Reviewerly/exHarmony huggingface dataset:
Motion Posture Recognition Method Based on Support Vector Machine

To solve the problem of differential human motion recognition of different users in human-computer interaction, SVM is used to classify and recognize the motion posture. We use Kinect sensor to capture human motion, generate depth image, and establish three-dimensional human model after processing. Then the target behavior recognition adopts a two-level SVM classifier to map various action signals to the feature space to form a feature vector with a certain dimension. During the process of outputting recognition results, corresponding confidence is output and the motion posture is determined by the change classification of motion features. The experimental results show that this research method uses the invariance and orthogonality of support vector machine to improve the recognition rate of vector optimization to more than 95%. Its the action recognition effect is good, which has better robustness with similar algorithms.
An Evaluating Method of QoS for Composite Services Based on Interval Muti-attribute Decision Making

Composite Web services needs to meet the global Quality of Service (QoS) requirements of Customers. The evaluation of the services QoS is a important standard to make decision from numerous candidate composite services. However, due to the difficulty on the exact measurement of services QoS and exact expression of customers’ QoS requirements, the decision of Web services composition should be made on the fuzzy information. In this paper, an interval muti-attribute decision-making based evaluating method of QoS for composite services is presented. The method uses the interval numbers to describe the QoS parameters of the Web services, as well as the customers’ QoS requirements. The method for measuring interval-valued QoS parameters and aggregation are also proposed. With the consideration of the Characteristics of the customers’ requirements, the evaluation on QoS parameters is made to construct the decision-making matrix following the principle of "To be appropriate for Customers’ requirements". Finally, based on the matrix, a comprehensive evaluation is made by using TOPSIS. By assessing the customer satisfaction and interactivity, it gives a comprehensive evaluation on the composite Web service based on uncertain multi-attribute decision making method. With the help of the adaptive genetic algorithm, it tries to find the appropriate composite service that meets the global QoS requirements of the customers.
Extra-Trees for Human Activity Recognition Using Wearable Sensors

Intelligent recognition is a hot keyword nowadays, meanwhile Human Activity Recognition (HAR) is one of the most popular research fields, and it also has a wide range of applications such as entertainment, health and safety. In recent years, machine learning methods have developed rapidly and have been applied to various scenarios. Therefore, we propose to apply the Extra-Trees algorithm to the HAR problem, and collect motion data through the wearable sensor device carried by the experimenter. In the test work on the public domain data set, the accuracy of the Extra-Trees algorithm reached 93.25%, while the accuracy of the KNN algorithm as a comparison was 90.77%, and the accuracy of the Decision Tree algorithm was only 87.75%.
Moving Object Tracking Based on Infrared Thermal Imager and Vision Sensor

In this paper, we propose a moving object tracking method that fuses infrared thermal imager and vision data for solving the limitations of single mode image information. First, contour feature matching is realized by the proposed shape context and Hog feature on the basis of foreground detection by Gaussian Mixture Model. Second, the regularization and scaling characteristics are used to reorganize the corresponding relation and estimate the transformation. In order to extend match to the whole shape, a TPS transformation model is established. A regularization method is used to relax the interpolation requirements, and the estimation error is reduced by iteratively reorganizing corresponding relationship and estimating the transformation. Finally, moving objects are determined by adaptive background subtraction. In complex and changeable lighting and meteorological conditions, the application of vision sensor has certain limitations, and multi-sensor fusion can realize information complementarity, this method combines edge and contour feature information, with better fault tolerance, real-time and robustness than a single sensor.
Face Feature Extraction Algorithm Based on Wavelet Transform and CNN

To improve the accuracy and efficiency of face recognition algorithm, a convolution neural network(CNN) face feature recognition scheme based on wavelet transform is proposed in this paper. Firstly, the face image is decomposed into four regions with different frequencies and scales by wavelet transform. Then, the compressed image is transformed by discrete cosine transform, and the weighted distance is used for classification and recognition. The improved lightweight CNN is adopted in face recognition algorithm to effectively eliminate the noise. Based on MATLAB platform, the feasibility of such method is tested in the collected face image database. The simulation results show it has higher recognition rate and robustness, and better comprehensive performance compared with the traditional algorithm.
Research on Mining Methods of User Behavior Patterns in Smart Home

