Reviewerly/exHarmony huggingface dataset:
Neighborhood Preserving Hashing for Scalable Video Retrieval

In this paper, we propose a Neighborhood Preserving Hashing (NPH) method for scalable video retrieval in an unsupervised manner. Unlike most existing deep video hashing methods which indiscriminately compress an entire video into a binary code, we embed the spatial-temporal neighborhood information into the encoding network such that the neighborhood-relevant visual content of a video can be preferentially encoded into a binary code under the guidance of the neighborhood information. Specifically, we propose a neighborhood attention mechanism which focuses on partial useful content of each input frame conditioned on the neighborhood information. We then integrate the neighborhood attention mechanism into an RNN-based reconstruction scheme to encourage the binary codes to capture the spatial-temporal structure in a video which is consistent with that in the neighborhood. As a consequence, the learned hashing functions can map similar videos to similar binary codes. Extensive experiments on three widely-used benchmark datasets validate the effectiveness of our proposed approach.
Secure and Efficient Outsourced k-Means Clustering using Fully Homomorphic Encryption With Ciphertext Packing Technique

Nowadays, more individuals and corporations tend to use machine learning as a service (MLaaS) in cloud computing environment. However, when enjoying the pay-as-you-go mode and flexible capacity of cloud computing, it also increases the risk of privacy leakage for sensitive data. In this paper, we aim to efficiently implement privacy-preserving MLaaS, and focus on k-means clustering over outsourced encrypted cloud databases. Previous works mainly utilize partially homomorphic encryptions, which require a great number of interactive protocols with high computation and communication costs, making them not practical in real-world applications. To better solve this problem, we propose a new secure and efficient outsourced k-means clustering (SEOKC) scheme using fully homomorphic encryption with ciphertext packing technique, which achieves parallel computation without extra cost. The proposed scheme preserves privacy in three aspects: (1) database security, (2) privacy of clustering results and (3) hiding of data access patterns. We provide formal security analysis and evaluate the performance of the proposed scheme through extensive experiments. The experiment results show that our scheme needs much less computation cost (more than three orders of magnitude lower) than the state-of-the-art schemes, and is suitable to be applied on large databases.
BshapeNet: Object detection and instance segmentation with bounding shape masks

We propose a modularizable component that can predict the boundary shapes and boxes of an image, along with a new masking scheme for improving object detection and instance segmentation. Specifically, we introduce two types of novel masks: a bounding box (bbox) mask and a bounding shape (bshape) mask. For each of these types, we consider two variants—the "Thick" model and the "Scored" model—both of which have the same morphology but differ in ways that make their boundaries thicker. To evaluate our masks, we design extended frameworks by adding a bshape mask (or a bbox mask) branch to a Faster R-CNN, and call this BshapeNet (or BboxNet). Furthermore, we propose BshapeNet+, a network that combines a bshape mask branch with a Mask R-CNN. Among our various models, BshapeNet+ demonstrates the best performance in both tasks. In addition, BshapeNet+ markedly outperforms the baseline models on MS COCO and Cityscapes and achieves highly competitive results with state-of-the-art models. In particular, the experimental results show that our branch works well on small objects and is easily applicable to various models, such as PANet as well as Faster R-CNN and Mask R-CNN.
Avoiding Insurance Fraud: A Blockchain-based Solution for the Vehicle Sector

Blockchain is a relatively new technology originally created to store Bitcoin's transaction records. The system is highly redundant and distributed, making it very difficult for fraudulent financial transactions. While cryptocurrencies might be the most well-known use case of blockchain technology, it is wrong to assume that this technology is restricted to the financial area. Indeed, many blockchain use cases are being developed today in different areas. Due to the complexity of certain processes, a new technology associated to blockchain has appeared – smart contracts. These digital contracts act like traditional contracts, with the major difference being their automaticity. In this article, we aim to discuss how blockchain and smart contracts may be used together in order to improve organizational operations. More specifically, we demonstrate how these technologies might be used to develop a solution that avoids certain types of fraud in the area of vehicle insurance.
Machine unlearning: linear filtration for logit-based classifiers

Recently enacted legislation grants individuals certain rights to decide in what fashion their personal data may be used and in particular a "right to be forgotten". This poses a challenge to machine learning: how to proceed when an individual retracts permission to use data which has been part of the training process of a model? From this question emerges the field of machine unlearning, which could be broadly described as the investigation of how to "delete training data from models". Our work complements this direction of research for the specific setting of class-wide deletion requests for classification models (e.g. deep neural networks). As a first step, we propose linear filtration as an intuitive, computationally efficient sanitization method. Our experiments demonstrate benefits in an adversarial setting over naive deletion schemes.
A chaotic owl search algorithm based bilateral negotiation model

