Reviewerly/exHarmony huggingface dataset:
Unsupervised Feature Selection Method based on Structural Particularity of Minimum Spanning Tree

Unsupervised Feature Selection (UFS) methods try to extract features that can well keep the intrinsic structure of data. To make full use of such information in this paper we use one of the simplest graph sparsification strategies MST (Minimum Spanning Tree) for the task of UFS. A novel graph structural information method is proposed for unsupervised feature selection, we simplify and preserve correlation between features via MST through a structure that simultaneously captures the local and global structure of data, and then use graph structural information directly to achieve the subset representative features with minimum redundancy and more discriminative power. To show the effectiveness of our method, some of the most representative and referenced UFS methods are used for conducting experiments on some benchmark datasets. Experimental results verify that the proposed feature subset selection algorithm is effective, more specifically at the running time.
Rapid Person Re-Identification via Sub-space Consistency Regularization

Person Re-Identification (ReID) matches pedestrian across disjoint cameras. Existing ReID methods adopting real-value feature descriptors have achieved high accuracy, but they are low in efficiency due to the slow Euclidean distance computation as well as complex quick-sort algorithms. Recently, some works propose to yield binary encoded person descriptors which instead only require fast Hamming distance computation and simple counting-sort algorithms. However, the performances of such binary encoded descriptors, especially with short code (e.g, 32 and 64 bits), are hardly satisfactory given the sparse binary space. To strike a balance between the model accuracy and efficiency, we propose a novel Sub-space Consistency Regularization (SCR) algorithm that can speed up the ReID procedure by 0.25 times than real-value features under same dimensions whilst maintain a competitive accuracy, especially under short codes. SCR transforms real-value features vector (e.g, 2048 float32) with short binary codes (e.g, 64 bits) by first dividing real-value features vector into M sub-spaces, each with C clustered centroids. Thus the distance between two samples can be expressed as the summation of respective distance to the centroids, which can be sped up by offline calculation and maintained via a look-up-table. On the other side, these real-value centroids help to achieve significantly higher accuracy than using binary code. Lastly, we convert the distance look-up-table to be integer and apply the counting-sort algorithm to speed up the ranking stage. We also propose a novel consistency regularization with an iterative framework. Experimental results on Market-1501 and DukeMTMC-reID show promising and exciting results. Under short code, our proposed SCR enjoys Real-value-level accuracy and Hashing-level speed.
The G-I of cyber security

Computer Fraud & SecurityVol. 2022, No. 8 The SandboxThe G-I of cyber securityKaren RenaudKaren RenaudUniversity of StrathclydeSearch for more papers by this authorKaren RenaudPublished Online:24 Aug 2022https://doi.org/10.12968/S1361-3723(22)70586-6AboutSectionsView articleView Full TextView EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareShare onFacebookTwitterLinked InEmail View article References 1. Human Factors in Diving Conference, home page. Accessed Aug 2022. www.hf-in-diving-conference.com. Google Scholar FiguresReferencesRelatedDetails 1 August 2022Volume 2022Issue 8ISSN (print): 1361-3723ISSN (online): 1873-7056 Metrics History Published online 24 August 2022 Published in print 1 August 2022 Information© MA Business Ltd.
Get It in Writing: Formal Contracts Mitigate Social Dilemmas in Multi-Agent RL

Multi-agent Reinforcement Learning (MARL) is a powerful tool for training autonomous agents acting independently in a common environment. However, it can lead to sub-optimal behavior when individual incentives and group incentives diverge. Humans are remarkably capable at solving these social dilemmas. It is an open problem in MARL to replicate such cooperative behaviors in selfish agents. In this work, we draw upon the idea of formal contracting from economics to overcome diverging incentives between agents in MARL. We propose an augmentation to a Markov game where agents voluntarily agree to binding transfers of reward, under pre-specified conditions. Our contributions are theoretical and empirical. First, we show that this augmentation makes all subgame-perfect equilibria of all Fully Observable Markov Games exhibit socially optimal behavior, given a sufficiently rich space of contracts. Next, we show that for general contract spaces, and even under partial observability, richer contract spaces lead to higher welfare. Hence, contract space design solves an exploration-exploitation tradeoff, sidestepping incentive issues. We complement our theoretical analysis with experiments. Issues of exploration in the contracting augmentation are mitigated using a training methodology inspired by multi-objective reinforcement learning: Multi-Objective Contract Augmentation Learning (MOCA). We test our methodology in static, single-move games, as well as dynamic domains that simulate traffic, pollution management and common pool resource management.
Resource theory of quantum scrambling

