Reviewerly/exHarmony huggingface dataset:
Hyperparameter importance and optimization of quantum neural networks across small datasets

Abstract As restricted quantum computers become available, research focuses on finding meaningful applications. For example, in quantum machine learning, a special type of quantum circuit called a quantum neural network is one of the most investigated approaches. However, we know little about suitable circuit architectures or important model hyperparameters for a given task. In this work, we apply the functional ANOVA framework to the quantum neural network architectures to analyze which of the quantum machine learning hyperparameters are most influential for their predictive performance. We restrict our study to 7 open-source datasets from the OpenML-CC18 classification benchmark, which are small enough for simulations on quantum hardware with fewer than 20 qubits. Using this framework, three main levels of importance were identified, confirming expected patterns and revealing new insights. For instance, the learning rate is identified as the most important hyperparameter on all datasets, whereas the particular choice of entangling gates used is found to be the least important on all except for one dataset. In addition to identifying the relevant hyperparameters, for each of them, we also learned data-driven priors based on values that perform well on previously seen datasets, which can then be used to steer hyperparameter optimization processes. We utilize these priors in the hyperparameter optimization method hyperband and show that these improve performance against uniform sampling across all datasets by, on average, $$0.53 \%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>0.53</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> , up to $$6.11 \%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>6.11</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> , in cross-validation accuracy. We also demonstrate that such improvements hold on average regardless of the configuration hyperband is run with. Our work introduces new methodologies for studying quantum machine learning models toward quantum model selection in practice. All research code is made publicly available.
How to Evaluate Blame for Gradual Types, Part 2

Equipping an existing programming language with a gradual type system requires two major steps. The first and most visible one in academia is to add a notation for types and a type checking apparatus. The second, highly practical one is to provide a type veneer for the large number of existing untyped libraries; doing so enables typed components to import pieces of functionality and get their uses type-checked, without any changes to the libraries. When programmers create such typed veneers for libraries, they make mistakes that persist and cause trouble. The question is whether the academically investigated run-time checks for gradual type systems assist programmers with debugging such mistakes. This paper provides a first, surprising answer to this question via a rational-programmer investigation: run-time checks alone are typically less helpful than the safety checks of the underlying language. Combining Natural run-time checks with blame, however, provides significantly superior debugging hints.
Modularity, Code Specialization, and Zero-Cost Abstractions for Program Verification

For all the successes in verifying low-level, efficient, security-critical code, little has been said or studied about the structure, architecture and engineering of such large-scale proof developments. We present the design, implementation and evaluation of a set of language-based techniques that allow the programmer to modularly write and verify code at a high level of abstraction, while retaining control over the compilation process and producing high-quality, zero-overhead, low-level code suitable for integration into mainstream software. We implement our techniques within the F proof assistant, and specifically its shallowly-embedded Low toolchain that compiles to C. Through our evaluation, we establish that our techniques were critical in scaling the popular HACL library past 100,000 lines of verified source code, and brought about significant gains in proof engineer productivity. The exposition of our methodology converges on one final, novel case study: the streaming API, a finicky API that has historically caused many bugs in high-profile software. Using our approach, we manage to capture the streaming semantics in a generic way, and apply it “for free” to over a dozen use-cases. Six of those have made it into the reference implementation of the Python programming language, replacing the previous CVE-ridden code.
Haskell Library for Safer Virtual Machine Introspection (Experience Report)

Virtual machine introspection (VMI) is a technique for inspecting a virtual machine from the outside, typically to analyze the operating system (guest OS) running on it. LibVMI is a C library for VMI and provides APIs for accessing guest OS's memory. However, in using LibVMI APIs directly in C, the programmer must compute target addresses in the kernel memory and then access them with their exact bit widths and types. This is an enormous burden for the programmer and is prone to introducing statically undetected but fatal errors. We create HaVMI, a Haskell library that facilitates VMI programming. HaVMI provides meta-functions for compile-time code generation by Template Haskell. These meta-functions make it easy to write safer VMI programs. HaVMI uses Haskell language features to detect the programmer's errors statically.
With or Without You: Programming with Effect Exclusion

