Reviewerly/exHarmony huggingface dataset:
Training on the optical system: local search method

Optical neural networks hold tremendous potential for energy efficiency and low latency. Despite this potential, the mismatch between simulation training and experimental setups can negatively impact the system’s performance. To address this issue, we present a local search method for training optical neural networks in the system. The implementation of this training method allows optical neural networks to reach their state-of-the-art performance levels within the constraints of current experimental settings.
Application-adapted beam shaping with cutting-edge optical elements in laser materials processing

The quality, efficiency and robustness of laser materials processing significantly benefits from an application-adapted beam shaping. For a successful realization of tailored beam shapes that utilizes state-of-the-art optic concepts, research in two different steps is necessary. First, information about the required beam shape (especially in terms of intensity distribution) is needed. During the last years, we have developed and validated a method to solve the so-called inverse heat conduction problem which enables the derivation of an intensity distribution that specifically tailors the induced temperature profile within the processed material. Here, we present the latest enhancement of the algorithm to complex time-dependent scenarios and new applications, e.g. from the field of surface treatment and tape placement. With the knowledge of the target beam shape, in a second step, optical systems must be designed that enable the realization of the achieved, highly complex intensity distributions. We further demonstrate the potential and limits of novel optics such as freeform mirrors, LCoS-SLMs or diffractive neural networks as well as VCSELs for application-adapted beam shaping. We especially present specific design methods that can contribute to a robust, flexible, and practical realization in various applications.
Deep optics: learning cameras and optical computing systems

Neural networks and other advanced image processing algorithms excel in a wide variety of computer vision and imaging applications, but their high performance also comes at a high computational cost and their success is sometimes limited. Here, we explore hybrid optical-digital strategies to computational imaging that outsource parts of the algorithm into the optical domain. Using such a co-design of optics and image processing, we can learn application-domain-specific cameras using modern artificial intelligence techniques or compute parts of a convolutional neural network in optics. Optical computing happens at the speed of light and without any memory or power requirements, thereby opening new directions for intelligent imaging systems.
Integrated optical output layer for a reservoir computer based on frequency multiplexing

Reservoir Computers (RCs) are brain-inspired algorithms based on recurrent neural networks where only output weights are tuned, while internal weights remain untrained. We recently demonstrated a photonic frequency-multiplexing RC encoding neurons in the lines of a frequency comb. We also demonstrated a single-layer feed-forward neural network based on a similar frequency-multiplexing principle. Here we present the design for an integrated optical output layer for such frequency multiplexing based photonic neural networks. The all-optical output layer uses wavelength (de)multiplexers and wavelength converters to apply signed weights to neurons encoded in comb lines.
Deep learning super resolution for high-speed excitation emission matrix measurements

In many optical experiments, a long measurement time is necessary to collect enough information and improve the signal-to-noise ratio. This happens, for example, in total luminescence spectroscopy (TLS) where the data is acquired as excitation-emission matrices (EEMs). An EEM is an unique chemical fingerprint of the analyzed substance that allows its comprehensive characterization. To collect a high-resolution EEM, it is necessary to scan both the excitation and the emission wavelengths in small steps and, for each step, to collect the light for a long time to maximize the signal-to-noise ratio. Therefore, acquiring a high-resolution excitation emission matrix can take more than an hour, depending on the size of the wavelength steps, the intensity of the signal, and the spectral range to be analyzed. This paper proposes a new method to reconstruct a high-resolution EEM from low-resolution one using deep learning super-resolution techniques. Specifically, this work proposes a new artificial neural network architecture, a sub-pixel convolutional neural network, designed to be applied to fluorescence EEM images. The code used is made available via a GitHub repository with instructions for applying transfer learning to different types of images.
Parallel and deep reservoir computing based on frequency multiplexing

Reservoir Computers (RC) are brain-inspired algorithms that use partially untrained recurrent neural networks where only output connections are tuned. RCs can perform signal-analysis tasks such as distortion compensation. We recently demonstrated a photonic RC in which neurons are encoded in a frequency comb, untrained interconnections are realized by phase modulation, and trained output connections are realized by spectral filters. Here, we present a further development of this scheme in which the same substrate is used to implement two RCs simultaneously. The two RCs can either be used in parallel on different tasks, or in series, thereby implementing a “deep” RC.
Photonic tensor core machine learning accelerator

