Reviewerly/exHarmony huggingface dataset:
A Framework for the Experience of Meaning in Human-Computer Interaction

The view of quality in human-computer interaction continuously develops, having in past decades included consistency, transparency, usability, and positive emotions. Recently, meaning is receiving increased interest in the user experience literature and in industry, referring to the end, purpose or significance of interaction with computers. However, the notion of meaning remains elusive and a bewildering number of senses are in use. We present a framework of meaning in interaction, based on a synthesis of psychological meaning research. The framework outlines five distinct senses of the experience of meaning: connectedness, purpose, coherence, resonance, and significance. We illustrate the usefulness of the framework by analyzing a selection of recent papers at the CHI conference and by raising a series of open research questions about the interplay of meaning, user experience, reflection, and well-being.
Analyzing Basketball Movements and Pass Relationships Using Realtime Object Tracking Techniques Based on Deep Learning

In this paper, we present techniques for automatically classifying players and tracking ball movements in basketball game video clips under poor conditions, where the camera angle dynamically shifts and changes. In the core of our system lies Yolo, a realtime object detection system. Given the ground truth boxes collected by our data specialists, Yolo is trained to detect the presence of objects in every video frame. In addition, Yolo uses Darknet that implements convolution neural networks to classify a detected object to a player and to recognize its jersey numbers of specific movements. By identifying players and ball possessions, we can automatically compute ball distributions that are reflected on complex networks. With original Yolo system, player movement can be interrupted, when the players move out of the frame due to camera shift and when players overlap each other on a two-dimensional frame. We have adapted Yolo to keep track of players even under such poor condition by considering contextual information available from the framework preceding and/or succeeding problematic video frames. In addition to the novel movement inference method, we provide a framework for analyzing the pass networks in various perspectives to help the managing staff to reveal critical determinants of team performance and to design better game strategies. We assess the performance of our system in terms of accuracy by making a comparison with the analytical reports generated by human experts.
Genuinely nonlocal product bases: Classification and entanglement-assisted discrimination

An orthogonal product basis of a composite Hilbert space is genuinely nonlocal if the basis states are locally indistinguishable across every bipartition. From an operational point of view, such a basis corresponds to a separable measurement that cannot be implemented by local operations and classical communication unless all the parties come together in a single location. In this work we classify genuinely nonlocal product bases into different categories. Our classification is based on the state elimination property of the set via orthogonality-preserving measurements when all the parties are spatially separated or different subsets of the parties come together. We then study local state discrimination protocols for several such bases with additional entangled resources shared among the parties. Apart from consuming less entanglement than teleportation-based schemes, our protocols indicate operational significance of the proposed classification and exhibit nontrivial use of genuine entanglement in the local state discrimination problem.
Transparent predictive modelling of catalytic hydrodesulfurization using an interval type-2 fuzzy logic

Optimizing the desulfurization process in reactors would assist in predicting the performance of catalysts, and in turn, the final sulfur concentration in fuel. Machine learning (ML) techniques have proven their predictive capacity in solving challenging problems in the petroleum industry. Several ML-based sulfur predictors have been designed to predict the sulfur concentration in fuel products. The capability to handle the impression and uncertainty present in the real-world environment has made fuzzy logic (FL) one of the most commonly used soft computing paradigms in modeling. This paper proposes an interval type-2 fuzzy logic model to optimize the use of catalysts for hydrodesulfurization (HDS) in an oil refinery for cleaner production. Such optimization assists decision makers in identifying the precise required conditions such as temperature, pressure and the amount of catalyst required to avoid unexpected redesulfurization. The catalyst was synthesized from AlMoCo modified with boron, phosphorus or bismuth. A series of hydrodesulfurization was performed using these catalysts under various conditions. The collected data were used to build and test the model. The results showed a promising prediction performance in terms of average absolute relative error (AARE = 0.0647) and the squared correlation coefficient (R2=0.995). Further experimental validation done on unexplored parameter settings demonstrates that the predicted values obtained from the proposed model corresponds closely with the follow-up experimental results where an average absolute difference of less than 4 ppm was recorded. This proves the capacity of the interval type-2 FL model in handling uncertainty, which holds great promise for cleaner production of oil. The reported model prove to be good for prediction of catalysts performance in a single unit, however, it cannot be used for catalysts in dual units.
SmartCert BlockChain Imperative for Educational Certificates

