Reviewerly/exHarmony huggingface dataset:
Stability and Replicability in Learning

Replicability is essential in science as it allows us to validate and verify research findings. Impagliazzo, Lei, Pitassi and Sorrell ('22) recently initiated the study of replicability in machine learning. A learning algorithm is replicable if it typically produces the same output when applied on two i.i.d. inputs using the same internal randomness. We study a variant of replicability that does not involve fixing the randomness. An algorithm satisfies this form of replicability if it typically produces the same output when applied on two i.i.d. inputs (without fixing the internal randomness). This variant is called global stability and was introduced by Bun, Livni and Moran ('20) in the context of differential privacy. Impagliazzo et al. showed how to boost any replicable algorithm so that it produces the same output with probability arbitrarily close to 1. In contrast, we demonstrate that for numerous learning tasks, global stability can only be accomplished weakly, where the same output is produced only with probability bounded away from 1. To overcome this limitation, we introduce the concept of list replicability, which is equivalent to global stability. Moreover, we prove that list replicability can be boosted so that it is achieved with probability arbitrarily close to 1. We also describe basic relations between standard learningtheoretic complexity measures and list replicable numbers. Our results, in addition, imply that besides trivial cases, replicable algorithms (in the sense of Impagliazzo et al.) must be randomized. The proof of the impossibility result is based on a topological fixed-point theorem. For every algorithm, we are able to locate a "hard input distribution by applying the Poincaré-Miranda theorem in a related topological setting. The equivalence between global stability and list replicability is algorithmic.
UML Miner: A Tool for Mining UML Diagrams

Modeling is a key activity in conceptual design and system design, learning and understanding modeling languages such as the Unified Modeling Language (UML) is important. Current UML learning applications and gamification-based alternatives lack guidance for novice modelers regarding possible modeling activities. To overcome this problem, in this demo paper, we present a tool called UML Miner, implemented as a plug-in, that evaluates all UML diagrams realized in the Visual Paradigm environment. UML Miner records all modeling events used in a modeling software project as event logs. By analyzing event logs generated from the use of Visual Paradigm, UML Miner can perform conformance checking to provide useful tips to modelers. These tips are based on the UML model created by highly skilled modelers. In particular, UML Miner can compare different modeling process executions through conformance checking and identify behavioral similarities and differences. The goal of this approach is to improve learner motivation and increase learning outcomes.
The Full Landscape of Robust Mean Testing: Sharp Separations between Oblivious and Adaptive Contamination

We consider the question of Gaussian mean testing, a fundamental task in high-dimensional distribution testing and signal processing, subject to adversarial corruptions of the samples. We focus on the relative power of different adversaries, and show that, in contrast to the common wisdom in robust statistics, there exists a strict separation between adaptive adversaries (strong contamination) and oblivious ones (weak contamination) for this task. Specifically, we resolve both the information-theoretic and computational landscapes for robust mean testing. In the exponential-time setting, we establish the tight sample complexity of testing $\mathcal{N}(0, I)$ against $\mathcal{N}(\alpha v, I)$, where $\|v\|_{2}=1$, with an $\varepsilon$-fraction of adversarial corruptions, to be $\tilde{\Theta}\left(\max \left(\frac{\sqrt{d}}{\alpha^{2}}, \frac{d \varepsilon^{3}}{\alpha^{4}}, \min \left(\frac{d^{2 / 3} \varepsilon^{2 / 3}}{\alpha^{8 / 3}}, \frac{d \varepsilon}{\alpha^{2}}\right)\right)\right)$ while the complexity against adaptive adversaries is $\tilde{\Theta}\left(\max \left(\frac{\sqrt{d}}{\alpha^{2}}, \frac{d \varepsilon^{2}}{\alpha^{4}}\right)\right)$ which is strictly worse for a large range of vanishing $\varepsilon, \alpha$. To the best of our knowledge, ours is the first separation in sample complexity between the strong and weak contamination models. In the polynomial-time setting, we close a gap in the literature by providing a polynomial-time algorithm against adaptive adversaries achieving the above sample complexity $\tilde{\Theta}\left(\max \left(\sqrt{d} / \alpha^{2}, d \varepsilon^{2} / \alpha^{4}\right)\right)$, and a low-degree lower bound (which complements an existing reduction from planted clique) suggesting that all efficient algorithms require this many samples, even in the oblivious-adversary setting.
Attribute-Based Encryption for Circuits of Unbounded Depth from Lattices

