Reviewerly/exHarmony huggingface dataset:
Generation of non-classical states of photons from a metal–dielectric interface: a novel architecture for quantum information processing

We show the possibility to generate photons in a certain class of non-classical states from a metal-dielectric interface using dipole emitters on the interface. The photons emitted into the surface plasmon mode from the initially excited emitters radiate out in free space in a cone-shaped geometry. When detected at two detectors, these photons exhibit coalescence, a clear signature of non-classicality. Such a system can also be employed as a building block for a distributed quantum network. We further show that it is indeed feasible to implement our model using available technology.
HInDoLA: A Unified Cloud-Based Platform for Annotation, Visualization and Machine Learning-Based Layout Analysis of Historical Manuscripts

Palm-leaf manuscripts are one of the oldest medium of inscription in many Asian countries. Especially, manuscripts from the Indian subcontinent form an important part of the world's literary and cultural heritage. Despite their significance, large-scale datasets for layout parsing and targeted annotation systems do not exist. Addressing this, we propose a web-based layout annotation and analytics system. Our system, called HInDoLA, features an intuitive annotation GUI, a graphical analytics dashboard and interfaces with machine-learning based intelligent modules on the backend. HInDoLA has successfully helped us create the first ever large-scale dataset for layout parsing of Indic palm-leaf manuscripts. These manuscripts, in turn, have been used to train and deploy deep-learning based modules for fully automatic and semi-automatic instance-level layout parsing.
Performance Analysis of Flappy Bird Playing Agent Using Neural Network and Genetic Algorithm

The aim of this paper is to develop and study an artificial intelligence based game-playing agent using genetic algorithm and neural networks. We first create an agent which learns how to optimally play the famous “Flappy Bird” game by safely dodging all the barriers and flapping its way through them and then study the effect of changing various parameters like number of neurons on the hidden layer, gravity, speed, gap between trees has on the learning process. The gameplay was divided into two level of difficulty to facilitate study on the learning process. Phaser Framework was used to facilitate HTML5 programming for introducing real-life factors like gravity, collision and Synaptic Neural Network library was used to implement neural network so as to avoid creating a neural network from scratch. Machine Learning Algorithm which we have adopted in this project is based on the concept of Neuro-Evolution and this form of machine learning uses algorithms which can evolve and mature over time such as a genetic algorithm to train artificial neural networks.
Universal Foreground Segmentation Based on Deep Feature Fusion Network for Multi-Scene Videos

Foreground/background (fg/bg) classification is an important first step for several video analysis tasks such as people counting, activity recognition and anomaly detection. As is the case for several other Computer Vision problems, the advent of deep Convolutional Neural Network (CNN) methods has led to major improvements in this field. However, despite their success, CNN-based methods have difficulties in coping with multi-scene videos where the scenes change multiple times along the time sequence. In this paper, we propose a deep features fusion network based foreground segmentation method (DFFnetSeg), which is both robust to scene changes and unseen scenes comparing with competitive state-of-the-art methods. In the heart of DFFnetSeg lies a fusion network that takes as input deep features extracted from a current frame, a previous frame, and a reference frame and produces as output a segmentation mask into background and foreground objects. We show the advantages of using a fusion network and the three frames group in dealing with the unseen scene and bootstrap challenge. In addition, we show that a simple reference frame updating strategy enables DFFnetSeg to be robust to sudden scene changes inside video sequences and prepare a motion map based post-processing method which further reduces false positives. Experimental results on the test dataset generated from CDnet2014 and Lasiesta demonstrate the advantages of the DFFnetSeg method.
A Novel Context Migration Model for Fog-Enabled Cross-Vertical IoT Applications

With the maturity of Internet of things (IoT) paradigm, many innovative services are being conceived by integration of existing IoT services. Such services are termed as cross-vertical or cross-domain services. However, catering to real-time response requirement of such services is challenging and resource-constrained Fog nodes provide an alternative to cloud computing to realize end services. In this paper, we present a novel Fog resource aware and forecast based context migration model to address this delay requirement of such unified IoT applications by employing a Fog resource and forecast based mechanism among Fog nodes for minimizing system delay. Algorithms for context migration and required conditions for migrations been presented and simulation results carried out demonstrate the efficacy of the proposed methodology.
On the WalkerMaker–WalkerBreaker games

