Reviewerly/exHarmony huggingface dataset:
A Biased Proportional-Integral-Derivative-Incorporated Latent Factor Analysis Model

Nowadays, as the number of items is increasing and the number of items that users have access to is limited, user-item preference matrices in recommendation systems are always sparse. This leads to a data sparsity problem. The latent factor analysis (LFA) model has been proposed as the solution to the data sparsity problem. As the basis of the LFA model, the singular value decomposition (SVD) model, especially the biased SVD model, has great recommendation effects in high-dimensional sparse (HiDs) matrices. However, it has the disadvantage of requiring several iterations before convergence. Besides, the model PID-incorporated SGD-based LFA (PSL) introduces the principle of discrete PID controller into the stochastic gradient descent (SGD), the learning algorithm of the SVD model. It could solve the problem of slow convergence speed, but its accuracy of recommendation needs to be improved. In order to make better solution, this paper fuses the PSL model with the biased SVD model, hoping to obtain better recommendation result by combining their advantages and reconciling their disadvantages. The experiments show that this biased PSL model performs better than the traditional matrix factorization algorithms on different sizes of datasets.
Plastic and Stable Gated Classifiers for Continual Learning

Conventional neural networks are mostly high in plasticity but low in stability. Hence, catastrophic forgetting tends to occur over the sequential training of multiple tasks and a backbone learner loses its ability in solving a previously learnt task. Several studies have shown that catastrophic forgetting can be partially mitigated through freezing the feature extractor weights while only sequentially training the classifier network. Though these are effective methods in retaining knowledge, forgetting could still become severe if the classifier network is over-parameterised over many tasks. As a remedy, this paper presents a novel classifier design with high stability. Highway-Connection Classifier Networks (HCNs) leverage gated units to alleviate forgetting. When employed alone, they exhibit strong robustness against forgetting. In addition, they synergise well with many existing and popular continual learning archetypes. We release our codes at https://github.com/Nic5472K/CLVISION2021_CVPR_HCN
QuiCK

We present QuiCK, a queuing system built for managing asynchronous tasks in CloudKit, Apple's storage backend service. QuiCK stores queued messages along with user data in CloudKit, and supports CloudKit's tenancy model including isolation, fair resource allocation, observability, and tenant migration. QuiCK is built on the FoundationDB Record Layer, an open source transactional DBMS. It employs massive two-level sharding, with tens of billions of queues on the first level (separately storing the queued items for each user of every CloudKit app), and hundreds of queues on a second level (one per FoundationDB cluster used by CloudKit). Our evaluation demonstrates that QuiCK scales linearly with additional consumer resources, effectively avoids contention, provides fairness across CloudKit tenants, and executes deferred tasks with low latency.
Reorganizing local image features with chaotic maps: an application to texture recognition

Texture images are those where the focus of the analysis is on the spatial arrangement of pixels (primitives or textons) rather than on particular objects in the scene. The recognition of such images is naturally challenging as those primitives can be perceived in different manners depending on the image context. Despite the recent success of convolutional neural networks in texture recognition, model-based descriptors are still competitive, especially when we do not have access to large amounts of annotated data for training and the interpretation of the model is an important issue. Among the model-based approaches, fractal geometry has been one of the most popular, especially in biological applications. Nevertheless, fractals are part of a much broader family of models, which are the non-linear operators, studied in chaos theory. This scenario raises the question whether techniques from the broader area of chaos theory could be useful in texture modeling. Those techniques have been used for a long time in image cryptography, but studies on modeling in a broad sense have been scarce in the literature. In this context, we propose here a chaos-based local descriptor for texture recognition. More specifically, we map the image into the three-dimensional Euclidean space, iterate a chaotic map over this three-dimensional structure and convert it back to the original image. From such chaos-transformed image at each iteration we collect local descriptors (here we use local binary patters) and those descriptors compose the feature representation of the texture. The performance of our method was verified on the classification of benchmark databases and in the identification of Brazilian plant species based on the texture of the leaf surface. The achieved results confirmed our expectation of a competitive performance, even when compared with some learning-based modern approaches in the literature.
Time-Division Multiplexing for FPGA Considering CNN Model Switch Time

