Reviewerly/exHarmony huggingface dataset:
Modeling functional control unit by matrix indicator on basis of programmable logic integrated circuit

The article presents the process of simulation of the functional control unit of a matrix indicator on the basis of a programmable logic integrated circuit in the Xilinx Vivado Design Suite tool programming environment. Analysis and synthesis of the hardware language constructs, as well as developing RTL models were carried out using the Xilinx ISE software. There have been defined the functional modules common to the matrix indicator control unit: &#x0D; a finite state machine that generates a sequence of characters; a matrix indicator control unit that outputs a sequence &#x0D; of characters to a matrix indicator generating a finite automaton; a button bounce filter; frequency divider. A logic unit model combining functional nodes has been realized. Xilinx ISE CAD produced an RTL model that shows interaction of the nodes and the appearance of the top level. The matrix indicator control unit is presented as four interrelated devices: a column counter that enumerates the columns; a column decoder that takes input values and produces the value "1" only to one of the outputs, the number of which is equal to the number of the column whose value is put to the input; a character code register which receives, stores and transmits character codes; read-only memory which stores encodings of lines for sending an information signal in case of arrival of a particular word or a column. The specific features of implementing the modules on programmable logic are considered. A test module developed in the Verilog language was designed for verification, which is a step-by-step input of values and delays between input. Testing the modules that make up the matrix indicator control unit was carried out.
How do we quantify the utility of quantum algorithms?

Quantum computing advantage emerges not from brute force power, but from subtle differences in information processing that can occur for key bottleneck subroutines. In 2019, the Google Quantum AI team performed a landmark experiment demonstrating quantum computational supremacy (Arute et al., 2019) where they performed a quantum computation that, at the time, could not be done on a classical supercomputer. This was remarkable because it was achieved by a processor with only 53 qubits, an observation that emerged from theoretical work which identified that quantum computers could have a massive advantage for certain specially designed benchmarking tasks (Boixo et al., 2018; Bremner et al., 2016).
McQueen: a Benchmark for Multimodal Conversational Query Rewrite

The task of query rewrite aims to convert an in-context query to its fully-specified version where ellipsis and coreference are completed and referred-back according to the history context. Although much progress has been made, less efforts have been paid to real scenario conversations that involve drawing information from more than one modalities. In this paper, we propose the task of multimodal conversational query rewrite (McQR), which performs query rewrite under the multimodal visual conversation setting. We collect a large-scale dataset named McQueen based on manual annotation, which contains 15k visual conversations and over 80k queries where each one is associated with a fully-specified rewrite version. In addition, for entities appearing in the rewrite, we provide the corresponding image box annotation. We then use the McQueen dataset to benchmark a state-of-the-art method for effectively tackling the McQR task, which is based on a multimodal pre-trained model with pointer generator. Extensive experiments are performed to demonstrate the effectiveness of our model on this task\footnote{The dataset and code of this paper are both available in \url{https://github.com/yfyuan01/MQR}
Heterogeneous Information Crossing on Graphs for Session-based Recommender Systems

Recommender systems are fundamental information filtering techniques to recommend content or items that meet users' personalities and potential needs. As a crucial solution to address the difficulty of user identification and unavailability of historical information, session-based recommender systems provide recommendation services that only rely on users' behaviors in the current session. However, most existing studies are not well-designed for modeling heterogeneous user behaviors and capturing the relationships between them in practical scenarios. To fill this gap, in this paper, we propose a novel graph-based method, namely Heterogeneous Information Crossing on Graphs (HICG). HICG utilizes multiple types of user behaviors in the sessions to construct heterogeneous graphs, and captures users' current interests with their long-term preferences by effectively crossing the heterogeneous information on the graphs. In addition, we also propose an enhanced version, named HICG-CL, which incorporates contrastive learning (CL) technique to enhance item representation ability. By utilizing the item co-occurrence relationships across different sessions, HICG-CL improves the recommendation performance of HICG. We conduct extensive experiments on three real-world recommendation datasets, and the results verify that (i) HICG achieves the state-of-the-art performance by utilizing multiple types of behaviors on the heterogeneous graph. (ii) HICG-CL further significantly improves the recommendation performance of HICG by the proposed contrastive learning module.
Research on the Application of Computer Data Analysis Technology in the Image Processing of Ocean Survey

De-noising, enhancing, and edge extraction of ocean engineering images can improve the utilization of information. After bilateral filtering pre-processed the image, a multi-scale spatial representation is introduced, and finally an approximate sparse model iterative learning strategy is used to group and detect the candidate corner points set to select the corner points at the smallest scale as the best corner points. Experimental results show that the improved algorithm improves the corner detection rate and reduces the number of redundant corner points, which is better than the multi-scale Harris algorithm and Harris-Laplace algorithm.
From Farm to FAIR: The Trials of Linking and Sharing Wheat Research Data

