Reviewerly/exHarmony huggingface dataset:
Providing Upgrade Plans for Third-party Libraries: A Recommender System using Migration Graphs

During the development of a software project, developers often need to upgrade third-party libraries (TPLs), aiming to keep their code up-to-date with the newest functionalities offered by the used libraries. In most cases, upgrading used TPLs is a complex and error-prone activity that must be carefully carried out to limit the ripple effects on the software project that depends on the libraries being upgraded. In this paper, we propose EvoPlan as a novel approach to the recommendation of different upgrade plans given a pair of library-version as input. In particular, among the different paths that can be possibly followed to upgrade the current library version to the desired updated one, EvoPlan is able to suggest the plan that can potentially minimize the efforts being needed to migrate the code of the clients from the library's current release to the target one. The approach has been evaluated on a curated dataset using conventional metrics used in Information Retrieval, i.e., precision, recall, and F-measure. The experimental results show that EvoPlan obtains an encouraging prediction performance considering two different criteria in the plan specification, i.e., the popularity of migration paths and the number of open and closed issues in GitHub for those projects that have already followed the recommended migration paths.
The Lower Bound of Second-Order Nonlinearity of a Class of Boolean Functions

The r-th nonlinearity of Boolean functions is an important cryptographic criterion associated with higher order linearity attacks on stream and block ciphers. In this paper, we tighten the lower bound of the second-order nonlinearity of a class of Boolean function over finite field F2n, fλ(x)=Tr(λxd), where λ∈F*2r, d=22r+2r+1 and n=7r. This bound is much better than the lower bound of Iwata-Kurosawa.
Hard Example Guided Hashing for Image Retrieval

Compared with the traditional hashing methods, deep hashing methods generate hash codes with rich semantic information and greatly improves the performances in the image retrieval field. However, it is unsatisfied for current deep hashing methods to predict the similarity of hard examples. It exists two main factors affecting the ability of learning hard examples, which are weak key features extraction and the shortage of hard examples. In this paper, we give a novel end-to-end model to extract the key feature from hard examples and obtain hash code with the accurate semantic information. In addition, we redesign a hard pair-wise loss function to assess the hard degree and update penalty weights of examples. It effectively alleviates the shortage problem in hard examples. Experimental results on CIFAR-10 and NUS-WIDE demonstrate that our model outperformances the mainstream hashing-based image retrieval methods.
Dynatask: A Framework for Creating Dynamic AI Benchmark Tasks

We introduce Dynatask: an open source system for setting up custom NLP tasks that aims to greatly lower the technical knowledge and effort required for hosting and evaluating state-of-the-art NLP models, as well as for conducting model in the loop data collection with crowdworkers. Dynatask is integrated with Dynabench, a research platform for rethinking benchmarking in AI that facilitates human and model in the loop data collection and evaluation. To create a task, users only need to write a short task configuration file from which the relevant web interfaces and model hosting infrastructure are automatically generated. The system is available at https://dynabench.org/ and the full library can be found at https://github.com/facebookresearch/dynabench.
Chaotical PRNG based on composition of logistic and tent maps using deep-zoom

We proposed the deep zoom analysis of the composition of the logistic map and the tent map, which are well-known discrete unimodal chaotic maps. The deep zoom technique transforms each point of a given chaotic orbit by removing its first k-digits after the fractional part. We found that the pseudo-random qualities of the composition map as a pseudo-random number generator (PRNG) improves as the k parameter increases. This was proven by the fact that it successfully passed the randomness tests and even outperformed the k-logistic map and k-tent map PRNG. These dynamical properties show that using the deep-zoom on the composition of chaotic maps, at least on these two known maps, is suitable for better randomization for PRNG purposes as well as for cryptographic systems.
A Blockchain-based Data Governance Framework with Privacy Protection and  Provenance for e-Prescription

Real-world applications in healthcare and supply chain domains produce, exchange, and share data in a multi-stakeholder environment. Data owners want to control their data and privacy in such settings. On the other hand, data consumers demand methods to understand when, how, and who produced the data. These requirements necessitate data governance frameworks that guarantee data provenance, privacy protection, and consent management. We introduce a decentralized data governance framework based on blockchain technology and proxy re-encryption to let data owners control and track their data through privacy-enhancing and consent management mechanisms. Besides, our framework allows the data consumers to understand data lineage through a blockchain-based provenance mechanism. We have used Digital e-prescription as the use case since it has multiple stakeholders and sensitive data while enabling the medical fraternity to manage patients' prescription data, involving patients as data owners, doctors and pharmacists as data consumers. Our proof-of-concept implementation and evaluation results based on CosmWasm, Ethereum, and pyUmbral PRE show that the proposed decentralized system guarantees transparency, privacy, and trust with minimal overhead.
Teaching Networks to Solve Optimization Problems

