Reviewerly/exHarmony huggingface dataset:
Trust-aware Multi-task Knowledge Graph for Recommendation

Data sparsity and cold start problems are common in recommender systems. Adding some side information, such as knowledge graph and users' trust relationship, is an effective method to alleviate these problems. However, few work jointly explore the fine-grained implicit relationships between the external heterogeneous graphs to enhance the recommendation accuracy. To address this issue, in this paper, we propose a new method named Trust-aware Multi-task Knowledge Graph (TMKG), which uses multi-task learning to integrate two kinds of side information of trust graph and knowledge graph in an end-to-end manner. Firstly, we mine the intra-graph and inter-graph high-order connections through the node propagation and aggregation, and optimize the embedding of nodes through the implicit relationships obtained. Furthermore, through the shared cross unit, the connection relationships between each layer is mined, and the high-order interaction of nodes of different layers is obtained. We conduct extensive experiments on real-world datasets and prove that our model has the superior performance compared with the state-of-the-art models.
VFKD: Voxelized Fractal Keypoint Detector

In three-dimensional data, despite deep learning-based approaches achieve decent accuracy when applied to classification, they struggle on other more complex tasks such as registration. This is why traditional keypoint detection, description and matching pipelines are even currently state-of-the-art. In this paper we propose VFKD, a Voxelized Fractal Keypoint Detector. We leverage the fractal dimension of the neighborhood of a point to either classify it as a keypoint or not. Our approach accepts a set of parameters which should be tuned for each final application, thus providing high versatility. VFKD outperforms state-of-the-art 3D keypoint extractors in terms of mean displacement error by a large margin.
Quantum Channel Decoding

Channel Coding is the technique that enables reliable delivery of digital data over unreliable communication channels. For most high performance channel coding techniques, the existing classical algorithms are computationally expensive, making them impractical for throughput-demanding applications with large code sizes. Today’s Noisy Intermediate-Scale Quantum (NISQ) computers, although limited due to a modest number of qubits, short coherence time, and poor gate fidelity, are useful tools for exploring and experimenting with possible solutions to a wide variety of computational problems.In this paper we show how careful initialization of qubits combined with a simple quantum circuit, enables us to perform channel decoding for different linear block codes. We first explain our novel qubit initialization technique which we call "Quantum Soft Decision". We then show how to build a simple quantum circuit based on the Generator or Parity-check matrix using another technique called "Quantum Generator". Using these universal concepts, we implement Quantum Decoders for two different types of linear block codes, namely Hamming codes and Polar codes. Our simple quantum circuits achieve decoding performances comparable with best classical algorithms such as Maximum Likelihood (ML) for Hamming codes and Successive Cancellation (SC) and Successive Cancellation List (SCL) for Polar codes. Using Qiskit, we implemented and compared the decoding performance at different code sizes and noise levels on simulated (both ideal and noisy) quantum computers. Also using Amazon Braket, we verified the algorithm on real quantum computers.
Objective Measurements of Background Color Shifts Caused by Optical See-Through Head-Mounted Displays

Optical see-through head-mounted displays (OST-HMDs) have been increasingly used in research and industrial applications as Augmented Reality (AR) support devices. However, problems still exist that prevent their use as general-purpose devices. One of these issues is the color blending problem between content and background colors. More specifically, the light from the background overlaps with the light from the OST-HMD and shifts the color of OST-HMD's light from its intended display intensity and color. Though color compensation methods exist, in order to properly compensate for light shifts, we need to know how the background color will affect the light that eventually hits the user's eye when combined with the OST-HMD image. In this paper, we study how background colors shift as a result of passing through the OST-HMD's optics in order to better inform the development of color compensation methods. We measured the background color objectively for the Magic Leap 1, the HoloLens (first gen), and the HoloLens 2 and evaluated results. We found that all three OST-HMDs shift background color to a perceptible degree, and that the degree of shift depends on the original background color.
Underwater Image Enhancement Based on Light Attenuation Prior and Dual-Image Multi-Scale Fusion

The focus of this work is solving the degradation of underwater images. Different from images taken on land, underwater images suffer from color bias and fuzzy details due to absorption and scattering. In order to solve the above problems of underwater images, we propose a novel method called LAPF, which includes two stages of restoration and dual-image multi-scale fusion. Restoration based on underwater light attenuation prior can initially restore the details of the underwater image by building a scene depth estimation model. Coupled with the use of image fusion, obtain the contrast-enhanced picture and color-corrected picture as fusion inputs, and calculate the normalized weight maps to add detail. Carrying out multi-scale decomposition and fusion of these two parts, successfully further enhance the contrast while realizing the color correction. Experimental results on a variety of underwater images show the power of the method proposed in this paper, which achieves good results in both subjective visual perception and objective quality evaluation parameters.
Research on Underwater Image Recognition based on Transfer Learning