Although we have known about fully homomorphic encryption (FHE) from circular security assumptions for over a decade [Gentry, FOCS ’10; Brakerski-Vaikuntanathan, STOC ’11], there is still a significant gap in understanding related homomorphic primitives supporting all unrestricted polynomial-size computations. One prominent example is attribute-based encryption (ABE). The state-of-the-art constructions, relying on the hardness of learning with errors (LWE) [Gorbunov-Vaikuntanathan-Wee, STOC ’13; Boneh et al., Eurocrypt ’14], only accommodate circuits up to all predetermined depth, akin to leveled homomorphic encryption. In addition, their components (master public key, secret keys, and ciphertexts) have sizes polynomial in the maximum circuit depth. Even in the simpler setting where a single key is published (or a single circuit is involved), the depth dependency persists, showing up in constructions of 1-key ABE and related primitives, including laconic function evaluation (LFE), 1-key functional encryption (FE), and reusable garbling schemes. So far, the only approach of eliminating depth dependency relies on indistinguishability obfuscation. Intriguingly, for over a decade, it has remained unclear whether the circular security assumptions empowering FHE can similarly benefit ABE. In this work, we introduce new lattice-based techniques to overcome the depth-dependency limitations: •Relying on a circular security assumption, we construct LFE, 1-key FE, 1-key ABE, and reusable garbling schemes capable of evaluating circuits of unbounded depth and size.•Based on the evasive circular LWE assumption, a stronger variant of the recently proposed evasive LWE assumption [Wee, Eurocrypt ’22; Tsabary, Crypto ’22], we construct a full-fledged ABE scheme for circuits of unbounded depth and size. Our constructions eliminate the multiplicative overheads polynomial in depth from previous constructions. Our LFE, 1key FE, and reusable garbling schemes achieve almost optimal succinctness. Their ciphertexts and input encodings are proportional in length to the input, while function digest, secret keys, and garbled circuits maintain a constant size independent of circuit parameters. Our ABE schemes offer short components, with master public key and ciphertext sizes linear in the attribute length and secret key being constant-size.
A New Approach to Post-Quantum Non-Malleability

We provide the first constant-round construction of post-quantum non-malleable commitments under the minimal assumption that post-quantum one-way functions exist. We achieve the standard notion of non-malleability with respect to commitments. Prior constructions required $\Omega\left(\log ^{*} \lambda\right)$ rounds under the same assumption. We achieve our results through a new technique for constant-round non-malleable commitments which is easier to use in the post-quantum setting. The technique also yields an almost elementary proof of security for constant-round non-malleable commitments in the classical setting, which may be of independent interest. When combined with existing work, our results yield the first constant-round quantum-secure multiparty computation for both classical and quantum functionalities in the plain model, under the polynomial hardness of quantum fully-homomorphic encryption and quantum learning with errors.
Sensitivity and Dynamic Distance Oracles via Generic Matrices and Frobenius Form