Type and effect systems have been successfully used to statically reason about effects in many different domains, including region-based memory management, exceptions, and algebraic effects and handlers. Such systems’ soundness is often stated in terms of the absence of effects. Yet, existing systems only admit indirect reasoning about the absence of effects. This is further complicated by effect polymorphism which allows function signatures to abstract over arbitrary, unknown sets of effects. We present a new type and effect system with effect polymorphism as well as union, intersection, and complement effects. The effect system allows us to express effect exclusion as a new class of effect polymorphic functions: those that permit any effects except those in a specific set. This way, we equip programmers with the means to directly reason about the absence of effects. Our type and effect system builds on the Hindley-Milner type system, supports effect polymorphism, and preserves principal types modulo Boolean equivalence. In addition, a suitable extension of Algorithm W with Boolean unification on the algebra of sets enables complete type and effect inference. We formalize these notions in the λ ∁ calculus. We prove the standard progress and preservation theorems as well as a non-standard effect safety theorem: no excluded effect is ever performed. We implement the type and effect system as an extension of the Flix programming language. We conduct a case study of open source projects identifying 59 program fragments that require effect exclusion for correctness. To demonstrate the usefulness of the proposed type and effect system, we recast these program fragments into our extension of Flix.
Analysis of Non-Experts' Security- and Privacy-Related Questions on a Q&amp;A Site

Although security and privacy technologies are incorporated into every device and service, the complexity of these concepts confuses non-expert users. Prior research has shown that non-expert users ask strangers for advice about digital media use online. In this study, to clarify the security and privacy concerns of non-expert users in their daily lives, we investigated security- and privacy-related question posts on a Question-and-Answer (Q&A) site for non-expert users. We conducted a thematic analysis of 445 question posts. We identified seven themes among the questions and found that users asked about cyberattacks the most, followed by authentication and security software. We also found that there was a strong demand for answers, especially for questions related to privacy abuse and account/device management. Our findings provide key insights into what non-experts are struggling with when it comes to privacy and security and will help service providers and researchers make improvements to address these concerns.
深層学習を用いた午睡中の乳幼児の頭部検出と頭の向き推定

In this paper, we describe head detection and pose estimation of infants sleeping in a nursery school. Every year, there are many cases of infants dying from put in a prone sleeping position during nap time. Because a nursery teacher cares for several infants, and even if they pay attention, they cannot check on the condition of all the infants. We have been working on the development of a system that attaches a camera to the ceiling to take video of infants, detects infants of face down position, and sends alerts to the smartphone of a nursery teacher. Pose estimation focusing only on the infant's head cannot use existing methods for estimating face orientation because the infant's face may not be in the video. Therefore, we propose a two-step method for head detection and head orientation estimation. For head detection and head orientation estimation, YOLOv5 and VGG-16 with deep learning are employed to create a head detector and a head orientation estimator. Based on the experimental results, we will compare the accuracy of head detection and head orientation estimation with conventional methods, and verify their usefulness in the system.
On the Weakness of Non-Dual Ring-LWE Mod Prime Ideal q by Trace Map

The recent decision by the National Institute of Standards and Technology (NIST) to standardize lattice-based cryptography has further increased the demand for security analysis. The Ring-Learning with Error (Ring-LWE) problem is a mathematical problem that constitutes such lattice cryptosystems. It has many algebraic properties because it is considered in the ring of integers, R, of a number field, K. These algebraic properties make the Ring-LWE based schemes efficient, although some of them are also used for attacks. When the modulus, q, is unramified in K, it is known that the Ring-LWE problem, to determine the secret information s ∈ R/qR, can be solved by determining s (mod q) ∈ 𝔽qf for all prime ideals q lying over q. The χ2-attack determines s (mod q) ∈𝔽qf using chi-square tests over R/q ≅ 𝔽qf. The χ2-attack is improved in the special case where the residue degree f is two, which is called the two-residue-degree χ2-attack. In this paper, we extend the two-residue-degree χ2-attack to the attack that works efficiently for any residue degree. As a result, the attack time against a vulnerable field using our proposed attack with parameter (q,f)=(67, 3) was 129 seconds on a standard PC. We also evaluate the vulnerability of the two-power cyclotomic fields.
Preventing SNS Impersonation: A Blockchain-Based Approach

