Reviewerly/exHarmony huggingface dataset:
Identification of Risk Factors Using ANFIS-Based Security Risk Assessment Model for SDLC Phases

In the field of software development, the efficient prioritizing of software risks was essential and play significant roles. However, finding a viable solution to this issue is a difficult challenge. The software developers have to adhere strictly to risk management practice because each phase of SDLC is faced with its individual type of risk rather than considering it as a general risk. Therefore, this study proposes an adaptive neuro-fuzzy inference system (ANFIS) for selection of appropriate risk factors in each stages of software development process. Existing studies viewed the SDLC’s Security risk assessment (SRA) as a single integrated process that did not offer a thorough SRA at each stage of the SDLC process, which resulted in unsecure software development. Hence, this study identify and validate the risk factors needed for assessing security risk at each phase of SDLC. For each phase, an SRA model based on an ANFIS was suggested, using the identified risk factors as inputs. For the logical representation of the fuzzification as an input and output variables of the SRA risk factors for the ANFIS-based model employing the triangular membership functions. The proposed model utilized two triangular membership functions to represent each risk factor’s label, while four membership functions were used to represent the labels of the target SRA value. Software developers chose the SRA risk factors that were pertinent in their situation from the proposed taxonomy for each level of the SDLC process as revealed by the results. As revealed from the study’s findings, knowledge of the identified risk factors may be valuable for evaluating the security risk throughout the SDLC process.
Score-based Denoising Diffusion with Non-Isotropic Gaussian Noise Models

Generative models based on denoising diffusion techniques have led to an unprecedented increase in the quality and diversity of imagery that is now possible to create with neural generative models. However, most contemporary state-of-the-art methods are derived from a standard isotropic Gaussian formulation. In this work we examine the situation where non-isotropic Gaussian distributions are used. We present the key mathematical derivations for creating denoising diffusion models using an underlying non-isotropic Gaussian noise model. We also provide initial experiments with the CIFAR-10 dataset to help verify empirically that this more general modeling approach can also yield high-quality samples.
MetaLogic: Logical Reasoning Explanations with Fine-Grained Structure

In this paper, we propose a comprehensive benchmark to investigate models' logical reasoning capabilities in complex real-life scenarios. Current explanation datasets often employ synthetic data with simple reasoning structures. Therefore, it cannot express more complex reasoning processes, such as the rebuttal to a reasoning step and the degree of certainty of the evidence. To this end, we propose a comprehensive logical reasoning explanation form. Based on the multi-hop chain of reasoning, the explanation form includes three main components: (1) The condition of rebuttal that the reasoning node can be challenged; (2) Logical formulae that uncover the internal texture of reasoning nodes; (3) Reasoning strength indicated by degrees of certainty. The fine-grained structure conforms to the real logical reasoning scenario, better fitting the human cognitive process but, simultaneously, is more challenging for the current models. We evaluate the current best models' performance on this new explanation form. The experimental results show that generating reasoning graphs remains a challenging task for current models, even with the help of giant pre-trained language models.
Federated Calibration and Evaluation of Binary Classifiers

We address two major obstacles to practical use of supervised classifiers on distributed private data. Whether a classifier was trained by a federation of cooperating clients or trained centrally out of distribution, (1) the output scores must be calibrated, and (2) performance metrics must be evaluated -- all without assembling labels in one place. In particular, we show how to perform calibration and compute precision, recall, accuracy and ROC-AUC in the federated setting under three privacy models (i) secure aggregation, (ii) distributed differential privacy, (iii) local differential privacy. Our theorems and experiments clarify tradeoffs between privacy, accuracy, and data efficiency. They also help decide whether a given application has sufficient data to support federated calibration and evaluation.
The Better Your Syntax, the Better Your Semantics? Probing Pretrained Language Models for the English Comparative Correlative

Construction Grammar (CxG) is a paradigm from cognitive linguistics emphasising the connection between syntax and semantics. Rather than rules that operate on lexical items, it posits constructions as the central building blocks of language, i.e., linguistic units of different granularity that combine syntax and semantics. As a first step towards assessing the compatibility of CxG with the syntactic and semantic knowledge demonstrated by state-of-the-art pretrained language models (PLMs), we present an investigation of their capability to classify and understand one of the most commonly studied constructions, the English comparative correlative (CC). We conduct experiments examining the classification accuracy of a syntactic probe on the one hand and the models' behaviour in a semantic application task on the other, with BERT, RoBERTa, and DeBERTa as the example PLMs. Our results show that all three investigated PLMs are able to recognise the structure of the CC but fail to use its meaning. While human-like performance of PLMs on many NLP tasks has been alleged, this indicates that PLMs still suffer from substantial shortcomings in central domains of linguistic knowledge.
Dual unitaries as maximizers of the distance to local product gates