Leveraging machine learning to facilitate the optimization process is an emerging field that holds the promise to bypass the fundamental computational bottleneck caused by classic iterative solvers in critical applications requiring near-real-time optimization. The majority of existing approaches focus on learning data-driven optimizers that lead to fewer iterations in solving an optimization. In this paper, we take a different approach and propose to replace the iterative solvers altogether with a trainable parametric set function, that outputs the optimal arguments/parameters of an optimization problem in a single feed forward. We denote our method as Learning to Optimize the Optimization Process (LOOP). We show the feasibility of learning such parametric (set) functions to solve various classic optimization problems including linear/nonlinear regression, principal component analysis, transport-based coreset, and quadratic programming in supply management applications. In addition, we propose two alternative approaches for learning such parametric functions, with and without a solver in the LOOP. Finally, through various numerical experiments, we show that the trained solvers could be orders of magnitude faster than the classic iterative solvers while providing near optimal solutions.
Cyclical Curriculum Learning

Artificial neural networks (ANN) are inspired by human learning. However, unlike human education, classical ANN does not use a curriculum. Curriculum Learning (CL) refers to the process of ANN training in which examples are used in a meaningful order. When using CL, training begins with a subset of the dataset and new samples are added throughout the training, or training begins with the entire dataset and the number of samples used is reduced. With these changes in training dataset size, better results can be obtained with curriculum, anti-curriculum, or random-curriculum methods than the vanilla method. However, a generally efficient CL method for various architectures and data sets is not found. In this paper, we propose cyclical curriculum learning (CCL), in which the data size used during training changes cyclically rather than simply increasing or decreasing. Instead of using only the vanilla method or only the curriculum method, using both methods cyclically like in CCL provides more successful results. We tested the method on 18 different data sets and 15 architectures in image and text classification tasks and obtained more successful results than no-CL and existing CL methods. We also have shown theoretically that it is less erroneous to apply CL and vanilla cyclically instead of using only CL or only vanilla method. The code of Cyclical Curriculum is available at https://github.com/CyclicalCurriculum/Cyclical-Curriculum.
Can you even tell left from right? Presenting a new challenge for VQA

Visual Question Answering (VQA) needs a means of evaluating the strengths and weaknesses of models. One aspect of such an evaluation is the evaluation of compositional generalisation, or the ability of a model to answer well on scenes whose scene-setups are different from the training set. Therefore, for this purpose, we need datasets whose train and test sets differ significantly in composition. In this work, we present several quantitative measures of compositional separation and find that popular datasets for VQA are not good evaluators. To solve this, we present Uncommon Objects in Unseen Configurations (UOUC), a synthetic dataset for VQA. UOUC is at once fairly complex while also being well-separated, compositionally. The object-class of UOUC consists of 380 clasess taken from 528 characters from the Dungeons and Dragons game. The train set of UOUC consists of 200,000 scenes; whereas the test set consists of 30,000 scenes. In order to study compositional generalisation, simple reasoning and memorisation, each scene of UOUC is annotated with up to 10 novel questions. These deal with spatial relationships, hypothetical changes to scenes, counting, comparison, memorisation and memory-based reasoning. In total, UOUC presents over 2 million questions. UOUC also finds itself as a strong challenge to well-performing models for VQA. Our evaluation of recent models for VQA shows poor compositional generalisation, and comparatively lower ability towards simple reasoning. These results suggest that UOUC could lead to advances in research by being a strong benchmark for VQA.
Reframing Human-AI Collaboration for Generating Free-Text Explanations

Large language models are increasingly capable of generating fluent-appearing text with relatively little task-specific supervision. But can these models accurately explain classification decisions? We consider the task of generating free-text explanations using human-written examples in a few-shot manner. We find that (1) authoring higher quality prompts results in higher quality generations; and (2) surprisingly, in a head-to-head comparison, crowdworkers often prefer explanations generated by GPT-3 to crowdsourced explanations in existing datasets. Our human studies also show, however, that while models often produce factual, grammatical, and sufficient explanations, they have room to improve along axes such as providing novel information and supporting the label. We create a pipeline that combines GPT-3 with a supervised filter that incorporates binary acceptability judgments from humans in the loop. Despite the intrinsic subjectivity of acceptability judgments, we demonstrate that acceptability is partially correlated with various fine-grained attributes of explanations. Our approach is able to consistently filter GPT-3-generated explanations deemed acceptable by humans.
Pengembangan Komik Online Mengenai Operasi Penjumlahan dan Pengurangan Bilangan Bulat dengan Model Neutralization On A Number Line