With the spread of real-time data analysis by artificial intelligence (Al), the use of accelerators in edge computing has been attracting attention due to their low power consumption and low latency. In this paper, we propose a system that further reduces the power consumption and cost by sharing an accelerator among multiple users while maintaining real-time performance. Four requirements are defined: high utilization of device, fair device usage among users, real-time performance, and resource abstraction. Targeting a use case of Al inference, we propose a system that can share a field-programmable gate array (FPGA) among multiple users while satisfying the requirements by switching convolutional neural network (CNN) models stored in the device memory on the FPGA. The system enables a time-division multiplexed accelerator with real-time performance and high device utilization by using a scheduling algorithm that considers the switch time of the CNN models. User fairness is also achieved by adopting ageing techniques in the scheduling algorithm, in which priority increases in accordance with job waiting time. In addition, a thread manager has been integrated to the system that absorbs the difference among CNN models to abstract underlying hardware resources. The system was implemented on an FPGA device and evaluated to be 24-94 % fairer and 31-33 % more resource efficient than the conventional system using first-come first-served and round robin algorithms.
Stealth: A Highly Secured End-to-End Symmetric Communication Protocol

Symmetric key cryptography is applied in almost all secure communications to protect all sensitive information from attackers, for instance, banking, and thus, it requires extra attention due to diverse applications. Moreover, it is vulnerable to various attacks, for example, cryptanalysis attacks. Cryptanalysis attacks are possible due to a single-keyed encryption system. The state-of-the-art symmetric communication protocol uses a single secret key to encrypt/decrypt the entire communication to exchange data/message that poses security threats. Therefore, in this paper, we present a new secure communication protocol based on Diffie-Hellman cryptographic algorithms, called Stealth. It is a symmetric-key cryptographic protocol to enhance the security of modern communication with truly random numbers. Additionally, it applies a pseudo-random number generator. Initially, Stealth uses the Diffie-Hellman algorithm to compute four shared secret keys. These shared secret keys are used to generate four different private keys to encrypt for the first block of the message for symmetric communication. Stealth changes its private keys in each communication, making it very hard to break the security protocol. Moreover, the four shared secret keys create additional complexity for the adversary to overcome, and hence, it can provide highly tight security in communications. Stealth neither replaces the existing protocol nor authentication mechanism, but it creates another security layer to the existing protocol to ensure the security measurement’s tightness.
Evaluation of Vietnamese Speech Recognition Platforms

research-article Evaluation of Vietnamese Speech Recognition Platforms Share on Authors: Hai Thanh Diep Eastern International University, Viet Nam Eastern International University, Viet NamView Profile , Thanh Thi My Nguyen Eastern International University, Viet Nam Eastern International University, Viet NamView Profile , Bich Ngoc Le Eastern International University, Viet Nam Eastern International University, Viet NamView Profile , Quy Xuan Dao Eastern International University, Viet Nam Eastern International University, Viet NamView Profile Authors Info & Claims ICMLSC'21: 2021 The 5th International Conference on Machine Learning and Soft ComputingJanuary 2021 Pages 141–146https://doi.org/10.1145/3453800.3453826Online:18 June 2021Publication History 0citation29DownloadsMetricsTotal Citations0Total Downloads29Last 12 Months29Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Deep Learning based Approach for Facilitating Online Proctoring using Transfer Learning

This paper aims at developing an algorithm which helps to ensure the reliability of online examinations. The proposed algorithm provides an automated approach to facilitate online proctoring which alleviates the cumbersome nature of its manual counterpart. It makes use of transfer learning to realize deep learning and it combines three models namely- YOLO (for fraudulent object and multi-person detection), MPGazeII (for abnormal gaze detection) and VGG16 (for Face recognition) and combines the results of all the individual anomaly detection algorithms to ultimately predict whether the examinee has been engaging in malpractice so that necessary action could be taken. The existing algorithms make use of huge amounts of processing for localization as well as tedious feature extraction to realize online proctoring. The proposed algorithm used Deep Learning to overcome the drawbacks of existing online Proctoring algorithms such that no hand-crafted feature extraction is involved.
Machine Learning Approach to Detection of Offensive Language in Online Communication in Arabic

This paper presents the results of several machine learning experiments, conducted with a dataset of YouTube comments in Arabic. The experiments aim at studying the impact of various text preprocessing, feature-extraction and feature-selection techniques on the accuracy of a document classifier for detection of offensive language in online communication in Arabic. Regarding data pre-processing, our experiments focus on filtering out noisy characters and normalising inconsistencies present in casual online writing in Arabic. The combined effect of these data preprocessing techniques and a few feature-extraction and feature-selection methods is then evaluated by training document classifiers. Our results give evidence that it is possible to train a classifier for the detection of offensive language on Arabic social media with reasonable overall accuracy of 0.84, and precision, recall and F1-score of 0.89, 0.76 and 0.81, respectively.
Saying the Unseen: Video Descriptions via Dialog Agents

