Reviewerly/exHarmony huggingface dataset:
Embedded Devices Security: Design and Implementation of a Light RDBMS Encryption Utilizing Multi-Core Processors

The pervasive proliferation of embedded, mobile, and IoT devices continue to change our lifestyle dramatically. However, the huge increase in these devices has come with critical breaches to data resting inside them. Many types of such data are considered to be sensitive and confidential. Because the most sensitive data of such devices are resting in databases, focusing on encrypting SQLite databases will be more efficient than full disk encryption (FDE). While SQLite is a very popular, lightweight, and easy-to-use relational database suitable for embedded and mobile devices, its stored data suffers serious security risks. If an attacker can gain access to higher system privileges or find a way to access the database plain file, he can tamper with the database files and user-sensitive data, which breaches the security CIA triad of SQLite. To ensure data confidentiality in SQLite databases of embedded devices, we present a design and implementation of a parallel database encryption system, called SQLite-XTS. The developed system encrypts the database pages on-the-fly in a transparent manner without user intervention. Because performance is a critical issue, SQLite-XTS utilizes multi-core processors coming with most current mobile and embedded devices. The developed parallel SQLite-XTS was successfully implemented and integrated into a testbed device. To assess the performance and feasibility of this system, it was compared to three other SQLite implementations: plain SQLite, serial XTS SQLite, and SQLCipher-CBC. The results show that SQLite-XTS reduces the overhead of database encryption from 30.8% with serial implementation to 17.8% when SQLite-XTS is used. This provides the developed system with an efficiency of 73% compared with its serial counterpart. The results clarify that SQLite-XTS introduces significant performance improvements compared to other implementations. Experiments also show that the system has a very low impact on the memory of these resource-limited devices.
Ship Formation Algorithm Based on the Leader–Follower Method

In recent years, marine engineers have increasingly relied on multiship collaborative work to complete complex tasks at sea and improve maritime transport efficiency.Ship formation significantly contributes to the maintenance of stability during multiship-coordinated operations, but establishing the formation structure while ensuring the safety of each ship remains challenging.Therefore, this study proposes an improved leader-follower-based formation algorithm as a solution to this issue.First, the fast marching square method was used for global static path planning.The leader-follower formation control method then enabled the path tracking of the follower ship to the leader ship.Finally, an improved artificial potential field method was used for local collision avoidance, and virtual obstacles were added to improve the situation in which the ship probably entered a local minimum point.The experimental results revealed that the proposed algorithm could plan a route with time, smoothness, and safety advantages for the formation leader and quickly form a stable formation.Notably, the ship can quickly initiate collision avoidance operations when it encounters an obstacle posing a collision risk.
Multi-Channel Sampling on Graphs and Its Relationship to Graph Filter Banks

In this paper, we consider multi-channel sampling (MCS) for graph signals. We generally encounter full-band graph signals beyond the bandlimited ones in many applications, such as piecewise constant/smooth graph signals and union of bandlimited graph signals. Full-band graph signals can be represented by a mixture of multiple signals conforming to different generation models. This requires the analysis of graph signals via multiple sampling systems, i.e., MCS, while existing approaches only consider single-channel sampling. We develop a MCS framework based on generalized sampling. We also present a sampling set selection (SSS) method for the proposed MCS so that the graph signal is best recovered. Furthermore, we reveal that existing graph filter banks can be viewed as a special case of the proposed MCS. In signal recovery experiments, the proposed method exhibits the effectiveness of recovery for full-band graph signals.
Robust Stereo Matching Using Discriminative Multilevel Features and Multimodal Bifurcated Cost Volume Network

Recent introduction of deep convolutional neural network (DCNN) into the task of stereo matching has led to a remarkable progress, achieving superior performance over the traditional methods. However, current stereo matching DCNN architectures still struggle to resolve matching ambiguities and local minima caused by violation of feature distinctiveness constraint and/or consistency constraint. Taking this underlying problem into account, we propose a robust stereo matching solution using discriminative multilevel feature network (DML-Net) and multimodal bifurcated cost volume network (MBCV-Net). The DML-Net first extracts multilevel (ML) features and then learns to discriminate among the ML features to facilitate accurate dense matching. The MBCV-Net consists of two parallel matching pathways and a fusion module for leveraging geometric relationship between stereo images via feature concatenation matching, in addition to feature-appearance-based matching. Finally, we train the proposed stereo matching network from a unified optimization perspective to allow effective combination of multimodal cost volumes. Experiment on standard stereo benchmarks revealed that the proposed method is effective and surpasses many state-of-the-art methods.
FedSeq: A Hybrid Federated Learning Framework Based on Sequential In-Cluster Training