The electronic authentication system authenticates the documents electronically using the blockchain technology, which enables us to implement an integrated system of official documents of all kinds for Al-Zaytoonah University of Jordan. This technology allows us to build a robust database system that cannot be tampered with, modified, destroyed or altered. It further ensures the safety of documents issued within the University departments and those exported or deported outside the University system; such as, financial documents, official documents and academic certificates. Moreover, this system guarantees us a very high level of data and information security and confidentiality. Our system will be built using a database that provides 100% true information about the exports of that system. This technology will help users prevent the forgery of the data and official documents issued by the University of Al-Zaytoonah, both internally and externally, and also enables the departments within the University to issue documents to third parties while ensuring confirmed, undeniable delivery and sound content.
Elastic and cost-effective data carrier architecture for smart contract in blockchain

Smart contract, which could help developer deploy decentralized and secure blockchain application, is one of the most promising technologies for modern Internet of things (IoT) ecosystem today. However, Ethereum smart contract lacks of ability to communicate with outside IoT environment. To enable smart contracts to fetch off-chain data, this paper proposes a data carrier architecture that is cost-effective and elastic for blockchain-enabled IoT environment. Three components, namely Mission Manager, Task Publisher and Worker, are presented in the data carrier architecture to interact with contract developer, smart contract, Ethereum node and off-chain data sources. Selective solutions are also proposed for filtering smart contract event and decoding event log to fit different requirements. The evaluation results and discussions show the proposed system will decrease about 20USD deployment cost in average for every smart contract, and it is more efficient and elastic compared with Oraclize Oracle data carrier service.
Erlay

Bitcoin is a top-ranked cryptocurrency that has experienced huge growth and survived numerous attacks. The protocols making up Bitcoin must therefore accommodate the growth of the network and ensure security.
Stochastic Configuration Networks Based Adaptive Storage Replica Management for Power Big Data Processing

In the power industry, processing business big data from geographically distributed locations, such as online line-loss analysis, has emerged as an important application. How to achieve highly efficient big data storage to meet the requirements of low latency processing applications is quite challenging. In this paper, we propose a novel adaptive power storage replica management system, named PARMS, based on stochastic configuration networks (SCNs), in which the network traffic and the data center (DC) geodistribution are taken into consideration to improve data real-time processing. First, as a fast learning model with less computation burden and sound prediction performance, the SCN model is employed to estimate the traffic state of power data networks. Then, a series of data replica management algorithms is proposed to lower the effects of limited bandwidths and a fixed underlying infrastructure. Finally, the proposed PARMS is implemented using data-parallel computing frameworks (DCFs) for the power industry. Experiments are carried out in an electric power corporation of 230 million users, China Southern power grid, and the results show that our proposed solution can deal with power big data storage efficiently and the job completion times across geodistributed DCs are reduced by 12.19% on average.
Eager pruning

Today's big and fast data and the changing circumstance require fast training of Deep Neural Networks (DNN) in various applications. However, training a DNN with tons of parameters involves intensive computation. Enlightened by the fact that redundancy exists in DNNs and the observation that the ranking of the significance of the weights changes slightly during training, we propose Eager Pruning, which speeds up DNN training by moving pruning to an early stage.
IoT Device Fingerprinting: Machine Learning based Encrypted Traffic Analysis

Even in the face of strong encryption, the spectacular Internet of Things (IoT) penetration across sectors such as e-health, energy, transportation, and entertainment is expanding the attack surface, which can seriously harm users' privacy. We demonstrate in this paper that an attacker is able to disclose sensitive information about the IoT device, such as its type, by identifying specific patterns in IoT traffic. To perform the fingerprint attack, we train machine-learning algorithms based on selected features extracted from the encrypted IoT traffic. Extensive simulations involving the baseline approach show that we achieve not only a significant mean accuracy improvement of 18.5% and but also a speedup of 18.39 times for finding the best estimators. Obtained results should spur the attention of policymakers and IoT vendors to secure the IoT devices they bring to market.
Data Recovery on Encrypted Databases with k-Nearest Neighbor Query Leakage

