Reviewerly/exHarmony huggingface dataset:
$Q$-Mapping: Learning User-Preferred Operation Mappings With Operation-Action Value Function

User interfaces have been designed to fit typical users and their usage styles as assumed by designers. However, it is impossible to cover all the possible use cases. To address this problem, we propose <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Q$</tex-math></inline-formula> -Mapping, which is a method for user interfaces to acquire the operation mapping, or mapping from user operations to their effects. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Q$</tex-math></inline-formula> -Mapping has an advantage over previous techniques in that it can acquire operation mapping interactively. The core idea of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Q$</tex-math></inline-formula> -Mapping is that what a user selects as an ideal action has a tendency to be the same as the action that has the highest <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Q$</tex-math></inline-formula> -value. On the basis of this concept, we defined the operation-action value function, which can be calculated from the value that a user expects to gain when a particular mapping is given in that state and is updated each time an operation occurs. We conducted a simulation experiment and a user study to investigate the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Q$</tex-math></inline-formula> -Mapping performance and the effects of the acquisition of interactive operation mapping. The simulation results showed that the changeability of operation mapping could be controlled by a coefficient called the balancing parameter. As for the user study, we found that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Q$</tex-math></inline-formula> -Mapping with a balancing parameter that decays with time was able to acquire operation mapping that was easy for users to understand. These results demonstrate the importance of balancing consistency and adaptability in the interactive acquisition of operation mapping.
Efficient Gaussian Mixture Modeling of Long-Range 2D Radar Measurements

In some practical radar systems of interest, the standard EKF linearization approach applied to a nonlinear measurement transformation is ineffective. In particular, if measurements are performed in polar coordinates and the crossrange error is much greater than the range error, then the true error region in Cartesian space is no longer well approximated by a Gaussian distribution. This effect is known as the “contact lens” problem, and various approaches have been proposed to alleviate it, including particle and Gaussian mixture (GM) filters. In this work, a method is presented for modeling Cartesian converted measurement distributions which suffer from the contact lens effect using Maximum Likelihood (ML) GM parameters. In order to allow an efficient implementation of this process in a GM Kalman filter, a normalization of the ML parameters is introduced so that parameters can be computed offline and efficiently stored in a lookup table. Simulation results are presented to illustrate the method and confirm the results.
Multiobjective End-to-End Design Space Exploration of Parameterized DNN Accelerators

Deep neural network (DNN) hardware accelerators enable the execution of complex DNN inferences on resource-constrained IoT devices. Inference performance and energy figures depend on how the DNN layers are mapped into the accelerator and how the architecture of the accelerator fits the variety of layers’ shapes of the actual DNN. The mapping determines the execution order of the operations, both temporally and spatially. Thus, selecting the best mapping that allows fitting the DNN model to the specific accelerator is of paramount importance to meet the strong constraints imposed by resource-scarce IoT platforms. Although several mapping space exploration techniques have been proposed in the literature, they are focused on determining the best mapping for a given layer, for a given architecture, and for optimizing a single objective. This article largely extends the scope of the exploration by considering the huge design space spanned by mapping related and architectural parameters, considering all the layers of the DNN, and optimizing multiple objectives simultaneously. We present EPOCA, end-to-end Pareto optimization of DNN accelerators, whose goal is to determine the accelerator’s architecture and the mapping for each layer that optimizes end-to-end and in a multiobjective fashion a set of conflicting design criteria. We assess EPOCA on different DNN models on a parameterized hardware accelerator designed for IoT applications and compare them with a state-of-the-art mapping space explorer, considering the area, inference latency, and inference energy as optimization metrics. We show that the set of Pareto solutions found by EPOCA provides the designer with a range of choices from which to select the best tradeoff with respect to the specific application.
Biometric Identity Verification as Part of Physical Protection Systems

The presented article is focused on access control systems like the basic elements of the property protection system, designed for intelligent access/exit control of persons to/from the protected area and for their movement tracking. The authors highlight the importance of identity verification within alarm systems designed to detect unauthorized access and describe the possibilities of applying various authentication information within access control systems. They focus on the method of authentication using the unique biometric characteristics of a man. In addition to the many advantages, that this approach presents, there are limitations in its application, resulting from the technological means and procedures used. These limitations can be summarized as metrics of biometric system performance. However, these metrics are often evaluated on ideal, so-called zero effort impostor datasets, which distort the design of physical protection systems. In this article, the authors emphasize the need to focus on the quantification of biometric performance, including spoof attacks. By testing the selected biometric system, authors outline the possibilities for real evaluation of the performance level. The results from testing can be used for physical protection systems models and for simulating the violation of the protected area (e.g. elements of critical infrastructure).
A new hybdrid steganographic technique for images