With the rise of social network service (SNS) in recent years, the security of SNS users' private information has been a concern for the public. However, due to the anonymity of SNS, identity impersonation is hard to be detected and prevented since users are free to create an account with any username they want. This could lead to cybercrimes like fraud because impersonation allows malicious users to steal private information. Until now, there are few studies about this problem, and none of them can perfectly handle this problem. In this paper, based on an idea from previous work, we combine blockchain technology and security protocol to prevent impersonation in SNS. In our scheme, the defects of complex and duplicated operations in the previous work are improved. And the authentication work of SNS server is also adjusted to resist single-point, attacks. Moreover, the smart contract is introduced to help the whole system runs automatically. Afterward, our proposed scheme is implemented and tested on an Ethereum test network and the result suggests that it is acceptable and suitable for nowadays SNS network.
A Lightweight and Efficient Infrared Pedestrian Semantic Segmentation Method

The thermal imaging pedestrian segmentation system has excellent performance in different illumination conditions, but it has some drawbacks(e.g., weak pedestrian texture information, blurred object boundaries). Meanwhile, high-performance large models have higher latency on edge devices with limited computing performance. To solve the above problems, in this paper, we propose a real-time thermal infrared pedestrian segmentation method. The feature extraction layers of our method consist of two paths. Firstly, we utilize the lossless spatial downsampling to obtain boundary texture details on the spatial path. On the context path, we use atrous convolutions to improve the receptive field and obtain more contextual semantic information. Then, the parameter-free attention mechanism is introduced at the end of the two paths for effective feature selection, respectively. The Feature Fusion Module (FFM) is added to fuse the semantic information of the two paths after selection. Finally, we accelerate method inference through multi-threading techniques on the edge computing device. Besides, we create a high-quality infrared pedestrian segmentation dataset to facilitate research. The comparative experiments on the self-built dataset and two public datasets with other methods show that our method also has certain effectiveness. Our code is available at https://github.com/mcjcs001/LEIPNet.
Physical modeling and sound synthesis of the hi-hat

In this paper, we propose a physical model of the hi-hat. Two cymbals are modeled with thin spherical shells, into which a point mass resembling a stick collides. Two models are proposed for the collision of two cymbals: a spring model and an algorithmically mimicked inelastic collision model. The governing equations for the two proposed models are derived, discretized using the finite difference method, and analyzed numerically. Several numerical experiments have revealed the validity of the two proposed models and the characteristics of the hi-hat.
Compact and Efficient Constant-Time GCD and Modular Inversion with Short-Iteration

Theoretically secure cryptosystems, digital signatures may not be secure after being implemented on Internet of Things (IoT) devices and PCs because of side-channel attacks (SCA). Because RSA key generation and ECDSA require GCD computations or modular inversions, which are often computed using the binary Euclidean algorithm (BEA) or binary extended Euclidean algorithm (BEEA), the SCA weaknesses of BEA and BEEA become a serious concern. Constant-time GCD (CT-GCD) and constant-time modular inversion (CTMI) algorithms are effective countermeasures in such situations. Modular inversion based on Fermat's little theorem (FLT) can work in constant time, but it is not efficient for general inputs. Two CTMI algorithms, named BOS and BY in this paper, were proposed by Bos, Bernstein and Yang, respectively. Their algorithms are all based on the concept of BEA. However, one iteration of BOS has complicated computations, and BY requires more iterations. A small number of iterations and simple computations during one iteration are good characteristics of a constant-time algorithm. Based on this view, this study proposes new short-iteration CT-GCD and CTMI algorithms over 𝔽p borrowing a simple concept from BEA. Our algorithms are evaluated from a theoretical perspective. Compared with BOS, BY, and the improved version of BY, our short-iteration algorithms are experimentally demonstrated to be faster.
Malicious Domain Detection Based on Decision Tree

Different types of malicious attacks have been increasing simultaneously and have become a serious issue for cybersecurity. Most attacks leverage domain URLs as an attack communications medium and compromise users into a victim of phishing or spam. We take advantage of machine learning methods to detect the maliciousness of a domain automatically using three features: DNS-based, lexical, and semantic features. The proposed approach exhibits high performance even with a small training dataset. The experimental results demonstrate that the proposed scheme achieves an approximate accuracy of 0.927 when using a random forest classifier.
セマンティックセグメンテーションによるパネル画像からの積雪率推定手法の検討