Abstract This paper describes progress towards an integrated data framework that supports the sharing of data from the Designing Future Wheat (DFW) strategic research programme funded by the UK BBSRC. DFW is a 5 year project ( https://designingfuturewheat.org.uk/ ) that spans eight research institutes and universities, and aims to deliver pre-breeding germplasm to breeders to improve and increase the genetic diversity of their breeding programs. DFW is committed to making its data open to the wider research community by adopting FAIR data sharing approaches. It is also a good example of a data-intensive strategic research programme which follows a cyclical Field-to-Lab-to-Field approach that is representative of much contemporary and multidisciplinary crop science research. However, even with dedicated funding to develop crop data research infrastructures within DFW, we found that there are many challenges that require pragmatic and flexible ways to enable them to interoperate. We present key DFW data resources as a case study to assess progress and discuss these challenges with a view to developing infrastructure that exposes metadata-rich datasets and that meets FAIR principles.
Surprise Minimization Revision Operators

Prominent approaches to belief revision prescribe the adoption of a new belief that is as close as possible to the prior belief, in a process that, even in the standard case, can be described as attempting to minimize surprise. Here we extend the existing model by proposing a measure of surprise, dubbed relative surprise, in which surprise is computed with respect not just to the prior belief, but also to the broader context provided by the new information, using a measure derived from familiar distance notions between truth-value assignments. We characterize the surprise minimization revision operator thus defined using a set of intuitive rationality postulates in the AGM mould, along the way obtaining representation results for other existing revision operators in the literature, such as the Dalal operator and a recently introduced distance-based min-max operator.
A distributed blossom algorithm for minimum-weight perfect matching

We describe a distributed, asynchronous variant of Edmonds's exact algorithm for producing perfect matchings of minimum weight. The development of this algorithm is driven by an application to online error correction in quantum computing, first envisioned by Fowler; we analyze the performance of our algorithm as applied to this domain in a sequel.
RoMQA: A Benchmark for Robust, Multi-evidence, Multi-answer Question Answering

We introduce RoMQA, the first benchmark for robust, multi-evidence, multi-answer question answering (QA). RoMQA contains clusters of questions that are derived from related constraints mined from the Wikidata knowledge graph. RoMQA evaluates robustness of QA models to varying constraints by measuring worst-case performance within each question cluster. Compared to prior QA datasets, RoMQA has more human-written questions that require reasoning over more evidence text and have, on average, many more correct answers. In addition, human annotators rate RoMQA questions as more natural or likely to be asked by people. We evaluate state-of-the-art large language models in zero-shot, few-shot, and fine-tuning settings, and find that RoMQA is challenging: zero-shot and few-shot models perform similarly to naive baselines, while supervised retrieval methods perform well below gold evidence upper bounds. Moreover, existing models are not robust to variations in question constraints, but can be made more robust by tuning on clusters of related questions. Our results show that RoMQA is a challenging benchmark for large language models, and provides a quantifiable test to build more robust QA methods.
In search of strong embedding extractors for speaker diarisation

Speaker embedding extractors (EEs), which map input audio to a speaker discriminant latent space, are of paramount importance in speaker diarisation. However, there are several challenges when adopting EEs for diarisation, from which we tackle two key problems. First, the evaluation is not straightforward because the features required for better performance differ between speaker verification and diarisation. We show that better performance on widely adopted speaker verification evaluation protocols does not lead to better diarisation performance. Second, embedding extractors have not seen utterances in which multiple speakers exist. These inputs are inevitably present in speaker diarisation because of overlapped speech and speaker changes; they degrade the performance. To mitigate the first problem, we generate speaker verification evaluation protocols that mimic the diarisation scenario better. We propose two data augmentation techniques to alleviate the second problem, making embedding extractors aware of overlapped speech or speaker change input. One technique generates overlapped speech segments, and the other generates segments where two speakers utter sequentially. Extensive experimental results using three state-of-the-art speaker embedding extractors demonstrate that both proposed approaches are effective.
On the Approximation and Complexity of Deep Neural Networks to Invariant Functions

Recent years have witnessed a hot wave of deep neural networks in various domains; however, it is not yet well understood theoretically. A theoretical characterization of deep neural networks should point out their approximation ability and complexity, i.e., showing which architecture and size are sufficient to handle the concerned tasks. This work takes one step on this direction by theoretically studying the approximation and complexity of deep neural networks to invariant functions. We first prove that the invariant functions can be universally approximated by deep neural networks. Then we show that a broad range of invariant functions can be asymptotically approximated by various types of neural network models that includes the complex-valued neural networks, convolutional neural networks, and Bayesian neural networks using a polynomial number of parameters or optimization iterations. We also provide a feasible application that connects the parameter estimation and forecasting of high-resolution signals with our theoretical conclusions. The empirical results obtained on simulation experiments demonstrate the effectiveness of our method.
Stochastic Mirror Descent in Average Ensemble Models

