Reviewerly/exHarmony huggingface dataset:
Discriminative Multi-View Privileged Information Learning for Image Re-Ranking

Conventional multi-view re-ranking methods usually perform asymmetrical matching between the region of interest (ROI) in the query image and the whole target image for similarity computation. Due to the inconsistency in the visual appearance, this practice tends to degrade the retrieval accuracy particularly when the image ROI, which is usually interpreted as the image objectness, accounts for a smaller region in the image. Since Privileged Information (PI), which can be viewed as the image prior, is able to characterize well the image objectness, we are aiming at leveraging PI for further improving the performance of multi-view re-ranking in this paper. Towards this end, we propose a discriminative multi-view re-ranking approach in which both the original global image visual contents and the local auxiliary PI features are simultaneously integrated into a unified training framework for generating the latent subspaces with sufficient discriminating power. For the on-the-fly re-ranking, since the multi-view PI features are unavailable, we only project the original multi-view image representations onto the latent subspace, and thus the re-ranking can be achieved by computing and sorting the distances from the multi-view embeddings to the separating hyperplane. Extensive experimental evaluations on the two public benchmarks, Oxford5k and Paris6k, reveal that our approach provides further performance boost for accurate image re-ranking, whilst the comparative study demonstrates the advantage of our method against other multi-view re-ranking methods.
Online Gesture Recognition Algorithm Applied to HUD Based Smart Driving System

In order to avoid driving distraction caused by traditional buttons or touch screens, gesture recognition technology has begun to be applied to the field of human-car interaction. Online gesture recognition system designed for vehicle needs powerful enough to satisfy the requirements of high classification accuracy, fast response time and low graphics memory consumption. To solve the above challenges, we propose an online gesture recognition algorithm based on RGB camera to identify motion, hand and gesture in sequence. We use the frame difference as a motion detection modality and apply the hand detection neural network to determine whether to activate the gesture classifier. In the gesture classifier, the frame difference is fused with the RGB image at data level based on Efficient Convolutional Network. We combined gesture recognition and Heads-Up Display to create a simulated driving system that allows users control auxiliary information through gestures, which used for usability analysis and user evaluation. For the purpose of finding the gestures that best match the various interactive tasks, we use the entropy weight method to analyze the usability of the gestures in the JESTER dataset and derive seven best gestures. The offline gesture classification accuracy on the JESTER dataset is 95.96% and online recognition algorithm runs on average at 306 fps when there is no motion and 164 fps in the presence of hand. According to the questionnaire results after the subjects used our system, more than 86.25% of the subjects expressed satisfaction with our gesture recognition system.
Towards the Internet of Augmented Things: An Open-source Framework to Interconnect IoT Devices and Augmented Reality Systems

The latest Augmented Reality (AR) and Mixed Reality (MR) systems are able to provide innovative methods for user interaction, but their full potential can only be achieved when they are able to exchange bidirectional information with the physical world that surround them, including the objects that belong to the Internet of Things (IoT). The problem is that elements like AR display devices or IoT sensors/actuators often use heterogeneous technologies that make it difficult to intercommunicate them in an easy way, thus requiring a high degree of specialization to carry out such a task. This paper presents an open-source framework that eases the integration of AR and IoT devices as well as the transfer of information among them, both in real time and in a dynamic way. The proposed framework makes use of widely used standard protocols and open-source tools like MQTT, HTTPS or Node-RED. In order to illustrate the operation of the framework, this paper presents the implementation of a practical home automation example: an AR/MR application for energy consumption monitoring that allows for using a pair of Microsoft HoloLens smart glasses to interact with smart power outlets.
Signal Detection for Full-duplex Cognitive Underwater Acoustic Communications with SIC Using Model-Driven Deep Learning Network

This paper aims to handle the model-driven deep learning network based signal detection for full-duplex cognitive underwater acoustic communications (FDCUACs) with self-interference cancellation (SIC). The FDCUACs play an important role in underwater wireless communications, which employs the index modulation-orthogonal frequency division multiplexing-spread spectrum (IM-OFDM-SS) that carries subcarrier index bits and symbol bits simultaneously to further enhance data rate. It is shown that the proposed signal detector for the FDCUACs with the SIC can be modelled as a model-driven deep learning network, i.e., an easy index bit recovering processor utilizing the uniqueness behavior of spread codes plus a deep learning network with eight essential layers employed to directly regain the data belonging to the pre-selected carrier index in the transmitter. Compared with the original receiver and signal detection for the IM-OFDM-SS communications, the proposed scheme omits the traditional steps such as channel estimation, equalization and demodulation, and demonstrates the remarkable performance.
Image Denoising Using Hybrid Singular Value Thresholding Operators

