Reviewerly/exHarmony huggingface dataset:
The uncertainty principle over finite fields

In this paper we study the uncertainty principle (UP) connecting a function over a finite field and its Mattson-Solomon polynomial, which is a kind of Fourier transform in positive characteristic. Three versions of the UP over finite fields are studied, in connection with the asymptotic theory of cyclic codes. We first show that no finite field satisfies the strong version of UP, introduced recently by Evra, Kowalsky, Lubotzky, 2017. A refinement of the weak version is given, by using the asymptotic Plotkin bound. A naive version, which is the direct analogue over finite fields of the Donoho-Stark bound over the complex numbers, is proved by using the BCH bound. It is strong enough to show that there exist sequences of cyclic codes of length $n$, arbitrary rate, and minimum distance $\Omega(n^\alpha)$ for all $0<\alpha<1/2$. Finally, a connection with Ramsey Theory is pointed out.
A Hierarchical Subspace Model for Language-Attuned Acoustic Unit Discovery

In this work, we propose a hierarchical subspace model for acoustic unit discovery. In this approach, we frame the task as one of learning embeddings on a low-dimensional phonetic subspace, and simultaneously specify the subspace itself as an embedding on a hyper-subspace. We train the hyper-subspace on a set of transcribed languages and transfer it to the target language. In the target language, we infer both the language and unit embeddings in an unsupervised manner, and in so doing, we simultaneously learn a subspace of units specific to that language and the units that dwell on it. We conduct our experiments on TIMIT and two low-resource languages: Mboshi and Yoruba. Results show that our model outperforms major acoustic unit discovery techniques, both in terms of clustering quality and segmentation accuracy.
RDPNet: A single-path lightweight CNN with re-parameterization for CPU-type edge devices

Abstract Deep convolutional neural networks have produced excellent results when utilized for image classification tasks, and they are being applied in a growing number of contexts. Model inference on edge devices is challenging due to the unending complicated structures needed to improve performance, which adds a significant computing burden.According to recent research, the often utilized residual structure in models does not support model inference. The idea of structural reparameterization is put out to address this shortcoming. The RepVGG produced with this method is a high-performance, quick-inference single-path network. Even after reparameterization, the model still needs GPUs and other specialized computing libraries to accelerate inference, however this still has a limit on how quickly the model can infer at the edge. We construct RDPNet using depthwise separable convolution and structural reparameterization to further reduce model size and accelerate inference. When utilizing an Intel CPU, this is a straightforward network that may be utilized for inference. For re-parameterization, we specifically adopt Depthwise separable convolution as the basic convolution form. Create a multi-branch model for training on the training side, and then simplify it into a single-branch model that the edge devices can easily infer. Research demonstrates that compared to alternative lightweight networks that can attain SOTA performance, RDPNet offers a superior trade-off between accuracy and latency.
Reading Shakespeare

Interface theory and cognitive load theory (CLT) illuminate how Shakespeare’s plays, understood as information mediated by an interface, make specialized cognitive demands on readers. The challenges of such cognitive operations are a function both of the inherent difficulty of reading drama but also of the cognitive load associated with interpreting paratextual materials designed to facilitate comprehension or provide scholarly supports. The extraneous cognitive load defined by CLT demonstrates how book and digital designers of Shakespeare can mitigate these cognitive difficulties by considering the challenges to working memory caused by split attention in busy page and screen interfaces.
Development of Algorithmic Thinking in Primary School Students when Studying Computer Science

The relevance of the study is conditioned by the fact that modern society requires new generations to be able to plan their actions, find the information necessary to solve problems among huge information arrays (many of which are also not reliable), and model processes for the future. The set of certain tasks is synergised in the plane of the concept of algorithmic thinking. The purpose of the study is to track the educational and competence consequences of applying the developed approaches to stimulate algorithmic thinking in computer science lessons, to identify how they will contribute to the development of algorithmic thinking in primary school students. The paper states that categorisation of the concept of algorithmic thinking requires a broad understanding of the algorithm as a set and sequence of actions aimed at achieving the desired result. While algorithmic thinking is a way of organising mental actions and techniques for solving problems, as a result of which an algorithm is created – a specific product of human mental activity. Algorithmic thinking is not a compilation of algorithms, and its development is not limited to the mechanical memorisation of a number of algorithms. In the scientific literature and current policy documents on primary schools, the development of algorithmic thinking is perceived in the context of information and digital competence of schoolchildren and is associated with branches related to computer science: mathematics and logic. The emphasis on the potential for developing algorithmic thinking applies directly to the computer science course, but is not limited to it. The intention to stimulate the ability to think algorithmically is a requirement of the postmodern information era and the key to personal success, it is one of the tools of socialisation, and therefore, approaches to the development of algorithmic thinking should be comprehensively presented in the concepts of studying other compulsory and optional subjects. For the study, the authors developed and successfully tested a special eight-level diagnostic structure, which is presented in the paper. It can be used as a basis for conducting similar experimental studies. Further study on the topic should be organised around deepening the understanding and expanding the range of methodological approaches to the development of algorithmic thinking in schoolchildren of the New Ukrainian School (NUS), in particular, in computer science lessons
American Sign Language Translator