The stochastic mirror descent (SMD) algorithm is a general class of training algorithms, which includes the celebrated stochastic gradient descent (SGD), as a special case. It utilizes a mirror potential to influence the implicit bias of the training algorithm. In this paper we explore the performance of the SMD iterates on mean-field ensemble models. Our results generalize earlier ones obtained for SGD on such models. The evolution of the distribution of parameters is mapped to a continuous time process in the space of probability distributions. Our main result gives a nonlinear partial differential equation to which the continuous time process converges in the asymptotic regime of large networks. The impact of the mirror potential appears through a multiplicative term that is equal to the inverse of its Hessian and which can be interpreted as defining a gradient flow over an appropriately defined Riemannian manifold. We provide numerical simulations which allow us to study and characterize the effect of the mirror potential on the performance of networks trained with SMD for some binary classification problems.
Block Codes on Pomset Metric

Given a regular multiset $M$ on $[n]=\{1,2,\ldots,n\}$, a partial order $R$ on $M$, and a label map $\pi : [n] \rightarrow \mathbb{N}$ defined by $\pi(i) = k_i$ with $\sum_{i=1}^{n}\pi (i) = N$, we define a pomset block metric $d_{(Pm,\pi)}$ on the direct sum $ \mathbb{Z}_{m}^{k_1} \oplus \mathbb{Z}_{m}^{k_2} \oplus \ldots \oplus \mathbb{Z}_{m}^{k_n}$ of $\mathbb{Z}_{m}^{N}$ based on the pomset $\mathbb{P}=(M,R)$. The pomset block metric extends the classical pomset metric introduced by I. G. Sudha and R. S. Selvaraj and generalizes the poset block metric introduced by M. M. S. Alves et al over $\mathbb{Z}_m$. The space $ (\mathbb{Z}_{m}^N,~d_{(Pm,\pi)} ) $ is called the pomset block space and we determine the complete weight distribution of it. Further, $I$-perfect pomset block codes for ideals with partial and full counts are described. Then, for block codes with chain pomset, the packing radius and Singleton bound are established. The relation between MDS codes and $I$-perfect codes for any ideal $I$ is investigated. Moreover, the duality theorem for an MDS pomset block code is established when all the blocks have the same size.
Deletion-Based Tangle Architecture for Edge Computing

IOTA Tangle offers a promising approach for distributed ledger technology with the capability to compete with the traditional blockchain. To enable microtransactions the Internet of things (IoT) environment, IOTA employs a direct acrylic graph that ensures the integrity and immutability of the transactions. However, IoT data exhibit time sensitivity, wherein the value is lost after a period. Storing these temporary data for immutable storage would not be affordable in the distributed ledger. This study proposes a novel approach—referred to as D-Tangle—that enables data deletions in the Tangle architecture. To achieve this goal, D-Tangle divides transactions into three categories based on their expiration features and employs the climb-up writing technique. Extensive evaluations prove that D-Tangle enables instant deletions in finite lifetime data. Moreover, immutability and deletion upon request are guaranteed for unknown lifetime data.
User-Level Differential Privacy against Attribute Inference Attack of Speech Emotion Recognition in Federated Learning

Many existing privacy-enhanced speech emotion recognition (SER) frameworks focus on perturbing the original speech data through adversarial training within a centralized machine learning setup. However, this privacy protection scheme can fail since the adversary can still access the perturbed data. In recent years, distributed learning algorithms, especially federated learning (FL), have gained popularity to protect privacy in machine learning applications. While FL provides good intuition to safeguard privacy by keeping the data on local devices, prior work has shown that privacy attacks, such as attribute inference attacks, are achievable for SER systems trained using FL. In this work, we propose to evaluate the user-level differential privacy (UDP) in mitigating the privacy leaks of the SER system in FL. UDP provides theoretical privacy guarantees with privacy parameters $ε$ and $δ$. Our results show that the UDP can effectively decrease attribute information leakage while keeping the utility of the SER system with the adversary accessing one model update. However, the efficacy of the UDP suffers when the FL system leaks more model updates to the adversary. We make the code publicly available to reproduce the results in https://github.com/usc-sail/fed-ser-leakage.
Multistage attention network for human pose estimation

Human pose estimation is a fundamental yet challenging task in computer vision. Although many methods have achieved significant improvement, they are still insufficient for the fusion of feature maps at different stages, such as the stacked hourglass network (SHNet). The SHNet is a classic human pose estimation network that extracts multiscale features through stacked multistage downsampling and upsampling operations. We propose a multistage attention mechanism to fuse the multistage feature maps. Furthermore, we apply it in the SHNet to propose a multistage attention network (MANet). In the experiments, we demonstrated the effectiveness of MANet in human pose estimation on the common objects in context dataset and the MPII human pose dataset.
BI AVAN: Brain inspired Adversarial Visual Attention Network

