Reviewerly/exHarmony huggingface dataset:
Disentangled representation learning for collaborative filtering based on hyperbolic geometry

In the realm of recommender systems, the exploration of hyperbolic geometry-based embeddings for users and items has emerged as a promising avenue, particularly in the context of collaborative filtering through graph convolution networks. Despite the advancements in this domain, there are two significant questions have received limited attention: (i) Most hyperbolic geometry-based methods ignore the influence of different users’ intents on the preferences of historical behaviors; (ii) They usually learn high-dimensional embeddings akin to Euclidean geometry-based methods, without taking good advantage of the capacity of hyperbolic spaces. To tackle these limitations head-on, we propose a novel method called Disentangled Hyperbolic Collaborative Filtering (DHCF). DHCF learns multiple embeddings in different low-dimensional hyperbolic spaces, enabling the disentanglement of users’ intents and the separate modeling of user and item representations. Moreover, we design an intent-aware graph reconstruction module, which adaptively allocates intent-aware relation strengths to build a dynamic interaction graph. Additionally, this module reduces the risk of oversmoothing in low-dimension spaces, facilitating the stacking of multiple graph aggregation layers. To the best of our knowledge, our method achieves competitive performance compared to state-of-the-art approaches.
Densely Parallelized Photonic Tensor Processor on Hybrid Waveguide/Free-Space-Optics

We demonstrate photonic tensor processor based on hybrid waveguide and free-space-optics. Our device can realize 32x100λ matrix operation thanks to the dense space/wavelength parallelization. By constructing the optical benchtop, we confirmed all matrix value can be controlled with good accuracy (>7 bit). Benchmark experiments for the standard neural network models showed good performance compatible to the simulated ones, suggesting the successful operation of our device. The device is considered to be able to perform 188 tera operations per second, which is one magnitude faster than previous approach. We also demonstrate the computation on the optical link using our device.
Heterogeneous III-V-on-Silicon photonic Non-Linear Activation Function for Scalable Photonic Neural Networks

Targeting a large scale deep neural network implementation in the photonic domain, we experimentally demonstrate an optoelectronic activation function generated on our heterogeneous III-V-on-Si platform and the combination of a quantum dot avalanche photodiode with a quantum dot distributed feedback laser. The power threshold for the light activation out of the ReLU function is 5.25 mW.
Validation of Photonic Neural Networks in Health Scenarios

Photonic hardware represents a promising alternative to speed-up Neural Network (NN) computations, outperforming electronic counterparts in terms of speed, energy consumption and computing density. In this paper we exploit a Photonic-Aware Neural Network (PANN) architecture with unipolar and bipolar weight implementations, considering ReLU and photonic sigmoid as candidate activation functions to solve a heartbeat sound classification task. Results indicate that increasing the bitwidths during quantization improves the F1-score. The use of bipolar implementation for weight choice demonstrates better performance. ReLU is identified as a better nonlinearity. Finally, a multi-resolution scenario in the bipolar photonic-sigmoid experiment is evaluated, revealing that incorporating multi-resolution does not enhance the model's generalization ability if the bitwidth for the first layer remains fixed. However, the importance of the highest bitwidth at the NN inputs is highlighted.
Programmable Photonic Architecture Solving Systems of Ordinary Differential Equations

Photonic integrated hardware accelerators promise to be a viable technology to complement digital electronic devices in applications requiring massive computing, such as high-performance computers and artificial intelligence systems. In this work we report on a fully programmable photonic architecture implementing an analogue solver of systems of ordinary differential equations (ODEs). We show how to map the coefficients of an arbitrary system of ODEs onto the optical parameters of the photonic circuit, which consists of a recursive mesh of tunable Mach-Zehnder interferometers (MZIs). Numerical simulations are carried out to evaluate, both in the frequency and time-domain, the error between the exact solution of the ODE system and the device response, and conditions to minimize such error are provided.
Low Loss Fan-Out Structure for Multimode Waveguide-Based Reservoir Computing

In this paper, we present a compact and efficient structure designed to extract optical output from a multimode waveguide (MMW), which facilitates the propagation of multiple waveguide modes. Our focus is on its application in reservoir computing systems. Through extensive simulations and parameter adjusting, we demonstrate that the average loss across all guided modes can be effectively reduced to as low as 0.3 dB. Our proposed structure is specifically designed for a 25 μm wide MMW implemented on a silicon photonics platform.
How to Boost Ising Machines' Ability to Find Optimum Solutions: a Bifurcation Analysis

