Reviewerly/exHarmony huggingface dataset:
Variable Rate Image Compression Method with Dead-zone Quantizer

Deep learning based image compression methods have achieved superior performance compared with transform based conventional codec. With end-to-end Rate-Distortion Optimization (RDO) in the codec, compression model is optimized with Lagrange multiplier λ. For conventional codec, signal is decorrelated with orthonormal transformation, and uniform quantizer is introduced. We propose a variable rate image compression method with dead-zone quantizer. Firstly, the autoencoder network is trained with RaDOGAGA [6] framework, which can make the latents isometric to the metric space, such as SSIM and MSE. Then the conventional dead-zone quantization method with arbitrary step size is used in the common trained network to provide the flexible rate control. With dead-zone quantizer, the experimental results show that our method performs comparably with independently optimized models within a wide range of bitrate.
Sketched Newton--Raphson

We propose a new globally convergent stochastic second-order method. Our starting point is the development of a new sketched Newton--Raphson (SNR) method for solving large scale nonlinear equations of the form $F(x)=0$ with $F:\mathbb{R}^p \rightarrow \mathbb{R}^m$. We then show how to design several stochastic second-order optimization methods by rewriting the optimization problem of interest as a system of nonlinear equations and applying SNR. For instance, by applying SNR to find a stationary point of a generalized linear model, we derive completely new and scalable stochastic second-order methods. We show that the resulting method is very competitive as compared to state-of-the-art variance reduced methods. Furthermore, using a variable splitting trick, we also show that the stochastic Newton method (SNM) is a special case of SNR and use this connection to establish the first global convergence theory of SNM. We establish the global convergence of SNR by showing that it is a variant of the online stochastic gradient descent (SGD) method, and then leveraging proof techniques of \textttSGD. As a special case, our theory also provides a new global convergence theory for the original Newton--Raphson method under strictly weaker assumptions as compared to the classic monotone convergence theory.
Quantum wireless network communication based on cluster states

Due to the high security of quantum transmission and the more flexible and economical implementation of wireless communication, quantum wireless network communication attracts lots of attention. Because of the high entanglement persistence and robustness of cluster states against decoherence and loss, we investigate the application of cluster states in quantum wireless network communication (QWNC) and propose several kinds of QWNC schemes based on 1D, 2D and 3D cluster states, respectively. Finally we propose a QWNC scheme under the bilayer quantum network architecture. Comparing with other multi-hop teleportation schemes, it is unnecessary for the intermediate nodes to perform entanglement swapping to establish the required entanglement. The computational complexity is independent of the number of intermediate nodes and cluster states are allocated on demand, thus reducing the computational complexity and resource consumption largely.
Towards the Impact of Gamers Strategy and User Inputs on the Delay Sensitivity of Cloud Games

Cloud Gaming is an emerging service that is considered by many as the future of the gaming industry. This service requires a highly reliable network with low latency and high bandwidth. If these requirements are not satisfied, cloud gaming services cannot create a good Quality of Experience (QoE) for its users. However, gaming QoE can vary significantly among different game scenarios and users. For an optimal resource allocation and quality estimation, it is highly important for cloud providers, game developers, and network planners to consider the influence of the game content and gamers. This paper presents the result of a subjective study that investigated the impact of different player strategies and user inputs on their perceived delay. The results indicated that the user input characteristics vary among the games but stays the same between different users and different strategies. In addition to the users' inputs, the input quality and the overall gaming experience of the users were also investigated, and results did not show any main effect of user strategy on the delay sensitivity of the games.
Deep Structured Mixtures of Gaussian Processes

Gaussian Processes (GPs) are powerful non-parametric Bayesian regression models that allow exact posterior inference, but exhibit high computational and memory costs. In order to improve scalability of GPs, approximate posterior inference is frequently employed, where a prominent class of approximation techniques is based on local GP experts. However, local-expert techniques proposed so far are either not well-principled, come with limited approximation guarantees, or lead to intractable models. In this paper, we introduce deep structured mixtures of GP experts, a stochastic process model which i) allows exact posterior inference, ii) has attractive computational and memory costs, and iii) when used as GP approximation, captures predictive uncertainties consistently better than previous expert-based approximations. In a variety of experiments, we show that deep structured mixtures have a low approximation error and often perform competitive or outperform prior work.
Leveraging deep learning for seismic image denoising