To realize the user interaction of smart home, this paper focuses on the cluster analysis and association analysis technology, which is used to mine the control habits of smart home users, and proposes a mining and home appliance control strategy based on user behavior pattern. The algorithm mines frequent itemsets from user behavior data, evaluates it with association rules and obtains strong positive correlation association rules. Then it analyzes the association of user behavior in different time periods by using temporal association rule technology, uses hierarchical clustering method to analyze user behavior data, and obtains similar usage patterns between household appliances. Finally, according to the above-mentioned user behavior habits, the intelligent control design of household appliances is performed. The experimental results show that the association rule data mining method can predict the future behavior of users in smart home environment, and the Apriori algorithm based on hash technology can improve the efficiency of user behavior prediction in smart home.
Research on mobile real time monitoring technology based on Intelligent mountaineering stick

In this paper, the threshold judgment method of acceleration and angular velocity is used to determine the guardian's body posture, and the adaptive filtering method is used to process the pulse wave, which can effectively judge the abnormal body posture and heart rate data of the ward and send out alarm information. The fall detection and heart rate data detection are designed and tested. According to the acceleration and angular velocity information compared with the set threshold, the attitude of intelligent crutches is detected and judged. In the aspect of heart rate detection, the adaptive filtering algorithm is used to make the obtained heart rate data more accurate. The guardian can set the normal heart rate range according to the physical condition of the guardian.
Summary of Research on Whispered Speech Recognition Technology

Speech recognition technology has been studied in normal speech and has achieved better recognition performance, but whispered speech recognition still needs a lot of research work. This article briefly introduces the development of whispered speech recognition technology in recent years, summarizes the application and improvement of traditional models in whispered speech recognition, and explains the main technologies, advantages and disadvantages of these methods. Due to the great contributions of deep learning and end-to-end to the field of whispered speech recognition, this article introduces their research results in the field of whispered speech recognition, and explains their main methods, advantages and disadvantages. On this basis, the whispered corpus and pseudo-whispered generation methods are summarized. Finally, the process of whispered speech research was discussed and analyzed objectively.
A LSTM-based Sentiment Analysis Network on Classifying Natural Language Comments

Machine learning techniques, especially deep learning, are currently widely studied in the sentiment analysis task. Automatically classifying natural language comments to positive or negative is an essential and challenging task. In this paper, the sentiment analysis network based on LSTM is utilized to predict human emotion via natural language. Firstly, several variants of LSTM networks are introduced and developed. Then, the proposed network is designed to be able to inference and classify five levels of rating via human comments as input. In addition, a movie reviews benchmark dataset is utilized to evaluate our proposed network with other baseline algorithms. Finally, the results demonstrate promising and competitive performance against other classical methods with the accuracy of 0.8743.
HEELS: A Host-Enabled eBPF-Based Load Balancing Scheme

Layer 4 (L4) load balancing is crucial in cloud computing and elastic microservices. Existing L4 load balancer designs can be split into two main categories: centralized designs using a hardware or software middlebox, and decentralized designs in which every node can play the role of the load balancer. Centralized designs offer better scheduling policies and easier worker node management, but suffer from I/O and CPU limitations. Decentralized designs scale better, but are harder to manage. We introduce HEELS, a novel load balancing scheme designed for internal cloud workloads and microservices, achieving the best of both worlds. HEELS uses the load balancer only during the connection establishment and allows clients and servers to communicate directly after that. Supporting general L4 load balancers and requiring no kernel changes, HEELS is readily deployable on the public cloud. We implement HEELS as a set of eBPF programs split across the client and server. Our evaluation shows that HEELS introduces minimal overheads, works with off-the-shelf load balancers (e.g., Katran by Meta), and significantly reduces the costs of cloud load balancers.
RingGuard: Guard io_uring with eBPF

io_uring offers a flexible yet efficient asynchronous I/O paradigm for Linux. Despite a significant performance improvement, it also brings many security concerns to the kernel. Not only does io_uring itself contain multiple vulnerabilities, but it can also be used to bypass existing security mechanisms such as seccomp. To address these problems, this paper proposes a security mechanism named RingGuard that safeguards io_uring with eBPF programs. RingGuard is carefully designed to reduce the overhead of I/O request submission and to ensure the security of inserted eBPF programs. Our evaluation shows that RingGuard provides encouraging security benefits with moderate overhead. For instance, the overhead of RingGuard in file I/O scenarios is merely 7.8%.
Practical and Flexible Kernel CFI Enforcement using eBPF

Enforcing control flow integrity (CFI) in the kernel (kCFI) can prevent control-flow hijack attacks. Unfortunately, current kCFI approaches have high overhead or are inflexible and cannot support complex context-sensitive policies. To overcome these limitations, we propose a kCFI approach that makes use of eBPF (eKCFI) as the enforcement mechanism. The focus of this work is to demonstrate through implementation optimizations how to overcome the enormous performance overhead of this approach, thereby enabling the potential benefits with only modest performance tradeoffs.
Seeing the Invisible: Auditing eBPF Programs in Hypervisor with HyperBee