Negotiation is a core activity between stakeholders with different goals. This paper offers a solution based on the automated bilateral negotiation model using a recent meta-heuristic algorithm Owl Search Algorithm (OSA), and chaos theory. The proposed algorithm called Chaotic Owl Search Algorithm (COSA). This algorithm is used to adapt negotiation strategies for computing negotiation offers throughout the negotiation process. For this aim, a negotiation grasped between two parties during several negotiation rounds. The results of the proposed algorithm compared with the standard OSA, PSO and the most common negotiation tactics. Different control parameters considered for accurate judgments of the suggested optimization techniques. The comparative study proved that the COSA provides accurate results over compared algorithms in terms of Average buyers' /seller's utility, Average negotiation rounds and Average processing time. This paper is the first of integrating chaos theory with the OSA in optimization problems and especially in the negotiation process.
Morphometry of drainage basins and stream networks as an indicator of active fold growth (Gorm anticline, Fars Province, Iran)

The Zagros Folded belt, as one of the most tectonically active areas in the world, contains NW–SE trending whaleback anticlines that are growing vertically and laterally. Morphometric indices are widely used in tectonic geomorphological research because of their low cost and relative ease of application. Drainage basins and their networks adapt to tectonically induced changes in surface slope, and hence their qualitative and quantitative analysis can reveal aspects of active anticlinal growth. This study evaluates the morphometric properties of drainage basins and their networks developed on the fold limbs, using this quantitative data to detect lateral propagation of the Gorm anticline in the Zagros Simply Folded Belt (ZSFB). The Gorm anticline was considered to be suitable for this approach because of the variability of drainage basins and their networks along the axis of the anticline and also transversely across its forelimb and backlimb. The morphometric properties of stream networks including drainage density (Dd), drainage frequency (Df), ratio of 1st-order streams to the total number of streams of all orders (N1/N), length of 1st-order streams (L1), spacing ratio (R) of main streams on flanks, and drainage patterns were analyzed in three zones of the anticline (southeastern (SE), central, and northwestern (NW)). Also, the morphometric indices of drainage basins such as basin area (Ba), shape (Bs), asymmetry factor (AF), hypsometric integral (HI), a new proposed crescentness index (CI), and sinuosity of anticline divide (SAD) were evaluated. Results show that an increase in N1/N ratio and L1 from the center towards both the southeastern and northwestern fold noses suggests lateral growth of the anticline towards the southeast and northwest. The SE and NW zones are characterized by the smaller, elongated and crescent-shaped basins, lower elevations, lower dip slopes, higher hypsometric integral, and lower sinuosity of anticline ridge. These results collectively suggest that the southeastern and northwestern fold noses are younger, less eroded and laterally growing. The formation of crescent-shaped basins with asymmetric forked drainages developed in the SE and NW zones are considered to be useful indicators of lateral growth of the Gorm anticline. Although higher values of Ba, SAD, Df and S indexes in the central part of the fold can be attributed to the older relative age of this area, the activity of the Nezamabad Fault and the exposure of some weak rocks also play some roles in explaining the increase the values of parameters. Overall, the N1/N, L1, CI, SAD indexes, as well as the fan-shaped and asymmetric forked drainage pattern are the most useful tectonic geomorphological criteria to detect lateral propagation of anticlines.
Digital harm and addiction: An anthropological view

A broad trend of anti‐tech rhetoric has raised fears about the dangers of digital overuse and reliance and suggests that technology is addictive, unnatural or harmful. Some people have limited their hours of ‘screen time’ or resorted to the practice of ‘digital detoxing’ – a catch‐all term to describe temporarily ‘leaving’ the digital world. Using her ethnographic work in a North American digital detox retreat, the author considers an anthropological approach to digital harm and addiction that emphasizes their socially constructed nature. Following Horst and Miller (2012) and the view that digital harms are socially constructed, she argues that digital technology will be removed in different places for different reasons, and that geographically bound cultural values are vital to understanding how digital harms come to be imagined and counteracted. Whether or not digital use will ever be proven to be clinically harmful, digital harm is best viewed as a ‘social fact’.
SHA-2 Acceleration Meeting the Needs of Emerging Applications: A Comparative Survey