Disclaimer Text and Data Mining. Any automated analytical technique aimed at analysing text and data in digital form in order to generate information including - but not limited to - patterns, trends and correlations (so-called Text and Data Mining) may only be applied if it is done by a research organisation or cultural heritage institution solely for the purpose of (non-commercial) scientific research as mentioned in Clause 3 of Directive (EU) 2019/790 (the Digital Single Market Directive) and Clause 15n of the Dutch Copyright Act [Auteurswet]. Text and Data Mining for commercial purposes is not allowed; all rights in this respect are reserved pursuant to Clause 4 paragraph 3 of the Digital Single Market Directive and Clause 15o paragraph 1 of the Dutch Copyright Act.
Innovation of English teaching model based on machine learning neural network and image super resolution

At present, applying image recognition technology to promote English teaching is a kind of teaching innovation that meets the needs of the times. Therefore, based on machine learning neural network and image super-resolution, this study conducted an innovative analysis of English teaching mode. This paper combined the current situation of English teaching classroom to study and analyze English classroom, combined classroom characteristics as the basis of English teaching innovation and constructed a feature recognition model suitable for current English teaching status. Moreover, this paper formed an initial high-resolution image for low-resolution image reconstruction by sparse representation method, and then established a mixed sample spine regression model to re-estimate the high-frequency components of the initial high-resolution image to realize various behavioral characteristics of students in English teaching classroom. In addition, this article builds a verification test. The research shows that the proposed algorithm has certain effects and can provide theoretical reference for subsequent related research.
Effect of Non-mandatory Use of an Intelligent Tutoring System on Students’ Learning

Numerous controlled studies prove the effectiveness of Intelligent Tutoring Systems (ITSs). But what happens when ITSs are available to students for voluntary practice? EER-Tutor is a mature ITS which was previously found effective in controlled experiments. Students can use EER-Tutor for tutored problem solving, and there is also a special mode allowing students to develop solutions for the course assignment without receiving feedback. In this paper, we report the observations from two classes of university students using EER-Tutor. In 2018, the system was available for completely voluntary practice. We hypothesized that the students' pre-existing knowledge and the time spent in EER-Tutor, mediated by the number of attempted EER-Tutor problems, contribute to the students' scores on the assignment. All but one student used EER-Tutor to draw their assignment solutions, and 77% also used it for tutored problem solving. All our hypotheses were confirmed. Given the found benefits of tutored problem solving, we modified the assignment for the 2019 class so that the first part required students to solve three problems in EER-Tutor (without feedback), while the second part was similar to the 2018 assignment. Our hypothesized model fits the data well and shows the positive relationship between the three set problems on the overall system use, and the assignment scores. In 2019, 98% of the class engaged in tutored problem solving. The 2019 class also spent significantly more time in the ITS, solved significantly more problems and achieved higher scores on the assignment.
CANU-ReID: A Conditional Adversarial Network for Unsupervised person Re-IDentification

Unsupervised person re-ID is the task of identifying people on a target data set for which the ID labels are unavailable during training. In this paper, we propose to unify two trends in unsupervised person re-ID: clustering & fine-tuning and adversarial learning. On one side, clustering groups training images into pseudo-ID labels, and uses them to fine-tune the feature extractor. On the other side, adversarial learning is used, inspired by domain adaptation, to match distributions from different domains. Since target data is distributed across different camera viewpoints, we propose to model each camera as an independent domain, and aim to learn domain-independent features. Straightforward adversarial learning yields negative transfer, we thus introduce a conditioning vector to mitigate this undesirable effect. In our framework, the centroid of the cluster to which the visual sample belongs is used as conditioning vector of our conditional adversarial network, where the vector is permutation invariant (clusters ordering does not matter) and its size is independent of the number of clusters. To our knowledge, we are the first to propose the use of conditional adversarial networks for unsupervised person re-ID. We evaluate the proposed architecture on top of two state-of-the-art clustering-based unsupervised person re-identification (re-ID) methods on four different experimental settings with three different data sets and set the new state-of-the-art performance on all four of them. Our code and model is publicly available at https://team.inria.fr/perception/research/canu-reid/.
Gathering People’s Happy Moments from Collective Human Eyes and Ears for a Wellbeing and Mindful Society

Information technologies are dramatically and rapidly changing our world. Human well-being is one of the most important factors in our everyday lives. Although the importance of well-being is widely discussed, there are very few actual digital services to facilitate human well-being. Remembering happy moments significantly increases human well-being, so memorizing happy moments from the past and presenting those moments in the present is a promising future direction for promoting well-being in our everyday lives. In this paper, we present CollectiveEyes, an invention that allows us to share collective people’s eyes and ears, and use CollectiveEyes to memorize and present visuals and sound recordings of various people’s happy moments to explore human well-being. We first analyze happy moments and introduce an analysis framework for designing happy moments. We also conduct experiments using a scenario-based analysis to investigate how to gather happy moments with CollectiveEyes.
Mobile Application Based Automatic Caption Generation for Visually Impaired

