Reviewerly/exHarmony huggingface dataset:
SAMO: Optimised Mapping of Convolutional Neural Networks to Streaming Architectures

Significant effort has been placed on the development of toolflows that map Convolutional Neural Network (CNN) models to Field Programmable Gate Arrays (FPGAs) with the aim of automating the production of high performing designs for a diverse set of applications. However, within these toolflows, the problem of finding an optimal mapping is often overlooked, with the expectation that the end user will tune their generated hardware for their desired platform. This is particularly prominent within Streaming Architecture toolflows, where there is a large design space to explore . In this work, we establish the framework SAMO: a Streaming Architecture Mapping Optimiser. SAMO exploits the structure of CNN models and the common features that exist in Streaming Architectures, and casts the mapping optimisation problem under a unified methodology. Furthermore, SAMO explicitly explores the reconfigurability property of FPGAs, allowing the methodology to overcome mapping limitations imposed by certain toolflows under resource-constrained scenarios, as well as improve on the achievable throughput. Three optimisation methods - Brute-Force, Simulated Annealing and Rule-Based - have been developed in order to generate valid, high performance designs for a range of target platforms and CNN models. Results show that SAMO-optimised designs can achieve 4-20x better performance compared to existing hand-tuned designs. The SAMO framework is open-source: https://github.com/AlexMontgomerie/samo.
Learning Rich Representation of Keyphrases from Text

In this work, we explore how to train task-specific language models aimed towards learning rich representation of keyphrases from text documents. We experiment with different masking strategies for pre-training transformer language models (LMs) in discriminative as well as generative settings. In the discriminative setting, we introduce a new pre-training objective - Keyphrase Boundary Infilling with Replacement (KBIR), showing large gains in performance (upto 8.16 points in F1) over SOTA, when the LM pre-trained using KBIR is fine-tuned for the task of keyphrase extraction. In the generative setting, we introduce a new pre-training setup for BART - KeyBART, that reproduces the keyphrases related to the input text in the CatSeq format, instead of the denoised original input. This also led to gains in performance (upto 4.33 points in F1@M) over SOTA for keyphrase generation. Additionally, we also fine-tune the pre-trained language models on named entity recognition (NER), question answering (QA), relation extraction (RE), abstractive summarization and achieve comparable performance with that of the SOTA, showing that learning rich representation of keyphrases is indeed beneficial for many other fundamental NLP tasks.
A Survey for Deep RGBT Tracking

Visual object tracking with the visible (RGB) and thermal infrared (TIR) electromagnetic waves, shorted in RGBT tracking, recently draws increasing attention in the tracking community. Considering the rapid development of deep learning, a survey for the recent deep neural network based RGBT trackers is presented in this paper. Firstly, we give brief introduction for the RGBT trackers concluded into this category. Then, a comparison among the existing RGBT trackers on several challenging benchmarks is given statistically. Specifically, MDNet and Siamese architectures are the two mainstream frameworks in the RGBT community, especially the former. Trackers based on MDNet achieve higher performance while Siamese-based trackers satisfy the real-time requirement. In summary, since the large-scale dataset LasHeR is published, the integration of end-to-end framework, e.g., Siamese and Transformer, should be further considered to fulfil the real-time as well as more robust performance. Furthermore, the mathematical meaning should be more considered during designing the network. This survey can be treated as a look-up-table for researchers who are concerned about RGBT tracking.
Aplikasi Jago (Baca Dan Tulis Pego) Dengan Voice Recognition Berbasis Android

Abstrak -Pego is an Arabic script that is modified to write Javanese, Indonesian, and Sundanese languages.At first glance, Pego's writing is similar to Arabic hijaiyah writing as usual.But there are still additional letters and writing rules that make it difficult for students to adjust vocabulary in Indonesian or other languages.MI Darul Ulum II School is one of the schools that has "Arabic pego" material as local content.The learning is in the form of teaching writing on the board in recognizing the letters of pego and how to write pego.Unfortunately, this method makes it difficult for students to recognize pego letters and the function of reading pego letters for writing Indonesian or Javanese.Therefore we need an application that can help students in the process of learning the letter pego.This study aims to create an Android-based learning media application in Pego learning.The application in this study was developed using the waterfall system development model.This application is designed with unfield modeling language (UML).The result of this research is that a good learning application (learning to write and read pego) with android-based voice recognition has been built using Adobe Flash Professional CS6.This android-based Pego learning application was built with reference to Arabic-Malay reading and writing guidelines and with an interesting teaching.Kata Kunci -Pego, Android, Adobe Flash Profesional CS6.
Exact matrix product state representation and convergence of a fully  correlated electronic wavefunction in the infinite basis limit