Photonic Tensor Cores (PTCs) have been raised as one of the major candidates to accelerate Neural Network hardware, taking advantage of the high bandwidth, low latency, and energy efficiency that propagating light has over the electrical counterpart. However, actual solutions still rely on external bulk components, such as lasers, modulators, and photodetectors. In this work, we show the first fully integrated hybrid Silicon Photonics PTC for computing Matrix-Vector Multiplication (MVM), one of the main steps for any Neural Network layer. The PTC is formed by a WDM 3-fold InP laser array connected to an active Silicon Photonic PIC through Photonic Wire Bondings. The CW lasers are modulated by high-speed Mach-Zehnder modulators to generate the input vector. The mix of all the signals is sent into a 3x3 matrix, formed by high-speed add-drop coupled microring resonators, whose tuning signals are the weights of the matrix. Outputs are collected by a bank of high-speed integrated photodetectors. The whole photonics IC has a footprint of 4.1×1.7 mm<sup>2</sup>, including lasers, allowing to have just electrical I/O. The full integration of input modulators, weights, and photodetectors can allow the PIC to work at over 20 GHz bandwidth with extremely low latency. This integration is a key step toward the actual deployment of photonics as an NN-accelerator for future AI systems.
Speckle-based optical cryptosystem for face recognition

Face recognition has been widely implemented in public places for security purposes. However, face photos are sensitive biometric data, and their privacy is a common concern, which often needs to be protected via cryptosystems. Popular software-based cryptosystems have limitations on short secret key lengths, posing a significant threat when facing high performance quantum computing. Recently, in order to achieve higher level security, hardware-based optical cryptosystems have been investigated. However, due to the complexity of optical system designs, it is difficult to integrate the extensively studied optical double random phase encryption into current face recognition systems. Speckle-based cryptosystems, on the contrary, affords high-level safety with high adaptivity, high speed, and low cost, using simpler optical setups. In this study, a speckle-based optical cryptosystem for face recognition is proposed, and encrypted face recognition is experimentally demonstrated. During encryption, a scattering ground glass is utilized as the only physical secret key with 17.2 gigabit length, so as to encrypt face images via random optical speckles at light speed. During decryption, a specially designed neural network is pre-trained to reconstruct face images from speckles with high fidelity, allowing for up to 98% accuracy in the subsequent face recognition process. Apart from face recognition, the proposed speckle-based optical cryptosystem can also be transferred to other high-security cryptosystems due to its high security, high adaptivity, fast speed, and low cost.
Michelson interferometric methods for full optical complex convolution

Dynamic real-time optical processing has significant potential for accelerating specific tensor algebra. Here we present the first demonstration of simultaneous amplitude and phase modulation of an optical two-dimension signal in the Fourier plane of a thin lens. Two spatial light modulators (SLMs) arranged in a Michelson interferometer modulate the amplitude and the phase while being simultaneously in the focal plane of two Fourier lenses. The lenses frame an interferometer in a 4f-system enabling full modulation in the Fourier domain of a telescope. Main sources of phase noise and losses are discussed such as native to SLMs non-linear inter-pixel crosstalk, variability in modulation efficiency as a function of projected mask parameters, and Fresnel’s optics limitations. Such a system is of extreme utility in rapidly progressing fields of optical computing, hardware acceleration, encryption, and machine learning, where neglecting phase modulation can lead to impractical bit-error rates.
Decentralized power grid control scheme utilizing photonic sensing and computing

A shift in the way we generate and distribute electricity from few, large-scale units; to many, small-scale, decentralized units has created power distribution complications that cannot be managed by one centralized authority in reasonable time. We hypothesize that through the utilization of photonic-based computing, physics-based hierarchical machine learning frameworks will allow for optimized grid operation at lower computational cost. Behavioral analysis of trained agents demonstrates that local, modular control of microgrids could provide a reliable alternative to the existing method of power grid control. It is our vision that these agents could discover new methods for optimal grid control.
Identifying the advantageous latent space dimensionality for StyleGANs used in industrial machine vision applications

State-of-the-art style-based generative adversarial networks (StyleGANs) synthesize high-quality images by learning a mapping from a disentangled latent space onto the image manifold. Thereby, learned representations can be analyzed by interpreting the latent space and used subsequently to control the properties of the synthesized industrial machine vision data. StyleGANs in combination with an embedding into the latent space enable the assessment of the properties of embedded images by means of their latent space representations, however, a trade-off between the dimensionality of the StyleGAN’s latent space and the quality of generated images must be found. While a smaller latent space is easier to interpret, it might not capture all quality characteristics if lossless compression cannot be achieved. This work presents an evaluation scheme that uses statistical hypothesis testing to identify an advantageous dimensionality of the latent space for industrial machine vision applications. As quality measure of images synthesized by GANs, often the Fr´echet Inception distance (FID) based on features learned from the ImageNet dataset is used. However, the features of the underlying Inception network are opaque and might not be representative for application specific quality characteristics. Herein, synthetic data is evaluated instead by means of a Fr´echet distance based on selected and application specific features extracted from the used industrial machine vision dataset. With these application specific features, the image quality of multiple StyleGANs trained with different latent space dimensionalities is compared using statistical tests to select an advantageous latent space dimension.
Human activity classification using deep learning based on 3D motion feature