To address the problem that underwater image data is difficult to obtain and the quantity is small, this paper proposes an image recognition method based on improved secondary transfer learning on the basis of direct transfer learning. Firstly, cifar-10 is used as an intermediate transition data set for secondary transfer learning to improve the accuracy of underwater image recognition network model. Secondly, the fully connected layer of the constitutional neural network is designed to be refined and simplified for the problems of many parameters, slow training and large storage capacity of the secondary transfer network. The experimental results show that the accuracy of the streamlined model is improved by 3.33% compared with direct transfer learning.
On the Decomposability of Homogeneous Binary Planar Configurations with Respect to a Given Exact Polyomino

A binary planar configuration A associates to each point in $$\mathbb {Z}^2$$ an element in $$\{0,1\}$$ . Provided a finite window probe P, we locally inspect A by moving P in all its possible positions and counting the 1s elements that fit inside it. In case all the computed values have the same value k, then we say that A is k-homogeneous w.r.t. P. A recent conjecture states that a binary planar configuration is k-homogeneous with respect to an exact polyomino P, i.e., a polyomino that tiles the plane by translation, if and only if it can be decomposed into k configurations that are 1-homogeneous with respect to P. In this paper we define a class of exact polyominoes called perfect pseudo-squares ( $$\mathcal {PPS}$$ ) and we investigate the periodicity behaviors of the homogeneous configurations that are related to them. Then, we show that some elements in $$\mathcal {PPS}$$ allow 2-homogeneous or 3-homogeneous non-decomposable planar configurations, so providing evidence that the conjecture does not hold for the whole class of exact polyominoes.
Luminance-Guided Chrominance Image Enhancement for HEVC Intra Coding

In this paper, we propose a luminance-guided chrominance image enhancement convolutional neural network for HEVC intra coding. Specifically, we firstly develop a gated recursive asymmetric-convolution block to restore each degraded chrominance image, which generates an intermediate output. Then, guided by the luminance image, the quality of this intermediate output is further improved, which finally produces the high-quality chrominance image. When our proposed method is adopted in the compression of color images with HEVC intra coding, it achieves 28.96% and 16.74% BD-rate gains over HEVC for the U and V images, respectively, which accordingly demonstrate its superiority. The code is available online: https://github.com/Nickyang4900/Luminance-Guided-Chrominance-Enhancement-for-HEVC-Intra-Coding.
Accelerating the creation of instance segmentation training sets through bounding box annotation

Collecting image annotations remains a significant burden when deploying CNN in a specific applicative context. This is especially the case when the annotation consists in binary masks covering object instances. Our work proposes to delineate instances in three steps, based on a semi-automatic approach: (1) the extreme points of an object (left-most, right-most, top, bottom pixels) are manually defined, thereby providing the object bounding-box, (2) a universal automatic segmentation tool like Deep Extreme Cut is used to turn the bounded object into a segmentation mask that matches the extreme points; and (3) the predicted mask is manually corrected. Various strategies are then investigated to balance the human manual annotation resources between bounding-box definition and mask correction, including when the correction of instance masks is prioritized based on their overlap with other instance bounding-boxes, or the outcome of an instance segmentation model trained on a partially annotated dataset. Our experimental study considers a teamsport player segmentation task, and measures how the accuracy of the Panoptic-Deeplab instance segmentation model depends on the human annotation resources allocation strategy. It reveals that the sole definition of extreme points results in a model accuracy that would require up to 10 times more resources if the masks were defined through fully manual delineation of instances. When targeting higher accuracies, prioritizing the mask correction among the training set instances is also shown to save up to 80% of correction annotation resources compared to a systematic frame by frame correction of instances, for a same trained instance segmentation model accuracy.
HE-to-PAS histological stain conversion by GAN in renal pathology

This paper presents the evaluation of the accurate-ness of an automatic HE to PAS stain conversion. We collected the unique HE-PAS (stain and restain specimens) database of renal specimens, and we have developed and compared a set of GAN methods for the automatic staining conversion. The detailed evaluation includes 10 numerical metrics and a visual evaluation, which was performed to investigate and closely look at the accuracy of an automatic stain transformation. The main contribution and novelty of this paper is the multi-aspect evaluation of the accurateness of staining transformation using a unique HE-PAS stain-restain dataset. Achieved results show that an automatic HE to PAS staining conversion achieved Frechet Inception Distance (FID) metric below 7, whereas visual evaluation is up to 96%. Presented analysis and results give insights into possibilities to build automatic stain transformations tools to replace the need of performing manual staining.
Privacy-Preserving Online Ride-Hailing Matching System with an Untrusted Server