We study the unbiased WalkerMaker–WalkerBreaker games on the edge set of the complete graph on n vertices, Kn, a variant of the well-known Maker–Breaker positional games, where both players have the restriction on the way of playing. Namely, each player has to choose her/his edges according to a walk. Here, we focus on two standard graph games — the Connectivity game and the Hamilton cycle game and show how quickly WalkerMaker can win both games.
Classification of Mammogram Images Using Radial Basis Function Neural Network

Recently, computer aided diagnosis and image processing have received considerable attention from a number of researchers. Mammography is the most effective method for exposure of early breast cancer to increase the survival rate. This paper presents the classification method for mammogram Image using Radial Basis Function Network (RBF) technique. This method is focused on features extracted from the breast cancer mammogram image processing algorithms. The actual decision about the presence of the disease is then made by RBF network classifiers. We conducted this study in five stages; collecting images, Region of Interest (ROI), features extracting, classification and end with testing and evaluating. The experimental results shown the classification accuracy results of the RBF neural network 79.166% while MLP algorithm was 54.1667%, that illustrate the capability of the RBF network to obtain better classification accuracy results.
Scalable Manifold-Regularized Attributed Network Embedding via Maximum Mean Discrepancy

Networks are ubiquitous in many real-world applications due to their capability of representing the rich information in the data. One fundamental problem of network analysis is to learn a low- dimensional vector representation for nodes within the attributed networks. However, there is little work theoretically considering the information heterogeneity from the attributed networks, and most of the existing attributed network embedding techniques are able to capture at most k-th order node proximity, thus leading to the information loss of the long-range spatial dependencies between individual nodes across the entire network. To address the above problems, in this paper, we propose a novel MAnifold-RegularIzed Network Embedding (MARINE) algorithm inspired by minimizing the information discrepancy in a Reproducing Kernel Hilbert Space via Maximum Mean Discrepancy. In particular, we show that MARINE recursively aggregates the graph structure information as well as individual node attributes from the entire network, and thereby preserves the long-range spatial dependencies between nodes across the network. The experimental results on real networks demonstrate the effectiveness and efficiency of the proposed MARINE algorithm over state-of-the-art embedding methods.
Client risk informedness in brokered cloud services: An experimental pricing study

Service brokerage in cloud computing markets aggregates cloud resources to lower the cost and risk of their services and to customize them to address different client needs. This research proposes a mechanism for pricing brokered cloud computing services that includes guaranteed computing job completion and risk-based pricing. Using a laboratory experiment with 42 participants playing the role of cloud computing clients, we compare the economic performance of the pricing mechanism under different levels of client risk informedness and job duration. The results suggest that such risk informedness plays a major role in influencing the willingness-to-pay of clients for brokered services. By investigating the heterogeneity of client risk propensity, we explain the performance differences and find an interaction effect for risk propensity and risk informedness on the willingness-to-pay of clients. Based on our findings and the related causal inferences, we discuss the implications for the pricing strategies of cloud services brokers, as well as for those of cloud services vendors.
Efficient private database queries using ring-LWE somewhat homomorphic encryption

We study the problem of private database queries over any outsourced encrypted database. In 2016, Kim et al. [IEEE Trans. on Dependable and Secure Comput.] showed three private query processing protocols for conjunctive, disjunctive, and threshold queries respectively over an encrypted database. First, we propose two more efficient protocols of processing conjunctive and disjunctive queries with a lower-depth equality circuit than Kim et al.’s one. To get the lower-depth circuit, we modify the packing methods of Saha and Koshiba [APWConCSE 2016] to support an efficient batch computation of our protocols within a few multiplications using binary encoded data. Secondly, we propose another efficient protocol engaging a batch technique for threshold query processing in which many inequality comparisons are indispensable. Finally, we propose the packing methods to support batch computation of many inequality circuits. In addition, our theoretical analysis along with practical experiments show that our protocols are more efficient than any other existing method. We further enhance the performance of conjunctive and disjunctive query protocols using a base-N fixed-length encoding.
The consequences of blockchain architectures for the governance of public services: A case study of the movement of excise goods under duty exemptions

Blockchain technology has the potential to provide public services directly to the public. This challenges the need for public organizations, who traditionally provided these services. Much of the current work is focused on the technology, whereas the influence on public administration structure has gained less attention. The goal of this paper is to investigate the impact of blockchain technology on the governance of public service provision. For this, we performed a case study of an EU-wide system that monitors the movement of excise goods under duty suspension. We developed two scenarios for blockchain technology’s use based on a permissionless blockchain architecture on the one hand and a permissioned one on the other. The scenarios were evaluated based on their impact on transaction validation, data quality and governance. The findings show that blockchain technology alone cannot be an alternative for the current data quality controls, equal access assurances and adaptations to legislation conducted by public administrations. As such, governments will remain playing a key role in registration of documents and assets, however, the governance will likely change depending on the type of blockchain architecture.
Towards Post-Interaction Computing: Addressing Immediacy, (un)Intentionality, Instability and Interaction Effects