Quantum scrambling refers to the spread of local quantum information into the many degrees of freedom of a quantum system. In this work, we introduce a resource theory of scrambling which incorporates two mechanisms, "entanglement scrambling" and "magic scrambling". We introduce two resource monotones called the Pauli growth and the OTOC (out-of-time-ordered correlator) magic for these two mechanisms, respectively. We use our resource theory to explain recent experimental observations of magic. We also show that both resource monotones can be used to bound the decoding fidelity in Yoshida's black hole decoding protocol. These applications provide an operational interpretation of the resource monotones defined in this work.
ProtoPFormer: Concentrating on Prototypical Parts in Vision Transformers for Interpretable Image Recognition

Prototypical part network (ProtoPNet) has drawn wide attention and boosted many follow-up studies due to its self-explanatory property for explainable artificial intelligence (XAI). However, when directly applying ProtoPNet on vision transformer (ViT) backbones, learned prototypes have a "distraction" problem: they have a relatively high probability of being activated by the background and pay less attention to the foreground. The powerful capability of modeling long-term dependency makes the transformer-based ProtoPNet hard to focus on prototypical parts, thus severely impairing its inherent interpretability. This paper proposes prototypical part transformer (ProtoPFormer) for appropriately and effectively applying the prototype-based method with ViTs for interpretable image recognition. The proposed method introduces global and local prototypes for capturing and highlighting the representative holistic and partial features of targets according to the architectural characteristics of ViTs. The global prototypes are adopted to provide the global view of objects to guide local prototypes to concentrate on the foreground while eliminating the influence of the background. Afterwards, local prototypes are explicitly supervised to concentrate on their respective prototypical visual parts, increasing the overall interpretability. Extensive experiments demonstrate that our proposed global and local prototypes can mutually correct each other and jointly make final decisions, which faithfully and transparently reason the decision-making processes associatively from the whole and local perspectives, respectively. Moreover, ProtoPFormer consistently achieves superior performance and visualization results over the state-of-the-art (SOTA) prototype-based baselines. Our code has been released at https://github.com/zju-vipa/ProtoPFormer.
FurryGAN: High Quality Foreground-aware Image Synthesis

Foreground-aware image synthesis aims to generate images as well as their foreground masks. A common approach is to formulate an image as an masked blending of a foreground image and a background image. It is a challenging problem because it is prone to reach the trivial solution where either image overwhelms the other, i.e., the masks become completely full or empty, and the foreground and background are not meaningfully separated. We present FurryGAN with three key components: 1) imposing both the foreground image and the composite image to be realistic, 2) designing a mask as a combination of coarse and fine masks, and 3) guiding the generator by an auxiliary mask predictor in the discriminator. Our method produces realistic images with remarkably detailed alpha masks which cover hair, fur, and whiskers in a fully unsupervised manner.
Mix-Pooling Strategy for Attention Mechanism

Recently many effective attention modules are proposed to boot the model performance by exploiting the internal information of convolutional neural networks in computer vision. In general, many previous works ignore considering the design of the pooling strategy of the attention mechanism since they adopt the global average pooling for granted, which hinders the further improvement of the performance of the attention mechanism. However, we empirically find and verify a phenomenon that the simple linear combination of global max-pooling and global min-pooling can produce pooling strategies that match or exceed the performance of global average pooling. Based on this empirical observation, we propose a simple-yet-effective attention module SPEM, which adopts a self-adaptive pooling strategy based on global max-pooling and global min-pooling and a lightweight module for producing the attention map. The effectiveness of SPEM is demonstrated by extensive experiments on widely-used benchmark datasets and popular attention networks.
When BERT Fails -- The Limits of EHR Classification

Transformers are powerful text representation learners, useful for all kinds of clinical decision support tasks. Although they outperform baselines on readmission prediction, they are not infallible. Here, we look into one such failure case, and report patterns that lead to inferior predictive performance.
SDBERT: SparseDistilBERT, a faster and smaller BERT model

In this work we introduce a new transformer architecture called SparseDistilBERT (SDBERT), which is a combination of sparse attention and knowledge distillantion (KD). We implemented sparse attention mechanism to reduce quadratic dependency on input length to linear. In addition to reducing computational complexity of the model, we used knowledge distillation (KD). We were able to reduce the size of BERT model by 60% while retaining 97% performance and it only took 40% of time to train.
On the binary linear constant weight codes and their autormorphism groups