TThe problem of finding the resource free, closest local unitary, to any bipartite unitary gate $U$ is addressed. Previously discussed as a measure of nonlocality, the distance $K_D(U)$ to the nearest product unitary has implications for circuit complexity and related quantities. Dual unitaries, currently of great interest in models of complex quantum many-body systems, are shown to have a preferred role as these are maximally and equally away from the set of local unitaries. This is proved here for the case of qubits and we present strong numerical and analytical evidence that it is true in general. An analytical evaluation of $K_D(U)$ is presented for general two-qubit gates. For arbitrary local dimensions, that $K_D(U)$ is largest for dual unitaries, is substantiated by its analytical evaluations for an important family of dual-unitary and for certain non-dual gates. A closely allied result concerns, for any bipartite unitary, the existence of a pair of maximally entangled states that it connects. We give efficient numerical algorithms to find such states and to find $K_D(U)$ in general.
Study on neural information decoding of ground and flight motion state of pigeons in free-moving in the field

The neural information decoding of birds in natural flight is very significant for the development of new biological robots, but there is little research at present. In this paper, we recorded the local field potential signals of pigeons under free movement in the wild; and constructed the brain function connectivity network underground and flight motion state by using the coherence between channels. The analysis shows that the information transmission of pigeons' brains is higher during flight, and its gamma-band contains more movement-related information. Finally, the clustering coefficients and global efficiency features of the gamma band are extracted for decoding. The average decoding accuracy of the three pigeons is 82.66%. This work can provide theoretical support for bird neural information decoding in the future natural environment.
An Analysis Of Students’ Motivation In Teaching And Learning Process By Using Kahoot

Kahoot! is a game-based learning platform used to review students’ knowledge, for formative assessment or as a break from traditional classroom activities. Play is a social-centred process, able to boost motivation and promote learning across all levels and ages. With the growing push for creativity in the classroom as well as the application of effective technology in teaching and learning. The key of teaching and learning process affects the students’ motivation as well as the learning goals. This study was carried out to describe an analysis of students’ motivation in learning process by using Kahoot! and to describe Kahoot! is effective to motivate students in learning writing process. This research used qualitative method with the observation and questionnaires as the instruments. It involved about three classes of the second grade students of SMPN 1 Sumber. The Sample was VIII A, VIII B, VIII H consist of 100 students. The data was analyzed descriptively. The findings showed that the result of ARCS (Attention, Relevance, Confidence, and Satisfaction) model of motivation defined the students motivated in learning process by using Kahoot! In addition, the result of the questionnaire Kahoot! was effetive to motivate student in learning writing process.
The Relationship Between Language and Speech: Formal Understanding of Natural Symbology

This paper aims to realize the natural language formal understanding through the mathematical logic principle of indirect formalization for Chinese and its characters, and its essence is to understand the relationship between language and speech.The method is: First, it is clear that monosyllabic Chinese characters have two basic attributes of language and text, English letters and morphemes, words, phrases and sentences, which belong to another type of text and language; It is found that in the past Chinese was regarded as a monosyllabic language and English was regarded as a polysyllabic language which is a complete misjudgment; Finally, it is further clarified that monosyllabic and mixed syllables not only have unique interpersonal communication in Chinese, but also have human-computer interaction and 邹晓辉 等：言和语的关系：自然语言的形式化理解 http://www.chlanglit.comeven automated batch processing with uniqueness, its fundamental feature is that the monosyllabic Chinese characters meet a certain arithmetic base that is indirectly formalized, that is, the typical P base.Its result is that, it was unexpectedly discovered that arithmetic and language have rigid constraints on the underlying sequence-position logic and surface linkage functions, but there are flexible rules for broad interpretation or translation for the symbolic objects of their division and combination.Its significance lies in: sequence-position logic, linkage function, generalized bilingual or translation or language, in essence, from the macroscopic global to the microscopic local, to systematically and formally understand natural language, especially to thoroughly understand the relationship between language and speech, namely: Global Chinese Sequencing and Positioning System.
A New Dimension in the World Economy - Fiat Digital Currencies: A Review