The flexibility of eBPF makes it widely used in performance, security, and monitoring. However, this flexibility is a double-edged sword, allowing attackers to use eBPF for malicious purposes. Security researchers have discovered multiple backdoors built by eBPF. Detecting malicious eBPF programs is challenging since eBPF exploits are almost invisible to inspection in both the user and kernel space. To defend against malicious eBPF programs, auditing an operating system's eBPF programs externally at load time is a more efficient approach. We propose HyperBee, a system integrated into the hypervisor that enables auditing of eBPF programs loaded in guests without performance impact during the execution. Guests relinquish their ability to load eBPF programs and must complete verification and JIT compilation of their eBPF programs through HyperBee. We implement a prototype of HyperBee on KVM and the HyperBee-aware guest based on Linux and evaluate its performance when loading eBPF programs. Our results show that HyperBee only brings overhead at load time: 9% extra load time when there is no security policy and 17% extra load time when using security policies against known eBPF malicious programs.
Cloud in Digital Government

The development and widespread use of cloud technologies promises a number of economic prospects at the local, regional, and global levels. Cloud technologies have numerous advantages in terms of management, storage, and exchange of information resources in the virtual space for the public and private sector, citizens, and general users. The chapter provides an overview of the research conducted in the field of cloud technologies in Azerbaijan. The results show that the studies are mostly devoted to the infrastructure issues of cloud technologies, to the research of the conceptual bases of the creation of the government cloud, to the issues of security and personal data protection, as well as to the implementation of measures for the development of the economy of cloud technologies. The international experience in the field of government cloud was studied, and the realization of this concept and the results to be achieved were analysed. Necessary measures for the formation and development of the cloud technologies economy at the government level have been indicated.
Enabling eBPF on Embedded Systems Through Decoupled Verification

eBPF (Extended Berkeley Packet Filter) is a Linux kernel subsystem that aims to allow developers to write safe and efficient kernel extensions by employing an in-kernel verifier and just-in-time compiler (JIT). We find that verification is prohibitively expensive for resource-constrained embedded systems. To solve this we describe a system that allows for verification to occur outside of the embedded kernel and before BPF program load time. The in-kernel verifier and JIT are coupled so they must be decoupled together. A designated verifier kernel accepts a BPF program, then verifies, compiles, and signs a native precompiled executable. The executable can then be loaded onto an embedded device without needing the verifier and JIT on the embedded device. Decoupling verification and JIT from load-time opens the door to much more than running BPF programs on embedded devices. It allows larger and more expressive BPF programs to be verified, provides a way for new approaches to verification to be used without extensive kernel modification and creates the possibility for BPF program verification as a service.
Unleashing Unprivileged eBPF Potential with Dynamic Sandboxing

For safety reasons, unprivileged users today have only limited ways to customize the kernel through the extended Berkeley Packet Filter (eBPF). This is unfortunate, especially since the eBPF framework itself has seen an increase in scope over the years. We propose SandBPF, a software-based kernel isolation technique that dynamically sandboxes eBPF programs to allow unprivileged users to safely extend the kernel, unleashing eBPF's full potential. Our early proof-of-concept shows that SandBPF can effectively prevent exploits missed by eBPF's native safety mechanism (i.e., static verification) while incurring 0%-10% overhead on web server benchmarks.
Fast-MFQE: A Fast Approach for Multi-Frame Quality Enhancement on Compressed Video

For compressed images and videos, quality enhancement is essential. Though there have been remarkable achievements related to deep learning, deep learning models are too large to apply to real-time tasks. Therefore, a fast multi-frame quality enhancement method for compressed video, named Fast-MFQE, is proposed to meet the requirement of video-quality enhancement for real-time applications. There are three main modules in this method. One is the image pre-processing building module (IPPB), which is used to reduce redundant information of input images. The second one is the spatio-temporal fusion attention (STFA) module. It is introduced to effectively merge temporal and spatial information of input video frames. The third one is the feature reconstruction network (FRN), which is developed to effectively reconstruct and enhance the spatio-temporal information. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods in terms of lightweight parameters, inference speed, and quality enhancement performance. Even at a resolution of 1080p, the Fast-MFQE achieves a remarkable inference speed of over 25 frames per second, while providing a PSNR increase of 19.6% on average when QP = 37.
A Hybrid Many-Objective Optimization Algorithm for Job Scheduling in Cloud Computing Based on Merge-and-Split Theory