While SHA-2 is a ubiquitous cryptographic hashing primitive, its role in emerging application domains, e.g. blockchains or trusted IoT components, has made the acceleration of SHA-2 very challenging due to new stringent classes of requirements, especially implementation cost and energy efficiency. The survey discusses these emerging applications and their fundamental requirements. Then, the work presents a comprehensive review of the different design techniques available in the literature for SHA-2 acceleration. The main focus of the presentation is placed on the impact of each design technique on the area, energy, power, and performance of the resulting accelerator, guiding the designer through the identification of the appropriate technique mixes which meet the constraints of any given application.
Enhancement of surgical hand gesture recognition using a capsule network for a contactless interface in the operating room

Background and objective:Hand gesture recognition systems in operating rooms (ORs) are crucial for browsing and controlling computer-aided devices, which have been developed to decrease the risk of contamination during surgical procedures. Methods:We proposed the use of hand gesture recognition to enhance accuracies and recognition areas with the capsule network (CapsNet) of deep neural network and Leap Motionâ Our method includes the i) extraction and preprocessing of infrared (IR) images (60 frames per second) from Leap Motion™, ii) training of various types of networks, and iii) gesture recognition evaluation in the OR. We trained the images of training dataset (N=903) and tested images (N=100) using five types of surgical hand gestures including hovering, grab, click, one peak, and two peaks by 10 subjects with various types of augmentation methods including rotate (0∘, 90∘, 180∘), scale, translation, illumination, and resize. Results: CapsNet achieved a classification accuracy of 86.46% (around 10% improvement) compared with 73.67% for the baseline convolutional neural network (CNN) and 76.4% for VGG16. Conclusions: In conclusion, the accuracy of hand gesture recognition with CapsNet was better than that of conventional CNNs, which could be used to navigate and manipulate various types of computer-aided devices and applications through contactless gesture interaction.
Towards a Material Landscape of TUIs, Through the Lens of the TEI Proceedings 2008-2019

Materials play an influential role in determining the way people interact with and experience objects. This impact is particularly important to TUI designers, as the artefacts they design often afford grasping and physical manipulation. As the ACM Conference on Tangible, Embedded and Embodied Interaction moves through its second decade, we sought to survey past proceedings to present a picture of the material choices for TUI design. In this paper we present an exhaustive survey of these proceedings and discuss insights revealed on TUI material trends. Our findings include highlighting the most popular materials choices, as well as the high percentage of materials that have only been used once throughout the years. Furthermore, we make recommendations on the future use of, and reporting on materials choice for TUI design and point toward future work that is needed to fully map the material landscape of TUIs.
Multi-label feature selection based on label distribution and feature complementarity

In the real-world, data in various domains usually tend to be high-dimensional, which may result in considerable time complexity and poor performance for multi-label classification problems. Multi-label feature selection is an important preprocessing step in machine learning, which can effectively solve the so-called "curse of dimensionality" by removing irrelevant and redundant features. Nevertheless, the significance of related labels for each instance is generally different, which is an issue that most of the existing multi-label feature selection algorithms have not addressed. Hence, in this paper, we integrate label-distribution learning into multi-label feature selection from the perspective of granular computing with considering multiple feature correlations. Then, a novel multi-label feature selection algorithm based on label distribution and feature complementarity is developed. In addition, the proposed algorithm consists of two primary parts: first, the different significances of related labels for each instance in the multi-label data are obtained based on granular computing; second, the feature complementarity is estimated based on neighborhood mutual information without discretization. Moreover, the superiority of our proposed method over other state-of-the-art methods is demonstrated by conducting comprehensive experiments with 10 publicly available multi-label datasets on six widely-used metrics. Finally, the proposed method can significantly improve the performance of the classifier while reducing the dimension of the original data.
Quantum Enhanced Optical Phase Estimation With a Squeezed Thermal State

Quantum phase-estimation protocols can provide a measuring method of phase shift with precision superior to the standard quantum limit (SQL) due to the application of a nonclassical state of light. A squeezed vacuum state, whose variance in one quadrature is lower than the corresponding SQL, has been pointed out as a sensitive resource for quantum phase estimation and the estimation accuracy is directly influenced by the properties of the squeezed state. Here we analyze in detail the influence of the purity and squeezing level of the squeezed state on the accuracy of quantum phase estimation. The maximum precision that can be achieved for a squeezed thermal state is evaluated, and the experimental results are in agreement with the theoretical analyses. It is also found that the width of the phase-estimation interval $\mathrm{\ensuremath{\Delta}}\ensuremath{\theta}$ beyond SQL is correlated with the purity of the squeezed state.
Simultaneous Utilization of Inertial and Video Sensing for Action Detection and Recognition in Continuous Action Streams