We give a characterization for the binary linear constant weight codes by using the symmetric difference of the supports of the codewords. This characterization gives a correspondence between the set of binary linear constant weight codes and the set of partitions for the union of supports of the codewords. By using this correspondence, we present a formula for the order of the automorphism group of a binary linear constant weight code in terms of its parameters. This formula is a key step to determine more algebraic structures on constant weight codes with given parameters. Bonisoli [Bonisoli, A.: Every equidistant linear code is a sequence of dual Hamming codes. Ars Combinatoria 18, 181--186 (1984)] proves that the $q$-ary linear constant weight codes with the same parameters are equivalent (for the binary case permutation equivalent). We also give an alternative proof for Bonisoli's theorem by presenting an explicit permutation on symmetric difference of the supports of the codewords which gives the permutation equivalence between the binary linear constant weight codes.
Generalized Attention Mechanism and Relative Position for Transformer

In this paper, we propose generalized attention mechanism (GAM) by first suggesting a new interpretation for self-attention mechanism of Vaswani et al. . Following the interpretation, we provide description for different variants of attention mechanism which together form GAM. Further, we propose a new relative position representation within the framework of GAM. This representation can be easily utilized for cases in which elements next to each other in input sequence can be at random locations in actual dataset/corpus.
To show or not to show: Redacting sensitive text from videos of electronic displays

With the increasing prevalence of video recordings there is a growing need for tools that can maintain the privacy of those recorded. In this paper, we define an approach for redacting personally identifiable text from videos using a combination of optical character recognition (OCR) and natural language processing (NLP) techniques. We examine the relative performance of this approach when used with different OCR models, specifically Tesseract and the OCR system from Google Cloud Vision (GCV). For the proposed approach the performance of GCV, in both accuracy and speed, is significantly higher than Tesseract. Finally, we explore the advantages and disadvantages of both models in real-world applications.
Learning Branched Fusion and Orthogonal Projection for Face-Voice Association

Recent years have seen an increased interest in establishing association between faces and voices of celebrities leveraging audio-visual information from YouTube. Prior works adopt metric learning methods to learn an embedding space that is amenable for associated matching and verification tasks. Albeit showing some progress, such formulations are, however, restrictive due to dependency on distance-dependent margin parameter, poor run-time training complexity, and reliance on carefully crafted negative mining procedures. In this work, we hypothesize that an enriched representation coupled with an effective yet efficient supervision is important towards realizing a discriminative joint embedding space for face-voice association tasks. To this end, we propose a light-weight, plug-and-play mechanism that exploits the complementary cues in both modalities to form enriched fused embeddings and clusters them based on their identity labels via orthogonality constraints. We coin our proposed mechanism as fusion and orthogonal projection (FOP) and instantiate in a two-stream network. The overall resulting framework is evaluated on VoxCeleb1 and MAV-Celeb datasets with a multitude of tasks, including cross-modal verification and matching. Results reveal that our method performs favourably against the current state-of-the-art methods and our proposed formulation of supervision is more effective and efficient than the ones employed by the contemporary methods. In addition, we leverage cross-modal verification and matching tasks to analyze the impact of multiple languages on face-voice association. Code is available: \url{https://github.com/msaadsaeed/FOP}
Unscented Kalman filter with stable embedding for simple, accurate and computationally efficient state estimation of systems on manifolds in Euclidean space

This paper proposes a simple, accurate and computationally efficient method to apply the ordinary unscented Kalman filter developed in Euclidean space to systems whose dynamics evolve on manifolds.We use the mathematical theory called stable embedding to make a variant of unscented Kalman filter that keeps state estimates in closeproximity to the manifold while exhibiting excellent estimation performance. We confirm the performance of our devised filter by applying it to the satellite system model and comparing the performance with other unscented Kalman filters devised specifically for systems on manifolds. Our devised filter has a low estimation error, keeps the state estimates in close proximity to the manifold as expected, and consumes a minor amount of computation time. Also our devised filter is simple and easy to use because our filter directly employs the off-the-shelf standard unscented Kalman filter devised in Euclidean space without any particular manifold-structure-preserving discretization method or coordinate transformation.
From Distributed Quantum Computing to Quantum Internet Computing: an Overview

The possibility of quantum computing has been proposed decades ago, at least as far back as the 1980s, and distributed quantum computing has been studied around two decades ago. Recent times have seen experimental successes and advances in quantum computer hardware and in quantum networking, leading towards the quantum Internet. We provide in this paper an overview of concepts and ideas in distributed quantum computing since over two decades ago as well as look at recent efforts in the area, and consider how, with the development of the quantum Internet, distributed quantum computing is evolving into quantum Internet computing.
Revising Image-Text Retrieval via Multi-Modal Entailment