Photonic Ising machines are a recent and promising non-von Neuman architecture to efficiently solve large NP-hard optimization problems. This is done by letting spins evolve to the ground state of the Ising system. However, the Ising machine does not always relax to this ground state and can get trapped in local minima, resulting in suboptimal solutions. Here, we show that Ising-encoded optimization problems implemented on analog Ising machines can be divided in three difficulty classes and we propose an annealing scheme to efficiently solve two of them. Furthermore, we show that changing the physical implementation of the Ising machine can cause a problem to change difficulty class, rendering it potentially a lot more easy to solve.
Quantum Interference Patterns for N-Photon States Using a Gires-Tournois Etalon Assisted Dual-Pass Mach-Zehnder Interferometer

We introduce the concept of GTE-assisted double-pass MZI and its operation using photon number states, based on quantum-optical formalism. We also theoretically show impressive interferometric fringes for different combinations of N-photon states as circuit inputs.
A Phase-Optimal Linear Photonic Architecture

We derive the information-theoretic limit to phase-shift efficiency of universal multiport interferometers, and propose an architecture that approaches this limit to within a factor of 2×, approximately a 10× improvement over the prior art.
Photonic Activation Function Using Sound Waves

We experimentally demonstrate an in-fiber optoacoustic nonlinear activation function based on stimulated Brillouin scattering. The activation function can be tuned all-optically and shows an input-output behaviour of a LeakyReLU, Sigmoid, and quadratic function. Our scheme is coherent and frequency selective, which is suitable for operation in multi-frequency neural networks. In addition, the optoacoustic activation function can amplify its input signal, allowing to compensate on-the-fly for optical losses during propagation.
Higher-Accuracy Photonic Neural Networks via Duplication Schemes for Noise Reduction

We present how feasible duplication schemes for reducing noise in optical neural networks achieve accuracy gains when compared to implementations without duplication. Performance gains are 7.4% at a practical chip size, and noise can be negated completely in a many-duplication regime.
Nonvolatile Switching in a Ring Resonator with Saturable Absorption

We investigate the theoretical properties of a novel all-optical switch design consisting of a saturable absorber in an add-drop ring resonator. Using a modified transfer matrix method we demonstrate nonvolatile switching, where the routing persists even after the control signal is deactivated.
Numerical Analysis Considering Modal Mixing for Multimode-Interference-Based Photonic Reservoir Computing

We present a simulation for multimode-interference-based photonic reservoir computing (RC) that includes modal mixing. By using the scattering parameters for each separated part of the waveguide and cascading them, we efficiently performed a more accurate light propagation analysis considering modal mixing, even for the larger devices. The effectiveness of the approach was confirmed by typical tasks for RC.
A Haskell Library for Adaptable Parsing Expression Grammars

Adaptable Parsing Expression Grammar (APEG) is a formal model for describing languages which provide a mechanism to extend its own syntax during the parsing process. In this work, we review the syntax and semantics of APEG and discuss the implementation of a Haskell library for building parsers for extensible languages. We also present an example of an APEG parser for a tiny extensible language.
Comparison of Depth Models in Real-World Scenarios: A Case Study

The aim of this study was to explore the effectiveness of deep learning methods in obtaining depth maps from RGB images. To achieve this, a series of experiments were conducted on both indoor and outdoor images using three different methods: AdaBins, MiDas, and ZoeDepth. AdaBins was tested using versions trained with NYU and KITTI datasets, while MiDaS was tested using three different versions: dpt_swin2_tiny_256, dpt_swin2_large_384, and dpt_beit_large_512. This resulted in a total of six comparisons (2 AdaBins, 3 MiDaS, 1 ZoeDepth) being made. As the images used in the study were real-world images, there was no ground-truth available to evaluate the results. Therefore, two researchers evaluated the results using observational methods. The findings indicated that ZoeDepth was the preferred model if resource consumption was not an issue. However, in real-time applications where resource consumption and time constraints are a concern, the MiDaS dpt_swin2_tiny_256 model was found to be the most suitable option. This was due to the fact that there were no significant visual differences between MiDaS and ZoeDepth, and the former was less resource-intensive. Overall, the study demonstrated the potential of deep learning methods in obtaining depth maps from RGB images. The findings provide valuable insights for researchers and practitioners working in the field of computer vision and image processing.
A Comprehensive Approach for Enhancing Deep Learning Datasets Quality Using Combined SSIM Algorithm and FSRCNN