Scheduling jobs within a cloud environment is a critical area of research that necessitates meticulous analysis. It entails the challenge of optimally assigning jobs to various cloud servers, each with different capabilities, and is classified as a non-deterministic polynomial (NP) problem. Many conventional methods have been suggested to tackle this difficulty, but they often struggle to find nearly perfect solutions within a reasonable timeframe. As a result, researchers have turned to evolutionary algorithms to tackle this problem. However, relying on a single metaheuristic approach can be problematic as it may become trapped in local optima, resulting in slow convergence. Therefore, combining different metaheuristic strategies to improve the overall system enactment is essential. This paper presents a novel approach that integrates three methods to enhance exploration and exploitation, increasing search process efficiency and optimizing many-objective functions. In the initial phase, we adopt cooperative game theory with merge-and-split techniques to train computing hosts at different utilization load levels, determining the ideal utilization for each server. This approach ensures that servers operate at their highest utilization range, maximizing their profitability. In the second stage, we incorporate the mean variation of the grey wolf optimization algorithm, making significant adjustments to the encircling and hunting phases to enhance the exploitation of the search space. In the final phase, we introduce an innovative pollination operator inspired by the sunflower optimization algorithm to enrich the exploration of the search domain. By skillfully balancing exploration and exploitation, we effectively address many-objective optimization problems. To validate the performance of our proposed method, we conducted experiments using both real-world and synthesized datasets, employing CloudSim software version 5.0. The evaluation involved two sets of experiments to measure different evaluation metrics. In the first experiment, we focused on minimizing factors such as energy costs, completion time, latency, and SLA violations. The second experiment, in contrast, aimed at maximizing metrics such as service quality, bandwidth utilization, asset utilization ratio, and service provider outcomes. The results from these experiments unequivocally demonstrate the outstanding performance of our algorithm, surpassing existing state-of-the-art approaches.
Semantic Graph Representation Learning for Handwritten Mathematical Expression Recognition

Handwritten mathematical expression recognition (HMER) has attracted extensive attention recently. However, current methods cannot explicitly study the interactions between different symbols, which may fail when faced similar symbols. To alleviate this issue, we propose a simple but efficient method to enhance semantic interaction learning (SIL). Specifically, we firstly construct a semantic graph based on the statistical symbol co-occurrence probabilities. Then we design a semantic aware module (SAM), which projects the visual and classification feature into semantic space. The cosine distance between different projected vectors indicates the correlation between symbols. And jointly optimizing HMER and SIL can explicitly enhances the model’s understanding of symbol relationships. In addition, SAM can be easily plugged into existing attention-based models for HMER and consistently bring improvement. Extensive experiments on public benchmark datasets demonstrate that our proposed module can effectively enhance the recognition performance. Our method achieves better recognition performance than prior arts on both CROHME and HME100K datasets.
Translating and interpreting in the Metaverse: potential opportunities and challenges

The concept of the Metaverse has started to be studied by scholars from different disciplines after the evolution of the idea of building a virtual 3D universe reflecting the real world in digital platforms. Since the impacts of such a digital universe will be enormous, it is beneficial to understand the main motives for developing such a system, the basic features of the Metaverse, its operational steps, and its possible impacts on different disciplines. When the literature is analyzed to find resources about the Metaverse, it has been seen that there are only a limited number of studies in the literature since the Metaverse is a relatively new concept in the sense that we know it today. The relationship between the Metaverse and the profession of translation and interpreting is one of the latest issues; therefore, only a few studies have been found in the literature associating somehow the Metaverse with translation, and the scope of these studies is quite limited. Therefore, the present study is innovative research elaborating on the potential advantages and disadvantages of the Metaverse in the field of translation and interpreting by referring to the studies in the literature. After a detailed analysis of the data and comprehensive interpretation of possible impacts of the Metaverse on the profession, it has been concluded that the Metaverse offers many advantages for translators, interpreters, and educators. It is also noted that such a digital environment fully integrated into people’s lives may result in social, mental, and physical problems.
An efficient identity-based signature protocol over lattices for the smart grid

As the promising next-generation power grid, the smart grid has developed rapidly in recent years. The smart grid enables energy to be stored and delivered more efficiently and safely, but user data’s integrity protection has been an important security issue in the smart grid. Although lots of digital signature protocols for the smart grid have been proposed to resolve this problem, they are vulnerable to quantum attacks. To deal with this problem, an efficient identity-based signature protocol on lattices is proposed in this paper. To improve our protocol’s efficiency, the tree of commitments is utilized. Moreover, a detailed performance evaluation of the proposed protocol is made. The performance analysis demonstrates that the potential utility of our protocol in the smart grid is huge.