Reviewerly/exHarmony huggingface dataset:
Towards Guaranteed Privacy in Stream Processing: Differential Privacy for Private Pattern Protection

Sensor data often contain private information that requires proper protection. Most existing privacy-preserving mechanisms (PPMs) for data streams undermine the utility of the entire data stream and limit the performance of data-driven applications. We attempt to break the limitation and establish a new foundation for PPMs by proposing novel pattern-level differential privacy (DP) guarantees and pattern-level PPMs that fulfill pattern-level DP. They operate only on data that correlate with private patterns rather than on the entire data stream, leading to higher data utility. We first describe results for sequence operator based patterns in a centralized system and outline future work to generalize it for other operators and to local solutions.
On Improving Streaming System Autoscaler Behaviour using Windowing and Weighting Methods

Distributed stream processing systems experience highly variable workloads. This presents a challenge when provisioning compute to meet the needs of these workloads. Rightsizing systems for peak demand leads to often-unacceptable financial cost, motivating the need for adaptive approaches to meet the needs of changing workloads. The choice of parallelism of workload operators are commonly governed by autoscalers, but their behaviour is often case specific and highly sensitive to the choice of tunable parameters and thresholds. This presents a challenge to practitioners wishing to understand the performance implications of their decisions.
Human-machine recognition based on mouse behavior modeling

User identity authentication of computer systems can be seen everywhere in daily life. Most systems use traditional onetime authentication methods, such as password, fingerprint and face recognition based on biometrics. However, there is a risk of password being cracked or forgotten, and fingerprint and face recognition require specific hardware devices. For the one-time authentication method, once the intruder passes the one-time authentication, the operation after the intrusion of the system will no longer be restricted. Human operation behavior can be regarded as a biological behavior feature, which cannot be stolen or copied, and can be used for identity authentication. If the user's mouse click and slide is abstracted into a behavior model, the model can be used to judge the user's identity and achieve user identity authentication. This paper studies human-machine recognition model based on mouse operation behavior. This paper deeply analyzes the advanced features of mouse click and slide, and uses machine learning algorithm to train mouse operation behavior model to identify whether the mouse is operated by human or software. This method can effectively detect the malicious behavior of hackers who use software to operate the mouse to automatically log in to the system.
Knowledge atlas storage optimization algorithm based on two-level compression

A knowledge graph is a special kind of graph data, which consists of a triad. Each node in the knowledge graph has several attributes and their attribute values. The storage of the knowledge graph has been the object of academic research, and in this paper, we conduct an in-depth study on the knowledge graph data indexing and compression storage algorithm supported by the RDF graph model, and propose an optimization algorithm for the storage query after the second-level compression. The core of this paper is that after the second-level compression of the k2-tree tree, the sub-matrices are prioritized in terms of the size of data blocks, and when retrieving data, they are retrieved according to the priority, so that the blocks in front are both subject and object at the same time, which can improve the efficiency of data reading, so that the parts with more information will be retrieved first, instead of the traditional sequential retrieval, which tends to retrieve the null values or the data with less information.
Practical Forecasting of Cryptocoins Timeseries using Correlation Patterns

Cryptocoins (i.e., Bitcoin, Ether, Litecoin) are tradable digital assets. Ownerships of cryptocoins are registered on distributed ledgers (i.e., blockchains). Secure encryption techniques guarantee the security of the transactions (transfers of coins among owners), registered into the ledger. Cryptocoins are exchanged for specific trading prices. The extreme volatility of such trading prices across all different sets of crypto-assets remains undisputed. However, the relations between the trading prices across different cryptocoins remains largely unexplored. Major coin exchanges indicate trend correlation to advise for sells or buys. However, price correlations remain largely unexplored. We shed some light on the trend correlations across a large variety of cryptocoins, by investigating their coin/price correlation trends over the past two years. We study the causality between the trends, and exploit the derived correlations to understand the accuracy of state-of-the-art forecasting techniques for time series modeling (e.g., GBMs, LSTM and GRU) of correlated cryptocoins. Our evaluation shows (i) strong correlation patterns between the most traded coins (e.g., Bitcoin and Ether) and other types of cryptocurrencies, and (ii) state-of-the-art time series forecasting algorithms can be used to forecast cryptocoins price trends. We released datasets and code to reproduce our analysis to the research community.
Power Internet of Things traffic classification system based on reinforcement learning

