Reviewerly/exHarmony huggingface dataset:
An efficient computer vision-based approach for acute lymphoblastic leukemia prediction

Leukemia (blood cancer) diseases arise when the number of White blood cells (WBCs) is imbalanced in the human body. When the bone marrow produces many immature WBCs that kill healthy cells, acute lymphocytic leukemia (ALL) impacts people of all ages. Thus, timely predicting this disease can increase the chance of survival, and the patient can get his therapy early. Manual prediction is very expensive and time-consuming. Therefore, automated prediction techniques are essential. In this research, we propose an ensemble automated prediction approach that uses four machine learning algorithms K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes (NB). The C-NMC leukemia dataset is used from the Kaggle repository to predict leukemia. Dataset is divided into two classes cancer and healthy cells. We perform data preprocessing steps, such as the first images being cropped using minimum and maximum points. Feature extraction is performed to extract the feature using pre-trained Convolutional Neural Network-based Deep Neural Network (DNN) architectures (VGG19, ResNet50, or ResNet101). Data scaling is performed by using the MinMaxScaler normalization technique. Analysis of Variance (ANOVA), Recursive Feature Elimination (RFE), and Random Forest (RF) as feature Selection techniques. Classification machine learning algorithms and ensemble voting are applied to selected features. Results reveal that SVM with 90.0% accuracy outperforms compared to other algorithms.
Noisy quantum gyroscope

Gyroscope for rotation sensing plays a key role in inertial navigation systems. Developing more precise gyroscopes than the conventional ones bounded by the classical shot-noise limit by using quantum resources has attracted much attention. However, existing quantum gyroscope schemes suffer severe deterioration under the influence of decoherence, which is called the no-go theorem of noisy metrology. Here, by using two quantized optical fields as the quantum probe, we propose a quantum gyroscope scheme breaking through the constraint of the no-go theorem. Our exact analysis of the non-Markovian noise reveals that both the evolution time as a resource in enhancing the sensitivity and the achieved super-Heisenberg limit in the noiseless case are asymptotically recoverable when each optical field forms a bound state with its environment. The result provides a guideline for realizing high-precision rotation sensing in realistic noisy environments.
Effects of interlayer reflection and interpixel interaction in diffractive optical neural networks

Multilayer diffractive optical neural networks (DONNs) can perform machine learning (ML) tasks at the speed of light with low energy consumption. Decreasing the number of diffractive layers can reduce inevitable material and diffraction losses to improve system performance, and incorporating compact devices can reduce the system footprint. However, current analytical DONN models cannot accurately describe such physical systems. Here we show the ever-ignored effects of interlayer reflection and interpixel interaction on the deployment performance of DONNs through full-wave electromagnetic simulations and terahertz (THz) experiments. We demonstrate that the drop of handwritten digit classification accuracy due to reflection is negligible with conventional low-index THz polymer materials, while it can be substantial with high-index materials. We further show that one- and few-layer DONN systems can achieve high classification accuracy, but there is a trade-off between accuracy and model-system matching rate because of the fast-varying spatial distribution of optical responses in diffractive masks. Deep DONNs can break down such a trade-off because of reduced mask spatial complexity. Our results suggest that new accurate and trainable DONN models are needed to advance the development and deployment of compact DONN systems for sophisticated ML tasks.
High-flexibility and high-accuracy phase delay calibration method for MEMS-based fringe projection systems

Microelectromechanical system (MEMS) mirror based laser beam scanning (LBS) projectors for fringe projection profilometry (FPP) are becoming increasingly popular attributing to their small size and low cost. However, the initial phase of the scanning MEMS mirror employed in an LBS projector may vary over time, resulting in unstable and distorted fringe patterns. The distorted fringe patterns will largely decrease the accuracy of the three-dimensional (3D) topographic reconstruction. In this paper, an efficient phase delay calibration method based on a unique fringe projection sequence and a corresponding image processing algorithm is proposed. The proposed method can compensate the phase uncertainty and variation with no need to add any extra components. One LBS projector has been constructed using a uniaxial electrostatic MEMS mirror that has a mirror size of 2.5 mm × 2.5 mm and a scanning field of view of 60 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:msup> <mml:mi /> <mml:mo>∘<!-- ∘ --></mml:mo> </mml:msup> </mml:math> at its resonance of 1523 Hz. 3D reconstruction experiments are conducted to study how the 3D reconstruction results are affected by the phase delay. The standard deviation of a sphere reconstruction is improved from 2.05 mm to 0.20 mm after the positive phase delay deviation of 5 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi>μ<!-- μ --></mml:mi> </mml:mrow> </mml:math> s is compensated using this new calibration method.
Language Composition via Kind-Typed Symbol Tables.

