Reviewerly/exHarmony huggingface dataset:
Fine-Grained Visual Categorization: A Spatial–Frequency Feature Fusion Perspective

Fine-grained visual categorization is a challenging issue owing to high intra-class and low inter-class variances. Classical approaches rely on pre-trained models or many fine annotations. In this paper, we observe that spatial and frequency information provides distinct image views, and propose a new spatial–frequency feature fusion (SFFF) perspective to handle this challenging issue. Specifically, we design a heterogeneous feature extraction loss function, construct a global and local fusion SFFF network, and propose an importance-sparsity selection strategy. For feature extraction, we focus on the frequency domain feature learning network, extract fine-grained features, and achieve feature complementarity. For feature selection, we propose importance ranking and sparse regularity to constrain spatial–frequency features. For feature fusion, we design a spatial–frequency loss and an inter-layer switching strategy to achieve local-global collaboration. Comparative experiments were performed on popular fine-grained datasets and classic datasets such as CUB200-2011, Stanford Cars, Stanford Dogs, FGVC-Aircraft, and CIFAR100. The effectiveness and outstanding performance of SFFF are confirmed by comparisons with more than 40 state-of-the-art fine-grained categorization methods. Ablation studies and visualizations are provided to facilitate an understanding of our approach.
A TD-RRT* Based Real-Time Path Planning of a Nonholonomic Mobile Robot and Path Smoothening Technique Using Catmull-Rom Interpolation

It is inevitable for a mobile robot to competently plan an optimal path from its starting, or current, location to a desired goal location. This is an insignificant task when the environment is unvarying. However, the practicable environment for the robot is hardly static, and it often has many moving obstacles. The robot may encounter one, or many, of these unknown and unforeseeable dynamic obstacles. The robot will now opt to proceed, when one of these obstacles is obstructing its path. The objective of this paper is to find a reasonable relation between parameters used in the path planning algorithm in a platform which a robot will be able to move from the start point in a dynamic environment with map and plan an optimal path to specified goal without any collision with moving and static obstacles. For this purpose, an asymptotically optimal version of Rapidly-exploring Random Tree (RRT algorithm), named RRT* is used. The algorithm is based on an incremental sampling which covers the whole space and acts fast. Moreover, this algorithm is computationally efficient, therefore it can be used in multidimensional environments.A method of dynamic replanning using TD-RRT* is presented. The robot will rectify or modify its path when unknown random moving or static snag obstructs the path. Various experimental results show the effectiveness of the proposed method which is faster than the basic RRT*, and the smooth path with the shortest distance can be obtained which satisfies the nonholonomic constraint of mobile robots.
A forensic investigation framework for Internet of Things monitoring

With low-energy wireless sensing platforms enabling communications between the physical world and numerous Internet based digital devices, Internet connected physical objects have blurred the lines that traditionally separated a physical scene from the digital scene. Whilst sophisticated new Internet enabled physical objects are rapidly introduced for various IoT applications, the increasing variety and similarities of the connected Things to ordinary physical objects has significant implications for the digital forensic process of identification. The often-constrained Things, however, communicate utilizing new and improved technologies that provide for robust wireless communications where other traditional wireless technologies are less suitable. This brings the opportunity to gather valuable insights from the radio frequency signals of a physical environment. This research introduces a novel concept of harnessing radio frequency signals to discover, determine and locate the wireless sensing deployments of a digital scene. A model of the system for IoT monitoring combines the processes of collecting communications between connected Things, building the logical topologies and predicting the locations of disparate Things. The design of this solution is suited to benefit both forensic investigators and law enforcement agencies.
Leveraging Towards Analytical Approach of Fixed batch-based Queueing assisted Blockchain System

Bitcoin is a virtual cryptocurrency built on the blockchain, a transaction-ledger database. The blockchain is updated and maintained by a miner passing through a mining process in which a group of miners competes to solve a tough puzzle-like challenge. Users’ transactions are grouped into blocks, and when an algorithmic problem specialized for the block is solved, the block is recorded to the blockchain. According to a recent study, newly arrived transactions are not included in the block being mined and waits in the unconfirmed transaction pool and mined by a miner till the number of transaction matches a minimum batch size i.e. the block size limit. The transaction-confirmation time is investigated in this paper by simulating the mining process using a queueing system with batch service. We assume a Markovian queue that processes transactions in fixed batch K. Additionally, we evaluate the model’s performance metrics, such as the estimated number of transactions seeking to enter the block from the queue, the mean number of transactions waiting in the unconfirmed transaction pool, the waiting time for a transaction, and the confirmation time for every transaction. The validation of the analytical model was performed utilizing the software packages MAPLE 18 to analyze the conclusions acquired by the queueing model.
Enhancing Document-Level Relation Extraction by Entity Knowledge Injection