In an international economic situation in a hyperinflationary environment, the competition for international digital currencies is becoming increasingly intense. The Federal Reserve released its first study on the US central bank's digital currency in early 2022 and China's digital RMB pilot has expanded to 23 regions in 15 provinces and cities... In today's highly digitalized world, the development of a legal digital currency will have a profound impact on national monetary systems, the development of international standards for central bank digital currencies and even the global monetary landscape and is worthy of focused attention and in-depth study. This paper summarizes the development and application of legal digital currency and its limitations at the present stage by combing and analyzing the legal digital currency research literature from CNKI, with a view to providing reference for legal digital currency to better support the innovative development of digital economy, finding a new direction for better development of digital currency and providing a prospect for the internationalization of legal digital currency.
The Influences and Improvement Strategies of the Use of Accurate English Phonetics on English Listening for Chinese University Freshmen

Due to less attention to the English phonetics improvements was paid by high school English teaching, many Chinese university freshmen lack of the most basic phonetics knowledge, for example, their verbal continuous reading is not coherent and their incomplete blasting phonetics is not clear, some freshmen even do not know how to spell words correctly according to their phonetics. This situation has made many negative effects on China’s university English EFL teaching, especially for their improvement of listening comprehension ,but if both teachers and freshmen are aware of this problems, the university English classroom teaching can be effectively improved.
Algorithmic Explainability and the Sufficient-Disclosure Requirement under the European Patent Convention

Artificial intelligence and its subsector machine learning differs from traditional programming. For this reason, coupled with its potential benefits to society in many arenas, it has been articulated as one of the key priorities in the European Union. Such characteristics specific to artificial intelligence as models with increased accuracy and generalisation power may accentuate issues of algorithmic explainability that can defy patentability. Accordingly, the article focuses on the legal requirements related to the ‘sufficient disclosure’ criterion under the legal framework for patents as one facet of deciding on the patentability of the invention, and it addresses potential solutions for overcoming issues of algorithmic explainability. The author argues that solutions introducing a system involving deposit of the algorithm, training data, or both might not be as effective a mechanism for tackling those issues as instead implementing a recognised certification system.
Toward a 'Standard Model' of Machine Learning

Machine learning (ML) is about computational methods that enable machines to learn concepts from experience.In handling a wide variety of experience ranging from data instances, knowledge, and constraints to rewards, adversaries, and lifelong interaction in an ever-growing spectrum of tasks, contemporary ML/AI (artificial intelligence) research has resulted in a multitude of learning paradigms and methodologies.Despite the continual progresses on all different fronts, the disparate, narrowly focused methods also make standardized, composable, and reusable development of ML approaches difficult, and preclude the opportunity to build AI agents that panoramically learn from all types of experience.This article presents a standardized ML formalism, in particular a 'standard equation' of the learning objective, that offers a unifying understanding of many important ML algorithms in the supervised, unsupervised, knowledge-constrained, reinforcement, adversarial, and online learning paradigms, respectively-those diverse algorithms are encompassed as special cases due to different choices of modeling components.The framework also provides guidance for mechanical design of new ML approaches and serves as a promising vehicle toward panoramic machine learning with all experience.
An Event Study of the Ethereum Transition to Proof-of-Stake

On September 15, 2022, the Ethereum network adopted a proof-of-stake (PoS) consensus mechanism. We study the impact on the network and competing platforms in a two month event window around the Beacon chain merge. We find that the transition to PoS has reduced energy consumption by 99.98%. Miners have not transformed into validators, and total block reward income (in USD) has fallen by 97%, though transaction fees (in ETH) for Ether have increased nearly 10%. The Herfindahl index for the top 10 is 1,009; the network is 19% less concentrated after the merge. Ethereum supply growth has been deflationary since the merge. The time between consecutive blocks is now steady at 12 seconds and transactions per day are up 7.0%. On Polygon, Matic fees rose but token fees fell. Polygon also slows, processing 3.3% fewer transactions per day. Solana's fees fall by $0.0003, and transactions per day are down 48%. Stablecoin transfer volumes fall on Ethereum and Polygon, but rise on Solana.
Predicting Long-Term Citations from Short-Term Linguistic Influence

A standard measure of the influence of a research paper is the number of times it is cited. However, papers may be cited for many reasons, and citation count offers limited information about the extent to which a paper affected the content of subsequent publications. We therefore propose a novel method to quantify linguistic influence in timestamped document collections. There are two main steps: first, identify lexical and semantic changes using contextual embeddings and word frequencies; second, aggregate information about these changes into per-document influence scores by estimating a high-dimensional Hawkes process with a low-rank parameter matrix. We show that this measure of linguistic influence is predictive of $\textit{future}$ citations: the estimate of linguistic influence from the two years after a paper's publication is correlated with and predictive of its citation count in the following three years. This is demonstrated using an online evaluation with incremental temporal training/test splits, in comparison with a strong baseline that includes predictors for initial citation counts, topics, and lexical features.
DEMETR: Diagnosing Evaluation Metrics for Translation