Abstract: Sign language is one of the ancient and most natural form of language for communication. Sign Language is being used by many people in the world, but only a small part of the population knows how to interpret the language. Which makes it tough for the Deaf people to communicate with other people. We used the Tensorflow object detection API in our research, which is an open-source framework for training and deploying object detection models.
B-SMALL: A Bayesian Neural Network approach to Sparse Model-Agnostic Meta-Learning

There is a growing interest in the learning-to-learn paradigm, also known as meta-learning, where models infer on new tasks using a few training examples. Recently, meta-learning based methods have been widely used in few-shot classification, regression, reinforcement learning, and domain adaptation. The model-agnostic meta-learning (MAML) algorithm is a well-known algorithm that obtains model parameter initialization at meta-training phase. In the meta-test phase, this initialization is rapidly adapted to new tasks by using gradient descent. However, meta-learning models are prone to overfitting since there are insufficient training tasks resulting in over-parameterized models with poor generalization performance for unseen tasks. In this paper, we propose a Bayesian neural network based MAML algorithm, which we refer to as the B-SMALL algorithm. The proposed framework incorporates a sparse variational loss term alongside the loss function of MAML, which uses a sparsifying approximated KL divergence as a regularizer. We demonstrate the performance of B-MAML using classification and regression tasks, and highlight that training a sparsifying BNN using MAML indeed improves the parameter footprint of the model while performing at par or even outperforming the MAML approach. We also illustrate applicability of our approach in distributed sensor networks, where sparsity and meta-learning can be beneficial.
BanglaBERT: Language Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in Bangla

In this work, we introduce BanglaBERT, a BERT-based Natural Language Understanding (NLU) model pretrained in Bangla, a widely spoken yet low-resource language in the NLP literature. To pretrain BanglaBERT, we collect 27.5 GB of Bangla pretraining data (dubbed `Bangla2B+') by crawling 110 popular Bangla sites. We introduce two downstream task datasets on natural language inference and question answering and benchmark on four diverse NLU tasks covering text classification, sequence labeling, and span prediction. In the process, we bring them under the first-ever Bangla Language Understanding Benchmark (BLUB). BanglaBERT achieves state-of-the-art results outperforming multilingual and monolingual models. We are making the models, datasets, and a leaderboard publicly available at https://github.com/csebuetnlp/banglabert to advance Bangla NLP.
Generative Deep Learning for Virtuosic Classical Music: Generative Adversarial Networks as Renowned Composers

Current AI-generated music lacks fundamental principles of good compositional techniques. By narrowing down implementation issues both programmatically and musically, we can create a better understanding of what parameters are necessary for a generated composition nearly indistinguishable from that of a master composer.
More than just an auxiliary loss: Anti-spoofing Backbone Training via Adversarial Pseudo-depth Generation

In this paper, a new method of training pipeline is discussed to achieve significant performance on the task of anti-spoofing with RGB image. We explore and highlight the impact of using pseudo-depth to pre-train a network that will be used as the backbone to the final classifier. While the usage of pseudo-depth for anti-spoofing task is not a new idea on its own, previous endeavours utilize pseudo-depth simply as another medium to extract features for performing prediction, or as part of many auxiliary losses in aiding the training of the main classifier, normalizing the importance of pseudo-depth as just another semantic information. Through this work, we argue that there exists a significant advantage in training the final classifier can be gained by the pre-trained generator learning to predict the corresponding pseudo-depth of a given facial image, from a Generative Adversarial Network framework. Our experimental results indicate that our method results in a much more adaptable system that can generalize beyond intra-dataset samples, but to inter-dataset samples, which it has never seen before during training. Quantitatively, our method approaches the baseline performance of the current state of the art anti-spoofing models with 15.8x less parameters used. Moreover, experiments showed that the introduced methodology performs well only using basic binary label without additional semantic information which indicates potential benefits of this work in industrial and application based environment where trade-off between additional labelling and resources are considered.
Role of Electronic Management in Achieving Competitive Advantage in Yemeni Pharmaceutical Manufacturers Sector in Presence of Intellectual Capital as A Mediating Variable