Current vision and language tasks usually take complete visual data (e.g., raw images or videos) as input, however, practical scenarios may often consist the situations where part of the visual information becomes inaccessible due to various reasons e.g., restricted view with fixed camera or intentional vision block for security concerns. As a step towards the more practical application scenarios, we introduce a novel task that aims to describe a video using the natural language dialog between two agents as a supplementary information source given incomplete visual data. Different from most existing vision-language tasks where AI systems have full access to images or video clips, which may reveal sensitive information such as recognizable human faces or voices, we intentionally limit the visual input for AI systems and seek a more secure and transparent information medium, i.e., the natural language dialog, to supplement the missing visual information. Specifically, one of the intelligent agents - Q-BOT - is given two semantic segmented frames from the beginning and the end of the video, as well as a finite number of opportunities to ask relevant natural language questions before describing the unseen video. A-BOT, the other agent who has access to the entire video, assists Q-BOT to accomplish the goal by answering the asked questions. We introduce two different experimental settings with either a generative (i.e., agents generate questions and answers freely) or a discriminative (i.e., agents select the questions and answers from candidates) internal dialog generation process. With the proposed unified QA-Cooperative networks, we experimentally demonstrate the knowledge transfer process between the two dialog agents and the effectiveness of using the natural language dialog as a supplement for incomplete implicit visions.
Auxiliary Role of Artificial Intelligence Technology in Landscape Architecture Design

With the advancement of computer science and technology, architects have an unprecedented range of mechanical applications, and the practice of robots in construction is creating a new building code system. The roles of designers and systems are not yet clear, and discussions on this potential new system are needed. The purpose of this article is to study the auxiliary role of artificial intelligence technology in landscape architecture design. It combines machine learning, big data and intelligent cloud display technology to integrate various advanced algorithms. Internet equipment allows architects and developers to perform routine analysis, planning and design tasks before construction, and accelerate architects through related tasks, support land acquisition such as conception and related analysis, and deepen design and construction after docking. Artificial intelligence technology The use of technology in artificial intelligence can save architects’ labor and planning time costs, reduce design time by 25%, and effectively improve architects’ work efficiency.
G-Code Machina: A Serious Game for G-code and CNC Machine Operation Training

In this paper, we present a desktop-based CNC machining training system developed as a serious game for CNC manufacturing motivating young digital-ready users to train in the machining sector. The proposed serious game trains users to write G-code and set up virtual machines for completing Milling and Turning tasks, contrary to previous work mainly focusing on machining simulations. G-code is a simple and usable low-level command set for manufacturing complex objects without prior knowledge of Computer-Aided Manufacturing (CAM) systems. The proposed CNC machining serious game can be used as a standalone introductory training application before being involved with CNC manufacturing. It contains CNC machining and G-code tutorials explaining processes without the need for a book or the assistance from a trainer. The user can select to train in turning and milling machines. The system adapts to each user's progression and performance, assigning missions to a user when mistakes are continuous, while hastening the progress of an experienced user to more advanced missions. The user can check the correctness of the code they have written and receive feedback in relation to potential mistakes.
Optimizing Multi-objective Functions in Fuzzy Association Rule Mining

Fuzzy association rule mining is a popular method to discover hidden patterns and associations in quantitative data. With fuzzy logic, fuzzy association rule mining is easier for explainable human linguistics in real-world applications. Finding a better performance of fuzzy association rules and building a more reliable model are two main challenges. Therefore, this paper aims to propose multi-objective functions based fuzzy association rule mining method. It can optimize all of the performance metrics of association rules simultaneously in each optimization step in mining fuzzy association rules.
OAG_know: Self-supervised Learning for Linking Knowledge Graphs

We propose a self-supervised embedding learning frameworkSelfLinKGto link concepts in heterogeneous knowledge graphs. Without any labeled data, SelfLinKG can achieve competitive performance against its supervised counterpart, and significantly outperforms state-of-the-art unsupervised methods by 26%-50%. The essential components of SelfLinKG are local attention-based encoding and momentum contrastive learning. The former aims to learn the graph representation using an attention network, while the latter is to learn a self-supervised model across knowledge graphs using contrastive learning. SelfLinKG has been deployed to build the the new version, called OAG_know of Open Academic Graph (OAG). All data and codes are publicly available.
A Novel Model-Based Reinforcement Learning Attitude Control Method for Virtual Reality Satellite

