Reviewerly/exHarmony huggingface dataset:
Adaptive Coding for Matrix Multiplication at Edge Networks

Edge computing is emerging as a new paradigm to allow processing data at the edge of the network, where data is typically generated and collected, by exploiting multiple devices at the edge collectively. However, exploiting the potential of edge computing is challenging mainly due to the heterogeneous and time-varying nature of edge devices. Coded computation, which advocates mixing data in sub-tasks by employing erasure codes and offloading these sub-tasks to other devices for computation, is recently gaining interest, thanks to its higher reliability, smaller delay, and lower communication cost. In this paper, our focus is on characterizing the cost-benefit trade-offs of coded computation for practical edge computing systems, and develop an adaptive coded computation framework. In particular, we focus on matrix multiplication as a computationally intensive task, and develop an adaptive coding for matrix multiplication (ACM <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) algorithm by taking into account the heterogeneous and time varying nature of edge devices. ACM <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> dynamically selects the best coding policy by taking into account the computing time, storage requirements as well as successful decoding probability. We show that ACM <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> improves the task completion delay significantly as compared to existing coded matrix multiplication algorithms.
Three-dimensional pose discrimination in natural images of humans

Perceiving 3D structure in natural images is an immense computational challenge for the visual system. While many previous studies focused on the perception of rigid 3D objects, we applied a novel method on a common set of non-rigid objects—static images of the human body in the natural world. Perception of body structure is particularly challenging due to various joint articulations with different frequency of occurrence, and appearance variations from changes due to occlusion, clothing, lighting, and viewpoint. As a result, natural images vary in pose typicality and the amount of information views provide to support body part parsing. We investigated (1) to what degree humans can interpret 3D body structure given viewpoint rotations about the vertical axis, and (2) to what extent this ability depends on a priori knowledge of 3D pose typicality and informativeness of viewpoints. Using a 2AFC pose matching task, we tested how well subjects were able to match a target natural pose image with one of two comparison, synthetic body images—one was rendered with the same 3D pose parameters as the target while the other was a distractor rendered with added noises on joint angles. Target natural images were drawn from the UP-3D dataset, whereas synthetic images were rendered with constant, predetermined clothing and lighting. Observers picked the synthetic pose which best matched the target despite changes in viewpoint about the vertical axis. We found that the ability to accurately match poses decreased with increasing differences between target and comparison viewpoints. When we grouped trials by typicality of underlying 3D poses and informativeness of viewpoints from natural images, we found that performance for typical poses was measurably better than atypical poses; however, we found no significant difference between informative and noninformative viewpoints. Our psychophysical results provide useful benchmarks for future model comparisons.
The Detection of Fake Reviews in Bestselling Books

This study detected the possible manipulation of reviews for bestseller books. The authors first used clustering analysis to identify the cluster of bestselling books and patterns of manipulated reviews and ratings. They then used an artificial neural network to predict the possibility of review manipulation in bestselling books based on the patterns identified. The prediction outcome has an accuracy rate of 89%. They found that fake or manipulated reviews for bestselling books could be identified by analyzing abnormal rating fluctuations. The findings could help e-commerce platforms identify review manipulations and thereby help customers make prudent purchase decisions.
Use Procedural Noise to Achieve Backdoor Attack

In recent years, more researchers pay their attention to the security of artificial intelligence. The backdoor attack is one of the threats and has a powerful, stealthy attack ability. There exist a growing trend towards the triggers is that the triggers become dynamic and global. In this paper, we propose a novel global backdoor trigger generated by procedural noise. Our backdoor triggers are much stealthy and straightforward to implement compared with most triggers. There are three types of procedural noise, and we evaluate the attack ability for the triggers with them on the different classification datasets, including CIFAR-10, GTSRB, CelebA, and ImageNet12.The experiment results show that our attack approach can bypass most defense approaches, even for the inspections of humans. We only need poison 5%-10% training data, and the attack success rate(ASR) can reach over 99%. To test the robustness of the backdoor model against the corruption methods that in practice, we introduce 17 corruption methods and compute the accuracy, attack success rate(ASR) of the backdoor model. The facts show that the backdoor models generated by our approaches have strong robustness for the most corruption methods and can be applied in reality. Our code is available at https://github.com/928082786/pnoiseattack.
Differentially private false discovery rate control