Algebraic techniques have had an important impact on graph algorithms so far. Porting them, e.g., the matrix inverse, into the dynamic regime improved best-known bounds for various dynamic graph problems. In this paper, we develop new algorithms for another cornerstone algebraic primitive, the Frobenius normal form (FNF). We apply our developments to dynamic and fault-tolerant exact distance oracle problems on directed graphs.For generic matrices A over a finite field accompanied by an FNF, we show (1) an efficient data structure for querying submatrices of the first $k \geq 1$ powers of A, and (2) a near-optimal algorithm updating the FNF explicitly under rank-1 updates.By representing an unweighted digraph using a generic matrix over a sufficiently large field (obtained by random sampling) and leveraging the developed FNF toolbox, we obtain:•a conditionally optimal distance sensitivity oracle (DSO) in the case of single-edge or single-vertex failures, providing a partial answer to the open question of Gu and Ren [ICALP 2021],•a multiple-failures DSO improving upon the state of the art (vd. Brand and Saranurak [FOCS 2019]) wrt. both preprocessing and query time,•improved dynamic distance oracles in the case of single-edge updates,•a dynamic distance oracle supporting vertex updates, i.e., changing all edges incident to a single vertex, in $\widetilde{O}\left(n^{2}\right)$ worst-case time and distance queries in $\widetilde{O}(n)$ time.
Locally Uniform Hashing

Hashing is a common technique used in data processing, with a strong impact on the time and resources spent on computation. Hashing also affects the applicability of theoretical results that often assume access to (unrealistic) uniform/fully-random hash functions. In this paper, we are concerned with designing hash functions that are practical and come with strong theoretical guarantees on their performance.To this end, we present tornado tabulation hashing, which is simple, fast, and exhibits a certain full, local randomness property that provably makes diverse algorithms perform almost as if (abstract) fully-random hashing was used. For example, this includes classic linear probing, the widely used HyperLogLog algorithm of Flajolet, Fusy, Gandouet, Meunier [AOFA’97] for counting distinct elements, and the one-permutation hashing of Li, Owen, and Zhang [NIPS’12] for large-scale machine learning. We also provide a very efficient solution for the classical problem of obtaining fully-random hashing on a fixed (but unknown to the hash function) set of n keys using $O(n)$ space. As a consequence, we get more efficient implementations of the splitting trick of Dietzfelbinger and Rink [ICALP’09] and the succinct space uniform hashing of Pagh and Pagh [SICOMP’08].Tornado tabulation hashing is based on a simple method to systematically break dependencies in tabulation-based hashing techniques.
ReSQueing Parallel and Private Stochastic Convex Optimization

We introduce a new tool for stochastic convex optimization (SCO): a Reweighted Stochastic Query (ReSQue) estimator for the gradient of a function convolved with a (Gaussian) probability density. Combining ReSQue with recent advances in ball oracle acceleration [CJJ+20], [ACJ+21], we develop algorithms achieving state-of-the-art complexities for SCO in parallel and private settings. For a SCO objective constrained to the unit ball in $\mathbb{R}^{d}$, we obtain the following results (up to polylogarithmic factors).1)We give a parallel algorithm obtaining optimization error $\epsilon_{\text {opt} }$ with $d^{1 / 3} \epsilon_{\text {opt} }^{-2 / 3}$ gradient oracle query depth and $d^{1 / 3} \epsilon_{\text {opt} }^{-2 / 3}+\epsilon_{\text {opt} }^{-2}$ gradient queries in total, assuming access to a bounded-variance stochastic gradient estimator. For $\epsilon_{\text {opt} } \in\left[d^{-1}, d^{-1 / 4}\right]$, our algorithm matches the state-of-the-art oracle depth of [BJL+19] while maintaining the optimal total work of stochastic gradient descent.2)Given n samples of Lipschitz loss functions, prior works [BFTT19], [BFGT20], [AFKT21], [KLL21] established that if $n \gt rsim d \epsilon_{\mathrm{dp}}^{-2},\left(\epsilon_{\mathrm{dp}}, \delta\right)$-differential privacy is attained at no asymptotic cost to the SCO utility. However, these prior works all required a superlinear number of gradient queries. We close this gap for sufficiently large $n \gt rsim d^{2} \epsilon_{{\bf d p}}^{-3}$, by using ReSQue to design an algorithm with near-linear gradient query complexity in this regime.
Co-Evolving Meta-Models and View Types in View-Based Development