Truncated singular value decomposition (TSVD) is a simple and efficient technique for patchbased image denoising, in which a hard thresholding operator is utilized to set some small singular values to zero.Before performing the hard thresholding, the noise variance should be accurately estimated in order to determine the rank of the patch matrices.However, when the noise level is high, the denoisied results from the TSVD still contain some residual noise.To solve this problem, we present a hybrid thresheldoing strategy that combines a hard thresholding operator and a soft one.The former is directly reused the thresholding derived from TSVD, the latter is derived by minimum variance estimator.Simulation experiments are conducted to verify the effectiveness of the proposed method.Experimental results show that the method can effectively denoise the images with high level noise.
Short-Range Audio Channels Security: Survey of Mechanisms, Applications, and Research Challenges

Short-range audio channels have appealing distinguishing characteristics: ease of use, low deployment costs, and easy to tune frequencies, to cite a few. Moreover, thanks to their seamless adaptability to the security context, many techniques and tools based on audio signals have been recently proposed. However, while the most promising solutions are turning into valuable commercial products, acoustic channels are also increasingly used to launch attacks against systems and devices, leading to security concerns that could thwart their adoption. To provide a rigorous, scientific, security-oriented review of the field, in this paper we survey and classify methods, applications, and use-cases rooted on short-range audio channels for the provisioning of security services - including Two-Factor Authentication techniques, pairing solutions, device authorization strategies, defense methodologies, and attack schemes. Moreover, we also point out the strengths and weaknesses deriving from the use of short-range audio channels. Finally, we provide open research issues in the context of short-range audio channels security, calling for contributions from both academia and industry.
A Novel Image Encryption Scheme Based on Reversible Cellular Automata and Chaos

In this paper, a new scheme for image encryption is presented.The scheme is based on a chaotic map and cellular automata (CA).CA is a collection of cells arranged in a grid, such that each cell changes state as a function of time according to a defined set of rules that includes the states of neighboring cells.The major disadvantages of cellular automata in cryptography include limited number of reversal rules and inability to produce long sequences of states by these rules.In this paper, reversible cellular automaton is presented and used to solve this problem.The presented scheme is applied in three individual steps.Firstly, the image is blocked and the pixels are substituted by a reversible cellular automaton.Then, image pixels are scrambled by a chaos map that is produced by an elementary cellular automata and finally the blocks are attached and pixels are substituted by an individual reversible cellular automaton.Due to reversibility of used cellular automata, decryption scheme can reversely be applied.The experimental results show that encrypted image is suitable visually and this scheme has satisfied quantitative performance.
Authenticated semi-quantum key distribution without entanglement

One important challenge of quantum key distribution is to preserve its unconditional security when a scheme is implemented. In semi-quantum key distribution (SQKD), one of the parties is only able to perform classical operations, thus alleviating the cost of its implementation. Authenticated SQKD (ASQKD) ensures also better security than SQKD against attacks aiming at the identity of the parties. In this regard, Li et al. have proposed a new ASQKD scheme that ensures better efficiency than the first proposed ASQKD scheme of Yu et al. However, both schemes present loopholes in their security. This study presents a new ASQKD without using entanglement, able to achieve higher security than the schemes of Yu et al. and Li et al. Our scheme is also simpler and demands less advanced quantum devices than ASQKD schemes using entanglement.
Fast Compression and Optimization of Deep Learning Models for Natural Language Processing

Nowadays, recurrent neural networks (RNN) and convolutional neural networks (CNN) play a major role in a lot of natural language domains like text document categorization, part of speech tagging, chatbots, language modeling or language translation. Very often RNN networks have a few stacked layers with several megabytes of memory, the same is in case of CNN networks. In many domains like automatic speech recognition the real time inference is a crucial factor to achieve satisfactory quality of service. Compressing the network layers parameters and outputs into a suitable precision formats and applying pruning process can reduce the required storage and computation cycles in embedded devices. It can drastically reduce the consumed power and the memory capacity. In this article, we present pruning and quantization on deep learning models used for sentiment analysis, language modelling and language translation. All of them with a minor degradation of performance metric compared to full floating-point version. We present our attention based modification of language modelling network which achieved state-of-the-art perplexity results and significantly shortened training time.
A gearbox model for processing large volumes of data by using pipeline systems encapsulated into virtual containers