Differential privacy provides a rigorous framework for privacy-preserving data analysis. This paper proposes the first differentially private procedure for controlling the false discovery rate (FDR) in multiple hypothesis testing. Inspired by the Benjamini-Hochberg procedure (BHq), our approach is to first repeatedly add noise to the logarithms of the p-values to ensure differential privacy and to select an approximately smallest p-value serving as a promising candidate at each iteration; the selected p-values are further supplied to the BHq and our private procedure releases only the rejected ones. Moreover, we develop a new technique that is based on a backward submartingale for proving FDR control of a broad class of multiple testing procedures, including our private procedure, and both the BHq step- up and step-down procedures. As a novel aspect, the proof works for arbitrary dependence between the true null and false null test statistics, while FDR control is maintained up to a small multiplicative factor.
Joint Vertex-Time Filtering on Graphs With Random Node-Asynchronous Updates

In graph signal processing signals are defined over a graph, and filters are designed to manipulate the variation of signals over the graph. On the other hand, time domain signal processing treats signals as time series, and digital filters are designed to manipulate the variation of signals in time. This study focuses on the notion of vertex-time filters, which manipulates the variation of a time-dependent graph signal both in the time domain and graph domain simultaneously. The key aspects of the proposed filtering operations are due to the random and asynchronous behavior of the nodes, in which they follow a collect-compute-broadcast scheme. For the analysis of the randomized vertex-time filtering operations, this study first considers the random asynchronous variant of linear discrete-time state-space models, in which each state variable gets updated randomly and independently (and asynchronously) in every iteration. Unlike previous studies that analyzed similar models under certain assumptions on the input signal, this study considers the model in the most general setting with arbitrary time-dependent input signals, which lay the foundations for the vertex-time graph filtering operations. This analysis shows that exponentials continue to be eigenfunctions in a statistical sense in spite of the random asynchronous nature of the model. This study also presents the necessary and sufficient condition for the mean-squared stability and shows that stability of the underlying state transition matrix is neither necessary nor sufficient for the mean-squared stability of the randomized asynchronous recursions. Then, the proposed filtering operations are proven to be mean-square stable if and only if the filter, the graph operator and the update probabilities satisfy a certain condition. The results show that some unstable vertex-time graph filters (in the synchronous case) can be implemented in a stable manner in the presence of randomized asynchronicity, which is also demonstrated by numerical examples.
Usage Data for Predicting User Trends and Behavioral Analysis in E-Commerce Applications

Reviewing and buying the right goods from online websites is growing day by day in today's fast internet environment. Numerous goods in the same label are available to consumers. It is thus a difficult job for consumers to pick up the correct commodity at a decent price under different market conditions. Therefore, it is important for owners of online shopping websites to better understand their customers' needs and offer better services. For these reasons, the access log documented a vast amount of data related to user interactions with the websites. This access log therefore plays a key role in predicting user access trends and in recommending the best product to consumers. This research work therefore focuses on one such methodology for evaluating the pattern and behavioral analysis of users in e-commerce websites.
MRD: A Memory Relation Decoder for Online Handwritten Mathematical Expression Recognition

Recently, attention based encoder-decoder methods have been widely used in online handwritten mathematical expression recognition, which achieve significant improvements compared to traditional methods. The encoder-decoder methods usually employ string decoders to generate the recognition result, which are not well matched for tree-structured languages like math expression. A novel sequential relation decoder (SRD) was introduced to recognize the online mathematical expression as a math tree, which can be decomposed into a subtree sequence and each subtree consists of a relation node and two symbol nodes (related symbol node and primary symbol node). However, the alignments between these two symbol nodes were implemented by spatial attention probabilities, leading to incorrect recognition if spatial attention is not accurate. In this paper, we propose a memory relation decoder (MRD), equipped with a memory based attention model to determine the correspondence between two symbol nodes. Specifically, at each decoding step, this memory based attention finds the corresponding primary symbol node in the memory and treats it as the related symbol node, which actually achieves the alignments between two symbol nodes in an explicit manner. Besides, we propose to introduce global visual information while calculating attention probabilities to help alleviate the ambiguous problems in online handwritten mathematical expression recognition. Evaluated on a benchmark published by CROHME competition, the proposed approach can substantially outperform previous encoder-decoder methods.
Fractional decoding of codes from Hermitian curves

We present a new probabilistic decoding algorithm that can be used to perform fractional decoding of codes from the Hermitian curve. Fractional decoding means that the original codeword may be obtained from a received word using only an <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\alpha$</tex> -proportion of symbols of the received word, provided not too many errors have occurred. The procedure presented makes use of fractional decoding of Reed-Solomon codes while allowing for the use of codes of similar lengths over smaller fields.
The Relationship Between Motivation, Satisfaction with Exercise, Immersion in Exercise, And Continuous Behaviors in Cycling Based on Gamification Content