Recent works by Kellaris et al. (CCS'16) and Lacharite et al. (SP'18) demonstrated attacks of data recovery for encrypted databases that support rich queries such as range queries. In this paper, we develop the first data recovery attacks on encrypted databases supporting one-dimensional k-nearest neighbor (k-NN) queries, which are widely used in spatial data management. Our attacks exploit a generic k-NN query leakage profile: the attacker observes the identifiers of matched records. We consider both unordered responses, where the leakage is a set, and ordered responses, where the leakage is a k-tuple ordered by distance from the query point. As a first step, we perform a theoretical feasibility study on exact reconstruction, i.e., recovery of the exact plaintext values of the encrypted database. For ordered responses, we show that exact reconstruction is feasible if the attacker has additional access to some auxiliary information that is normally not available in practice. For unordered responses, we prove that exact reconstruction is impossible due to the infinite number of valid reconstructions. As a next step, we propose practical and more realistic approximate reconstruction attacks so as to recover an approximation of the plaintext values. For ordered responses, we show that after observing enough query responses, the attacker can approximate the client's encrypted database with considerable accuracy. For unordered responses we characterize the set of valid reconstructions as a convex polytope in a k-dimensional space and present a rigorous attack that reconstructs the plaintext database with bounded approximation error. As multidimensional spatial data can be efficiently processed by mapping it to one dimension via Hilbert curves, we demonstrate our approximate reconstruction attacks on privacy-sensitive geolocation data. Our experiments on real-world datasets show that our attacks reconstruct the plaintext values with relative error ranging from 2.9% to 0.003%.
Homomorphic Latent Space Interpolation for Unpaired Image-To-Image Translation

Generative adversarial networks have achieved great success in unpaired image-to-image translation. Cycle consistency allows modeling the relationship between two distinct domains without paired data. In this paper, we propose an alternative framework, as an extension of latent space interpolation, to consider the intermediate region between two domains during translation. It is based on the fact that in a flat and smooth latent space, there exist many paths that connect two sample points. Properly selecting paths makes it possible to change only certain image attributes, which is useful for generating intermediate images between the two domains. We also show that this framework can be applied to multi-domain and multi-modal translation. Extensive experiments manifest its generality and applicability to various tasks.
Multi-source data fusion for aspect-level sentiment classification

Neural networks have achieved great success in aspect-level sentiment classification due to their ability to learn sentiment knowledge from text. Generally, the effectiveness of neural networks relies on sufficiently large training corpora. However, existing aspect-level corpora are relatively small, which greatly limits the performance of neural network-based systems. In this paper, we propose a novel approach to aspect-level sentiment classification based on multi-source data fusion, which allows our system to learn sentiment knowledge from different types of resources. Specifically, we design a unified framework to integrate data from aspect-level corpora, sentence-level corpora, and word-level sentiment lexicons. Moreover, we take advantage of BERT, a pre-trained language model based on deep bidirectional Transformers, to generate aspect-specific sentence representations for sentiment classification. We evaluate our approach using laptop and restaurant datasets from SemEval 2014. Experimental results show that our approach consistently outperforms the state-of-the-art methods on all datasets.
Scalable analysis for arbitrary photonic integrated waveguide meshes

The advances in fabrication processes in different material platforms employed in integrated optics are opening the path towards the implementation of circuits with increasing degree of complexity.In addition to the more conventional Application Specific Photonic Circuit (ASPIC) paradigm the Programmable Multifunctional Photonics (PMP) approach is a transversal concept inspired by similar approaches, which are already employed in other technology fields.For instance, in electronics Field Programmable Gate Array (FPGA) devices enable a much more flexible universal operation as compared to Application Specific Integrated Circuits (ASICs).In photonics, the PMP concept is enabled by two-dimensional (2D) waveguide meshes for which, the number of possible input/outputs ports quickly builds up and furthermore, internal signal flow paths make the computation of transfer functions an intractable problem.Here we report a scalable method based on mathematical induction that allows one to obtain the scattering matrix of any 2D integrated photonic waveguide mesh circuit composed of an arbitrary number of cells and which is easily programmable.To our knowledge this is the first report of the kind and our results open the path to unblock this important design bottleneck.
A Fusion Approach for Multi-Frame Optical Flow Estimation

To date, top-performing optical flow estimation methods only take pairs of consecutive frames into account. While elegant and appealing, the idea of using more than two frames has not yet produced state-of-the-art results. We present a simple, yet effective fusion approach for multi-frame optical flow that benefits from longer-term temporal cues. Our method first warps the optical flow from previous frames to the current, thereby yielding multiple plausible estimates. It then fuses the complementary information carried by these estimates into a new optical flow field. At the time of writing, our method ranks first among published results in the MPI Sintel and KITTI 2015 benchmarks. Our models will be available on https://github.com/NVlabs/PWC-Net.
Private PAC learning implies finite Littlestone dimension