The quality of the datasets used in deep learning problems is crucial in almost all application domains. There are a variety of common issues that can degrade datasets quality, including problems like data duplication, insufficient image resolution, and low data quality. Well-established techniques exist for mitigating these problems individually, such as using the SSIM algorithm to efficiently find similarities and duplicates in data or leveraging neural networks like FSRCNN to increase image resolution and quality. However, while each of these approaches offers its own unique benefits, they also possess some inherent limitations and disadvantages when applied in isolation. To mitigate the disadvantages of individual methods and provide a more comprehensive solution, a data preprocessing technique is proposed which involves combining the preceding algorithms and applying them as a single pipeline. The key insight is that SSIM's ability to identify duplicates combined with FSRCNN's power to super-resolve images can offer complementary strengths. Experiments conducted demonstrate that this hybrid approach achieves an improvement of approximately 3% in classification accuracy on the CIFAR-100 image dataset benchmark, with only 1.5 times increase in computational training duration. The joint technique outperforms using SSIM or FSRCNN independently. This highlights the potential for hybrid solutions to enhance datasets quality. Further research can explore combinations of other complementary data enhancement methods and study their performance across diverse datasets and machine learning models. The early findings clearly show that using multiple techniques together can improve model accuracy by addressing various issues with low-quality datasets.
Vulnerability of Atomic Patterns to Simple SCA

In this work we discuss the vulnerability of atomic pattern algorithms for elliptic curve point multiplication against simple side-channel analysis attacks using our own implementation as an example. One of the assumptions, on which the atomicity principle is based, is the indistinguishability of operations with different registers, i.e. storing of the data into two different registers cannot be distinguished if their old and new data values are the same. But before the data can be stored in a register/block, this register/block has to be addressed for storing the data. Different registers/blocks have different addresses. The key-dependent addressing of registers/blocks is an inherent feature of the binary <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$kP$</tex> algorithms and allows to reveal the key <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> . In our work we demonstrated it. This means that the main assumption, that addressing of different registers/blocks is an indistinguishable operation, may no longer be applied when realizing <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$kP$</tex> implementations, at least not for hardware implementations.
QR-Cloud-Edge Computing for Good Logistics

It is proposed to use QR-computing to solve the problem of saving money and time, increasing reliability when processing a large capacity of resources and processes using the cloud and terminal devices. The entire structure of the interaction of mechanisms, prescribed in cloud-edge computing, is preserved. The main idea of the article is how to speed up cargo export through multi-level access to information systems, using QR-coding methods. The main task is to formulate a model, explain its principle of operation and justify the advantage of a new method of multi-level access to information systems in the process of improving cargo circulation between states. Multi-level access to information systems (Database) allows to limit the access to it according to the status (permission) of individuals, meaning, individuals with different statuses can receive information intended only for them through the same QR-code. The QR code is generated only once, and it stays unchanged during the export process of a specific cargo. The only part that changes is information provided to the specific person according to his/her status. Such an approach to cargo export will significantly reduce the time spent on the process and make it much more comfortable and efficient for all organizations and individuals participating in it.
Motion Aware Data Sampling Using Sequential Frames for Deep Learning Models

In this study, an alternative data sampling method is proposed for deep learning models. In the suggested method, 3-stage sampling is done by using sequential frames. In the sampling process, a 5-minute 30 fps video is re-sampled to 5 fps and a total of 1500 frames are obtained. Optical flow and random sampling operations are performed sequentially on the frames obtained. In optical flow methods, 4 different thresholds are used while determining the magnitude vector. When performing random sampling, a total of 4 different data sets were created with 3 different sample rates. As a result of experiments with different sample rates and thresholds, a maximum of 1459 and a minimum of 757 frames are obtained from 1500 frames. The number of data obtained for each sub-data set is different. Obtained sub-data sets can be used for model generalization according to the needs of the environment in which the deep learning model will be used. To evaluate the performance of a deep learning model from different perspectives, experiments were carried out with the obtained data sets. As a result of the experiments, it was seen that the learning model cannot be directly associated with the number of data. According to different evaluation metrics, it has been evaluated which data set is more suitable for various environments.
Investigation of Noise Filtering Methods on Histological Images

The article presents the results of studies on the reduction and elimination of noise in histological images using filtering methods such as Unsharp mask, Non-local filter, Total variation filter, Block matching and 3d. Studies have shown that the characteristics of the original binary histological image improved more with Non-local filtering than with Unsharp filtering. For color histological images with the possible presence of artifacts, the BM3D filter should be used because it has good performance compared to the Total Variation method. Software has been created to reduce noise on histological images by applying the proposed filters, which can be used to compare the results visually.
Automated Negotiation - Preliminary Results of a Systematic Mapping Study