Document-level relation extraction (RE) aims to identify the relations between entities throughout an entire document. It needs complex reasoning skills to synthesize various knowledge such as coreferences and commonsense. Large-scale knowledge graphs (KGs) contain a wealth of real-world facts, and can provide valuable knowledge to document-level RE. In this paper, we propose an entity knowledge injection framework to enhance current document-level RE models. Specifically, we introduce coreference distillation to inject coreference knowledge, endowing an RE model with the more general capability of coreference reasoning. We also employ representation reconciliation to inject factual knowledge and aggregate KG representations and document representations into a unified space. The experiments on two benchmark datasets validate the generalization of our entity knowledge injection framework and the consistent improvement to several document-level RE models.
Multi-Context Grouped Attention for Unsupervised Person Re-Identification

Recent advancements like multiple contextual analysis, attention mechanisms, distance-aware optimization, and multi-task guidance have been widely used for supervised person re-identification (ReID), but the implementation and effects of such methods in unsupervised ReID frameworks are non-trivial and unclear, respectively. Moreover, with increasing size and complexity of image- and video-based ReID datasets, manual or semi-automated annotation procedures for supervised ReID are becoming labor intensive and cost prohibitive, which is undesirable especially considering the likelihood of annotation errors increase with scale/complexity of data collections. Therefore, we propose a new iterative clustering framework that is insensitive to annotation errors and over-fitting ReID annotations (i.e., labels). Our proposed unsupervised framework incorporates (a) a novel multi-context group attention architecture that learns a holistic attention map from multiple local and global contexts, (b) an unsupervised clustering loss function that down-weights easily discriminative identities, and (c) a background diversity term that helps cluster persons across different cross-camera views without leveraging any identification or camera labels. We perform extensive analysis using the DukeMTMC-VideoReID and MARS video-based ReID datasets and the MSMT17 image-based ReID dataset. Our approach is shown to provide a new state-of-the-art performance for unsupervised ReID, reducing the rank-1 performance gap between supervised and unsupervised ReID to 1.1%, 12.1%, and 21.9% from 6.1%, 17.9%, and 22.6% for DukeMTMC, MARS, and MSMT17 datasets, respectively.
Research on Edge Network Security Technology Based on DHR

This paper examines how the extent of the network has expanded from the traditional computer Internet to the field of edge computing based on mobile communication technology with the in-depth development of the mobile Internet and the Internet of Things. In particular, the introduction of 5G has enabled massive edge computing nodes to build a high-performance, energy-efficient and low-latency mobile edge computing architecture. Traditional network security technologies and methods are not fully applicable in this environment. The focus of this paper is on security protection for edge networks. Using virtualized networks builds a dynamic heterogeneous redundancy security model (i.e., DHR). It first designs and evaluates the DHR security model, then constructs the required virtualized heterogeneous entity set, and finally constructs a DHR-based active defense scheme. Compared with existing network security solutions, the security protection technology of the edge network studied this time has a better protective effect against the unknown security threats facing the edge network.
Leakage-resilient Linear Secret-sharing Against Arbitrary Bounded-size Leakage Family

Motivated by leakage-resilient secure computation of circuits with addition and multiplication gates, this work studies the leakage-resilience of linear secret-sharing schemes with a small reconstruction threshold against any bounded-size family of joint leakage attacks, i.e., the leakage function can leak global information from all secret shares. We first prove that, with high probability, the Massey secret-sharing scheme corresponding to a random linear code over a finite field F is leakage-resilient against any $$\ell $$ -bit joint leakage family of size at most $$|{F}|^{k-2.01}/8^\ell $$ , where k is the reconstruction threshold. Our result (1) bypasses the bottleneck due to the existing Fourier-analytic approach, (2) enables secure multiplication of secrets, and (3) is near-optimal. We use combinatorial and second-moment techniques to prove the result. Next, we show that the Shamir secret-sharing scheme over a prime-order field F with randomly chosen evaluation places and with threshold k is leakage-resilient to any $$\ell $$ -bit joint leakage family of size at most $$|{F}|^{2k-n-2.01}/(k!\cdot 8^\ell )$$ with high probability. We prove this result by marrying our proof techniques for the first result with the existing Fourier analytical approach. Moreover, it is unlikely that one can extend this result beyond $$k/n\leqslant 0.5$$ due to the technical hurdle for the Fourier-analytic approach.
Simon’s Algorithm and Symmetric Crypto: Generalizations and Automatized Applications