We show that every approximately differentially private learning algorithm (possibly improper) for a class H with Littlestone dimension d requires Ω(log*(d)) examples. As a corollary it follows that the class of thresholds over ℕ can not be learned in a private manner; this resolves open questions due to [Bun et al. 2015] and [Feldman and Xiao, 2015]. We leave as an open question whether every class with a finite Littlestone dimension can be learned by an approximately differentially private algorithm.
The Visual Centrifuge: Model-Free Layered Video Representations

True video understanding requires making sense of non-lambertian scenes where the color of light arriving at the camera sensor encodes information about not just the last object it collided with, but about multiple mediums -- colored windows, dirty mirrors, smoke or rain. Layered video representations have the potential of accurately modelling realistic scenes but have so far required stringent assumptions on motion, lighting and shape. Here we propose a learning-based approach for multi-layered video representation: we introduce novel uncertainty-capturing 3D convolutional architectures and train them to separate blended videos. We show that these models then generalize to single videos, where they exhibit interesting abilities: color constancy, factoring out shadows and separating reflections. We present quantitative and qualitative results on real world videos.
Adaptive Batch Size Image Merging Steganography and Quantized Gaussian Image Steganography

In digital image steganography, the statistical model of an image is essential for hiding data in less detectable regions and achieving better security. This has been addressed in the literature where different cost-based and statistical model-based approaches were proposed. However, due to the usage of heuristically defined distortions and non-constrained message models, resulting in numerically solvable equations, there is no closed-form expression for security as a function of payload. The closed-form expression is crucial for a better insight into image steganography problem and also improving performance of batch steganography algorithms. Here, we develop a statistical framework for image steganography in which the cover and the stego messages are modeled as multivariate Gaussian random variables. We propose a novel Gaussian embedding model by maximizing the detection error of the most common optimal detectors within the adopted statistical model. Furthermore, we extend the formulation to cost-based steganography, resulting in a universal embedding scheme that improves empirical results of current cost-based and statistical model-based approaches. This methodology and its presented solution, by reason of assuming a continuous hidden message, remains the same for any embedding scenario. Afterward, the closed-form detection error is derived within the adopted model for image steganography and it is extended to batch steganography. Thus, we introduce Adaptive Batch size Image Merging steganographer, AdaBIM, and mathematically prove it outperforms the state-of-the-art batch steganography method and further verify its superiority by experiments.
Recognizing Disguised Faces in the Wild

Research in face recognition has seen tremendous growth over the past couple of decades. Beginning from algorithms capable of performing recognition in constrained environments, existing face recognition systems achieve very high accuracies on large-scale unconstrained face datasets. While upcoming algorithms continue to achieve improved performance, many of them are susceptible to reduced performance under disguise variations, one of the most challenging covariate of face recognition. In this paper, the disguised faces in the wild (DFW) dataset is presented, which contains over 11000 images of 1000 identities with variations across different types of disguise accessories (the DFW dataset link: http://iab-rubric.org/resources/dfw.html). The images are collected from the Internet, resulting in unconstrained variations similar to real-world settings. This is a unique dataset that contains impersonator and genuine obfuscated face images for each subject. The DFW dataset has been analyzed in terms of three levels of difficulty: 1) easy; 2) medium; and 3) hard, in order to showcase the challenging nature of the problem. The dataset was released as part of the First International Workshop and Competition on DFW at the Conference on Computer Vision and Pattern Recognition, 2018. This paper presents the DFW dataset in detail, including the evaluation protocols, baseline results, performance analysis of the submissions received as part of the competition, and three levels of difficulties of the DFW challenge dataset.
Evaluation of a Dual Convolutional Neural Network Architecture for Object-wise Anomaly Detection in Cluttered X-ray Security Imagery

X-ray baggage security screening is widely used to maintain aviation and transport secure. Of particular interest is the focus on automated security X-ray analysis for particular classes of object such as electronics, electrical items and liquids. However, manual inspection of such items is challenging when dealing with potentially anomalous items. Here we present a dual convolutional neural network (CNN) architecture for automatic anomaly detection within complex security X-ray imagery. We leverage recent advances in region-based (R-CNN), mask-based CNN (Mask R-CNN) and detection architectures such as RetinaNet to provide object localisation variants for specific object classes of interest. Subsequently, leveraging a range of established CNN object and fine-grained category classification approaches we formulate within object anomaly detection as a two-class problem (anomalous or benign). Whilst the best performing object localisation method is able to perform with 97.9% mean average precision (mAP) over a six-class X-ray object detection problem, subsequent two-class anomaly/benign classification is able to achieve 66% performance for within object anomaly detection. Overall, this performance illustrates both the challenge and promise of object-wise anomaly detection within the context of cluttered X-ray security imagery.