While machine translation evaluation metrics based on string overlap (e.g., BLEU) have their limitations, their computations are transparent: the BLEU score assigned to a particular candidate translation can be traced back to the presence or absence of certain words. The operations of newer learned metrics (e.g., BLEURT, COMET), which leverage pretrained language models to achieve higher correlations with human quality judgments than BLEU, are opaque in comparison. In this paper, we shed light on the behavior of these learned metrics by creating DEMETR, a diagnostic dataset with 31K English examples (translated from 10 source languages) for evaluating the sensitivity of MT evaluation metrics to 35 different linguistic perturbations spanning semantic, syntactic, and morphological error categories. All perturbations were carefully designed to form minimal pairs with the actual translation (i.e., differ in only one aspect). We find that learned metrics perform substantially better than string-based metrics on DEMETR. Additionally, learned metrics differ in their sensitivity to various phenomena (e.g., BERTScore is sensitive to untranslated words but relatively insensitive to gender manipulation, while COMET is much more sensitive to word repetition than to aspectual changes). We publicly release DEMETR to spur more informed future development of machine translation evaluation metrics
A Survey on Artificial Intelligence for Music Generation: Agents, Domains and Perspectives

Music is one of the Gardner's intelligences in his theory of multiple intelligences. How humans perceive and understand music is still being studied and is crucial to develop artificial intelligence models that imitate such processes. Music generation with Artificial Intelligence is an emerging field that is gaining much attention in the recent years. In this paper, we describe how humans compose music and how new AI systems could imitate such process by comparing past and recent advances in the field with music composition techniques. To understand how AI models and algorithms generate music and the potential applications that might appear in the future, we explore, analyze and describe the agents that take part of the music generation process: the datasets, models, interfaces, the users and the generated music. We mention possible applications that might benefit from this field and we also propose new trends and future research directions that could be explored in the future.
Contrastive Search Is What You Need For Neural Text Generation

Generating text with autoregressive language models (LMs) is of great importance to many natural language processing (NLP) applications. Previous solutions for this task often produce text that contains degenerative expressions or lacks semantic consistency. Recently, Su et al. introduced a new decoding method, contrastive search, based on the isotropic representation space of the language model and obtained new state of the art on various benchmarks. Additionally, Su et al. argued that the representations of autoregressive LMs (e.g. GPT-2) are intrinsically anisotropic which is also shared by previous studies. Therefore, to ensure the language model follows an isotropic distribution, Su et al. proposed a contrastive learning scheme, SimCTG, which calibrates the language model's representations through additional training. In this study, we first answer the question: "Are autoregressive LMs really anisotropic?". To this end, we extensively evaluate the isotropy of LMs across 16 major languages. Surprisingly, we find that the anisotropic problem only exists in the two specific English GPT-2-small/medium models. On the other hand, all other evaluated LMs are naturally isotropic which is in contrast to the conclusion drawn by previous studies. Based on our findings, we further assess the contrastive search decoding method using off-the-shelf LMs on four generation tasks across 16 languages. Our experimental results demonstrate that contrastive search significantly outperforms previous decoding methods without any additional training. More notably, on 12 out of the 16 evaluated languages, contrastive search performs comparably with human-level performances as judged by human evaluations. Our code and other related resources are publicly available at https://github.com/yxuansu/Contrastive_Search_Is_What_You_Need.
Decorrelated Variable Importance

Because of the widespread use of black box prediction methods such as random forests and neural nets, there is renewed interest in developing methods for quantifying variable importance as part of the broader goal of interpretable prediction. A popular approach is to define a variable importance parameter - known as LOCO (Leave Out COvariates) - based on dropping covariates from a regression model. This is essentially a nonparametric version of R-squared. This parameter is very general and can be estimated nonparametrically, but it can be hard to interpret because it is affected by correlation between covariates. We propose a method for mitigating the effect of correlation by defining a modified version of LOCO. This new parameter is difficult to estimate nonparametrically, but we show how to estimate it using semiparametric models.
Wearable Sensors Applied in Movement Analysis

Recent advances in the miniaturization of electronics have resulted in sensors whose sizes and weights are such that they can be attached to living systems without interfering with their natural movements and behaviors [...].