Human activity classification is needed to support various fields. The health sector, for example, requires the ability to monitor the activities of patients, the elderly, or people with special needs to provide services with fast response as needed. In the traditional classification model, the steps taken to start from the input of data and then proceed with feature extraction, representation, classifier and end with semantic labels. The classification stage uses Convolutional Neural Network (CNN) deep learning to data input, CNN, and semantic labels. This paper proposes a novel method of classifying nine activities based on the movement features of changes in joint distance using Euclidean on the order of frames in each activity segment as input to the CNN model. This study's motion feature extraction technique was tested using various window sizes to obtain the best classification accuracy. The experimental results show that the selection of window size 16 on the motion feature setting will produce an optimal model accuracy of 94.08% in classifying human activities.
<scp>PriMed</scp> : Private federated training and encrypted inference on medical images in healthcare

Abstract In healthcare, patient information is a sparse critical asset considered as private data and is often protected by law. It is also the domain which is least explored in the field of Machine Learning. The main reason for this is to build efficient artificial intelligence (AI) based models for preliminary diagnosis of various diseases, it would require a large corpus of data which can be obtained by pooling in patient information from multiple sources. However, for these sources to agree to sharing their data across distributed systems for training algorithms and models, there has to be an assurance that there will be no disclosure of the personally identifiable information (PII) of the respective Data Owners. This paper proposes PriMed, an approach to build robust privacy preserving additions to convolutional neural networks (CNN) for training and performing inference on medical images without compromising privacy. Since privacy of the data is preserved, large amounts of data can be effectively accumulated to increase the accuracy and efficiency of AI models in the field of healthcare. This involves implementing a hybrid of privacy‐enhancing techniques like Federated Learning, Differential Privacy, and Homomorphic Encryption to provide a private and secure environment for learning through data.
Action knowledge for video captioning with graph neural networks

Many existing video captioning methods capture action information in the video by exploiting features extracted from an action recognition model. However, directly using the action features without object-specific representation may not well capture the object interactions. Consequently, the generated captions may not be accurate enough in describing the action and the object in the scenes. To address this issue, we propose to incorporate the action features as the edge features in a graph neural network where the nodes represent objects, thereby capturing a finer visual representation of object-action-object relationships. Previous graph-based video captioning methods commonly relied on a pretrained object detection model to create the node representations. The object detection model, however, may miss detecting some important objects. To alleviate this problem, we further introduce a grid-based node representation where the nodes are represented by the features extracted from grids of video frames. Using this representation, the important objects in the scenes are captured more thoroughly. To avoid adding any complexity during inference, the knowledge of the proposed graph is transferred to another neural network via knowledge distillation. Our proposed method achieved state-of-the-art results on two popular video captioning datasets, i.e., MSVD and MSR-VTT, on all metrics. The code of our proposed method is available at https://github.com/Sejong-VLI/V2T-Action-Graph-JKSUCIS-2023.
How information within the perceptual span guides visual search and aids perception

We explore the guiding effect of task-related information appearing within the perceptual span on visual search based on elements such as the range of effective information attention within the perceptual span and the orientation of task-related objects. In the first experiment, we recorded reaction time and eye movement data of 48 subjects performing a visual search task for target information in the navigation interface and divided them into four experimental groups according to the perceptual span (within or outside the range) and orientation (left or right) of the task-related information. The experimental results showed that: (a) task-related information appearing within the perceptual span had a guiding effect on target search; and (b) the right side closer to the target in task search have better performance in guiding. In the second experiment, eye-movement experiments of the navigation interface based on the eye-movement processing theoretical model of linguistics showed that: (a) within the perceptual span, the shifting of attention causing eye movements (saccade) and brings gaze into the area of interest, and (b) the perceptual span moves with attention shifts. This study provides evidence for cognitive processing of information responses within the perceptual span and dynamic adjustment of perceptual span location and provides a reliable method for effective information presentation and visual layout in navigation interfaces, with a view to providing reference and learning for related studies.
STUDENTS’ PERCEPTION OF LEARNING MANAGEMENT SYSTEM ON STUDENTS’ STUDY HABIT DURING COVID-19