Observing the universe with virtual reality satellite is an amazing experience. An intelligent method of attitude control is the core object of research to achieve this goal. Attitude control is essentially one of the goal-state reaching tasks under constraints. Using reinforcement learning methods in real-world systems faces many challenges, such as insufficient samples, exploration safety issues, unknown actuator delays, and noise in the raw sensor data. In this work, a mixed model with different input sizes was proposed to represent the environmental dynamics model. The predication accuracy of the environmental dynamics model and the performance of the policy trained in this paper were gradually improved. Our method reduces the impact of noisy data on the model’s accuracy and improves the sampling efficiency. The experiments showed that the agent trained with our method completed a goal-state reaching task in a real-world system under wireless circumstances whose actuators were reaction wheels, whereas the soft actor-critic method failed in the same training process. The method’s effectiveness is ensured theoretically under given conditions.
Rao distances and conformal mapping

In this chapter, we have described the Rao distance (due to C.R. Rao) and ideas of conformal mappings on 3D objects with angle preservations. Three propositions help us to construct distances between the points within the 3D objects in R3 and line integrals within complex planes. We highlight application of these concepts to virtual tourism.
A Practical Adaptive Key Recovery Attack on the LGM (GSW-like) Cryptosystem

We present an adaptive key recovery attack on the leveled homomorphic encryption scheme suggested by Li, Galbraith and Ma (Provsec 2016), which itself is a modification of the GSW cryptosystem designed to resist key recovery attacks by using a different linear combination of secret keys for each decryption. We were able to efficiently recover the secret key for a realistic choice of parameters using a statistical attack. In particular, this means that the Li, Galbraith and Ma strategy does not prevent adaptive key recovery attacks.
A Queueing Model for Industrial Public Blockchains and Validation

This paper presents a queueing model for mining-based public blockchains and validations with respect to specific yet practical characteristics of public blockchains such as Bitcoin and Ethereum, primarily in terms of transaction queue size and block waiting time, as an alternative solution to the conventional industrial networks for the trustworthiness it offers. A set of variables taken into account in this model lists the network traffic intensity, the maximum number of transactions in a block, the block time, and the transaction arrival rate, to mention a few. The proposed model provides a comprehensive yet fundamental basis to assure and ultimately optimize the design of blockchain technology-based applications in specific terms of performance. Numerical simulations have been conducted and the efficacy of the proposed model is validated in a quantitative yet practical manner versus Bitcoin and Ethereum.
A method to improve quantum state fidelity in circuits executed on IBM’s quantum computers

Recently, various quantum computing and communication tasks have been implemented using IBM’s superconductivity-based quantum computers. Here, we show that the circuits used in most of those works were not optimized and obtain the corresponding optimized circuits. Optimized circuits implementable in IBM quantum computers are also obtained for a set of reversible benchmark circuits. With a clear example, it is shown that the reduction in circuit cost enhances the fidelity of the output state (with respect to the theoretically expected state in the absence of noise) as fewer gates and less circuit depth introduce fewer errors during evolution of the state. Further, considering Mermin inequality as an example, it is shown that the violation of the classical limit is enhanced when we use optimized circuits. Thus, the present approach can be used to identify a relatively weaker signature of quantumness and to establish quantum supremacy in a stronger manner.
Unsupervised water scene dehazing network using multiple scattering model

In water scenes, where hazy images are subject to multiple scattering and where ideal data sets are difficult to collect, many dehazing methods are not as effective as they could be. Therefore, an unsupervised water scene dehazing network using atmospheric multiple scattering model is proposed. Unlike previous image dehazing methods, our method uses the unsupervised neural network and the atmospheric multiple scattering model and solves the problem of difficult acquisition of ideal datasets and the effect of multiple scattering on the image. In our method, in order to embed the atmospheric multiple scattering model into the unsupervised dehazing network, the unsupervised dehazing network uses four branches to estimate the scene radiation layer, transmission map layer, blur kernel layer and atmospheric light layer, the hazy image is then synthesized from the four output layers, minimizing the input hazy image and the output hazy image, where the output scene radiation layer is the final dehazing image. In addition, we constructed unsupervised loss functions which applicable to image dehazing by prior knowledge, i.e., color attenuation energy loss and dark channel loss. The method has a wide range of applications, with haze being thick and variable in marine, river and lake scenes, the method can be used to assist ship vision for target detection or forward road recognition in hazy conditions. Through extensive experiments on synthetic and real-world images, the proposed method is able to recover the details, structure and texture of the water image better than five advanced dehazing methods.