View-based development is a successful approach for the development of complex cyber-physical systems. It uses views to abstract from the complexity of the system and allows the developers to focus on exactly the necessary information for a certain task. With projective views, the information shown is derived from underlying models, and changes made to the views are reflected back to the models. Similar to how models conform to a meta-model, views conform to a view type, which describes what and how the information is presented. When the underlying meta-models need to evolve, e.g., due to new requirements, so do the view types that rely on them. In this work, we investigate how to assist the meta-model/view-type co-evolution process by providing suggestions for adapting a view type after a meta-model change. To this end, we provide an approach and conceptual model for such co-evolution, based on precisely describing what a suggestion is. We present a catalog of domain-independent suggestions for the most representative meta-model evolution steps. We believe such suggestions can provide significant assistance in the maintenance and management of the meta-model/view type co-evolution.
Fine Tuning Named Entity Extraction Models for the Fantasy Domain

Named Entity Recognition (NER) is a sequence classification Natural Language Processing task where entities are identified in the text and classified into predefined categories. It acts as a foundation for most information extraction systems. Dungeons and Dragons (D&D) is an open-ended tabletop fantasy game with its own diverse lore. DnD entities are domain-specific and are thus unrecognizable by even the state-of-the-art off-the-shelf NER systems as the NER systems are trained on general data for pre-defined categories such as: person (PERS), location (LOC), organization (ORG), and miscellaneous (MISC). For meaningful extraction of information from fantasy text, the entities need to be classified into domain-specific entity categories as well as the models be fine-tuned on a domain-relevant corpus. This work uses available lore of monsters in the D&D domain to fine-tune Trankit, which is a prolific NER framework that uses a pre-trained model for NER. Upon this training, the system acquires the ability to extract monster names from relevant domain documents under a novel NER tag. This work compares the accuracy of the monster name identification against; the zero-shot Trankit model and two FLAIR models. The fine-tuned Trankit model achieves an 87.86% F1 score surpassing all the other considered models.
IOPs with Inverse Polynomial Soundness Error

We show that every language in NP has an Interactive Oracle Proof (IOP) with inverse polynomial soundness error and small query complexity. This achieves parameters that surpass all previously known PCPs and IOPs. Specifically, we construct an IOP with perfect completeness, soundness error $1 / n$, round complexity $O(\log \log n)$, proof length poly $(n)$ over an alphabet of size $O(n)$, and query complexity $O(\log \log n)$. This is a step forward in the quest to establish the sliding-scale conjecture for IOPs (which would additionally require query complexity $O(1))$. Our main technical contribution is a high-soundness small-query proximity test for the Reed-Solomon code. We construct an IOP of proximity for Reed-Solomon codes, over a field $\mathbb{F}$ with evaluation domain L and degree d, with perfect completeness, soundness error (roughly) $\max \{1-\delta, O(\rho^{1 / 4})\}$ for $\delta$-far functions, round complexity $O(\log \log d)$, proof length $O(|L| / \rho)$ over $\mathbb{F}$, and query complexity $O(\log \log d)$; here $\rho=(d+1) /|L|$ is the code rate. En route, we obtain a new high-soundness proximity test for bivariate Reed-Muller codes.The IOP for NP is then obtained via a high-soundness reduction from NP to Reed-Solomon proximity testing with rate $\rho=1 / \operatorname{poly}(n)$ and distance $\delta=1-1 / \operatorname{poly}(n)$ (and applying our proximity test). Our constructions are direct and efficient, and hold the potential for practical realizations that would improve the state-of-the-art in real-world applications of IOPs.
Rotation Memory Proximity Two-Dimension Inductance Neural Network Identify Gesture Situation

This paper presented a rotation memory neural network (RMNN) identify user gesture situation in access control system. The access control system combined user gesture analysis have been a research topic in smart home fields that algorithms build in doorway real time detect user identification situation. The neural network have been used in many fields that including identification, control and classification. Neural network features is used neurons weight train and learn target results, and rotation memory used last information record neural signal to train weight state. Therefore, the RMNN methods to identify user gesture has satisfactory response.
We-TIPS: Weak-Block-Based Transaction Inclusion Protocol with Signaling in DAG-based Blockchain