Software pipelines enable organizations to chain applications for adding value to contents (e.g., confidentially, reliability, and integrity) before either sharing them with partners or sending them to the cloud. However, the pipeline components add overhead when processing large volumes of data, which can become critical in real-world scenarios. This paper presents a gearbox model for processing large volumes of data by using pipeline systems encapsulated into virtual containers. In this model, the gears represent applications, whereas gearboxes represent software pipelines. This model was implemented as a collaborative system that automatically performs Gear up (by using parallel patterns) and/or Gear down (by using in-memory storage) until all gears produce uniform data processing velocities. This model reduces delays and bottlenecks produced by the heterogeneous performance of applications included in software pipelines. The new container tool capsule has been designed to encapsulate both the collaborative system and the software pipelines into a virtual container and deploy it on IT infrastructures. We conducted case studies to evaluate the performance of capsule when processing medical images and PDF repositories. The incorporation of a capsule to a cloud storage service for pre-processing medical imagery was also studied. The experimental evaluation revealed the feasibility of applying the gearbox model to the deployment of software pipelines in real-world scenarios as it can significantly improve the end-user service experience when pre-processing large-scale data in comparison with state-of-the-art solutions such as Sacbe and Parsl.
Authentication and Verification of Social Networking Accounts Using Blockchain Technology

Social networking pages authenticate by blockchain technology, through authenticating personal information and profile pages in the form of block and then distributing them with Blockchain to become a trusted reference point.To identified and verified social network accounts.This research proposed an effective and easy technical mechanism to authenticate the personal pages on social networks.Using this mechanism, anyone can authenticate any account on social networks, as well as increasing the possibility of making sure of the real individual behind social networking accounts.Moreover, this technique will show the fake accounts in order to reach a more confident and secure social network environment.Blockchain technique requires only a simple update to the characteristics of its platforms by developers, which is only a participatory mechanism between Blockchain and personal information.It is then combined with personal pages, to indicate that these pages contain the real personal information of the account holder, which is stored in an encrypted block that is difficult to modify, copy, or steal.
Single-shot measurements by Fresnel diffraction of divergent waves from a phase plate

Recently, Fresnel diffraction (FD) of a plane wave from phase steps has been studied and applied for precise measurements of the light wavelength, and height and refractive index of the step, by changing the angle of incidence or step height to induce phase shifts. In this study, we formulate the FD of cylindrical and spherical wavefronts as 1D and 2D divergent waves from a phase plate. Since the phase difference of the divergent wave varies continuously along the edge of the phase plate, it can be applied for single-shot measurements. It is shown that the diffracted intensity distribution is a periodic function along the lines parallel to the plate edge. The phase distribution in this direction is a linearly varying function of the position squared, with a slope dependent on the light wavelength, plate thickness and refractive index, and the radius of wavefront curvature (RWC) on the observation plane. The diffraction patterns are simulated and experimentally verified. Also, the RWC and displacement are determined as examples of applications in the experimental part of the report.
Research on Integrity Check Method of Cloud Storage Multi-Copy Data Based on Multi-Agent

Aimed at the huge number of files in cloud storage and the low audit efficiency of singlenode audit mode, this paper proposes a multi-agent-based cloud storage multi-copy data integrity check scheme.The scheme uses a bilinear mapping method to construct the key generation process and a multibranch authentication tree to perform multi-copy data signature to implement multi-copy authentication, signature, and verification.It also uses a directed acyclic graph (DAG) to represent the task relationship in the workflow, task and resource allocation based on QoS demand preference settings to schedule multiple tasks.Experimental results show that the proposed scheme has obvious advantages over existing schemes in terms of communication, storage overhead, and audit efficiency, with an average audit efficiency improvement of 20%.
SmartGe: Identifying Pen-Holding Gesture With Smartwatch

Performing the correct pen-holding gesture plays an important role in handwriting efficiency and quality, especially for early education. In this paper, a detailed design and evaluation of the system, called SmartGe, is presented, which can identify the pen-holding gesture with smartwatch when writing Chinese and English. We firstly analyze the hand movement and propose a novel handwriting detection algorithm to segment each stroke or letter. Then we recognize the pen-holding gesture using deep convolution neural network(DCNN). To improve system performance in Chinese writing, we connect a vertical stroke and a horizontal stroke for pen-holding gesture recognition. SmartGe provides a convenient and natural way to improve users' writing habits, which is a lightweight system, and extensive experiments confirm its effectiveness and robustness.
Facilitating GDPR Compliance: The H2020 BPR4GDPR Approach