Federated learning (FL) enables multiple devices to collaboratively accomplish a machine learning task by iteratively exchanging their model updates instead of raw data with a parameter server (PS), thus protecting users' data privacy. Nevertheless, the communication efficiency and the nonindependent and identically distributed (non-IID) data are remaining intractable challenges in FL. This article proposes a hybrid FL framework called FedSeq, based on user clustering and sequential in-cluster training, to improve the communication efficiency and the test accuracy, especially on non-IID data. The FedSeq first divides users into multiple clusters and selects a cluster head (CH) on behalf on each cluster to upload the model updates to the PS, thus drastically reducing the uplink communication overhead. Within each cluster, a sequential training method is designed, which enables the CHs' models witness more categories of training data and go through more meta-updates per training epoch, thus promoting the test accuracy. Moreover, we also provide the convergence analysis of FedSeq on IID data with Random user clustering, with specific experiments validating our theoretical analysis from a simulation perspective. In the experiments, we compare our FedSeq with other baselines, including FedSGD, FedAVG, FedProx, FedCluster, and HierFAVG, of which the results demonstrate that FedSeq outperforms other FL paradigms in terms of model accuracy and training efficiency. We also test FedSeq with Random and Low-Energy Adaptive Clustering Hierarchy clustering strategies to demonstrate the robustness of FedSeq to different clustering strategies. The FedSeq offers a potential solution to cope with non-IID data and reduce uplink communication overhead in FL.
Performance Comparison of Different Deep Reinforcement Learning Algorithms for Task Scheduling Problem in Blockchain-Enabled Internet of Vehicles

Recently, blockchain has been widely considered as a promising technology to cope with the security issues in Internet of Vehicles (IoV). However, due to the high energy consumption, large data storage and heavy transmission load of blockchain and the limited resources of IoV devices, the resource management is urgently to be studied. In this paper, we propose a blockchain-based trust trading platform in IoV scenario and formulate the Task Scheduling (TS) problem which selects the transactions to assemble block and effects the utilization of the wireless resources and the performance of blockchain system. The optimization object is designed by jointly considering the characteristics of wireless communications, the Quality of Service (QoS) and the implementation process of blockchain. The DRL algorithms are utilized as the solutions, and MCQ-TS, PG-TS, TDQ-TS and TDAC-TS algorithms are proposed base on several typical DRL methods. The computational complexity of the proposed algorithms is analyzed mathematically. Additionally, a fair and comprehensive comparison of the various proposed DRL methods is also conducted through the complexity analysis and the simulation results. Accordingly, the features and approriate applied scenarios of the proposed algorithms are summarized at last. PG-TS has the best optimization performance while MCQ-TS, TDQ-TS and TDAC-TS perform similar. MCQ-TS has the smallest complexity, and TDQ-TS and TDAC-TS have the best training efficiency.
Alleviating the Impact of Abnormal Events Through Multi-Constrained VM Placement

As a simple and low-cost way to obtain enough computing resources, more and more tenants migrate their tasks to the cloud. However, the frequent occurrence of abnormal events (e.g., malicious tenants and node failures) in the cloud will seriously affect the tenants’ QoS. Conventionally, the cloud vendors reduce the frequency of abnormal events by deploying auxiliary systems, which requires additional costs and increases network complexity. Considering that it is an unrealistic expectation to eliminate the occurrence of abnormal events in clouds, this paper proposes a complementary scheme to alleviate the negative impact scope when an abnormal event occurs through multi-constrained VM placement without consuming additional resources. Specifically, when deploying VMs, we limit the number of pods (or service nodes) each tenant can access and the number of tenants hosted by each pod (or service node). However, the multi-dimensional interaction among numerous system parameters and performance/resource considerations makes the problem of multi-constrained VM placement for alleviating the impact of abnormal events very challenging. To solve this problem, we formulate an integer linear programming and propose a rounding-based algorithm with a logarithmic approximation ratio. We implement our proposed algorithm on a physical testbed. The experimental and simulation results show the high efficiency of the proposed algorithm. For example, our algorithm reduces the impact scope of service node failure by 60%, the impact scope of malicious tenants by 40%, and the tenant task makespan by 25% compared with other alternatives.
Dual Transformer With Multi-Grained Assembly for Fine-Grained Visual Classification