The purpose of this research development is to develop an online comic product about the operation of calculating the addition and subtraction of integers for class VII with the Neutralization on a Number Line (NNL) model. This research is research and development (R&amp;D) and uses Borg and Gall developmentmodel, which contains steps (1)Perform product analysis tobe developed, (2)Develop the initial product, (3) Expert validation and revision, (4)Small-scale field trials and product revisions, and (5) Large-scale field trials and final products. The research was conducted in January 2021 at SMP Negeri 2 East Karawang. Based on the results of research on learning media by material and language experts, and media experts obtained a percentage of 87% on all aspects of the material and language and 81% in all aspects of the media. Then, based on the results of field trials by teachers obtained a percentage of 89.6% in all aspects of materials, language, and media. Meanwhile, based on the results of field trials for students on a large scale, a percentage of 87.69% was obtained in all aspects of materials, language, and media. This research produced a product in the form of online comics about the operation of calculating the addition and subtraction of integers for class VII with the model Neutralization on a Number Line (NNL).
Non-diagonal Lindblad master equations in quantum reservoir engineering

Reservoir engineering has proven to be a practical approach to control open quantum systems, preserving quantum coherence by appropriately manipulating the reservoir and system-reservoir interactions. In this context, for systems comprised of different parts, it is common to describe the dynamics of a subsystem of interest by performing an adiabatic elimination of the remaining components of the system. This procedure often leads to an effective master equation for the subsystem that is not in the diagonal form of the Gorini-Kossakowski-Lindblad-Sudarshan master equation (here called diagonal Lindblad form). Instead, it has a more general structure (here called non-diagonal Lindblad form), which explicitly reveals the dissipative coupling between the various components of the subsystem. In this work, we present a set of dynamical equations for the first and second moments of the canonical variables for linear Gaussian systems, bosonic and fermionic, described by non-diagonal Lindblad master equations. Our method is efficient and allows one to obtain analytical solutions for the steady state. We supplement our findings with a review of covariance matrix methods, focusing on those related to the measurement of entanglement. Notably, our exploration yields a surprising byproduct: the Duan criterion, commonly applied to bosonic systems for verification of entanglement, is found to be equally valid for fermionic systems. We conclude with a practical example, where we revisit two-mode mechanical entanglement in an optomechanical setup. Our approach, which employs adiabatic elimination for systems governed by time-dependent Hamiltonians, opens the door to examine physical regimes that have not been explored before.
Heavy-tailed denoising score matching

Score-based model research in the last few years has produced state of the art generative models by employing Gaussian denoising score-matching (DSM). However, the Gaussian noise assumption has several high-dimensional limitations, motivating a more concrete route toward even higher dimension PDF estimation in future. We outline this limitation, before extending the theory to a broader family of noising distributions -- namely, the generalised normal distribution. To theoretically ground this, we relax a key assumption in (denoising) score matching theory, demonstrating that distributions which are differentiable almost everywhere permit the same objective simplification as Gaussians. For noise vector norm distributions, we demonstrate favourable concentration of measure in the high-dimensional spaces prevalent in deep learning. In the process, we uncover a skewed noise vector norm distribution and develop an iterative noise scaling algorithm to consistently initialise the multiple levels of noise in annealed Langevin dynamics (LD). On the practical side, our use of heavy-tailed DSM leads to improved score estimation, controllable sampling convergence, and more balanced unconditional generative performance for imbalanced datasets.
Danzer's Problem, Effective Constructions of Dense Forests and Digital Sequences

A 1965 problem due to Danzer asks whether there exists a set in Euclidean space with finite density intersecting any convex body of volume one. A recent approach to this problem is concerned with the construction of dense forests and is obtained by a suitable weakening of the volume constraint. A dense forest is a discrete point set of finite density getting uniformly close to long enough line segments. The distribution of points in a dense forest is then quantified in terms of a visibility function. Another way to weaken the assumptions in Danzer's problem is by relaxing the density constraint. In this respect, a new concept is introduced in this paper, namely that of an optical forest. An optical forest in $\mathbb{R}^{d}$ is a point set with optimal visibility but not necessarily with finite density. In the literature, the best constructions of Danzer sets and dense forests lack effectivity. The goal of this paper is to provide constructions of dense and optical forests which yield the best known results in any dimension $d \ge 2$ both in terms of visibility and density bounds and effectiveness. Namely, there are three main results in this work: (1) the construction of a dense forest with the best known visibility bound which, furthermore, enjoys the property of being deterministic; (2) the deterministic construction of an optical forest with a density failing to be finite only up to a logarithm and (3) the construction of a planar Peres-type forest (that is, a dense forest obtained from a construction due to Peres) with the best known visibility bound. This is achieved by constructing a deterministic digital sequence satisfying strong dispersion properties.
Qudit surface code and hypermap code