Steganography is a technique that aims to hide the communication between two interlocutors. Steganography, unlike cryptography, allows to hide a message within a cover medium that can allow it to be transferred without arousing suspicion. Currently there are numerous techniques depending on the digital file (also named cover media or briefly cover) that is used to hide the message for which it is desired to ensure the necessary protection (also named secret message). In this way, the desired recipient can only read it. As regards the images, some main techniques are based on the modification of the LSB (Least Significant Bit) of each pixel of the cover image and on the modification of the bits of the DCT (Discrete Cosine Transform) values of the image itself. The purpose of the present work is to illustrate a new hybrid steganographic technique that merges the two methodologies illustrated above. It also allows to use an appropriate parameter as a cryptographic key for encoding and decoding the secret message.
Dimensity 9000 – A Flagship Smartphone SoC

Display, Camera & Gaming drive higher performance
Analysing keystroke dynamics using wavelet transforms

Many smartphones are lost every year, with a meager percentage recovered. In many cases, users with malicious intent access these phones and use them to acquire sensitive data. There is a need for continuous monitoring and surveillance in smartphones, and keystroke dynamics play an essential role in identifying whether a phone is being used by its owner or an impersonator. Also, there is a growing need to replace expensive 2-tier authentication methods like One-time passwords (OTP) with cheaper and more robust methods. The methods proposed in this paper are applied to existing data and are proven to train more robust classifiers. A novel feature extraction method by wavelet transformation is demonstrated to convert keystroke data into features. The comparative study of classifiers trained on the extracted features vs. features extracted by existing methods shows that the processes proposed perform better than the state-of-art feature extraction methods.
Metrics for Measuring Error Extents of Machine Learning Classifiers

Metrics play a crucial role in evaluating the performance of machine learning (ML) models. Metrics for quantifying the extent of errors, in particular, have been intensively studied and widely used but only so far for regression models. This paper focuses instead on classifier models. A new approach is proposed in which datamorphic exploratory testing is used to discover the boundary values between classes and the distance of misclassified instances from that boundary is used to quantify the errors that the model makes. Empirical experiments and case studies are reported that validate and evaluate the proposed metrics.
On (in)Security of Edge-based Machine Learning Against Electromagnetic Side-channels

Machine (deep) learning represents mainstream research and development direction. This success can be linked to the ever-increasing computational capabilities and vast amounts of available data, resulting in ever more sophisticated machine learning models. The design and training of such machine learning models are challenging and expensive tasks, which incentivize companies to protect and keep it secret. Additionally, the wide applicability of machine learning results in diverse deployment scenarios. Many machine learning architectures are deployed on edge devices, such as sensors or actuators, making them susceptible to side-channel attacks. This work surveys the research works considering electromagnetic side-channel and edge-based machine learning models. After discussing state-of-the-art attacks and countermeasures, we propose several open problems to be investigated in future research.
Analysis of Current Preventive Approaches in the Context of Cybersecurity

Reliance on information technology and computer network solutions is on the increase, so are the activities of cyber criminals, the number of cyber-attacks and the impact. According to Identity theft Resource Centre, the number of data breaches jumped 68% over what was reported in 2020.IT Governance recorded 1,243 security incidents in 2021 representing an 11% increase compared to 2020 that recorded 1,120. Governments and companies continue to invest in preventing these cyber-attacks from occurring by adopting various methods such as patch management, end point protection, installation of fire walls, backup of data, security policies, access control and privileges, Wi-Fi security access, and most importantly security awareness training. According to Info Guard cybersecurity, 95% of security breach is caused by human error.This review paper evaluated the findings from various articles on methods used to prevent cyber-attacks in a simplified format.
Emotional analysis of safeness and risk perception of cybersecurity attacks during the COVID-19 pandemic