DAG-based blockchain faces the key challenge of transaction inclusion collision due to the high concurrency and network delay. In this paper, we propose “We-TIPS”, the weak-block-based transaction inclusion protocol with signaling to tackle this key challenge. In We-TIPS, during the mining process, the miner can broadcast their weak block header as a signal, which can indicate the miner's current transaction inclusion. With the prompt broadcast of the signal, the miner can effectively avoid the transaction inclusion collision and thus greatly boost the system performance. Besides, we develop a transaction inclusion game in We-TIPS to model miners' interaction and further show that it is a potential game. We propose a decentralized transaction inclusion algorithm that can achieve the approximate Nash equilibrium. Finally, we conduct intensive experiments to demonstrate the superior performance of the We-TIPS.
Unveiling Developers' Mindset Barriers to Software Modeling Adoption

This paper presents a qualitative empirical study that seeks to delve into and illuminate the mindset barriers confronted by software developers that hinder the acceptance and use of software modeling techniques. Although software modeling provides acknowledged advantages, such as improving quality, facilitating superior comprehension of system behavior, and enhancing stakeholder communication, it remains significantly underutilized among developers. The primary objective of this research is to understand these barriers not as a result of just technological challenges, but rather as deeply-rooted mindset barriers entrenched within the developer community. Utilizing a grounded theory approach, we conducted extensive interviews with a broad range of developers from various backgrounds. The data were analyzed to identify common themes and patterns. The findings provide insights into the developers' perspectives, revealing key mindset barriers such as tooling and support concerns, challenges posed by modern work paradigms, behavioral resistance, lack of perceived value, knowledge deficiency and skill deficiency. The results of this study serve as a stepping stone towards developing strategies to overcome these mindset barriers, with the aim of fostering a wider acceptance of software modeling techniques. The study contributes to our understanding of the human factors at play in the broader software engineering field.
Automating the Creation of Graph-Based NoSQL Databases in the Context of Big Data

This paper focuses on the transformation of a relational database to a NoSQL database using Model-Driven Architecture (MDA). The research question addressed in this paper is how to automate the process of migrating a relational database to a NoSQL database using MDA. The main findings of this research show that MDA can be used to transform a relational database schema into a NoSQL database schema. The results also show that the transformation process can be automated using Atlas Transformation Language (ATL). The conclusion of this paper is that MDA can help organizations to save time and resources by automating the transformation process, which is critical in modern data-driven applications.
Fractal analysis to BEMD’s IMFs: Application CT-Scan

Osteoporosis is a serious disease due to the fractures it causes which can lead to pain, impotence, loss of independence and excess mortality (femoral neck fractures). In addition, it is a disease with a high recurrence rate and is age-specific. Therefore, the impact of osteoporosis on the already sensitive healthcare system will increase, and thus several preventive measures can be taken to reduce its impact Based on texture analysis, which is crucial for image interpretation in the biomedical domain. We propose a fresh approach for classifying medical images in this context using bidimensional empirical multimodal decomposition (BEMD), this approach is based on the fractal analysis of BIMFs. BEMD is an extension of the one-dimensional case because it has proven to be an adaptive way to represent non-stationary and non-linear signals. Its application to image processing breaks down a image into the total of a number of hierarchical elements “bidimensional intrinsic mode functions (BIMFs)” and residues and the decomposition procedure is iterative. In order to objectively assess the effectiveness of the various BIMF modes and to characterize two states: osteoporotic and healthy, the fractal dimension was calculated for each BIMF using the DBC “Differential Box Counting” method. This novel strategy was applied on a database of CT-Scan medical images of bone textures which contains images of normal and pathological cases. Experimental results indicate that the third mode BIMF achieves higher separation rates compared to the other mode between normal and osteoporotic cases. We use classification rate evaluation criteria, such that the classification rate is given by KNN