Network traffic classification technology is an important means for power Internet of things to carry out network management and maintain network security. However, there are many existing traffic classification methods. Different traffic classification methods face different data sets, and the data sets used for training are limited, the update is slow, and the change of traffic characteristics is not obvious. Therefore, based on passive detection technology, this paper uses traffic analysis as a tool to collect the lossless traffic data of the target network, and then uses reinforcement learning Q-learning algorithm to classify the traffic and design the corresponding return function, and adopt <i>&epsilon;</i>-greedy exploration strategy and delayed return strategy to improve the learning effect of agents and improve the accuracy and efficiency of classification to a greater extent. Finally, the feasibility of the system is verified by experimental simulation. After 100 days of training, the classification accuracy has exceeded 85%, and with the increase of training time, the classification accuracy will be further improved.
A vulnerability analysis method for system target

In view of the vulnerability analysis requirements of system targets, this paper puts forward the concept of system target vulnerability, defines system target vulnerability, analyzes the difference between system targets and single targets, and puts forward the analysis process of system target vulnerability, including preliminary determination of system target range, determination of vulnerability purpose and level, determination of key entities, construction of system damage tree, determination of damage weight and damage criteria, based on the analysis of single target vulnerability research results and other steps, this method can guide relevant work.
Telecom package recommendation model based on convolutional neural network

In today's society, the demand for telecom packages is increasing and at the same time, there are many different types of telecom packages, so we need to recommend different types of telecom packages to different users so that each user can find the most suitable package for themselves among the many packages. We choose to build a click-through prediction model to model the interaction between user characteristics and telecom package characteristics. In this paper, we propose the FGCIN model, which generates new features by capturing important feature interactions through a convolutional neural network FGCNN, and then interacts the new features with the original features at the feature interaction layer through a compressed interaction network CIN for higher order features, followed by a deep neural network DNN for implicit feature interactions to finalize the output. In this paper we use the private dataset Telecom dataset and the public dataset Criteo for comparison experiments and ablation experiments, thus demonstrating the effectiveness and rationality of our proposed model FGCIN.
An automatic QoS-aware resource partitioning framework for cloud environment

The increasing use of computers in various fields has led to the popularization of cloud computing due to its advantages, such as large scale, high performance, low power consumption, high reliability, and low cost. However, the rapid growth of business volume and kinds of computer hardware resources, along with complex computer architecture, poses challenges in allocating hardware resources. Traditional methods struggle to meet the varying requirements of different businesses, hinder automatic tuning, and limit the improvement of overall system performance. In this regard, we propose an automatic QoS-aware resource partitioning framework that aims to maximize the overall performance of the system. Our contributions include an automatic performance tuning framework for cloud environments, a Deep Q-learning Network (DQN) based performance optimization method, and achieving a speed-up of at most 1.73 times compared to uniform partitions for CPU overload scenarios involving throughput-aware and latency-critical workloads. The proposed framework addresses the challenges faced by cloud computing by adopting different allocation methods for hardware resources that maximize performance in different application scenarios. The framework efficiently utilizes emerging hardware resource control capabilities to improve hardware and system performance. The use of DQN in performance optimization allows the framework to learn from past experiences and adapt to different situations, resulting in better resource allocation decisions. The proposed framework can significantly reduce the waste of resources and unnecessary expenditures for governments and enterprises. Our framework can serve as a guide for future research in cloud computing and related fields.
Decentralized Stream Reasoning Agents