Fine-grained visual classification requires distinguishing sub-categories within the same super-category, which suffers from small inter-class and large intra-class variances. This paper aims to improve the FGVC task towards better performance, for which we deliver a novel dual Transformer framework (coined Dual-TR) with multi-grained assembly. The Dual-TR is well-designed to encode fine-grained objects by two parallel hierarchies, which is amenable to capturing the subtle yet discriminative cues via the self-attention mechanism in ViT. Specifically, we perform orthogonal multi-grained assembly within the Transformer structure for a more robust representation, i.e., intra-layer and inter-layer assembly. The former aims to explore the informative feature in various self-attention heads within the Transformer layer. The latter pays attention to the token assembly across Transformer layers. Meanwhile, we introduce the constraint of center loss to pull intra-class samples’ compactness and push that of inter-class samples. Extensive experiments show that Dual-TR performs on par with the state-of-the-art methods on four public benchmarks, including CUB-200-2011, NABirds, iNaturalist2017, and Stanford Dogs. The comprehensive ablation studies further demonstrate the effectiveness of architectural design choices.
Fast Computation of RFD-Like Descriptors in Four Orientations

RFD-like binary descriptors have been designed to be fast and demonstrate good quality in image-matching tasks. One of those descriptors, RFDoc, produces state-of-the art results when applied to document localization systems. However, the computational efficiency of such descriptors strongly depends on their implementation. In this study, we consider the computation of RFD-like descriptors for an 8-bit single-channel image; provide a detailed implementation of the baseline algorithm; demonstrate its weak points; and propose four modifications. In those modifications, we investigate two ways to accelerate the descriptor computations: 1) compute common operations globally for the entire input image instead of computing them locally for every patch; or 2) use lookup tables to replace the most computationally demanding operations and minimize the number of conversions between integer and floating point types. Experiments on the document identification and localization task on the MIDV-2020 dataset have shown that the modifications with lookup tables are noticeably faster than the baseline, achieving a 2-2.6 times acceleration on x86 and ARM CPUs. Based on experimental results, modifications with global operations may outperform the baseline algorithm if there are many intersecting patches that require descriptor computation. We also demonstrated that one can use any of the proposed algorithms without loss of image-matching quality and without necessity to retrain the parameters of RFD-like descriptors. Finally, we propose an efficient way to compute the descriptors for four orientations of a patch, which is a important for document location systems. The proposed method reduces a common computation part for four orientations to a single run; thus, four descriptors are computed 3 times faster than the direct computation of the descriptors for four patches, as shown experimentally.
Developing a Video Game as an Awareness and Research Tool Based on SARS-CoV-2 Epidemiological Dynamics and Motivational Perspectives

In mid-2020, the University of Liège (ULiège, Belgium) commissioned the ULiège Video Game Research Laboratory (Liège Game Lab) and the AR/VR Lab of the HEC-Management School of ULiège to create a serious game to raise awareness of preventive measures for its university community. This project has its origins in two objectives of the institutional policy of ULiège in response to the crisis caused by SARS-CoV-2 to raise awareness among community members of various preventive actions that can reduce the spread of the virus and to inform about the emergence and progression of a pandemic. After almost two years of design, the project resulted in the creation of SARS Wars, a decision-making management game for browsers and smartphones. This article presents the creative process of the game, specifically the integration of an adapted SEIR (susceptible-exposed-infectious-recovered) model, as well as the modeling of intercompartmental circulation dynamics in the game’s algorithm, and the various limitations observed regarding the game’s original missions and possibilities for future work. The SARS-CoV-2 video game project may be considered an innovative way to translate epidemiology into a language that can be used in the scope of citizen sciences. On the one hand, it provides an engaging tool and encourages active participation of the audience. On the other hand, it allows us to have a better understanding of the dynamic changes of a pandemic or an epidemic (crisis preparedness, monitoring, and control) and to anticipate potential consequences in the given parameters at set time (emerging risk identification), while offering insights for impact on some parameters on motivation (social science aspect).
Exploring OpenMP GPU Offloading for Implementing Convolutional Neural Networks

Computing on heterogeneous architecture involving CPUs and accelerators is now a popular choice of parallel computing. As a directive-based programming model, OpenMP has become more and more comprehensive that supports a large variety of hardware architectures and commonly used parallel patterns for high performance computing applications. In this paper, we present our experience of using OpenMP offloading model for computer vision tasks with the implementation of Convolutional Neural Network (CNN) for image classification. We explore different approaches of using OpenMP directives for parallelizing computational loops and managing data copy between host and GPUs. We evaluate our implementation for CNN training using MNIST and ImageNet data sets. According to our evaluation, optimization of parallelizing computational loops and data copy leads to up to 3.1x and 1.6x improvements of overall execution time among single GPU cases. Besides, up to 2.7x improvement of overall execution time is observed from the comparison between 4-GPU cases and single GPU cases. We also compare our implementation with OpenMP CPU and cuDNN versions. We observed solid better performance comparing with OpenMP CPU version and on par performance with cuDNN version on ImageNet data set. Detailed breakdown analysis and comparison are also provided in the paper.
The solution to an open problem on the bentness of Mesnager's functions