The changes that have come about through the increased speed, ubiquity, and scale of computational systems require a reconceptualisation of how we think about and study the relationship between humans and computers. Driven by the increased production of data in interaction and the transfer of value from interaction to data, we argue that computing that fundamentally impacts human-computer relations is no longer happening only in interaction but also without and outside interaction. While recent arguments have highlighted interaction as a problematic concept for HCI — challenging what constitute users, use, the human, and the computer in interaction — we propose post-interaction computing as one means to conceptualise a fourth wave of HCI. We propose four concepts — immediacy, (un)intentionality, interaction effects, and instability — that can help us in identifying and slicing our objects of analysis in new ways that better match the challenges that HCI is now faced with.
Sky Image forecasting with Generative Adversarial Networks for cloud coverage prediction

This work focuses on the problem of sky image prediction using Generative Adversarial Networks (GANs). Sky images observation is used for cloud coverage short-term forecasting and therefore by predicting the future frames of a sky image sequence, a better estimate of the available solar resource for renewables could be achieved. For this purpose, a Deep Convolutional Neural Network (CNN) topology is trained with an Adversarial loss in order to produce realistic future frames of the input image sequences. The prediction of future frames is incurred by two different approaches. In the first approach, a network is receiving 4 frames and predicting the next frame and in the second approach, given a sequence of 8 input images a network is predicting the next 8 frames. In order to evaluate the quality of the generated images, a criterion based on cloud coverage is proposed. This cloud coverage metric provides an objective criterion for assessing the quality of the generated sky images, especially in the presence of clouds.
The Data Hungry Home

It's said that the pleasure is in the giving, not the receiving. This belief is validated by how humans interact with their family, friends and society as well as their gardens, homes, and pets. Yet for ubiquitous devices, this dynamic is reversed with devices as the donors and owners as the recipients. This paper explores an alternative paradigm where these devices are elevated, becoming members of Data Hungry Homes, allowing us to build relationships with them using the principles that we apply to family, pets or houseplants. These devices are developed to fit into a new concept of the home, can symbiotically interact with us and possess needs and traits that yield unexpected positive or negative outcomes from interacting with them. Such relationships could enrich our lives through our endeavours to "feed" our Data Hungry Homes, possibly leading us to explore new avenues and interactions outside and inside the home.
Effect of Population Size Over Parameter-less Firefly Algorithm

Nature-inspired optimization algorithms have proved their efficacy for solving highly nonlinear problems in science and engineering. Firefly Algorithm (FA), which is one of them, has been taken to be broadly discussed here. However, practical studies demonstrate that the proper setting of the FA's parameters is the key difficulty. FA associates with some sensitive parameters, and proper setting of their values is extremely time-consuming. Therefore, parameter less variants of FA have been proposed in this study by incorporating the adaptive formulation of the control parameters which are free from the tuning procedure. Population size greatly influences the convergence of any nature-inspired optimization algorithm. Generally, lower population size enhances convergence speed but may trap into local optima. While larger population size maintains the diversity but slows down the convergence rate. Therefore, the crucial effect of different population size over FA's efficiency has also been investigated here. The population size (of considered FA is varied within the size [10, 1280] where it gets doubled in each run starting with \(n = 10\). Therefore, eight instances of FA have been executed, and the best one is considered by the user over different classes of functions. Experimental results also show that the proposed parameter-less FA with a population size between 40 and 80 provides best results by considering optimization ability and consistency over any classes of functions.
Multiple Interaction Attention Model for Open-World Knowledge Graph Completion

Knowledge Graph Completion (KGC) aims at complementing missing relationships between entities in a Knowledge Graph (KG). While closed-world KGC approaches utilizing the knowledge within KG could only complement very limited number of missing relations, more and more approaches tend to get knowledge from open-world resources such as online encyclopedias and newswire corpus. For instance, a recent proposed open-world KGC model called ConMask learns embeddings of the entity's name and parts of its text-description to connect unseen entities to the KG. However, this model does not make full use of the rich feature information in the text descriptions, besides, the proposed relationship-dependent content masking method may easily miss to find the target-words. In this paper, we propose to use a Multiple Interaction Attention (MIA) mechanism to model the interactions between the head entity description, head entity name, the relationship name, and the candidate tail entity descriptions, to form the enriched representations. Our empirical study conducted on two real-world data collections shows that our approach achieves significant improvements comparing to state-of-the-art KGC methods.
Sequential Monte Carlo Cardinalized probability hypothesized density filter based on Track-Before-Detect for fluctuating targets in heavy-tailed clutter