This PhD project proposes the theoretical and technological foundations of an approach for decentralized processing of streaming knowledge graphs, where autonomous reasoners may combine individual and collective processing of continuous data. These decentralized stream processors shall be capable of sharing not only data stream knowledge, but also processing duties, using collaboration and negotiation protocols. Moreover, commonly agreed semantic vocabularies will be used to address the high dynamicity of reasoners' knowledge and goals. The approach proposed in this project goes beyond previous works on stream reasoning, enabling the self-organization and coordination among distributed stream reasoners, based on techniques and principles inspired by Multi-Agent systems. On the one hand, it adds the ability to explicate processing goals, capabilities and knowledge, while on the other it exploits potential ways of interconnecting them in ways that expand their combined capacity/efficacy for managing highly dynamic flows of streaming knowledge. Through this approach, efficient local stream processors can establish cooperative processing schemes, respecting data privacy restrictions and data locality requirements through the exchange of streaming Knowledge Graphs.
A Hardware-Conscious Stateful Stream Compression Framework for IoT Applications (Vision)

Data stream compression has attracted vast interest in emerging IoT (Internet of Things) applications. However, adopting stream compression on IoT applications is non-trivial due to the divergent demands, i.e., low energy consumption, high throughput, low latency, high compressibility, and tolerable information loss, which sometimes conflict with each other. This is particularly challenging when adopting stateful stream compression algorithms, which rely on states, e.g., a dictionary or model. This paper presents our vision of CStream, a hardware-conscious stateful stream compression framework for IoT applications. Through careful hardware-conscious optimizations, CStream will minimize energy consumption while striving to satisfy the divergent performance demands for parallelizing complex stateful stream compression algorithms for IoT applications.
Clothing pattern recognition based on attention mechanism and mask RCNN network

Clothing pattern recognition on social media is a key application of Internet marketing, but it is currently implemented manually, which is very inefficient. Our goal is to solve this problem through artificial intelligence. Based on the improved Mask RCNN network, this paper introduces the attention mechanism SENet to enable the feature extractor to extract the target areas that need to be focused on. And put more weight on this part to highlight significant useful features. And this article contributes a whole new dataset of clothing versions. The comparison experiment verifies that the improved Mask RCNN network has been significantly improved in the pattern recognition of clothing.
Adaptive Distributed Streaming Similarity Joins

How can we perform similarity joins of multi-dimensional streams in a distributed fashion, achieving low latency? Can we adaptively repartition those streams in order to retain high performance under concept drifts? Current approaches to similarity joins are either restricted to single-node deployments or focus on set-similarity joins, failing to cover the ubiquitous case of metric-space similarity joins. In this paper, we propose the first adaptive distributed streaming similarity join approach that gracefully scales with variable velocity and distribution of multi-dimensional data streams. Our approach can adaptively rebalance the load of nodes in the case of concept drifts, allowing for similarity computations in the general metric space. We implement our approach on top of Apache Flink and evaluate its data partitioning and load balancing schemes on a set of synthetic datasets in terms of latency, comparisons ratio, and data duplication ratio.
Image cutting in video media technology application based on detection algorithm

Speech activity detection algorithm belongs to a kind of means to judge the audio section and non audio section. Audio information diagnosis is composed of audio and noise section, and special treatment means are used for different signals. In the real model test, we can understand that RBF core has super stable attributes compared with other models, and has good self-renewal ability. In the audio information discrimination attribute diagnosis, we can understand the upgrading calculation process, and understand the large-scale counting audio information discrimination calculation process. The removal of pictograph is the primary obstacle to the solution scope of visual information. In the real use process, the classified noises are stored in the image removal with different attributes on a large scale, which plays a great role in the gradual diagnosis of image removal such as image upgrade and image classification. Therefore, in order to update the image level, eliminate the noise object and gradually diagnose the image, exploring the image removal method has become the first influential step in the first step of image diagnosis. In the era of interconnection, we began to further explore the merging means model of interconnection, visualization and information interoperability, focusing on the governance of the merging and upgrading means model on the premise of interconnection, visualization and information interoperability, We can also apply multi-channel upgrading to upgrade our own hardware and software strength, observe and learn the quality level of the mode and class, so that the practical role of the means of mutual transmission of interconnected visual information can go on for a longer time.
Softening overly demanding requirements in recommendation system