An outstanding image-text retrieval model depends on high-quality labeled data. While the builders of existing image-text retrieval datasets strive to ensure that the caption matches the linked image, they cannot prevent a caption from fitting other images. We observe that such a many-to-many matching phenomenon is quite common in the widely-used retrieval datasets, where one caption can describe up to 178 images. These large matching-lost data not only confuse the model in training but also weaken the evaluation accuracy. Inspired by visual and textual entailment tasks, we propose a multi-modal entailment classifier to determine whether a sentence is entailed by an image plus its linked captions. Subsequently, we revise the image-text retrieval datasets by adding these entailed captions as additional weak labels of an image and develop a universal variable learning rate strategy to teach a retrieval model to distinguish the entailed captions from other negative samples. In experiments, we manually annotate an entailment-corrected image-text retrieval dataset for evaluation. The results demonstrate that the proposed entailment classifier achieves about 78% accuracy and consistently improves the performance of image-text retrieval baselines.
Rancang Bangun Aplikasi Virtual Tour Berbasis Android untuk Pariwisata Pulau Belitung sebagai Media Informasi

Information Technology is expected to be a solution in tourism, especially in Belitung Island. The purpose of this study is to make it easier for tourists visiting Belitung Island to easily determine the tourist locations to be visited by showing the closest tourist locations and also virtual tours of each of the available tourist attractions. The method used in this research is the Multimedia Development Life Cycle (MDLC) method. This research was made so that tourists can easily determine the destination of tourist attractions based on the distance from the tourist location and virtual tours that utilize 360-degree images on the android application. At the search stage, the application will ask for gps access to see the starting point of the tourist. Then the system will automatically provide a list of tourist attractions sorted by the closest distance to tourists. Then after the tourist chooses a tourist spot, the application will bring up the details of the tourist spot and give the option to bring up a virtual tour of the tourist spot. With the addition of virtual tours and the Haversine formula for determining distances through the longitude and latitude of a location, it has proven to be effective in providing the detailed information travelers need.
Enhancing Security of IoT by Using Blockchain

In recent years, the Internet of Things has become urgent in our lives, as it is used in many areas to facilitate business and daily life.But unfortunately, there are concerns due to security and safety issues.Therefore, many researchers have turned their attention to identifying the best solutions for increasing security and raising privacy one of which is the usage of Blockchain in the Internet of Things.This paper presents the benefits of applying Blockchain technology to the Internet of Things, the challenges faced and the most up-to-date components that satisfy the Component-Based Development (CBD) principles to enhance security and privacy.
Penerapan Data Mining Untuk Memprediksi Ketersediaan Stok Produk HNI HPAI Menggunakan Algoritma C4.5

In a company, the availability of this stock must be considered to avoid product losses caused by expiration and also to avoid order cancellations. The data contained in the HPAI company application, called the HSIS application, can be utilized and applied for data mining in determining which products are in demand in everyday life. This HSIS application can be used by the Business Center (BC) and all Stock Agents in the HPAI HNI business. To maintain the availability of product stock, the purpose of this study is to test the Stock Agent using the C4.5 Algorithm to predict that the availability of products that must be maintained is in accordance with customer needs. The use of the C4.5 algorithm on stock agents can overcome stockpiling and finance or stock agents capital can be used to buy stock that must be available. The results of the analysis using the C4.5 algorithm stated that although the price of the product was expensive, it was still selling well as herbal products. So of the three types of HPAI HNI products (Herbs product, Health food &amp; beverage and Cosmetic &amp; home care), the stock that must be increased or available is the Herbs Product.
Analisa dan Perancangan Machine Learning Untuk Mendeteksi Kegagalan Job di Apache Spark

A collection of data stored in a database, so the longer the data, the bigger the data, because the data processed is very large, processing time in Apache Spark can take up to a dozen or tens of hours. Sometimes, the Apache Spark application even fails. Therefore, to minimize the waiting time that could have been avoided or reduced, artificial intelligence through Machine Learning will be used to detect whether an Apache Spark application will fail or run smoothly. Factors to determine this failure are called features and are generated through the feature engineering process. The purpose of this research is to design Machine Learning so that it is able to find out what features will determine the success or failure of the Apache Spark application. The research method used is the Prototyping process model.