The current study aimed to determine the mediating role of intellectual capital on relationship between e-management and competitive advantage. The descriptive analytical approach was used to achieve the objectives of the study. Due to the small population of the study (375 employees) in the Yemeni pharmaceutical manufacturers, a complete census was used. A questionnaire was used for data collection and the Structural Equation Modeling (SEM) was employed to analyze the collected data. The findings revealed a significant positive moral impact of e-management on both the intellectual capital and competitive advantage. The findings also showed a significant positive moral impact of intellectual capital on competitive advantage. Simultaneously, the findings revealed a significant mediating role of intellectual capital on relationship between e-management and competitive advantage. The study recommends that greater attention needs to be given to e-management and intellectual capital due to their positive impact on competitive advantage.
The Impact of Compliance with the Standards of Internal Audit Attributes on Reduction of Employee Fraud: A Field Study in Public Economic Units in Yemen

This study aimed to investigate the impact of compliance with the standards of internal audit attributes on reduction of employee fraud in public economic units in Republic of Yemen. In order to achieve the study objective, the questionnaire was used as the tool for collecting data from the study population which consisted of (83) public economic units in Yemen. A sample of (25) units in Sana'a city was taken as a stratified purposive sample that represented internal auditors in the selected units. The Statistical Program for the Social Sciences (SPSS) was used to analyze the study data. The results revealed that there is a statistically significant positive impact of the compliance with the standards of internal audit attributes on the reduction of employee fraud. The study recommends that the public economic unites in Yemen should comply with the standards of internal audit attributes for reducing the employee fraud.
e-PoS: Making Proof-of-Stake Decentralized and Fair

Blockchain applications that rely on the Proof-of-Work (PoW) have increasingly become energy inefficient with a staggering carbon footprint. In contrast, energy-efficient alternative consensus protocols such as Proof-of-Stake (PoS) may cause centralization and unfairness in the blockchain system. To address these challenges, we propose a modular version of PoS-based blockchain systems called epos that resists the centralization of network resources by extending mining opportunities to a wider set of stakeholders. Moreover, epos leverages the in-built system operations to promote fair mining practices by penalizing malicious entities. We validate epos's achievable objectives through theoretical analysis and simulations. Our results show that epos ensures fairness and decentralization, and can be applied to existing blockchain applications.
UNKs Everywhere: Adapting Multilingual Language Models to New Scripts

Massively multilingual language models such as multilingual BERT offer state-of-the-art cross-lingual transfer performance on a range of NLP tasks. However, due to limited capacity and large differences in pretraining data sizes, there is a profound performance gap between resource-rich and resource-poor target languages. The ultimate challenge is dealing with under-resourced languages not covered at all by the models and written in scripts unseen during pretraining. In this work, we propose a series of novel data-efficient methods that enable quick and effective adaptation of pretrained multilingual models to such low-resource languages and unseen scripts. Relying on matrix factorization, our methods capitalize on the existing latent knowledge about multiple languages already available in the pretrained model's embedding matrix. Furthermore, we show that learning of the new dedicated embedding matrix in the target language can be improved by leveraging a small number of vocabulary items (i.e., the so-called lexically overlapping tokens) shared between mBERT's and target language vocabulary. Our adaptation techniques offer substantial performance gains for languages with unseen scripts. We also demonstrate that they can yield improvements for low-resource languages written in scripts covered by the pretrained model.
Word-Class Embeddings for Multiclass Text Classification

Pre-trained word embeddings encode general word semantics and lexical regularities of natural language, and have proven useful across many NLP tasks, including word sense disambiguation, machine translation, and sentiment analysis, to name a few. In supervised tasks such as multiclass text classification (the focus of this article) it seems appealing to enhance word representations with ad-hoc embeddings that encode task-specific information. We propose (supervised) word-class embeddings (WCEs), and show that, when concatenated to (unsupervised) pre-trained word embeddings, they substantially facilitate the training of deep-learning models in multiclass classification by topic. We show empirical evidence that WCEs yield a consistent improvement in multiclass classification accuracy, using four popular neural architectures and six widely used and publicly available datasets for multiclass text classification. Our code that implements WCEs is publicly available at https://github.com/AlexMoreo/word-class-embeddings
Predicting cross-linguistic adjective order with information gain

Languages vary in their placement of multiple adjectives before, after, or surrounding the noun, but they typically exhibit strong intra-language tendencies on the relative order of those adjectives (e.g., the preference for `big blue box' in English, `grande bo\^{i}te bleue' in French, and `alsund\={u}q al'azraq alkab\={\i}r' in Arabic). We advance a new quantitative account of adjective order across typologically-distinct languages based on maximizing information gain. Our model addresses the left-right asymmetry of French-type ANA sequences with the same approach as AAN and NAA orderings, without appeal to other mechanisms. We find that, across 32 languages, the preferred order of adjectives largely mirrors an efficient algorithm of maximizing information gain.
Deriving Contextualised Semantic Features from BERT (and Other Transformer Model) Embeddings