The modularization of domain-specific modeling languages (DSMLs) fosters individual reuse of DSMLs in different contexts.Within this article, we discuss how it is possible to refer to model elements of other languages when composing different DSMLs.Related approaches usually rely on a DSML-agnostic language infrastructure that tests the compatibility of model elements via types encoded in Strings without any consistency checks.We propose the "strongly kind-typed" symbol table as an extension to the compiler approach to integrate the syntax of the languages using symbol tables that assign a symbol kind to each name definition.Our approach can be integrated into language workbenches that provide a symbol table infrastructure as part of a DSML implementation.The kind-typed symbol tables are integrated into the language workbench MontiCore.Strongly kind-typed symbol tables utilize the type system of the language workbench's host language to ensure type consistency between the language-specific symbol table infrastructures during DSML composition, which ultimately supports DSML engineering in the large.
Discovering Objects that Can Move

This paper studies the problem of object discovery - separating objects from the background without manual labels. Existing approaches utilize appearance cues, such as color, texture, and location, to group pixels into object-like regions. However, by relying on appearance alone, these methods fail to separate objects from the background in cluttered scenes. This is a fundamental limitation since the definition of an object is inherently ambiguous and context-dependent. To resolve this ambiguity, we choose to focus on dynamic objects - entities that can move independently in the world. We then scale the recent auto-encoder based frameworks for unsuper-vised object discovery from toy synthetic images to complex real-world scenes. To this end, we simplify their architecture, and augment the resulting model with a weak learning signal from general motion segmentation algorithms. Our experiments demonstrate that, despite only capturing a small subset of the objects that move, this signal is enough to generalize to segment both moving and static instances of dynamic objects. We show that our model scales to a newly collected, photo- realistic synthetic dataset with street driving scenarios. Additionally, we leverage ground truth segmentation and flow annotations in this dataset for thorough ablation and evaluation. Finally, our experiments on the real-world KITTI benchmark demonstrate that the proposed approach outperforms both heuristic- and learning-based methods by capitalizing on motion cues.
Semi-Supervised Learning of Semantic Correspondence with Pseudo-Labels

Establishing dense correspondences across semantically similar images remains a challenging task due to the significant intra-class variations and background clutters. Traditionally, a supervised learning was used for training the models, which required tremendous manually-labeled data, while some methods suggested a self-supervised or weakly-supervised learning to mitigate the reliance on the labeled data, but with limited performance. In this paper, we present a simple, but effective solution for semantic correspondence that learns the networks in a semi-supervised manner by supplementing few ground-truth correspondences via utilization of a large amount of confident correspondences as pseudo-labels, called SemiMatch. Specifically, our framework generates the pseudo-labels using the model's prediction itself between source and weakly-augmented target, and uses pseudo-labels to learn the model again between source and strongly-augmented target, which improves the robustness of the model. We also present a novel confidence measure for pseudo-labels and data augmentation tailored for semantic correspondence. In experiments, SemiMatch achieves state-of-the-art performance on various benchmarks.
A Catalog of Design Patterns for Compositional Language Engineering.

When composing a domain-specific language from several language components, it is also necessary to compose analysis and synthesis techniques, which are individually defined on these components in an efficient, ideally black-box form.An effective way of allowing such compositions is to use specific design patterns, which are partly reflected in the tooling code, partly reflected in the language, but also partly reflected in the language workbench (one meta-level higher), and the generated/synthesized product code (one meta-level downward).Based on the experiences gained in compositional language development using the language workbench MontiCore, we in detail discuss several of those design patterns, namely the Mill, the RealThis object composition, the Template/Hook, and the TOP-Generator Patterns, and the hidden complexity of an extended visitor infrastructure coping with the above patterns.The patterns are recorded and described in a reusable way, as usual, allowing readers to participate from the gained insights and possible solutions.
Accelerating Noisy VQE Optimization with Gaussian Processes

Hybrid variational quantum algorithms, which combine a classical optimizer with evaluations on a quantum chip, are the most promising candidates to show quantum advantage on current noisy, intermediate-scale quantum (NISQ) devices. The classical optimizer is required to perform well in the presence of noise in the objective function evaluations, or else it becomes the weakest link in the algorithm. We introduce the use of Gaussian Processes (GP) as surrogate models to reduce the impact of noise and to provide high quality seeds to escape local minima, whether real or noise-induced. We build this as a framework on top of local optimizations, for which we choose Implicit Filtering (ImFil) in this study. ImFil is a state-of-the-art, gradient-free method, which in comparative studies has been shown to outperform on noisy VQE problems. The result is a new method: "GP+ImFil". We show that when noise is present, the GP+ImFil approach finds results closer to the true global minimum in fewer evaluations than standalone ImFil, and that it works particularly well for larger dimensional problems. Using GP to seed local searches in a multi-modal landscape shows mixed results: although it is capable of improving on ImFil standalone, it does not do so consistently and would only be preferred over other, more exhaustive, multistart methods if resources are constrained.
Efficient Half-Period Phase Histogram Equalization for General Phase-Shifting Algorithms With Phase Shift Errors