Automated negotiation is a process of autonomously overcoming conflicts between intelligent agents and achieving agreement. The literature has proposed several approaches to automated negotiation. To this end, the aim of this study is to summarize the state-of-the-art on automated negotiation for the research community to identify the research gap and to conduct further research in the automated negotiation domain. To achieve this goal, we conducted a systematic mapping study (SMS) on the automated negotiation literature on a set of 73,760 candidate studies. Through a precise search and selection procedure, we identified a set of 21 primary studies, published between the year 2017 and June 2022. As preliminary results of this mapping study, we provide the classification framework to identify and evaluate the automated negotiation literature, and an up-to-date map of the state-of-the-art on automated negotiation focusing on (i) the specific purpose of negotiation, (ii) the application domain in which it is employed, and (iii) how it is carried out in terms of inputs, outputs, and used techniques.
Universal Design for Website Authentication: Views and Experiences of Senior Citizens

Using digital devices and online products and services requires users to regularly authenticate themselves. Given that the vast majority of websites use passwords to authenticate users, this study focuses on the accessibility and inclusivity of this mechanism, using Universal Design Principles as a lens. Collecting and analysing autobiographical narrative data from 50 respondents, we use a qualitative approach to explore the views and experiences of senior citizens across various phases of website authentication. Our analysis uncovers barriers and challenges, leading to several undesirable consequences, when authentication is not accessible and inclusive. Our findings also show how users, many of whom have cognitive and other age-related infirmities which are seldom accommodated in authentication design, try to cope with these issues. Our findings show how authentication may fail to align with the principles of universal design and highlight considerations in making authentication more accessible and inclusive for all users.
DIF-LUT: A Simple Yet Scalable Approximation for Non-Linear Activation Function on FPGA

Non-linear activation function plays an essential role in neural networks (NNs) for their generalization ability. However, deploying the intricate mathematical operations on hardware platforms like Field-Programmable Gate Array (FPGA) turns out a great challenge. Prior works based on piecewise functions or look-up table (LUT) either involve complex manual operations or neglect hardware overhead. To this end, this paper proposes a simple yet scalable and effective approximation called DIF-LUT, which is applicable to various non-linear functions. Specifically, the proposed method can achieve accurate approximation by piecewise linear matching to fit the function derivative roughly and range addressable LUT to offset the difference. Moreover, self-adaptive mechanisms are applied to automatically minimize hardware cost in terms of different accuracies. The experiments show that compared to state-of-the-art methods, DIF-LUT costs 43.68% fewer LUTs and 70.8% fewer flip-flops (FFs) without any digital signal processor (DSP), while achieving 2.7x approximation accuracy at 554.1MHz on Xilinx Zynq UltraScale+.
Exploiting On-Chip Heterogeneity of Versal Architecture for GNN Inference Acceleration

Graph Neural Networks (GNNs) have revolutionized many Machine Learning (ML) applications, such as social network analysis, bioinformatics, etc. GNN inference can be accelerated by exploiting data sparsity in the input graph, vertex features, and intermediate data in GNN computations. For dynamic sparsity exploitation, we leverage the heterogeneous computing capabilities of AMD Versal ACAP architecture to accelerate GNN inference. We develop a custom hardware module that executes the sparse primitives of the computation kernel on the Programmable Logic (PL) and efficiently computes the dense primitives using the AI Engine (AIE). To exploit data sparsity during inference, we devise a runtime kernel mapping strategy that dynamically assigns computation tasks to the PL and AIE based on data sparsity. Our implementation on the VCK5000 ACAP platform leads to superior performance compared with the state-of-the-art implementations on CPU, GPU, ACAP, and other custom GNN accelerators. Compared with these implementations, we achieve significant average runtime speedup across various models and datasets of 162.42x, 17.01×, 9.90×, and 27.23×, respectively. Furthermore, for Graph Convolutional Network (GCN) inference, our approach leads to a speedup of 3.9-96.7× compared to designs using PL only on the same ACAP device.
FPGA-Accelerated Causal Discovery with Conditional Independence Test Prioritization

Causal discovery is a data mining approach that finds causal relations between variables from data. Causal discovery algorithms are computationally demanding when the data set has a high dimensionality or a large sample size. A promising way to expedite causal discovery is by utilizing FPGAs, but a significant drawback is that FPGA designs become inefficient when the on-chip memory cannot store the entire data set. This paper proposes Conditional Independence Test Prioritization (CITP), a novel approach that overcomes this limitation and enables fast FPGA-based causal discovery for large datasets with comparable speed and adequate accuracy to state-of-the-art methods. The main idea behind CITP is to design a workflow that allows a small subset of data to be stored in on-chip memory for prioritizing conditional independence tests. The paper provides experimental results that demonstrate the effectiveness of CITP in terms of both accuracy and speed. Our experiments show that for specific datasets, the proposed approach can respectively be 79 times, 2.6 times and 2.1 times faster than current CPU, GPU and FPGA designs.