Models based on the transformer architecture, such as BERT, have marked a crucial step forward in the field of Natural Language Processing. Importantly, they allow the creation of word embeddings that capture important semantic information about words in context. However, as single entities, these embeddings are difficult to interpret and the models used to create them have been described as opaque. Binder and colleagues proposed an intuitive embedding space where each dimension is based on one of 65 core semantic features. Unfortunately, the space only exists for a small dataset of 535 words, limiting its uses. Previous work (Utsumi, 2018, 2020, Turton, Vinson & Smith, 2020) has shown that Binder features can be derived from static embeddings and successfully extrapolated to a large new vocabulary. Taking the next step, this paper demonstrates that Binder features can be derived from the BERT embedding space. This provides contextualised Binder embeddings, which can aid in understanding semantic differences between words in context. It additionally provides insights into how semantic features are represented across the different layers of the BERT model.
Synthesizing Decentralized Controllers with Graph Neural Networks and Imitation Learning

Dynamical systems consisting of a set of autonomous agents face the challenge of having to accomplish a global task, relying only on local information. While centralized controllers are readily available, they face limitations in terms of scalability and implementation, as they do not respect the distributed information structure imposed by the network system of agents. Given the difficulties in finding optimal decentralized controllers, we propose a novel framework using graph neural networks (GNNs) to \emph{learn} these controllers. GNNs are well-suited for the task since they are naturally distributed architectures and exhibit good scalability and transferability properties. We show that GNNs learn appropriate decentralized controllers by means of imitation learning, leverage their permutation invariance properties to successfully scale to larger teams and transfer to unseen scenarios at deployment time. The problems of flocking and multi-agent path planning are explored to illustrate the potential of GNNs in learning decentralized controllers.
A blockchain-based pattern for confidential and pseudo-anonymous contract enforcement

Blockchain has been praised for its capacity to hold data in a decentralized and tamper-proof way. It also supports the execution of code through blockchain's smart contracts, adding automation of actions to the network with high trustability. However, as smart contracts are visible by anybody on the network, the business data and logic may be at risk, thus companies could be reluctant to use such technology. This paper aims to propose a pattern that allows the execution of automatable legal contract clauses, where its execution states are stored in an on-chain smart-contract and the logic needed to enforce it wraps it off-chain. An engine completes this pattern by running a business process that corresponds to the legal contract. We then propose a pattern-based solution based on a real-life use case: transportation of refrigerated goods. We argue that this pattern guarantees companies pseudonymity and data confidentiality while ensuring that an audit trail can be reconstituted through the blockchain smart-contract to identify misbehavior or errors. This paper paves the way for a future possible implementation of the solution described, as well as its evaluation.
The quantum Zeno and anti-Zeno effects with driving fields in the weak and strong coupling regimes

Repeated measurements in quantum mechanics can freeze (the quantum Zeno effect) or enhance (the quantum anti-Zeno effect) the time-evolution of a quantum system. In this paper, we present a general treatment of the quantum Zeno and anti-Zeno effects for arbitrary driven open quantum systems, assuming only that the system-environment coupling is weak. In particular, we obtain a general expression for the effective decay rate of a two-level system subjected to arbitrary driving fields as well as periodic measurements. We demonstrate that the driving fields change the decay rate, and hence the quantum Zeno and anti-Zeno behavior, both qualitatively and quantitatively. We also extend our results to systems consisting of more than one two-level system, as well as a two-level system strongly coupled to an environment of harmonic oscillators, to further illustrate the non-trivial effect of the driving fields on the quantum Zeno and anti-Zeno effects.
Marginal probabilities in boson samplers with arbitrary input states

With the recent claim of a quantum advantage demonstration in photonics by Zhong et al, the question of the computation of lower-order approximations of boson sampling with arbitrary quantum states at arbitrary distinguishability has come to the fore. In this work, we present results in this direction, building on the results of Clifford and Clifford. In particular, we show: 1) How to compute marginal detection probabilities (i.e. probabilities of the detection of some but not all photons) for arbitrary quantum states. 2) Using the first result, how to generalize the sampling algorithm of Clifford and Clifford to arbitrary photon distinguishabilities and arbitrary input quantum states. 3) How to incorporate truncations of the quantum interference into a sampling algorithm. 4) A remark considering maximum likelihood verification of the recent photonic quantum advantage experiment.