In this article we present the exact representation of a fully correlated electronic wavefunction as the single-particle basis approaches completeness. It consists of a half-infinite chain of matrices of exponentially increasing size. The complete basis limit is illustrated numerically using the density matrix renormalization group method by computing the core-valence entanglement in the C$_2$ ground state in increasing subsets of cc-pVTZ and pVQZ bases until convergence is reached.
The complete weight enumerator of the Reed-Solomon code with dimension two or three

It is well-known that Reed-Solomon codes and extended Reed-Solomon codes are two special classes of MDS codes with wide applications in practice. The complete weight enumerators of these codes are very important for determining the capability of both error-detection and error-correction. In this paper, for any positive integer $m$ and prime $p$, basing on the character sums, we determine the complete weight enumerators of the Reed-Solomon code and the extended Reed-Solomon code with dimension $k$ $(k=2,3)$ over $\mathbb{F}_{p^m}$, explictly, which are generalizations of the corresponding results in \cite{BK91,K04}.
Alternative Data for Configurable and Personalized Commercial Insurance Products

Abstract The insurance sector has a very low degree of products’ personalization. This is because insurance companies are not adequately using the vast amounts of information that is available online. This chapter presents the under-explored avenue of using alternative or nontraditional sources of information to obtain data for the configuration of personalized insurance offers. Specifically, it illustrates how big data, synthetic data, and machine learning models can be used to draw up the risk map of companies in an individualized way. It also presents methods for constructing and offering insurance programs that are personalized to each business reality.
Quantum Codes, CFTs, and Defects

We give a general construction relating Narain rational conformal field theories (RCFTs) and associated 3d Chern-Simons (CS) theories to quantum stabilizer codes. Starting from an abelian CS theory with a fusion group consisting of $n$ even-order factors, we map a boundary RCFT to an $n$-qubit quantum code. When the relevant 't Hooft anomalies vanish, we can orbifold our RCFTs and describe this gauging at the level of the code. Along the way, we give CFT interpretations of the code subspace and the Hilbert space of qubits while mapping error operations to CFT defect fields.
Classifier Data Quality: A Geometric Complexity Based Method for Automated Baseline And Insights Generation

Testing Machine Learning (ML) models and AI-Infused Applications (AIIAs), or systems that contain ML models, is highly challenging. In addition to the challenges of testing classical software, it is acceptable and expected that statistical ML models sometimes output incorrect results. A major challenge is to determine when the level of incorrectness, e.g., model accuracy or F1 score for classifiers, is acceptable and when it is not. In addition to business requirements that should provide a threshold, it is a best practice to require any proposed ML solution to out-perform simple baseline models, such as a decision tree. We have developed complexity measures, which quantify how difficult given observations are to assign to their true class label; these measures can then be used to automatically determine a baseline performance threshold. These measures are superior to the best practice baseline in that, for a linear computation cost, they also quantify each observation' classification complexity in an explainable form, regardless of the classifier model used. Our experiments with both numeric synthetic data and real natural language chatbot data demonstrate that the complexity measures effectively highlight data regions and observations that are likely to be misclassified.
Fisher information analysis on post-selection involved quantum precision measurements using optical coherent states

The weak-value-amplification (WVA) technique has been extensively considered and debated in the field of quantum precision measurement, largely owing to the reduced Fisher information caused by the low probability of successful post-selection. %% In this work we show that, rather than the Gaussian meter state as typically considered, using the optical coherent state as a meter, the WVA measurement can definitely outperform the conventional measurement not involving the strategy of post-selection. %% We also show that the post-selection procedure involved in the WVA scheme can make a mixture of coherent states work better than a pure coherent state with identical average photon numbers. This is in sharp contrast to the claim proved in the absence of post-selection. The post-selection strategy can also result in the precision of Heisenberg (or even "super-Heisenberg") scaling with the photon numbers, but without using any expensive quantum resources. %% The present work may stimulate further investigations for the potential of the post-selection strategy in quantum precision measurements.
Designing a virtual laboratory within the development educational environment of the university

The article discusses the issues of designing a developing educational environment of a university in modern post-quarantine conditions for the development of distance and blended learning. The authors made an attempt to review and analyze existing studies of foreign and Kazakh authors on the subject under study and concluded that it is necessary to create a unified virtual laboratory using the capabilities and potential of a virtual learning environment. The main research principle of the proposed virtual laboratory is determined by the principle of reproducibility. In addition, the article provides the key points of the theoretical and methodological substantiation of the above project, such as the goal and objectives, the main structural components of the virtual laboratory. The authors also suggest that learning automation using online resources will be an effective addition to the traditional learning format. The relevance and practical orientation of the project being developed is also interesting for the thematic focus and pithiness of educational content.
Multi-scale Attention Guided Pose Transfer