Generating captions and text descriptions of images will enable visually impaired extended accessibility to the real world, thus reducing their social isolation, improving their well-being, employability and education experience. In this paper, we propose a captioning system based on a smartphone, which offers ultra-low-cost, portable and user-friendly platform for visually impaired. Image captioning aims to understand and describe a visual scene in terms of natural language expressions, which requires an advanced level of image understanding. In that sense, it brings computer vision and natural language process (NLP) fields together to generate meaningful sentences. In the computer vision side, more distinguished visual attributes need to be extracted from the images to feed the NLP for caption generation. The proposed captioning system based on a neural encoder-decoder framework that uses inception-v3 deep learning architecture as the encoder to extract the visual attributes. On the other hand, the decoder is based on the NLP architecture, which consists of multi-layer recurrent neural networks, to generate captions. Our captioning system is run on the remote server integrated to our custom-designed Android application via a cloud system after trained on MSCOCO 2017 captioning dataset. The user can choose an image from the gallery or capture a new image to transfer the image to the remote server. The caption is generated by the proposed captioning system and transferred back to the Android application to be displayed on the screen. The caption can be read out loudly using the narrator option. Experimental results show that the proposed system offers great advantages to the visually impaired in terms of portability, simple operation and rapid response.
Artificial Intelligence as Driver for Business Model Innovation in Smart Service Systems

Artificial Intelligence drives business model innovation. In this context, our paper examines major amendments of business model elements (value proposition, value creation and value capture) due to artificial intelligence implementation into products and services. Based on a literature review changes like enhanced autonomous and adaptive value proposition, intense use of data and stronger collaboration in ecosystems as well as adapted revenue models are identified, analyzed and presented in this paper.
Data Mining Visualization with the Impact of Nature Inspired Algorithms in Big Data

Data mining visualization is an important aspect of big data visualization and analysis. The impact of the nature-inspired algorithm along with the impact of computing traditions for the complete visualization of the storage and data communication needs have been studied. This paper also explores the possibilities of the hybridization of data mining in terms of association of cloud computing. It also explores the data analytical view in the exploration of these approaches in terms of data storage in big data. Based on these aspects the methodological advancement along with the problem statements has been analyzed. This will help in the exploration of computational capability along with the new insights in this domain.
On quantitative aspects of model interpretability.

Despite the growing body of work in interpretable machine learning, it remains unclear how to evaluate different explainability methods without resorting to qualitative assessment and user-studies. While interpretability is an inherently subjective matter, previous works in cognitive science and epistemology have shown that good explanations do possess aspects that can be objectively judged apart from fidelity), such assimplicity and broadness. In this paper we propose a set of metrics to programmatically evaluate interpretability methods along these dimensions. In particular, we argue that the performance of methods along these dimensions can be orthogonally imputed to two conceptual parts, namely the feature extractor and the actual explainability method. We experimentally validate our metrics on different benchmark tasks and show how they can be used to guide a practitioner in the selection of the most appropriate method for the task at hand.
Discriminative comparison classifier for generalized zero-shot learning

Comparison classifier based generalized zero-shot learning (GZSL) helps to achieve knowledge transfer from seen to unseen classes. However, it only utilizes the original semantic features, which are highly related and indistinguishable, to learn an embedding with no consideration of the relationship between them. Moreover, it cannot well encode discriminative information embedded in semantic features. To handle these problems, we present a discriminative comparison classifier for GZSL, which consists of semantic embedding network and relation network. Semantic embedding network takes as input original semantic features and relationship features which can be obtained by clustering, it ensures that the embedding network from semantic space to visual space can learn more discriminative features. Relation network is used to learn relationship between the embedded features and visual features, the validation information will guide embedding network to learn more discriminate features. Moreover, we adopt a novel semantic pivot regularization to keep inter-class discrimination in the visual space. Extensive experiments on several real-world datasets demonstrate the effectiveness of our method over the other state-of-the-arts.
A Heterogeneous Processor Design for CNN-Based AI Applications on IoT Devices

This paper proposes a heterogeneous processor design for CNN-based AI applications on IoT devices. The heterogeneous processor contains an embedded RISC-V CPU that works as a general processor and an efficient CNN-accelerator that supports a variety of CNN models with a list of macro instructions. For demonstration, we implement a prototype on an FPGA platform with the RISC-V CPU working under 20 MHz and the CNN accelerator working under 100 MHz. As a case study, we run a CNN-based face detection and recognition application on this prototype. The prototype can process one image in 0.72 seconds and an ASIC implementation working under 400 MHz can process one image in less than 0.15 seconds by estimation, which can satisfy the needs for many IoT scenarios such as access control systems and check-in systems.
A Lightweight Auction Framework for Spectrum Allocation with Strong Security Guarantees