This study aims to identify the relationship between motivation, satisfaction with exercise, immersion in exercise, and continuous behaviors in Cycling based on gamification content, and to provide basic data on the direction of the virtual reality-based home training market. The study set a population of 300 users who said they were using the gamification content-based cycle movement to identify the relationship between motivation, exercise satisfaction, exercise immersion, and continuous behavior. The survey was conducted first using snowball sampling for those surveyed according to the nature of the population, and data was obtained by obtaining survey consent from users who certified that they were using the content online and SNS through convenience sampling. The results obtained from this are as follows: First, motivation of cycle motion through gamification content has been shown to have a static effect on exercise satisfaction. Second, motivation of cycle motion through gamification content has been shown to have a static effect on motion immersion. Third, Undo-satisfaction has been shown to have a static effect on exercise immersion. Fourth, exercise satisfaction has been shown to have a static effect on continuous behavior. Fifth, exercise immersion has been shown to have a static effect on continuous behavior.
Learning Diverse Local Patterns for Deepfake Detection with Image-level Supervision

To prevent the Deepfake-like videos from spreading, researchers have proposed many anti-forgery methods. However, most approaches require pixel-wise annotation, which conflicts with the real Deepfake detection scenario. To make full use of the image-level label, we propose a Local-Prediction framework that indirectly allows the image-level label to supervise local regions. To further enrich the local feature, we introduced the Local-Diversity concept in the Deepfake detection field for the first time. We proposed the Local-Diversity Loss based on the motivation that regional pattern differences can provide semi-supervised information during training. Compared to the previous method, our approach limits each classification unit's receptive field and enriches the feature diversity. In the experiment, our method is evaluated on three benchmark datasets of four widely-used manipulation types. The result shows that the Local-Prediction framework is beneficial to different CNN backbones and achieved significant performance. The proposed LD loss enriches the learned patterns of binary classifiers. Furthermore, we provide visualization and ablation studies to understand the mechanism.
Electric heater mathematical model for cyber-physical systems

The article discusses the heat and mass transfer dynamic model for an electric heater with lumped parameters, which allows transient processes simulation for the main influences. The proposed model is recommended to be used in cyber-physical systems for forecasting and evaluating the effectiveness of control systems integrated into a single information management system. The developed model can be used by specialists for the analysis and synthesis of control systems for balanced ventilation systems or industrial air conditioners. As an example, a numerical simulation of transient processes along the action main channels for an electric heater HE 36/2 manufactured by VTS CLIMA was carried out. The significant advantage of the proposed model is the possibility for using it for the synthesis and analysis of multidimensional control systems.
Azure Spatial Anchors

In this chapter, you will learn how to use Azure Spatial Anchors. We’ll learn how to anchor the objects in Mixed Reality to experience the real world; you will also explore the various steps required to start and stop an Azure Spatial Anchors session and create, upload, and download Azure Spatial Anchors on a HoloLens 2.
Dynamic-graph-based Unsupervised Domain Adaptation

Unsupervised domain adaptation aims to learn an accurate classifier for a target domain by leveraging knowledge learned from a related (source) domain. Existing approaches focus on deriving new domain-invariant feature representations to align two domains and an extra classifier is required. In this paper, we propose a novel unsupervised domain adaptation method to train a classifier directly for the target domain without learning the domain-invariant feature representation. For our method, the pseudo labels are assigned to target samples. An effective method is proposed to measure the relationship among cross-domain samples more accurately, so that we can construct a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$p$</tex> -nearest neighbor graph. Then label propagation is employed to update the target sample labels. The graph model and labels of target samples are expected to be updated alternately within an iterative framework. To further improve the classifier, a fuzzy classification and pseudo-label selection mechanism are utilized. Extensive experiments validate that our proposed method is superior or comparable to the state-of-the-art unsupervised domain adaptation methods.
An accurate, scalable and verifiable protocol for federated differentially private averaging

Learning from data owned by several parties, as in federated learning, raises challenges regarding the privacy guarantees provided to participants and the correctness of the computation in the presence of malicious parties. We tackle these challenges in the context of distributed averaging, an essential building block of federated learning algorithms. Our first contribution is a scalable protocol in which participants exchange correlated Gaussian noise along the edges of a graph, complemented by independent noise added by each party. We analyze the differential privacy guarantees of our protocol and the impact of the graph topology under colluding malicious parties, showing that we can nearly match the utility of the trusted curator model even when each honest party communicates with only a logarithmic number of other parties chosen at random. This is in contrast with protocols in the local model of privacy (with lower utility) or based on secure aggregation (where all pairs of users need to exchange messages). Our second contribution enables users to prove the correctness of their computations without compromising the efficiency and privacy guarantees of the protocol. Our construction relies on standard cryptographic primitives like commitment schemes and zero knowledge proofs.
Quantum One-class Classification With a Distance-based Classifier