In this paper, a Sequential Monte Carlo Cardinalized Probability Hypothesized Density (SMC-CPHD) filter based on Track-Before-Detect (TBD) algorithm in heavy-tailed clutter is proposed. The CPHD filter, which jointly propagates the first-order statistical moment and cardinality distribution, is an effective and efficient solution to multitarget filter problem in the presence of data association uncertainty, detect uncertainty, and time-varying. The TBD algorithm is extremely sensitive to the presence of clutter spikes in the data as the formulation of the TBD filter is dependent on knowledge of underlying clutter behavior. Therefore, the clutter is modeled in terms of K distribution with sea surface background and G0 distribution with primary forest background, while the popular Swerling targets of type 1 and 3 are considered to capture the target amplitude fluctuation frame-to-frame in this paper. The utilization of K distribution and G0 distribution clutter models instead of conventional Rayleigh distribution are verified through analysis of simulated and realistic data. The relevant results prove that proposed SMC-CPHD-TBD algorithm based on K distribution model and G0 distribution model can improve the detect and tracking performance over the conventional method in heavy-tailed clutter scenario. Additionally, the numerical simulations show that proposed CPHD-TBD algorithm can output the multitarget trajectories in the better precision with considerably less computational complexity compared with DP-TBD algorithm.
Beyond HCI and CSCW: Challenges and Useful Practices Towards a Human-Centred Vision of AI and IA

Over the decades, technologies envisioned by pioneers such as Douglas Engelbart are becoming a reality. AI has become an important driver for technological progress, posing questions for the future of human-computer interaction. Our research group at Fraunhofer FIT looks back on a 51 year long research tradition that started with Engelbart’s vision and followed through the larger developments of HCI from the introduction of CSCW up to successful projects involving the engineering of large software systems in practice. In this paper, we outline the history of our institute against the background of trends in HCI, working out the cornerstones and “useful practices” from our research tradition towards our vision of the future of a human-centred AI/IA, with the expectation that this analysis may be useful for similar organisations. In doing so, we illustrate tensions between theory and application, humans and technology, and show how keeping those aspects in balance is an important challenge and chance for bringing disruptive emerging technologies successfully into practice.
Feature Enhancement via User Similarities Networks for Improved Click Prediction in Yahoo Gemini Native

Yahoo's native advertising marketplace (also known as Gemini native) serves billions of ad impressions daily, reaching many hundreds of millions USD in yearly revenue. Driving Gemini native models that are used to predict ad click probability (pCTR) is OFFSET - a feature enhanced collaborative-filtering (CF) based event prediction algorithm. While some of the user features used by OFFSET have high coverage, other features, especially those based on click patterns, suffer from extremely low coverage. In this work, we present a framework that simplifies complex interactions between users and other entities in a bipartite graph. The one mode projection of this bipartite graph onto users represents a user similarity network, allowing us to quantify similarities between users. This network is combined with existing user features to create an enhanced feature set. In particular, we describe the implementation and performance of our framework using user Internet browsing data (e.g., visited pages URLs) to enhance the user category feature. Using our framework we effectively increase the feature coverage by roughly 15%. Moreover, online results evaluated on 1% of Gemini native traffic show that using the enhanced feature increases revenue by almost 1% when compared to the baseline operating with the original feature, which is a substantial increase at scale.
Detection of Transcoding from H.264/AVC to HEVC Based on CU and PU Partition Types

High Efficiency Video Coding (HEVC) is a worldwide popular video coding standard due to its high coding efficiency. To make profits, forgers prefer to transcode videos from previous standards such as H.264/AVC to HEVC. To deal with this issue, an efficient method is proposed to expose such transcoded HEVC videos based on coding unit (CU) and prediction unit (PU) partition types. CU and PU partitioning are two unique syntactic units of HEVC that can reflect a video’s compression history. In this paper, CU and PU partition types of I pictures and P pictures are firstly extracted. Then, their mean frequencies are calculated and concatenated as distinguishing features, which are further sent to a support vector machine (SVM) for classification. Experimental results show that the proposed method can identify transcoded HEVC videos with high accuracy and has strong robustness against frame-deletion and shifted Group of Pictures (GOP) structure attacks.