Auction is an effective mechanism to distribute spectrum resources. Although many privacy-preserving auction schemes for spectrum allocation have been proposed, none of them is able to perform practical spectrum auctions while ensuring enough security for bidders' private information, such as geo-locations, bid values, and data access patterns. To address this problem, we propose SLISA, a lightweight auction framework which enables an efficient spectrum allocation without revealing anything but the auction outcome, i.e., the winning bidders and their clearing prices. We present contributions on two fronts. First, as a foundation of our design, we adopt a Shuffle-then-Compute strategy to build a series of secure sub-protocols based on lightweight cryptographic primitives (e.g., additive secret sharing and basic garbled circuits). Second, we improve an advanced spectrum auction mechanism to make it data-oblivious, such that data access patterns can be hidden. Meanwhile, the modified protocols adapt to our elaborate building blocks without affecting its validity and security. We formally prove the security of all protocols under a semi-honest adversary model, and demonstrate performance improvements compared with state-of-the-art works through extensive experiments.
Kemajuan Kelas Menulis: Studi Kasus Dema FU UIN Sunan Gunung Djati Bandung

This study aims to discuss the progress of writing classes. The research method is qualitative through literature and participatory studies. The results and discussion of this study include the progress of journal logins, the use of application references, and the preparation of research formulas. This study concludes that writing classes based on initiative are certain to bring progress. The recommendation of this study is the need for rewards for high achieving writing class participants.
Heralded spectroscopy with a fiber photon-pair source

The correlations between photons generated by nonlinear optical processes offer advantages for many quantum technology applications, including spectroscopy, imaging, and metrology. Here, we use spontaneous four-wave mixing in a birefringent single-mode fiber pumped by a tunable pulsed laser as a broadly tunable source of phase-matched non-degenerate photon pairs for spectroscopy. The pairs are tunable such that the idler beam measures the transmittance spectrum of a sample in the near infrared, while the visible signal beam independently reports correlation information. By the time-resolved counting of both signal and idler photons, we use photon-number correlations to remove uncorrelated noise from the probe beam. Here, we have used heralded spectroscopy to measure the absorption spectrum of gallium arsenide near its band edge, despite the idler photon spectrum being dominated by a large background from spontaneous Raman scattering.
Simple and secure device authentication mechanism for smart environments using Internet of things devices

Summary Enormous growth in digital electronics technology has paved path for the development of advanced networks, and smart environment is one among them. Smart environment is a network of smart devices connected to provide comfort, security, remote access and many more to end‐users. Devices in smart environments usually collect very sensitive data of the end‐users and disseminate it as required. The privacy of the end‐user has to be ensured in these applications, and security becomes a prime concern. The security system in smart environments has to ensure privacy and need to be more resource‐aware. An efficient authentication mechanism can ensure the privacy of end users and secrecy of the sensitive data; it may avoid unauthorized access of data and resources, ensuring privacy and security. In this paper, we proposed a mechanism for device authentication which enables smart devices to mutually authenticate each other. The proposed mechanism enables smart devices to acquire security credentials from the server and use these credentials to authenticate each other. The mechanism used exclusive OR (XOR), concatenation, one‐way hash, and symmetric key encryption techniques. The security analysis of the mechanism is done and proved that the mechanism is secure. The performance analysis proved that the computational and communications overheads are 8% less than existing mechanisms and storage overhead is also least.
Quantum-inspired satin bowerbird algorithm with Bloch spherical search for constrained structural optimization

&lt;p style='text-indent:20px;'&gt;To enhance the optimization ability of the satin bowerbird optimization (SBO) algorithm, in this paper, a novel quantum-inspired SBO with Bloch spherical search is proposed. In this algorithm, satin bowerbirds are encoded using qubits described on the Bloch sphere, each satin bowerbird occupies three locations in the search space and each location represents an optimization solution. Using the search method of general SBO to adjust the two parameters of the qubit, qubit rotation is performed on the Bloch sphere, which simultaneously updates the three locations occupied by a qubit and quickly approaches the global optimal solution. Finally, the experimental results of five examples of structural engineering design show that the proposed algorithm is superior to other state-of-the-art metaheuristic algorithms in terms of the performance measures.&lt;/p&gt;