The advancement of technology in Quantum Computing has brought possibilities for the execution of algorithms in real quantum devices. However, the existing errors in the current quantum hardware and the low number of available qubits make it necessary to use solutions that use fewer qubits and fewer operations, mitigating such obstacles. Hadamard Classifier (HC) is a distance-based quantum machine learning model for pattern recognition. We present a new classifier based on HC named Quantum One-class Classifier (QOCC) that consists of a minimal quantum machine learning model with fewer operations and qubits, thus being able to mitigate errors from NISQ (Noisy Intermediate-Scale Quantum) computers. Experimental results were obtained by running the proposed classifier on a quantum device and show that QOCC has advantages over HC.
Perhaps PTLMs Should Go to School – A Task to Assess Open Book and Closed Book QA

Our goal is to deliver a new task and leaderboard to stimulate research on question answering and pre-trained language models (PTLMs) to understand a significant instructional document, e.g., an introductory college textbook or a manual.PTLMs have shown great success in many question-answering tasks, given significant supervised training, but much less so in zero-shot settings.We propose a new task that includes two college-level introductory texts in the social sciences (American Government 2e) and humanities (U.S. History), hundreds of true/false statements based on review questions written by the textbook authors, validation/development tests based on the first eight chapters of the textbooks, blind tests based on the remaining textbook chapters, and baseline results given state-of-the-art PTLMs.Since the questions are balanced, random performance should be ~50%.T5, fine-tuned with BoolQ achieves the same performance, suggesting that the textbook's content is not prerepresented in the PTLM.Taking the exam closed book, but having read the textbook (i.e., adding the textbook to T5's pre-training), yields at best minor improvement (56%), suggesting that the PTLM may not have "understood" the textbook (or perhaps misunderstood the questions).Performance is better (~60%) when the exam is taken open-book (i.e., allowing the machine to automatically retrieve a paragraph and use it to answer the question).
Exploring the Relationship Between Technology Adoption and Cyber Security Compliance

IT infrastructure and systems are made up of technical and social systems that work together to ensure that organization's goals and objectives are met. Security controls and measures are developed and used to protect an organization's data and information systems. To improve cyber security, organizations focus most of their efforts on incorporating new technological approaches in products and processes, leaving out the most important and vulnerable factor. So this study intends to provide some practical implications to the technology developers and policymakers while identifying the factors that affect cyber security compliance in an organization or home environment for general users, HR, IT administrators, engineers, and others. It explored the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model and assessed the effect of its factors on cyber security compliance in organizations.
MSPPNet: A Lightweight Network for Real-time Semantic Image Segmentation

Abstract Real-time semantic segmentation is widely applied in many fields. However, current state-of-the-art methods ignore the inference speed, while some other models that have short run-times produce coarse segmentation results. To balance the inference speed and segmentation accuracy, we propose a Multi-scale Spatial Pyramid Pooling Network (MSPPNet), a lightweight and efficient network for real-time semantic segmentation. Here, we adopt modified Xception to obtain high-level and low-level feature maps, which fundamentally reduces computational complexity and the number of parameters. Besides, we design the Multi-scale Spatial Pyramid Pooling module (MSPP) to aggregate context information from high-level feature maps, which effectively improves segmentation accuracy. Furthermore, the spatial attention mechanism is employed to enrich the details of segmentation and recover object boundaries. Experiments on the Cityscapes dataset show that MSPPNet has less than 1M parameters, and achieves 64.55% mean IoU with a speed of 121 fps. It is demonstrated that MSPPNet achieves a balance between speed and accuracy.
Generation Challenges: Results of the Accuracy Evaluation Shared Task

The Shared Task on Evaluating Accuracy focused on techniques (both manual and automatic) for evaluating the factual accuracy of texts produced by neural NLG systems, in a sports-reporting domain. Four teams submitted evaluation techniques for this task, using very different approaches and techniques. The best-performing submissions did encouragingly well at this difficult task. However, all automatic submissions struggled to detect factual errors which are semantically or pragmatically complex (for example, based on incorrect computation or inference).