With the popularity of Online Ride-Hailing (ORH) service, there are growing concerns about location privacy because the taxis and passengers need to upload their locations to the service provider. These locations can be used to infer the users’ personal information. In this paper, we propose a privacy-preserving online ride-hailing matching system, which allows an untrusted service provider to calculate the distances between the taxis and one passenger and find the nearest taxi by itself while protecting the users’ location privacy. To calculate the distances in road networks, we leverage Road Network Embedding (RNE) in our proposed system. We propose a secure distance calculation scheme to conduct RNE distance calculation securely. In this scheme, we redesign Property-Preserving Hash (PPH) with Pseudo-Random Functions (PRF) and use PRF-based PPH to calculate the distance between two RNE location vectors securely. To enhance security, we embed the partition ID and generation time in PRF-based PPH ciphertext to limit the ciphertext match-ability. Our security analysis and experimental evaluation show that our proposed system is secure and efficient.
A Novel Distributed Nature-Inspired Algorithm for Solving Optimization Problems

Several bio-inspired algorithms use population evolution as analogies of nature. In this paper, we present an algorithm inspired by the biological Life-Cycle of animal species, which consists of several stages: birth, growth, reproduction, and death. As in nature, we intend to execute all these stages in parallel and asynchronously on a population that evolves constantly. From the ground up, we designed the algorithm as a cloud-native solution using the cloud available resources to divide the processing workload among several computers or running the algorithm as a cloud service. The algorithm works concurrently and asynchronously on a constantly evolving population, using different computers (or containers) independently, eliminating waiting times between processes. This algorithm seeks to imitate the natural life cycle, where new individuals are born at any moment and mature over time, where they age and suffer mutations throughout their lives. In reproduction, couples match by mutual attraction, where they may have offspring. Death can happen to everyone: from a newborn to an aged adult, where the individual's fitness will impact their longevity. As a proof-of-concept, we implemented the algorithm with Docker containers by solving the OneMax problem comparing it with a basic (sequential) GA algorithm, where it shows favorable and promising results.
Beam-Domain Image Mosaic of Forward-Looking Sonar Using Expression Domain Mapping Model

Since the existing forward-looking sonar (FLS) image mosaic algorithm is completed in the image-domain with reference to the optical image, and the characteristics of FLS imaging are not fully considered, obvious edge-seams and pseudo-target values are prone to generate after mosaic, resulting in poor visual effect and low operation efficiency. To avoid the aforementioned problems, the mosaic operation of beam-domain FLS images is proposed. Combined with the imaging principle of FLS, this study takes advantage of the variation of the sampling beams and sampling points of the same target point during sonar movement to analyze the deformation law of the target in beam-domain which is scanned in image-domain, and then proposes the concept of expression domain mapping model (EDMM). Considering the need of aligning acoustic origin, the target is deformed according to this model in the process of beam-domain mosaic, and the target alignment is completed. Subsequently, the Laplace pyramid (LP) layered algorithm is used for fusion and mosaic, and finally mapped to image-domain for display. Experimental results illustrate that this kind of algorithm can remove the mosaic edge-seams generated during mosaicking in the image-domain because it uses the sonar raw image data for mosaic in the beam-domain and avoids the appearance of sector edges in the image-domain; the actual pixel points sampled by sonar are used for mosaic, which effectively eliminates the pseudo-target value generated by the mosaic of the inter-beam interpolation in the image-domain; moreover, the number of image pixels in the beam-domain is small, and the mosaic time consuming of adjacent frames and continuous frames is more than half shorter than that of the image domain, which significantly improves the mosaic efficiency and has good mosaic performance.
Selective Presumed Benevolence in Multi-party System Verification

The functional correctness of particular components in a multi-party system may be dependent on the behaviour of other components and parties. Assumptions about how the other parties will act would thus have to be reflected in the specifications. In fact, one can find a substantial body of work on assume-guarantee reasoning with respect to the functional aspects of the component under scrutiny and those of other components. In this paper, we turn to look at non-functional assumptions about the behaviour of other parties. In particular, we look at smart contract verification under assumptions about presumed benevolence of particular parties and focusing on reentrancy issues—a class of bugs which, in the past few years, has led to huge financial losses. We make a case for allowing, in the specification, fine-grained assumptions on benevolence of certain parties, and show how these assumptions can be exploited in the verification process.
Spectral characterization of the Umbuzeiro Doce skarn target (Brazil): insights for the exploration of a W-Mo-gemological vesuvianite system