In this article, we define homological quantum code in arbitrary qudit dimension $D\geq{2}$ by directly defining CSS operators on a 2-complex $\Sigma$. When the 2-complex is from a surface, we get a qudit surface code. Then we prove that the dimension of the code we defined always equals the size of the first homology group of $\Sigma$. Next, we expand the hypermap-homology quantum code proposed by Martin Leslie to the qudit's case, and for every such hypermap code, we constructed an abstract 2-complex whose homological quantum code we just defined equals it.
Stubborn: A Strong Baseline for Indoor Object Navigation

We present a strong baseline that surpasses the performance of previously published methods on the Habitat Challenge task of navigating to a target object in indoor environments. Our method is motivated from primary failure modes of prior state-of-the-art: poor exploration, inaccurate object identification, and agent getting trapped due to imprecise map construction. We make three contributions to mitigate these issues: (i) First, we show that existing map-based methods fail to effectively use semantic clues for exploration. We present a semantic-agnostic exploration strategy (called Stubborn) without any learning that surprisingly outperforms prior work. (ii) We propose a strategy for integrating temporal information to improve object identification. (iii) Lastly, due to inaccurate depth observation the agent often gets trapped in small regions. We develop a multi-scale collision map for obstacle identification that mitigates this issue.
A Novel Incremental Learning Driven Instance Segmentation Framework to Recognize Highly Cluttered Instances of the Contraband Items

Screening cluttered and occluded contraband items from baggage X-ray scans is a cumbersome task even for the expert security staff. This paper presents a novel strategy that extends a conventional encoder-decoder architecture to perform instance-aware segmentation and extract merged instances of contraband items without using any additional sub-network or an object detector. The encoder-decoder network first performs conventional semantic segmentation and retrieves cluttered baggage items. The model then incrementally evolves during training to recognize individual instances using significantly reduced training batches. To avoid catastrophic forgetting, a novel objective function minimizes the network loss in each iteration by retaining the previously acquired knowledge while learning new class representations and resolving their complex structural inter-dependencies through Bayesian inference. A thorough evaluation of our framework on two publicly available X-ray datasets shows that it outperforms state-of-the-art methods, especially within the challenging cluttered scenarios, while achieving an optimal trade-off between detection accuracy and efficiency.
Searching, Learning, and Subtopic Ordering: A Simulation-Based Analysis

Complex search tasks—such as those from the Search as Learning (SAL) domain—often result in users developing an information need composed of several aspects. However, current models of searcher behaviour assume that individuals have an atomic need, regardless of the task. While these models generally work well for simpler informational needs, we argue that searcher models need to be developed further to allow for the decomposition of a complex search task into multiple aspects. As no searcher model yet exists that considers both aspects and the SAL domain, we propose, by augmenting the Complex Searcher Model (CSM), the Subtopic Aware Complex Searcher Model (SACSM)—modelling aspects as subtopics to the user's need. We then instantiate several agents (i.e., simulated users), with different subtopic selection strategies, which can be considered as different prototypical learning strategies (e.g., should I deeply examine one subtopic at a time, or shallowly cover several subtopics?). Finally, we report on the first large-scale simulated analysis of user behaviours in the SAL domain. Results demonstrate that the SACSM, under certain conditions, simulates user behaviours accurately.
The limited-memory recursive variational Gaussian approximation (L-RVGA)

We consider the problem of computing a Gaussian approximation to the posterior distribution of a parameter given a large number N of observations and a Gaussian prior when the dimension of the parameter d is also large. To address this problem we build on a recently introduced recursive algorithm for variational Gaussian approximation of the posterior, called recursive variational Gaussian approximation (RVGA), which is a single pass algorithm, free of parameter tuning. In this paper, we consider the case where the parameter dimension d is high, and we propose a novel version of RVGA that scales linearly in the dimension d (as well as in the number of observations N), and which only requires linear storage capacity in d. This is afforded by using a novel recursive expectation maximization (EM) algorithm applied for factor analysis introduced herein, to approximate at each step the covariance matrix of the Gaussian distribution conveying the uncertainty in the parameter. The approach is successfully illustrated on the problems of high dimensional least-squares and logistic regression, and generalized to a large class of nonlinear models.
Paradoxical decomposition of Group Actions and Configuration

In this paper we study the notion of configuration for group actions. It is proved that some properties concerning configuration of groups can be extended for the case of group actions. The relationship between configuration and different types of well-known ping-pong lemma is also investigated.