Visual attention is a fundamental mechanism in the human brain, and it inspires the design of attention mechanisms in deep neural networks. However, most of the visual attention studies adopted eye-tracking data rather than the direct measurement of brain activity to characterize human visual attention. In addition, the adversarial relationship between the attention-related objects and attention-neglected background in the human visual system was not fully exploited. To bridge these gaps, we propose a novel brain-inspired adversarial visual attention network (BI-AVAN) to characterize human visual attention directly from functional brain activity. Our BI-AVAN model imitates the biased competition process between attention-related/neglected objects to identify and locate the visual objects in a movie frame the human brain focuses on in an unsupervised manner. We use independent eye-tracking data as ground truth for validation and experimental results show that our model achieves robust and promising results when inferring meaningful human visual attention and mapping the relationship between brain activities and visual stimuli. Our BI-AVAN model contributes to the emerging field of leveraging the brain's functional architecture to inspire and guide the model design in artificial intelligence (AI), e.g., deep neural networks.
Secure Distributed Optimization Under Gradient Attacks

In this paper, we study secure distributed optimization against arbitrary gradient attack in multi-agent networks. In distributed optimization, there is no central server to coordinate local updates, and each agent can only communicate with its neighbors on a predefined network. We consider the scenario where out of $n$ networked agents, a fixed but unknown fraction $\rho$ of the agents are under arbitrary gradient attack in that their stochastic gradient oracles return arbitrary information to derail the optimization process, and the goal is to minimize the sum of local objective functions on unattacked agents. We propose a distributed stochastic gradient method that combines local variance reduction and clipping (CLIP-VRG). We show that, in a connected network, when unattacked local objective functions are convex and smooth, share a common minimizer, and their sum is strongly convex, CLIP-VRG leads to almost sure convergence of the iterates to the exact sum cost minimizer at all agents. We quantify a tight upper bound of the fraction $\rho$ of attacked agents in terms of problem parameters such as the condition number of the associated sum cost that guarantee exact convergence of CLIP-VRG, and characterize its asymptotic convergence rate. Finally, we empirically demonstrate the effectiveness of the proposed method under gradient attacks in both synthetic dataset and image classification datasets.
Pengaruh Penggunaan Media Animasi Berbasis Multimedia terhadap Minat Belajar Siswa pada Mata Pelajaran Pendidikan Agama dan Budi Pekerti

In PAI learning the learning process is still often done conventionally and the learning media used are less varied which results in student interest in PAI learning is still low. The purposes of this study are: 1. To find out how the use of multimedia-based animation media in PAI subjects at SMK Negeri 2 Padang. 2. To find out students' interest in learning by using multimedia-based animation media in PAI learning. 3. To determine the effect of multimedia-based animation media on PAI subjects. This study uses a pre-experimental method with a one-shot case study design. The sample in this study was class XI TKJ 1 and 2 at SMK N 2 Padang. Data was collected through a questionnaire and the analysis used was simple linear regression. From the analysis proves that animation media has an effect on students' interest in learning, as evidenced by the data generated in class XI TKJ 1 on average all students pay attention and focus during learning only a few people do not focus or follow the lesson well. Likewise with class XI TKJ 2 the number of students who did not take part in learning was 4 people. Based on the results of the f test, the results of the f table &gt; f count are 9.543 &gt; 4.00 with a sign value of 0.003 &lt;0.05, which means that the X variable has an influence on the Y variable and H1 is accepted. And the results of the t test are found that the results of t count&gt; t table 3.089&gt; 2.00100, which means that H1 is accepted and H0 is rejected. It is hoped that teachers will be more active in teaching and learning activities and teachers are expected to be able to make other learning media to make it easier for students to understand learning with the help of media including animation media.
Sentiment Analysis in Telugu–English CMSM Text

The most common method for determining positive or negative sentiment within a text is sentiment analysis. It is frequently utilized by businesses to grasp customer’s opinion regarding the products. A sentiment analysis examines message discussions and assesses the tone, expectation, and feeling behind each message. It is turning into a significant tool to notice and figure out the sentiment. It naturally analyzes whether a person is happy/frustrated/sad. It deals with the task of determining the difference during a document or a sentence and has gotten loads of consideration lately for national language. With the ascent of social media, a lot of data is available in provisional language other than English. Telugu is one such language with bountiful information accessible in social media, and it is difficult to look out labeled data of sentences for Telugu sentiment analysis. In this project, we differentiate positive and negative sentiment classes based on the polarity of code-mixed sentences, and the metrics are evaluated using machine learning approaches such as Naive Bayes, support vector machine, and recurrent neural network.