This paper outlines the approach followed by the H2020 BPR4GDPR project to facilitate GDPR compliance. Its goal is to provide a holistic framework able to support end-to-end GDPR-compliant intra- and inter-organisational ICT-enabled processes at various scales, while also being generic enough, fulfilling operational requirements covering diverse application domains. To this end, solutions proposed by BPR4GDPR cover the full process lifecycle addressing major challenges and priorities posed by the Regulation.
Key-Aggregate Searchable Encryption, Revisited: Formal Foundations for Cloud Applications, and Their Implementation

In the use of a cloud storage, sharing of data with efficient access control is an important requirement in addition to data security and privacy. Cui et al. (IEEE Trans. on Comp. 2016) proposed key-aggregate searchable encryption (KASE), which allows a data owner to issue an aggregate key that enables a user to search in an authorized subset of encrypted files by generating an encrypted keyword called trapdoor. While the idea of KASE is elegant, to the best of our knowledge, its security has never been discussed formally. In this paper, we discuss the security of KASE formally and propose provably secure schemes. We first introduce our provably secure scheme, named first construction, with respect to encrypted files and aggregate keys in a single-server setting. In comparison with the scheme of Cui et al., the first construction is secure without increased computational costs. Then, we introduce another provably secure scheme, named main construction, with respect to trapdoors in a two-server setting. The main construction guarantees the privacy of a search, encrypted files, and aggregate keys. Considering 5,000 encrypted keywords, the first construction can finish search within three seconds and the main construction can finish search within six seconds.
Path Planning of Redundant Series Manipulators Based on Improved RRT Algorithms

Aiming at the path planning problem of the redundant manipulator with seven degrees of freedom, this paper presents a new algorithm based on improved RRT (Rapidly-exploring Random Tree algorithm), Gm-RRT algorithm. The new algorithm performs path planning in the joint space of the robot to improve the efficiency of the planning. The new algorithm combines the target probability offset to improve the orientation of the growth of extended random trees; uses the method of generating random points instead of single random points to improve the computational efficiency of path planning; and introduces the method of adaptive virtual gravity of targets to ensure the balance of the new algorithm in target orientation and exploration to the surrounding space. Lastly, a simulation experiment is carried out on the MATLAB platform. The results show that the improved algorithm has advantages in time and keeps the algorithm concise.
Algorithmic interactive music generation in videogames

In this article, I review the concept of algorithmic generative and interactive music and discuss the advantages and challenges of its implementation in videogames. Excessive repetition caused by low interactivity in music sequences through gameplay has been tackled primarily by using random or sequential containers, coupled with overlapping rules and adaptive mix parameters, as demonstrated in the Dynamic Music Units in Audiokinetic’s Wwise middleware. This approach provides a higher variety through re-combinatorial properties of music tracks and also a responsive and interactive music stream. However, it mainly uses prerecorded music sequences that reappear and are easy to recognize throughout gameplay. Generative principles such as single-seed design have been occasionally applied in game music scoring to generate material. Some of them are complemented with rules and are assigned to sections with low emotional requirements, but support for real-time interaction in gameplay situations, although desirable, is rarely found.While algorithmic note-by-note generation can offer interactive flexibility and infinite diversity, it poses significant challenges such as achieving human-like performativity and producing a distinctive narrative style through measurable parameters or program arguments. Starting with music generation, I examine conceptual implementations and technical challenges of algorithmic composition studies that use Markov models, a-life/evolutionary music, generative grammars, agents, and artificial neural networks/deep learning. For each model, I evaluate rule-based strategies for interactive music transformation using parameters provided by contextual gameplay situations. Finally, I propose a compositional tool design based in modular instances of algorithmic music generation, featuring stylistic interactive control in connection with an audio engine rendering system.
Axiomatizing logics of fuzzy preferences using graded modalities

The aim of this paper is to propose a many-valued modal framework to formalize reasoning with both graded preferences and propositions, in the style of van Benthem et al.'s classical modal logics for preferences. To do so, we start from Bou et al.'s minimal modal logic over a finite and linearly ordered residuated lattice. We then define appropriate extensions on a multi-modal language with graded modalities, both for weak and strict preferences, and with truth-constants. Actually, the presence of truth-constants in the language allows us to show that the modal operators □ and ◇ of the minimal modal logic are inter-definable. Finally, we propose an axiomatic system for this logic in an extended language (where the preference modal operators are definable), and prove completeness with respect to the intended graded preference semantics.