The assessment of perceived safeness and risk by persons is especially valuable for security and safety managing. Each person is founded on the opinion of other people to make a selection and the Internet embodies the place where these opinions are mostly pursued, taken, and estimated. From this point of view, social networks are characterized by a considerable influence. Due to this reason, Opinion Mining and Sentiment Analysis have found outstanding employs in diverse environments and one of the most remarkable is expressed by public security and safety.The purpose of the present work is to investigate the perception of safeness and risk of cybersecurity attacks during COVID-19 pandemic by means of the semantic analysis of the textual contents existing in Twitter.
A Two-Level Auto-Encoder for Distributed Stereo Coding

We propose a new technique for stereo image compression that is based on Distributed Source Coding (DSC). In our setting, two cameras transmit their image back to a processing unit. Naively doing so requires each camera to compress and transmit its image independently. However, the images are correlated because they observe the same scene, and our goal is to take advantage of this fact. In our solution, one camera, assume the left camera, sends its image to the processing unit, as before. The right camera, on the other hand, transmits its image conditioned on the left image, even though the two cameras do not communicate. The processing unit can then decode the right image, using the left image. The solution is based on a two level Auto-Encoder (AE). During training, the first level AE learns a standard single image compression code. The second level AE further compresses the code of the right image, conditioned on the code of the left image. During inference, the left camera uses the first level AE to transmit its image to the processing unit. The right camera, on the other hand, uses the encoders of both levels to transmit its code to the processing unit. The processing unit uses the top level decoder to recover the left image, and the decoders of both levels, as well as the recovered left image, to recover the right image. The system achieves state of the art results in image compression on several popular datasets.
Differentiable Appearance Acquisition from a Flash/No-flash RGB-D Pair

Reconstructing 3D objects in natural environments requires solving the ill-posed problem of geometry, spatially-varying material, and lighting estimation. As such, many approaches impractically constrain to a dark environment, use controlled lighting rigs, or use few handheld captures but suffer reduced quality. We develop a method that uses just two smartphone exposures captured in ambient lighting to reconstruct appearance more accurately and practically than baseline methods. Our insight is that we can use a flash/no-flash RGB-D pair to pose an inverse rendering problem using point lighting. This allows efficient differentiable rendering to optimize depth and normals from a good initialization and so also the simultaneous optimization of diffuse environment illumination and SVBRDF material. We find that this reduces diffuse albedo error by 25%, specular error by 46%, and normal error by 30% against single-and paired-image baselines that use learning-based techniques. Given that our approach is practical for everyday solid objects, we enable photorealistic relighting for mobile photography and easier content creation for augmented reality.
Time-of-Day Neural Style Transfer for Architectural Photographs

Architectural photography is a genre of photography that focuses on capturing a building or structure in the foreground with dramatic lighting in the background. Inspired by recent successes in image-to-image translation methods, we aim to perform style transfer for architectural photographs. However, the special composition in architectural photography poses great challenges for style transfer in this type of photographs. Existing neural style transfer methods treat the architectural images as a single entity, which would generate mismatched chrominance and destroy geometric features of the original architecture, yielding unrealistic lighting, wrong color rendition, and visual artifacts such as ghosting, appearance distortion, or color mismatching. In this paper, we specialize a neural style transfer method for architectural photography. Our method addresses the composition of the foreground and background in an architectural photograph in a two-branch neural network that separately considers the style transfer of the foreground and the background, respectively. Our method comprises a segmentation module, a learning-based image-to-image translation module, and an image blending optimization module. We trained our image-to-image translation neural network with a new dataset of unconstrained outdoor architectural photographs captured at different magic times of a day, utilizing additional semantic information for better chrominance matching and geometry preservation. Our experiments show that our method can produce photorealistic lighting and color rendition on both the foreground and background, and outperforms general image-to-image translation and arbitrary style transfer baselines quantitatively and qualitatively. Our code and data are available at https://github.com/hkust-vgd/architectural_style_transfer.
Robust Scene Inference under Noise-Blur Dual Corruptions

Scene inference under low-light is a challenging problem due to severe noise in the captured images. One way to reduce noise is to use longer exposure during the capture. However, in the presence of motion (scene or camera motion), longer exposures lead to motion blur, resulting in loss of image information. This creates a trade-off between these two kinds of image degradations: motion blur (due to long exposure) vs. noise (due to short exposure), also referred as a dual image corruption pair in this paper. With the rise of cameras capable of capturing multiple exposures of the same scene simultaneously, it is possible to overcome this trade-off. Our key observation is that although the amount and nature of degradation varies for these different image captures, the semantic content remains the same across all images. To this end, we propose a method to leverage these multi exposure captures for robust inference under low-light and motion. Our method builds on a feature consistency loss to encourage similar results from these individual captures, and uses the ensemble of their final predictions for robust visual recognition. We demonstrate the effectiveness of our approach on simulated images as well as real captures with multiple exposures, and across the tasks of object detection and image classification. Project: https://wisionlab.com/project/noiseblurdual
PLEN: A Protocol-Theoretic Logic of Epistemic Norms