In this paper we deepen our understanding of how to apply Simon’s algorithm to break symmetric cryptographic primitives. On the one hand, we automate the search for new attacks. Using this approach we automatically find the first efficient key-recovery attacks against constructions like 5-round MISTY L-FK or 5-round Feistel-FK (with internal permutation) using Simon’s algorithm. On the other hand, we study generalizations of Simon’s algorithm using non-standard Hadamard matrices, with the aim to expand the quantum symmetric cryptanalysis toolkit with properties other than the periods. Our main conclusion here is that none of these generalizations can accomplish that, and we conclude that exploiting non-standard Hadamard matrices with quantum computers to break symmetric primitives will require fundamentally new attacks.
An Integrated Model of Customers’ Intention to Reuse Information Service: What’s New for Conversational Agents?

The application of conversational agents empowered by artificial intelligence (AI) is becoming more common among tourism and hospitality businesses. Past research on AI has mostly investigated embodied robots rather than disembodied conversational agents. This study investigates the determinants of customers’ intention to reuse chatbots through integrating three different perspectives – technology acceptance model, computer-mediated communication theories, and interpersonal communication theories. Using online self-reported data analyzed by hierarchical regression and multi-group structural equation modeling, the efficacy of different theoretical models in predicting chatbot adoption were compared. A separate data set was collected from instant messaging (IM) users to provide implications that further explain the distinction between disembodied conversational agents and human conversational partners. Results suggest that variables associated with TAM and interpersonal attraction exert greatest influence on customers’ trust and reuse intention toward conversational agents. Managerial implications are suggested for practitioners to improve chatbot and IM design.
On the Pragmatics of Moving from System Models to Program Contracts

Consider a software development process supported by Formal Methods that includes an abstract system model and contract-based program verification. Relative to the total size of the formal system model and the information captured in program contracts, the interface between those two levels of abstraction is relatively small. This encourages a clear separation of concerns between the two and a well-structured transition from system model to program contracts even if they are expressed in different formalisms. The paper discusses the implications of such a development process, and illustrates it by way of the Hagrid key server, with Alloy as a system modeling language and VCC’s annotated C as a contract language.
A Systematic Review for Crypto Currency Price Prediction using Machine Learning

The present research is reviewing the requirement of a novel approach in the case of cryptocurrency price prediction. Several types of research in the area of cryptocurrency and price prediction have been considered. Moreover, researches that are related to machine learning and deep learning are considered. The research paper considers the role of Artificial intelligence and machine learning in the price prediction of cryptocurrency. The issues in the case of previous research and the need for research in the area of cryptocurrency price prediction have been considered. The future scope of BTC, Wave, Ethereum, and Made cryptocurrency value prediction is also discussed.
Deep Learning Based Symbol Detection With Natural Redundancy for Non-Uniform Memoryless Sources

In this letter, we consider improving the performance of symbol detection by utilizing the natural redundancy (NR) that widely exists in the transmission sources. As an initial study, the NR considered in this letter is the redundancy inherent in the data caused by the non-uniform distribution of the memoryless transmission sources. We first theoretically analyze the utilization of NR to improve the performance of symbol detection from the perspective of information theory, and then propose a deep learning (DL) based iterative symbol detection algorithm with the source’s prior distribution unknown. Simulation results demonstrate that the performance of symbol detection can be improved by utilizing NR and our proposed DL-based iterative symbol detection algorithm can obtain the theoretically optimal performance even when the source distribution is blind to the detector.
Adaptive Fast XGBoost for Regression

The increasing generation of data by devices, people and systems arises the need for processing non-stationary data streams, which continuously change over time. It was noticed that when compared to data stream classification, there is a lack of data stream regression studies. This work proposes AFXGBReg-D, an Adaptive Fast regression algorithm using XGBoost and active concept drift detectors. AFXGBReg uses an alternate model training strategy to achieve lean models adapted to concept drift, combined with a set of drift detector algorithms: ADWIN, KSWIN and DMM. We compared two AFXGBReg variants with other regressors and data stream regressors, simulating using synthetic datasets with different kinds of concept drifts. We show that AFXGBReg models have similar MSE to ARFReg, with these models achieving the best performance than others as proven statistically. Also AFXGBReg is 33 times faster than ARFReg, meaning that it is able to keep the same MSE level while being much faster. Another improvement is its ability of doing a faster recovery from concept drifts, having a smaller MSE peak.
Identifying similar sentences when processed by Apache Spark