Pose transfer refers to the probabilistic image generation of a person with a previously unseen novel pose from another image of that person having a different pose. Due to potential academic and commercial applications, this problem is extensively studied in recent years. Among the various approaches to the problem, attention guided progressive generation is shown to produce state-of-the-art results in most cases. In this paper, we present an improved network architecture for pose transfer by introducing attention links at every resolution level of the encoder and decoder. By utilizing such dense multi-scale attention guided approach, we are able to achieve significant improvement over the existing methods both visually and analytically. We conclude our findings with extensive qualitative and quantitative comparisons against several existing methods on the DeepFashion dataset.
Paving the Way towards 800 Gbps Quantum-Secured Optical Channel Deployment in Mission-Critical Environments

This article describes experimental research studies conducted towards understanding the implementation aspects of high-capacity quantum-secured optical channels in mission-critical metro-scale operational environments using Quantum Key Distribution (QKD) technology. To the best of our knowledge, this is the first time that an 800 Gbps quantum-secured optical channel -- along with several other Dense Wavelength Division Multiplexed (DWDM) channels on the C-band and multiplexed with the QKD channel on the O-band -- was established at distances up to 100 km, with secret key-rates relevant for practical industry use cases. In addition, during the course of these trials, transporting a blockchain application over this established channel was utilized as a demonstration of securing a financial transaction in transit over a quantum-secured optical channel. The findings of this research pave the way towards the deployment of QKD-secured optical channels in high-capacity, metro-scale, mission-critical operational environments, such as Inter-Data Center Interconnects.
HeterMPC: A Heterogeneous Graph Neural Network for Response Generation in Multi-Party Conversations

Recently, various response generation models for two-party conversations have achieved impressive improvements, but less effort has been paid to multi-party conversations (MPCs) which are more practical and complicated. Compared with a two-party conversation where a dialogue context is a sequence of utterances, building a response generation model for MPCs is more challenging, since there exist complicated context structures and the generated responses heavily rely on both interlocutors (i.e., speaker and addressee) and history utterances. To address these challenges, we present HeterMPC, a heterogeneous graph-based neural network for response generation in MPCs which models the semantics of utterances and interlocutors simultaneously with two types of nodes in a graph. Besides, we also design six types of meta relations with node-edge-type-dependent parameters to characterize the heterogeneous interactions within the graph. Through multi-hop updating, HeterMPC can adequately utilize the structural knowledge of conversations for response generation. Experimental results on the Ubuntu Internet Relay Chat (IRC) channel benchmark show that HeterMPC outperforms various baseline models for response generation in MPCs.
Light Clients for Lazy Blockchains

Lazy blockchains decouple consensus from transaction verification and execution to increase throughput. Although they can contain invalid transactions (e.g., double spends) as a result, these can easily be filtered out by full nodes that check if there have been previous conflicting transactions. However, creating light (SPV) clients that do not see the whole transaction history becomes a challenge: A record of a transaction on the chain does not necessarily entail transaction confirmation. In this paper, we devise a protocol that enables the creation of efficient light clients for lazy blockchains. The number of interaction rounds and the communication complexity of our protocol are logarithmic in the blockchain execution time. Our construction is based on a bisection game that traverses the Merkle tree containing the ledger of all - valid or invalid - transactions. We prove that our proof system is succinct, complete and sound, and empirically demonstrate the feasibility of our scheme.
Editorial for TISMIR Special Collection: AI and Musical Creativity

This special issue focuses on research developments and critical thought in the domain of artificial intelligence (AI) applied to modeling and creating music. It is motivated by the AI Song Contests of 2020 and 2021, in which the four guest editors adjudicated or participated among many teams from around the world. The 2020 edition had 13 submissions and the 2021 edition had 38. The 2022 edition is now being planned. These unique events provide exciting opportunities for AI music researchers to test the state of the art and push the boundaries of what is possible, within the context of music creation. They portend a future when humans and machines work together as partners in music creation. Maybe “portend” is not the right term, but we must not think that the future of AI and music is only warm and fuzzy. It is important and timely to consider how we, in local and global contexts, can effectively and ethically develop and apply AI in contexts of music creation.
Linear Variational State-Space Filtering