The Umbuzeiro Doce skarn target in the Seridó Belt (NE Brazil) was studied using reflectance spectroscopy and petrography. Interpretation of the 61 reflectance spectra obtained in the study established the following zones: marble zone, with diagnostic absorption features at 2,340 and 2,475 nm (C-O); tremolite marble zone, with main absorption at 1,393 (OH−) and 2,313 nm (Mg-OH); garnet-vesuvianite zone, with the main absorption at 2,215 nm (OH−); diopside-hornblende zone, marked by a broad absorption at 1,153 nm (Fe2+); late wollastonite zone, which lacks a diagnostic spectral signature; and a local violet vesuvianite-bearing marble, characterized by potential gemological vesuvianite marked by Cr3+ absorption features (~548 and 680 nm). Centimeter-sized molybdenite aggregates occur at the interface of the marble and calc-silicate alteration zones. Spectral indices were produced to automate identification of potential zones to explore Mo, W, and gemological vesuvianite. Indices for the zones of marble, tremolite marble, garnet-vesuvianite, diopside-hornblende and violet vesuvianite were produced and tested. This methodology appears as a powerful exploratory guide for Mo, W, and violet vesuvianite occurrences or deposits that show a mineral zoning like the Umbuzeiro Doce target.
Find the Real: A Study of Individuals’ Ability to Differentiate Between Authentic Human Faces and Artificial-Intelligence Generated Faces

As advances in Artificial-Intelligence (AI) generated technology accelerate, much of the general population is unaware when they may come across AI-generated content, including AI-generated images of humans. In attempting to see if the general population can ascertain the real vs. fake as society encounters an increasing amount of deep fake imagery, this study was conducted using AI human portraits generated at thispersondoesnotexist.com [1] using SyleGan2 (a generative adversarial network developed by Nvidia) [2]. The deep fake portraits were then juxtaposed with authentic portraits of humans from the Flickr Faces HQ Dataset (FFHQ) of Creative Commons licenses and public domain images [3]. Survey respondents were asked to select which face they believed to be an authentic human face. This is an important study in the realm of Human-Computer Interaction as AI resources are utilized in a variety of forms, including the spread of information, and can be linked to disinformation with the implementation of deep fakes.
THE LANGUAGE STYLE USED IN PRESENTATION ON YOUTUBE BY ENGLISH LANGUAGE EDUCATION STUDENTS

People's thoughts on a particular topic were represented through language style. This study aimed at investigating kinds and functions of language style that students use when giving presentations on YouTube. There were a total of 15 students who took part in the study. A descriptive qualitative design was used in this study. The observation sheet and table classification of the types of language styles were used as research instruments. The findings revealed that concrete word (14%), abstract word (32%), general word (4%), specific word (4%), and scientific word (10%) were used in the language style based on diction. Meanwhile, antithesis (10%), repetition (10%), climax (12%), and anticlimax (4%) were among the language style based on sentence structure. Abstract word and climax made prominent appearances in both classifications, according to both data. The functions of the language style were expressive (18%), directive (43%), referential (17%), metalinguistic (7%), and phatic (15%). It suggested that directive had the most prominent expressions.
ZoomPass: A Zoom-Based Android Unlock Scheme on Smart Devices

Modern smart devices such as smartphones are able to provide various services, including checking online banking, watching video and listening to music. Due to the intelligence and popularity, such devices are also used as storage space for recording users’ private information such as personal photos, credit card number, videos and more. This creates a need for protecting the devices from unauthorized access. Currently, many unlock schemes have been developed, in which Android unlock pattern is the most widely used scheme. However, traditional Android unlock pattern is vulnerable to different threats due to the limited pattern space. To enhance the security, one major direction is to combine biometric features with Android unlock pattern. In this work, motivated by this trend, we propose an enhanced Android unlock scheme based on zoom actions. More specifically, users can select two dots and perform a zoom action (either zoom-in or zoom-out) over each dot for authentication. In the study with 30 participants, our scheme demonstrates good performance in terms of usability and security.
An Improved Method for Removal of Thin Clouds in Remote Sensing Images by Generative Adversarial Network

Thin clouds will attenuate surface radiation, causing color distortion in remote sensing images and loss of detailed information. Aiming at the color cast phenomenon of the existing thin cloud removal methods, this paper proposes a remote sensing image thin cloud removal method based on generative adversarial networks with color consistency constraints. The RICE1 thin cloud dataset was used to verify the effectiveness of the method. CIEDE2000 was added to the evaluation index to measure the consistency of the thin cloud removal results and the color of the cloud-free image, and the reasonable value of the color constraint parameter <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\lambda$</tex> was explored. The experimental results show that the method in this paper is significantly better than homomorphic filtering, dark channel prior method and non-color-constrained generative adversarial network cloud removal method.