In previous research, we estimated snow cover rate on the panel from panel image for the purpose of improving the prediction accuracy of the PV power considering the effect of the snow accumulation on the panel in the snowfall area. A method to predict the quantity was proposed. However, in previous research, the snow cover rate was estimated by dividing it into 11 categories from 0% to 100% in increments of 10%. There is a question as to whether or not it is possible, and we think that estimating with even finer granularity will lead to further improvement in the prediction accuracy of PV power. We will compare and evaluate the prediction of PV power generation using the snow cover rate predicted from panel image with related research and confirm the effectiveness of the proposed method.
Thoreau’s philosophy of work in frost’s “mowing”

Click to increase image sizeClick to decrease image sizeKeywords: Frostlife without principlemowingThoreautranscendentalismwork
Communication-efficient federated learning with stagewise training strategy

The efficiency of communication across workers is a significant factor that affects the performance of federated learning. Though periodic communication strategy is applied to reduce communication rounds in training, the communication cost is still high when the training data distributions are not independently and identically distributed (non-IID) which is common in federated learning. Recently, some works introduce variance reduction to eliminate the effect caused by non-IID data among workers. Nevertheless the provable optimal communication complexity O(log(ST)) and convergence rate O(1/(ST)) cannot be achieved simultaneously, where S denotes the number of sampled workers in each round and T is the number of iterations. To deal with this dilemma, we propose an optimization algorithm SQUARFA that adopts stagewise training framework coupling with variance reduction and uses a quick-start phase in each loop. Theoretical results show that SQUARFA achieves both optimal convergence rate and communication complexity for both strongly convex objectives and non-convex objectives under PL condition, thus fills the gap mentioned above. Then, a variant of SQUARFA yields the optimal theoretical results for general non-convex objectives. We further extend the technique in SQUARFA to the large batch setting and achieve optimal communication complexity. Experimental results demonstrate the superiority of the proposed algorithms.
Significant improvement of fidelity for encoded quantum bell pairs at long and short-distance communication along with generalized circuit

Quantum entanglement is a unique criterion of the quantum realm and an essential tool to secure quantum communication. Ensuring high-fidelity entanglement has always been a challenging task owing to interaction with the hostile channel environment created due to quantum noise and decoherence. Though several methods have been proposed, correcting almost all arbitrary errors is still a gigantic task. As one of the main contributions of this work, a new model for 'large distance communication' has been proposed, which may correct all bit flip errors or other errors quite extensively if proper encoding and subspace measurements are used. To achieve this purpose, at the very first step, the idea of differentiating the 'long-distance communication' and 'short-distance applications' has been introduced. Short-distance is determined by the maximum range of applying unitary control gates by the qubit technology. How the error correcting ability of Quantum codes change for short and long-distance application is investigated in this work, which was not explored in previous literatures as far as we know. At the beginning, we have applied stabilizer formalism and Repetition Code for decoding to distinguish the error correcting ability in long and short distance communication. Particularly for short-distance communication, it has been demonstrated that a 'properly encoded' bell state can identify all the bit flip, or phase flip errors with 100% accuracy theoretically. In contrast, if the bell states are used in long-distance communication, the error-detecting and correcting ability reduces at huge amounts. To increase the fidelity significantly and correct the errors quite extensively for long-distance communication, a new model based on classical communication protocol has been suggested. All the required circuits in these processes have been generalized for arbitrary (even) numbers of ancilla qubits during encoding. Proposed analytical results have also been verified with the Simulation results of IBM QISKIT QASM.
A novel graph-based feature interaction model for click-through rate prediction

Click-through rate (CTR) prediction is a crucial issue in recommender systems. In addition, data sparsity is a notable challenge for recommender systems compared to other applications. To overcome it, many learning-based models are studied to model feature interactions and improve CTR prediction. However, current inflexible and non-explicit feature combination methods have limitations that hinder accurate prediction. To address this issue, we propose a sophisticated feature interaction model based on a graph and factorization machine (FM). In this model, each node in the graph corresponds to a raw feature, the edge and its weight between two nodes are determined by the learnable latent vectors in the FM. This interaction method integrates the flexible and explicit representative ability of the graph with the learnability of the FM. Furthermore, it can be combined with most learning-based CTR prediction models to improve their performance. To verify this viewpoint, we apply it to improve three prominent models, including one deep-forest-based model and two deep-learning-based models, and compare them with the state-of-the-art techniques. Experimental results show that they significantly outperform to the original ones, and are competitive with the comparison models.