Let n=2m. In the present paper, we study the binomial Boolean functions of the formfa,b(x)=Tr1n(ax2m−1)+Tr12(bx2n−13), where m is an even positive integer, a∈F2n⁎ and b∈F4⁎. We show that fa,b is a bent function if the Kloosterman sumKm(a2m+1)=1+∑x∈F2m⁎(−1)Tr1m(a2m+1x+1x) equals 4, thus settling an open problem of Mesnager. The proof employs tools including computing Walsh coefficients of Boolean functions via multiplicative characters, divisibility properties of Gauss sums, and graph theory.
mBERT-GRU multilingual deep learning framework for hate speech detection in social media

One major issue plaguing online social media is hate speech, a complex phenomenon whose identification and target categorization have been studied by the natural language processing community. In recent years, notable studies have been made towards hate speech detection using various mechanisms varying from traditional machine learning to complex deep neural network models. However, these studies mainly focus on high-resource English language. The multilingual societies such as the Indian subcontinent: English, Hindi and Hindi-English code-mixed languages are widespread and convenient for the users. The research works studying hate speech detection in these languages are still very limited. To fill this gap, we propose an mBERT-GRU framework comprising of multilingual BERT embedding and bidirectional GRU layers to learn the cumulative features for hate speech detection and its target categorization. We evaluated our work on three datasets HASOC-2019, HS and HEOT to prove the competitive performance. Our results show that the proposed framework outperformed monolingual and state-of-the-art methods on English, Hindi and Hindi-English code-mixed datasets with Macro-F1 measure values of 0.87, 0.83 and 0.77, respectively.
Relative vectors clustering and temporal constraint based generalized Hough transform for high frame rate and ultra-low delay arbitrary shape detection

Arbitrary shape detection, compared with analytic shape detection, plays a more significant role in machine vision for industrial automation. With the development of industrial automation, the requirements for low-delay detection and highprecision operation are gradually increasing. However, existing works on arbitrary shape detection pay more attention to detection accuracy, but few researchers attempt to achieve ultra-low detection delay, because of the limitation of the bandwidth between memory to CPU. This paper proposes clustering relative-vectors-based parallelization and temporal constraint for generalized Hough transform (GHT) algorithm compression to achieve the ultra-low delay process system, implemented on FPGA. By clustering relative vectors among closed edge pixels as a clustered vector, and defining a regularized R-Table structure, the parallelization of GHT has been increased. Moreover, fully utilizing the temporal information in high frame rate video leads to the compression of accumulator memory consumption, by confining the search widow and restricting the rotation range according to the detection result from the previous frame. The evaluation shows that the proposed architecture finishes the detection in VGA sized sequence with an ultra-low process delay of 1.851ms per frame.
CROMA: a crowdsourcing web-based application for image resources platform

As computer vision advances, the Philippines falls behind on the resources of a web-based tool that features image annotation. In line with this, this research develops a web application that users can use to crowdsource annotations from an uploaded file and have a web interface that summarizes all the stored images. Image annotation uses a computer and labels multiple methods in recognizing the data, classification, and object detection to identify and mark the prominent feature of an image to train the computer to recognize and identify an unlabeled image. It provides open access to any user and researcher who uses this app and collaborates with others. Although this app requires multiple image data to improve the app's overall performance, this app also features inputs from the user to widen the identifier feature of the app. This research used agile software development to ensure continuous development. In addition, the testing is used to continuously test and advise providers' feedback per step that the developers are doing. Ten evaluators are invited to test the system. The results show that the scenario completion time of using the CROMA Web Application is easy to use and navigate and uses the users' time well. The scores of each usability component show that the system is easy to navigate and that learns to use the system well. It is a good result and suggests that users can perform the specific task they need to do in the system. Furthermore, the user acceptance test scores of each usability component show that the web application is doing its function efficiently, and the users are satisfied and happy with the features. After three layers of agile testing, CROMA Web Application has been given the go-ahead to be deployed in the cloud server. For future work, automated labeling and a search function would help the user navigate preferences for every category of the images.
Research on target detection based table tennis drop point recognition system

Science and technology have changed life. With the increasingly widespread application of image processing technology in sports competitions, in ball games, the landing point recognition system that recognizes and locates fast moving spheres plays an important role in competitions and daily training, and has become one of the research hotspots of artificial intelligence in the field of cultural entertainment. By collecting the players' playing videos, accurately identifying the landing points of table tennis and analyzing the landing areas, applying the target detection and tracking technology to the intelligent table tennis training scene, the system algorithm has carried out a number of experiments in the table tennis intelligent training system of the project team, accurately and effectively identifying the landing points and scoring the areas of table tennis, and realizing the functions of landing point identification and scoring the areas of table tennis, It has high recognition accuracy and real-time, and basically meets the functional requirements.