Phase-shifting algorithms are widely used for optical metrology that extract the phase from several fringe patterns with phase shifts. However, accurate phase shifts are always the crucial premise to guarantee the accuracy of phase extraction, and phase shift errors are usually the major error source. This article proposes an efficient half-period phase histogram equalization (HPHE) method to handle the phase shift errors in phase-shifting algorithms, which does not estimate any parameters and only require processing one half-period phase. Through analysis, we know that the period of the phase errors is half that of the wrapped phase, and the period of its histogram is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\pi $ </tex-math></inline-formula> in phase domain. Therefore, we can obtain the half-period phase ranging from 0 to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\pi $ </tex-math></inline-formula> by computing the remainder of the original phase. Then we directly apply histogram equalization on the distorted half-period phase to estimate the corrected half-period phase, then we can obtain the corrected whole-period phase. Some simulations and experiments have been conducted for three-, four-, and five-step phase-shifting algorithms, and their results indicate that the HPHE method can effectively reduce the phase errors and performs much better than the whole-period phase histogram equalization (WPHE) method.
Trust in the Context of Blockchain Applications

Measuring trust in different Blockchain application contexts is challenging. There is a gap between building an accountable system with Blockchain and measuring the actual trust enhancement. This paper reviews trust factors in different Blockchain application contexts. We propose a trust assessment model for accountable trust factor collection while accounting for both quantitative and qualitative aspects of trust assessment. Our goal is to help users determine the trustworthiness of an entity by analyzing trust factors collected from intermediaries during business transactions. We use security and incentive assumptions to enhance the reliability of trust assessment data on Blockchain.
Explainability and Graph Learning From Social Interactions

Social learning algorithms provide models for the formation of opinions over social networks resulting from local reasoning and peer-to-peer exchanges. Interactions occur over an underlying graph topology, which describes the flow of information among the agents. To account for drifting conditions in the environment, this work adopts an adaptive social learning strategy, which is able to track variations in the underlying signal statistics. Among other results, we propose a technique that addresses questions of explainability and interpretability of the results when the graph is hidden. Given observations of the evolution of the beliefs over time, we aim to infer the underlying graph topology, discover pairwise influences between the agents, and identify significant trajectories in the network. The proposed framework is online in nature and can adapt dynamically to changes in the graph topology or the true hypothesis.
Legal Accountability as Software Quality: A U.S. Data Processing Perspective

Software and hardware innovation has led to new consumer products and services with significant benefits to consumers and society. These advances, however, can come with great cost to society when they fail to comply with government laws and regulations. While compliance failures do result from technical missteps in design, there is also a wide gap between the technical expertise and culture of legal analysts and software engineers, as well as competing priorities between legal requirements and business objectives. In this perspective paper, we propose changing legal compliance from a corporate oversight activity to a principal design activity, wherein lawyers and software engineers employ enhanced methods and tools tailored to bridge the cultural and knowledge gap and assess legal and business trade-offs. To that end, we describe a new software quality, called Legal Accountability, which can be evaluated alongside other qualities, such as usability, modifiability, performance and testing. Legal Accountability has five properties that lawyers and designers must attend to, including legal traceability, completeness, validity, auditability and continuity. We illustrate the quality with examples from the U.S. data processing perspective, and prior work in requirements engineering, before concluding with future and ongoing research challenges.
Adaptive QIM With Minimum Embedding Cost for Robust Video Steganography on Social Networks

Sharing videos on social networks has become more and more popular, which provides a new scenario for covert communication. Video sharing-based hidden communication can conceal the contact relation between the sender and receiver and achieve the one-to-many delivery of secret messages. However, most video steganographic methods are unable to complete reliable hidden communication on social networks because of lossy video recompression. In this paper, we propose adaptive QIM (Quantization Index Modulation) with minimum embedding cost, which decreases the quantization distortion as much as possible to improve the security performance against steganalysis under the same level of robustness. Furthermore, based on the proposed quantization modulation scheme, we implement two robust steganographic methods in the DWT-SVD domain and the DTCWT-SVD domain. Experimental results show that the overall performance of our proposed modulation scheme outperforms QIM and adaptive QIM. Compared with existing robust video data hiding, two proposed steganographic methods demonstrate superior robustness and security on local lossy channels and social networks.
Gestalt Driven Augmented Collimator Widget for Precise 5 DOF Dental Drill Tool Positioning in 3D Space