The study investigated perception of students on Learning Management System and how it affects and benefits their study habit during COVID-19, as well as its effects on their study performance. The study adopted quantitative research approach and survey design where data was gathered using a questionnaire that was created to acquire pertinent information from respondents. The study participants are the 200 and 300 level students of Faculty of Education, University of Ilorin, while a total of 358 students administered the questionnaire only 303 responses are used for the study. The results demonstrated that students' study habits are significantly impacted by the use of Learning Management Systems (LMS) as majority of the respondents attest that the use of LMS improved their study habit which in turn affected their study result positively. These could also assist lecturers and university administration to know how well students are able to relate with Learning Management System, how it affects their academic performance and their preparedness to fully adopt the system.
Laser transmission of quantum bits and multi-tera-bits over multi-hop satellite orbital constellations

We present the transmission of continuous variable quantum key distribution (CV-QKD) qubits, ultra-high capacity of multi-tera bits over atmospheric and inter-satellite channels via the C-band. With the aid of advanced modulation formats and modern photonic technologies, we argue that multi-satellite jumps are both feasible and realizable. Photonic transceivers utilizing photonic integrated circuits in Silicon and InP technology of electro-absorption modulation lasers are presented. It is shown that optical amplification techniques, booster power amplifiers, and highly sensitive optical preamplifiers can compensate for exceedingly high attenuation of links between optical ground stations and satellites with all optical routing and link distances of 12,000 to 45,000 km. The digital signal processing algorithms in coherent receivers for ultra-high capacity and ultra-low power quantum bit systems are demonstrated to play a critical role in system performance. We specifically provide a Nyquist-equivalent sampling theorem that studies the quantum bit error rate based on both the Nyquist sampling theorem and the Heisenberg uncertainty principle. Furthermore, the Nyquist pulse shaping method and constellation probability shaping are used to maximize tera-bit transmission over spatial channels.
Evaluation and assessment of machine learning based user story grouping: A framework and empirical studies

User stories are the main approach to specifying requirements in agile development. Nowadays, a software project normally involves a huge amount of user stories, which makes it a challenging job to efficiently manage these stories. One mainstream way for user story management is to allocate different user stories into a number of groups based on the similarity among them. However, most of existing user story grouping techniques involve lots of manual work, which is not only time-consuming but also fault-prone. In this paper, we propose an automated framework for grouping user stories based on a family of machine learning techniques. Data preprocessing is first conducted to analyze user stories and obtain keywords in them. Features are then extracted, based on which user stories can be automatically clustered into different groups. We conduct a series of experiments to evaluate the framework, using five feature extraction algorithms and seven clustering techniques, on 13 datasets. Our experimental results show that TextCNN is the best feature extraction algorithm for user story grouping, while K-means and hierarchical clustering are superior to other clustering techniques. This study demonstrates the applicability and effectiveness of the proposed framework. It also provides some practical guidelines on further improving the efficacy of user story grouping, for example, via adjusting parameters for better feature extraction and clustering.
Toward Prompt-Enhanced Sentiment Analysis with Mutual Describable Information Between Aspects

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Exuberance by design? Hyping cryptocurrencies markets through token underpricing

ABSTRACTABSTRACTWe investigate potential factors influencing token after-market returns to explain the extreme levels of underpricing experienced in the cryptocurrencies market. This research analyses a sample of 300 tokens issued between September 2015 and May 2018, fully capturing the window of maximum ICO activity. The results confirm the presence of significant underpricing with an average of 180.66% and a median of 32.21%, which far exceed that of the IPO market. The cumulative after-market mean returns at three months, six months, nine months, and twelve months continue to be remarkably high and above 100%. However, the return distributions exhibit substantial negative median values, highlighting the poor post-ICO performance of most tokens. The research also confirms the IPO 'fads hypothesis' as a reasonable explanation of token underpricing, which attributes positive initial returns to investor overreactions. This hypothesis provides a rationale for ICO higher underpricing with respect to the IPO market as well. Finally, the quality of the management team positively affects token after-market returns while the project technical validity does not significantly impact on them. Blockchain entrepreneurs may use these results to design tokens and the offering campaigns with the aim of reducing negative swings in the after-market.KEYWORDS: CryptocurrenciesICOtoken underpricingirrational market movements Disclosure statementNo potential conflict of interest was reported by the author(s).