In order to motivate the protocol-theoretic framework for the logic of epistemic norms, the prior chapters articulated and defended several clusters of principles characterizing the structure, regulative role, and logic of epistemic norms. This chapter develops the core of the protocol-theoretic framework: the protocol-theoretic logic of epistemic norms (PLEN). PLEN is an elaborate revision of propositional dynamic logic (PDL). It encodes the content of epistemic norms with protocols, syntactic expressions that represent complex procedural operations on actions, and it models epistemic dynamics with process models that distinguish epistemic transitions from the actions, processes, and procedures that result in them while also characterizing their interrelations. The main elaborations involve formulae that encode properties of protocols that are invisible in the multi-modal logic of PDL. The main work of the chapter is defining the framework, and exploring it mainly from a conceptual perspective, interpreting the logic of protocols in PLEN as a logic of procedural epistemic norms defined over a model of single-agent epistemic dynamics, with minimal discussion of the standard metatheory. As far as the standard metatheory goes, it is shown that the deductive system of PLEN is sound with respect to PLEN’s semantics, and it is conjectured that the standard proofs of completeness and decidability for PDL can be extended to PLEN. This main work lays the initial groundwork for codifying epistemic proceduralism in PLEN.
MicroStream: A Distributed In-memory Caching Service For Data Production

Data-driven innovation and optimization have become an important direction for the intelligent transformation of enterprises. Data processing tasks have been developed and orchestrated to extract data insights, creating direct or indirect data dependencies between tasks or between tasks and the presentation layer. Traditional ETL (Extract-Transformation-Load) solutions share data through persistent storage, which has certain performance bottlenecks in hybrid cloud and multisource data scenarios. In this paper, we propose MicroStream, a distributed data virtualization and caching middleware service. MicroStream shields the direct access of ETL tasks to the storage layer and converts batch access to the source database into microstream access. ETL jobs share data through the distributed in-memory caching of MicroStream. In resource-constrained scenarios, such a solution significantly improves the performance of data transformation while reducing the extra load that the transformation jobs imply on the source persistent layer. We present a detailed performance evaluation of MicroStream and show that its performance compares favorably with traditional database-oriented solutions.
A Content-Based Medical Image Retrieval Algorithm

With the inexpensive and innovative computational advancements in the digital imaging and storage technologies, the application of the medical imagery information repositories into the medical field has been rapidly growing with the ongoing expansion of medical image databases. Currently, the resources related to storage of medical images are not an issue since effective storage capacity in computer systems is now available at an affordable cost. Therefore, big medical image data is afford-able and achievable solution for medical institutions. However, retrieval of relevant medical images out of large-scale databases effectively remains a challenging problem. The primary issues for retrieving an image from the medical repository are the large time taken in retrieving the image, low precision, and recall rates of the retrieval process. This paper proposes a content-based image retrieval algorithm that can effectively retrieve a medical image. The paper is based on the research application carried out using the proposed innovative solution and the ideas that have been explored. The algorithm performs by exploiting the similarities in the query image with those of database images with an adaptive approach. The average retrieval matching of the query image with the database images is 95.35%, 87.91%, and 71.54% respectively for the red, green, and blue color values.
Immersive Technology Systematic Review and Challenges

Since the starting of Immersive technologies field a rapid and continues research done and linked strongly to different areas of immersive technologies. An immersive technology systematic literature review (SLR) is done in this research in variant settings: Tele-immersive, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3\mathbf{D}$</tex> , gaming, virtual reality, augmented reality, etc. To address the immersive technologies issues a wide range of articles had been studied ACM publications in the period 2008 - 2021 to cover the latest issues in immersive technologies. Based on exclusion and inclusion conditions, ninety-three studies were chosen for the final investigation. This SLR analyzed the information from the specified research, their hypothetical, challenges that had been address, limitations and future work of the studies. Therefore, this paper specifies the constraints of recent research and future domain in the field of immersive technologies.