Drill tool positioning in dental implantology is a challenging task requiring 5DOF precision as the rotation around the tool axis is not influential. This work improves the quasi-static visual elements of the state-of-the-art with a novel Augmented Collimation Widget (ACW), an interactive tool of position and angle error visualization based on the gestalt reification, the human ability to group geometric elements. The user can seek in a quick, pre-attentive way the collimation of five (three positional and two rotational) error component widgets (ECWs), taking advantage of three key aspects: component separation and reification, error visual amplification, and dynamic hiding of the collimated components. We compared the ACW with the golden standard in a within-subjects (N=30) user test using 32 implant targets, measuring the time, error, and usability. ACW performed significantly better in positional (+19%) and angular (+47%) precision accuracy and with less mental demand (-6%) and frustration (-13%), but with an expected increase in task time (+59%) and physical demand (+64%). The interview indicated the ACW as the main preference and aesthetically more pleasant than GSW, candidating it as the new golden standard for implantology, but also for other applications where 5DOF positioning is key.
Done is better than perfect: Iterative Adaptation via Multi-grained Requirement Relaxation

In the studies of self-adaptive systems (SAS), requirement relaxation is a widely discussed approach for managing the system’s requirements when dealing with the runtime environment changes (e.g., ignoring low-priority requirements to guarantee high-priority requirements). Guaranteeable requirement analysis (GRA) is recently proposed to determine the relaxation by checking the feasibility of all requirement combinations, enabling the SAS to realize the relaxation autonomously. However, a critical problem of GRA is the trade-off between analysis/relaxation precision and computation time at different granularity levels of requirements. Specifically, the analysis may not be precise enough if the requirements are coarse-grained (i.e., high granularity level), while the analysis may take a too long time if the requirements are fine-grained (i.e., low granularity level). This paper proposed a method, namely iterative adaptation via multi-grained requirement relaxation, to achieve the advantages of high precision and short computation time. Specifically, the SAS first deploys a rapid (but imprecise) relaxation using high granularity-level requirements. It then repeatedly iterates to a preciser (but slower) relaxation with a progressive decrease in the granularity level. An experiment based on the warehouse robot system demonstrates the validity of our proposal.
Characterization of State-Preparation Uncertainty in Quantum Key Distribution

To achieve secure quantum key distribution, all imperfections in the source unit must be incorporated in a security proof and measured in the lab. Here we perform a proof-of-principle demonstration of the experimental techniques for characterising the source phase and intensity fluctuation in commercial quantum key distribution systems. When we apply the measured phase fluctuation intervals to the security proof that takes into account fluctuations in the state preparation, it predicts a key distribution distance of over 100 km of fiber. The measured intensity fluctuation intervals are however so large that the proof predicts zero key, indicating a source improvement may be needed. Our characterisation methods pave the way for a future certification standard.
A Nearly Tight Proof of Duc et al.’s Conjectured Security Bound for Masked Implementations

We prove a bound that approaches Duc et al.’s conjecture from Eurocrypt 2015 for the side-channel security of masked implementations. Let $$\textsc {Y}$$ be a sensitive intermediate variable of a cryptographic primitive taking its values in a set $$\mathcal {Y}$$ . If $$\textsc {Y}$$ is protected by masking (a.k.a. secret sharing) at order $$d$$ (i.e., with $$d+1$$ shares), then the complexity of any non-adaptive side-channel analysis—measured by the number of queries to the target implementation required to guess the secret key with sufficient confidence—is lower bounded by a quantity inversely proportional to the product of mutual informations between each share of $$\textsc {Y}$$ and their respective leakage. Our new bound is nearly tight in the sense that each factor in the product has an exponent of $$-1$$ as conjectured, and its multiplicative constant is $$\mathcal {O}\left( \log \left| \mathcal {Y}\right| \cdot \left| \mathcal {Y}\right| ^{-1} \cdot C^{-d}\right) $$ , where $$C \le 2 \log (2) \approx 1.38$$ . It drastically improves upon previous proven bounds, where the exponent was $$-1/2$$ , and the multiplicative constant was $$\mathcal {O}\left( \left| \mathcal {Y}\right| ^{-d}\right) $$ . As a consequence for side-channel security evaluators, it is possible to provably and efficiently infer the security level of a masked implementation by simply analyzing each individual share, under the necessary condition that the leakage of these shares are independent.