Machine learning, simply put, is the practice of using computer programs to learn from data without hard-coded instructions. One use for this type of AI computation is ‘similarity matching’ - identifying how two texts might be similar. Advanced natural language processing technologies can now be used on a large scale to identify similar sentences when processed by Apache Spark and other open-source frameworks on top of distributed file systems such as HDFS, Hadoop Distributed File System (HDFS). Basic sentence similarity matching techniques are more appropriate for large scale tasks where limited resources are expected but overtime they would provide more accuracy. This paper will be focused on how to setup the training of specific question pairs on Apache Spark, which can allow algorithms to learn patterns in close matrices. The authors apply clustering algorithms, Apache Spark, and machine learning algorithms in this research to develop a model that evaluates whether two questions are the same in a dataset. This method yielded a 70% accuracy using relatively simple methods.
Fingertip detection based on Freeman chain code analysis

In order to meet the requirements of real-time fingertip positioning and detection in some scenes, a fingertip detection method based on Freeman chain code analysis is proposed in this paper. Firstly, the image collected by the camera needs to be preprocessed. The edge information of the palm is obtained by median filtering, analyzing the skin color space, binarization and morphological processing. The Freeman chain code of the hand contour is obtained by contour tracking calculation. After the convex points of the chain code of the hand contour are repaired, the matching points are analyzed according to the chain code difference, so as to realize the function of fingertip detection. The test shows that the fingertip detection based on Freeman chain code can detect the fingertip position quickly and accurately, and has good detection performance.
IC9600: A Benchmark Dataset for Automatic Image Complexity Assessment

Image complexity (IC) is an essential visual perception for human beings to understand an image. However, explicitly evaluating the IC is challenging, and has long been overlooked since, on the one hand, the evaluation of IC is relatively subjective due to its dependence on human perception, and on the other hand, the IC is semantic-dependent while real-world images are diverse. To facilitate the research of IC assessment in this deep learning era, we built the first, to our best knowledge, large-scale IC dataset with 9,600 well-annotated images. The images are of diverse areas such as abstract, paintings and real-world scenes, each of which is elaborately annotated by 17 human contributors. Powered by this high-quality dataset, we further provide a base model to predict the IC scores and estimate the complexity density maps in a weakly supervised way. The model is verified to be effective, and correlates well with human perception (with the Pearson correlation coefficient being 0.949). Last but not the least, we have empirically validated that the exploration of IC can provide auxiliary information and boost the performance of a wide range of computer vision tasks. The dataset and source code can be found at https://github.com/tinglyfeng/IC9600.
AutoAvatar: Autoregressive Neural Fields for Dynamic Avatar Modeling

Neural fields such as implicit surfaces have recently enabled avatar modeling from raw scans without explicit temporal correspondences. In this work, we exploit autoregressive modeling to further extend this notion to capture dynamic effects, such as soft-tissue deformations. Although autoregressive models are naturally capable of handling dynamics, it is non-trivial to apply them to implicit representations, as explicit state decoding is infeasible due to prohibitive memory requirements. In this work, for the first time, we enable autoregressive modeling of implicit avatars. To reduce the memory bottleneck and efficiently model dynamic implicit surfaces, we introduce the notion of articulated observer points, which relate implicit states to the explicit surface of a parametric human body model. We demonstrate that encoding implicit surfaces as a set of height fields defined on articulated observer points leads to significantly better generalization compared to a latent representation. The experiments show that our approach outperforms the state of the art, achieving plausible dynamic deformations even for unseen motions. https://zqbai-jeremy.github.io/autoavatar .
K-Qbot: Language Learning Chatbot Based on Reinforcement Learning

The application of Reinforcement Learning (RL) as an emergent field of Machine Learning has shown positive results in interdisciplinary fields. Although research has proven its effectiveness in language education through various agents (e.g., chatbots, robots, talking avatars), its application in letter acquisition is relatively new. In light of the alphabet transition from Cyrillic to Latin for the Kazakh language, potential challenges might be associated with learning and memorizing the new alphabet. Specifically, students with extant alphabet knowledge might struggle in using the later-learnt new alphabet given no sufficient practice. In this paper, we present a chatbot based on Reinforcement Learning that is anticipated to assist university students in learning the Kazakh Latin alphabet during an interaction in a letter acquisition scenario. Thus, we attempt to identify whether the RL chatbot is efficient for this learning scenario through an online survey study involving pre-test, chatbot interaction, and post-test.
Design of English Production Education Integration Platform Based on Genetic Algorithm

With the promotion of cooperation between school and business, and the combination of education and education, the cloud computing environment of the university must be responsible for not only the information of the teacher office and the curriculum but also the customer’s education and student experiment. Such demand has made a major challenge to the construction of cloud platforms. Based on the genetic algorithm, we have designed an integrated platform for English industry education, and solved the safety sequestration and resource sharing problem of the University cloud platform environment in the industrial education integration scene.