This paper describes the simultaneous utilization of inertial and video sensing for the purpose of achieving human action detection and recognition in continuous action streams. Continuous action streams mean that actions of interest are performed randomly among actions of non-interest in a continuous manner. The inertial and video data are captured simultaneously via a wearable inertial sensor and a video camera, which are turned into 2D and 3D images. These images are then fed into a 2D and a 3D convolutional neural network with their decisions fused in order to detect and recognize a specified set of actions of interest from continuous action streams. The developed fusion approach is applied to two sets of actions of interest consisting of smart TV gestures and sports actions. The results obtained indicate the fusion approach is more effective than when each sensing modality is used individually. The average accuracy of the fusion approach is found to be 5.8% above inertial and 14.3% above video for the TV gesture actions of interest, and 23.2% above inertial and 1.9% above video for the sports actions of interest.
Early Action Recognition With Category Exclusion Using Policy-Based Reinforcement Learning

The goal of early action recognition is to predict action label when the sequence is partially observed. The existing methods treat the early action recognition task as sequential classification problems on different observation ratios of an action sequence. Since these models are trained by differentiating positive category from all negative classes, the diverse information of different negative categories is ignored, which we believe can be collected to help improve the recognition performance. In this paper, we step towards to a new direction by introducing category exclusion to early action recognition. We model the exclusion as a mask operation on the classification probability output of a pre-trained early action recognition classifier. Specifically, we use policy-based reinforcement learning to train an agent. The agent generates a series of binary masks to exclude interfering negative categories during action execution and hence help improve the recognition accuracy. The proposed method is evaluated on three benchmark recognition datasets, NTU-RGBD, First-Person Hand Action, as well as UCF-101. The proposed method enhances the recognition accuracy consistently over all different observation ratios on the three datasets, where the accuracy improvements on the early stages are especially significant.
A Survey on Spark Ecosystem: Big Data Processing Infrastructure, Machine Learning, and Applications

With the explosive increase of big data in industry and academic fields, it is important to apply large-scale data processing systems to analyze Big Data. Arguably, Spark is the state-of-the-art in large-scale data computing systems nowadays, due to its good properties including generality, fault tolerance, high performance of in-memory data processing, and scalability. Spark adopts a flexible Resident Distributed Dataset (RDD) programming model with a set of provided transformation and action operators whose operating functions can be customized by users according to their applications. It is originally positioned as a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fast</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">general</i> data processing system. A large body of research efforts have been made to make it more efficient (faster) and general by considering various circumstances since its introduction. In this survey, we aim to have a thorough review of various kinds of optimization techniques on the generality and performance improvement of Spark. We introduce Spark programming model and computing system, discuss the pros and cons of Spark, and have an investigation and classification of various solving techniques in the literature. Moreover, we also introduce various data management and processing systems, machine learning algorithms and applications supported by Spark. Finally, we make a discussion on the open issues and challenges for large-scale in-memory data processing with Spark.
Patients-Controlled Secure and Privacy-Preserving EHRs Sharing Scheme Based on Consortium Blockchain

The large-scale deployment of eHealth systems has brought deep impact on human society. However, the centralized Electronic health records (EHRs) outsourcing system faces some critical security and privacy issues, which have raised wide concerns in both academia and industry. Moreover, the patients lose control of their health data. There is a need to construct a decentralized and secure EHRs with more flexible control by patients themselves instead of the third party. Fortunately, we observe that the characteristics of blockchain technology such as decentralization, immutability, and auditability perfectly match these aforementioned requirements. Specifically, to satisfy our application requirements, we build a consortium blockchain (PESchain) which is maintained by a set of medical institutions. The EHRs of patients are encrypted and stored in the medical institutions by local cloud while the corresponding hash values are stored on PESchain. Moreover, to enable privacy-preserving EHRs sharing, we construct a stealth authorization scheme to achieve access authorization delivery on the blockchain. Besides, we pack the transactions according to different types to guarantee efficient block deletion. The security analysis and performance evaluation show that PESchain is secure and practical for EHRs sharing.
Performance Analysis of Distributed and Federated Learning Models on Private Data

There has been significant research in privacy-related aspects of machine learning and large scale data processing. In traditional methods of training a model, data is gathered at a centralized machine where training on the entire data takes place. This has led to a major problem of not only scalability but also of preserving the anonymity and privacy of sensitive user data. As a consequence, there has been extensive work done towards distributed machine learning. In more recent times, Federated Learning has gained a lot of traction. This is because of the features that make it highly suitable to train models collaboratively while preserving the privacy of sensitive data. In this paper, we compare basic machine learning, distributed machine learning, and federated learning by modelling on the Fashion MNIST dataset. Our results show that federated learning model not only maintains privacy but is also fast, and allows deployment at scale- even with low compute, mobile devices.