient digit recognition system for handwritten Devanagari script. The system uses a novel 196-element Mask Oriented Directional (MOD) features for the recognition purpose. The methodology is tested using five conventional classifiers on 6000 handwritten digit samples. On applying 3-fold cross-validation scheme, the proposed system yields the highest recognition accuracy of 95.02% using Support Vector Machine (SVM) classifier.
Efficient Multichannel in XML Wireless Broadcast Stream
Venue: arXiv cs.DC 2018
Year: 2018

In this paper we recommend the use of multi-channel for XML data in wireless broadcasting. First we divide XML data into information units as bucket, then extract path information (XPath) for any unit and build an index tree from the data path. Finally, make wireless data stream with merging parts of index tree and parts of XML data in multichannel XML. Then, create a protocol that allows mobile users access to the wireless XML stream generated with our method. We study 11 channels in server side and 3 orthogonal channels in client side.
High-Performance Parallel Implementation of Genetic Algorithm on FPGA
Venue: arXiv cs.DC 2018
Year: 2018

Genetic Algorithms (GAs) are used to solve search and optimization problems in which an optimal solution can be found using an iterative process with probabilistic and non-deterministic transitions. However, depending on the problem's nature, the time required to find a solution can be high in sequential machines due to the computational complexity of genetic algorithms. This work proposes a parallel implementation of a genetic algorithm on field-programmable gate array (FPGA). Optimization of the system's processing time is the main goal of this project. Results associated with the processing time and area occupancy (on FPGA) for various population sizes are analyzed. Studies concerning the accuracy of the GA response for the optimization of two variables functions were also evaluated for the hardware implementation. However, the high-performance implementation proposes in this paper is able to work with more variable from some adjustments on hardware architecture.
Computer Simulation of Neural Networks Using Spreadsheets: The Dawn of the Age of Camelot
Venue: arXiv cs.CY 2018
Year: 2018

The article substantiates the necessity to develop training methods of computer simulation of neural networks in the spreadsheet environment. The systematic review of their application to simulating artificial neural networks is performed. The authors distinguish basic approaches to solving the problem of network computer simulation training in the spreadsheet environment, joint application of spreadsheets and tools of neural network simulation, application of third-party add-ins to spreadsheets, development of macros using the embedded languages of spreadsheets; use of standard spreadsheet add-ins for non-linear optimization, creation of neural networks in the spreadsheet environment without add-ins and macros. After analyzing a collection of writings of 1890-1950, the research determines the role of the scientific journal "Bulletin of Mathematical Biophysics", its founder Nicolas Rashevsky and the scientific community around the journal in creating and developing models and methods of computational neuroscience. There are identified psychophysical basics of creating neural networks, mathematical foundations of neural computing and methods of neuroengineering (image recognition, in particular). The role of Walter Pitts in combining the descriptive and quantitative theories of training is discussed. It is shown that to acquire neural simulation competences in the spreadsheet environment, one should master the models based on the historical and genetic approach. It is indicated that there are three groups of models, which are promising in terms of developing corresponding methods - the continuous two-factor model of Rashevsky, the discrete model of McCulloch and Pitts, and the discrete-continuous models of Householder and Landahl.
Deciding the Closure of Inconsistent Rooted Triples is NP-Complete
Venue: arXiv cs.DS 2018
Year: 2018

Interpreting three-leaf binary trees or {\em rooted triples} as constraints yields an entailment relation, whereby binary trees satisfying some rooted triples must also thus satisfy others, and thence a closure operator, which is known to be polynomial-time computable. This is extended to inconsistent triple sets by defining that a triple is entailed by such a set if it is entailed by any consistent subset of it. Determining whether the closure of an inconsistent rooted triple set can be computed in polynomial time was posed as an open problem in the Isaac Newton Institute's "Phylogenetics" program in 2007. It appears (as NC4) in a collection of such open problems maintained by Mike Steel, and it is the last of that collection's five problems concerning computational complexity to have remained open. We resolve the complexity of computing this closure, proving that its decision version is NP-Complete. In the process, we also prove that detecting the existence of {\em any} acyclic B-hyperpath (from specified source to destination) is NP-Complete, in a significantly narrower special case than the version whose {\em minimization} problem was recently proven NP-hard by Ritz et al. This implies it is NP-hard to approximate (our special case of) their minimization problem to within {\em any} factor.
Citizen Social Lab: A digital platform for human behaviour experimentation within a citizen science framework
Venue: arXiv cs.CY 2018
Year: 2018

Cooperation is one of the behavioral traits that define human beings, however we are still trying to understand why humans cooperate. Behavioral experiments have been largely conducted to shed light into the mechanisms behind cooperation and other behavioral traits. However, most of these experiments have been conducted in laboratories with highly controlled experimental protocols but with varied limitations which limits the reproducibility and the generalization of the results obtained. In an attempt to overcome these limitations, some experimental approaches have moved human behavior experimentation from laboratories to public spaces, where behaviors occur naturally, and have opened the participation to the general public within the citizen science framework. Given the open nature of these environments, it is critical to establish the appropriate protocols to maintain the same data quality that one can obtain in the laboratories. Here, we introduce Citizen Social Lab, a software platform designed to be used in the wild using citizen science practices. The platform allows researchers to collect data in a more realistic context while maintaining the scientific rigour, and it is structured in a modular and scalable way so it can also be easily adapted for online or brick-and-mortar experimental laboratories. Following citizen science guidelines, the platform is designed to motivate a more general population into participation, but also to promote engaging and learning of the scientific research process. We also review the main results of the experiments performed using the platform up to now, and the set of games that each experiment includes. Finally, we evaluate some properties of the platform, such as the heterogeneity of the samples of the experiments and their satisfaction level, and the parameters that demonstrate the robustness of the platform and the quality of the data collected.
AI in Game Playing: Sokoban Solver
Venue: arXiv cs.AI 2018
Year: 2018

Artificial Intelligence is becoming instrumental in a variety of applications. Games serve as a good breeding ground for trying and testing these algorithms in a sandbox with simpler constraints in comparison to real life. In this project, we aim to develop an AI agent that can solve the classical Japanese game of Sokoban using various algorithms and heuristics and compare their performances through standard metrics.
Adversarial Examples in Deep Learning: Characterization and Divergence
Venue: arXiv cs.LG 2018
Year: 2018

The burgeoning success of deep learning has raised the security and privacy concerns as more and more tasks are accompanied with sensitive data. Adversarial attacks in deep learning have emerged as one of the dominating security threat to a range of mission-critical deep learning systems and applications. This paper takes a holistic and principled approach to perform statistical characterization of adversarial examples in deep learning. We provide a general formulation of adversarial examples and elaborate on the basic principle for adversarial attack algorithm design. We introduce easy and hard categorization of adversarial attacks to analyze the effectiveness of adversarial examples in terms of attack success rate, degree of change in adversarial perturbation, average entropy of prediction qualities, and fraction of adversarial examples that lead to successful attacks. We conduct extensive experimental study on adversarial behavior in easy and hard attacks under deep learning models with different hyperparameters and different deep learning frameworks. We show that the same adversarial attack behaves differently under different hyperparameters and across different frameworks due to the different features learned under different deep learning model training process. Our statistical characterization with strong empirical evidence provides a transformative enlightenment on mitigation strategies towards effective countermeasures against present and future adversarial attacks.