We introduce Variational State-Space Filters (VSSF), a new method for unsupervised learning, identification, and filtering of latent Markov state space models from raw pixels. We present a theoretically sound framework for latent state space inference under heterogeneous sensor configurations. The resulting model can integrate an arbitrary subset of the sensor measurements used during training, enabling the learning of semi-supervised state representations, thus enforcing that certain components of the learned latent state space to agree with interpretable measurements. From this framework we derive L-VSSF, an explicit instantiation of this model with linear latent dynamics and Gaussian distribution parameterizations. We experimentally demonstrate L-VSSF's ability to filter in latent space beyond the sequence length of the training dataset across several different test environments.
Sequential Bayesian Inference for Factor Analysis

We develop an efficient Bayesian sequential inference framework for factor analysis models observed via various data types, such as continuous, binary and ordinal data. In the continuous data case, where it is possible to marginalise over the latent factors, the proposed methodology tailors the Iterated Batch Importance Sampling (IBIS) of Chopin (2002) to handle such models and we incorporate Hamiltonian Markov Chain Monte Carlo. For binary and ordinal data, we develop an efficient IBIS scheme to handle the parameter and latent factors, combining with Laplace or Variational Bayes approximations. The methodology can be used in the context of sequential hypothesis testing via Bayes factors, which are known to have advantages over traditional null hypothesis testing. Moreover, the developed sequential framework offers multiple benefits even in non-sequential cases, by providing posterior distribution, model evidence and scoring rules (under the prequential framework) in one go, and by offering a more robust alternative computational scheme to Markov Chain Monte Carlo that can be useful in problematic target distributions.
Improving Automatic Speech Recognition for Non-Native English with Transfer Learning and Language Model Decoding

ASR systems designed for native English (L1) usually underperform on non-native English (L2). To address this performance gap, \textbf{(i)} we extend our previous work to investigate fine-tuning of a pre-trained wav2vec 2.0 model \cite{baevski2020wav2vec,xu2021self} under a rich set of L1 and L2 training conditions. We further \textbf{(ii)} incorporate language model decoding in the ASR system, along with the fine-tuning method. Quantifying gains acquired from each of these two approaches separately and an error analysis allows us to identify different sources of improvement within our models. We find that while the large self-trained wav2vec 2.0 may be internalizing sufficient decoding knowledge for clean L1 speech \cite{xu2021self}, this does not hold for L2 speech and accounts for the utility of employing language model decoding on L2 data.
A Survey on Deep Reinforcement Learning-based Approaches for Adaptation and Generalization

Deep Reinforcement Learning (DRL) aims to create intelligent agents that can learn to solve complex problems efficiently in a real-world environment. Typically, two learning goals: adaptation and generalization are used for baselining DRL algorithm's performance on different tasks and domains. This paper presents a survey on the recent developments in DRL-based approaches for adaptation and generalization. We begin by formulating these goals in the context of task and domain. Then we review the recent works under those approaches and discuss future research directions through which DRL algorithms' adaptability and generalizability can be enhanced and potentially make them applicable to a broad range of real-world problems.
Active Token Mixer

The three existing dominant network families, i.e., CNNs, Transformers, and MLPs, differ from each other mainly in the ways of fusing spatial contextual information, leaving designing more effective token-mixing mechanisms at the core of backbone architecture development. In this work, we propose an innovative token-mixer, dubbed Active Token Mixer (ATM), to actively incorporate flexible contextual information distributed across different channels from other tokens into the given query token. This fundamental operator actively predicts where to capture useful contexts and learns how to fuse the captured contexts with the query token at channel level. In this way, the spatial range of token-mixing can be expanded to a global scope with limited computational complexity, where the way of token-mixing is reformed. We take ATM as the primary operator and assemble ATMs into a cascade architecture, dubbed ATMNet. Extensive experiments demonstrate that ATMNet is generally applicable and comprehensively surpasses different families of SOTA vision backbones by a clear margin on a broad range of vision tasks, including visual recognition and dense prediction tasks. Code is available at https://github.com/microsoft/ActiveMLP.
Knowledge-enhanced Session-based Recommendation with Temporal Transformer

Recent research has achieved impressive progress in the session-based recommendation. However, information such as item knowledge and click time interval, which could be potentially utilized to improve the performance, remains largely unexploited. In this paper, we propose a framework called Knowledge-enhanced Session-based Recommendation with Temporal Transformer (KSTT) to incorporate such information when learning the item and session embeddings. Specifically, a knowledge graph, which models contexts among items within a session and their corresponding attributes, is proposed to obtain item embeddings through graph representation learning. We introduce time interval embedding to represent the time pattern between the item that needs to be predicted and historical click, and use it to replace the position embedding in the original transformer (called temporal transformer). The item embeddings in a session are passed through the temporal transformer network to get the session embedding, based on which the final recommendation is made. Extensive experiments demonstrate that our model outperforms state-of-the-art baselines on four benchmark datasets.