Used in multiple industries, recommender systems enhance the user experience by creating personalized item suggestions. However, without bias awareness, the system could output unfavorable results, leading to the item under-recommendation bias. To decrease this type of bias, creating a fair top-k list (i.e., list of top-ranked items by the user) is needed. Based on the debiased personalized ranking model (DPR) proposed in the works of Zhu et al., the paper aims to reduce item under-recommendation bias in recommender systems in the Bayesian Personalized Ranking (BPR) recommendation model. This is done by applying several fairness metrics, creating fairness through an adversarial component, and using an autoencoder on the data. The advantages of the new proposed model are proven through two sets of experiments based on multiple datasets and baselines, respectively. This results in a solution that is dozens of times more efficient compared to previously proposed ones.
LiDAR and RGB camera based feature extraction and evaluation for adverse weather driving

Modern automobiles are commonly equipped with a variety of perception modalities, i. e. sensors, to implement various levels of Advanced Driver Assistance Systems (ADAS). The main sensing modalities include camera, light detection and ranging (LiDAR), radar, as well as inertial measurement and global positioning devices. In addition, ultrasonic and acoustic sensors complete the possible perception modalities. ADAS features autonomous driving systems that have been initially developed assuming ideal perception, i.e., weather conditions; however, in reality, the quality of a sensor modality may degrade significantly due to adherent dirt, dust, fog, wet weather, or changing illumination conditions on the road. It is important to quantitatively study the degradation of sensor modalities in non-ideal conditions such as fog, rain, snow, bright sunlight, dust, etc., in order to evaluate sensor degradation and its effects on higher-level functionalities such as lane-keeping, object detection, recognition, and tracking, as well as ego-motion estimation and localization. This paper quantitatively examines the variability of several LiDAR and RGB camera-based features in variable weather conditions, including clear sky, overcast, fog, rain, and snow. Specifically, we derived LiDAR point-cloud-based features such as mean and standard deviation of distances and point cloud size. Also, image color space features were examined, in addition to blurriness and structural detail. Furthermore, we perform feature selection to explore the importance of various combinations of features for the prediction of road weather conditions.
A new GNSS outlier mitigation method for GNSS/INS integrated system

Abstract High-precision positioning with global navigation satellite systems (GNSS) remains a significant challenge in urban environments, due to the outliers caused by the insufficient number of accessible satellites and environmental interference. A GNSS outlier mitigation algorithm with effective fault detection and exclusion (FDE) is required for high-precision positioning. The traditional methods are designed to deal with zero-mean noise in GNSS, which leads to instabilities under biased measurements. Considering that GNSS data are typical time series data, a dynamic FDE scheme is constructed by combining a prediction-model-based method and a dissimilarity-based method. First, a hybrid prediction model which combines autoregressive integrated moving average (ARIMA) model and multilayer perceptron (MLP) model is proposed to provide pseudo-GNSS series by predicting the vehicle’s location for several future steps. Then, a dissimilarity-based method of dynamic time warping measure is utilized to analyze the pairwise dis-similarity between the pseudo-GNSS series and the received GNSS series. The performance of the different models in forecasting is evaluated, and the results show that the positioning accuracy is significantly improved by applying the ARIMA-MLP. The effectiveness of the proposed FDE method is verified through simulation experiments and real experiments based on a typical urban canyon public dataset collected in Tokyo.
A thumbnail preserving encryption scheme based on bit plane scrambling

With the rapid development of cloud services, more and more people use cloud services to store personal image privacy. However, cloud services lack security, which leads to the leakage of personal privacy. Although some scholars have proposed that images be encrypted before being uploaded to cloud services, it will cause the images to lose their availability. Recently, Zhao et al. proposed a thumbnail preserving encryption scheme, which can ensure the security of the image while maintaining the visual usability of the image, but the encryption efficiency of the algorithm is low. Therefore, this paper proposes a thumbnail preserving encryption scheme based on bit plane scrambling. In this scheme, a new one-dimensional chaotic system is proposed for the image chaos process. An improved Zigzag transformation method is proposed to change the position of pixels. The control parameters in the new one-dimensional chaotic system have a large value range, which ensures that the key space of the encryption algorithm is large enough. The improved Zigzag transformation method adds random numbers to improve the randomness of the encryption process. Experimental results show that the scheme has high encryption efficiency, and can make the encrypted image retain the visual usability of the original image, balancing security and usability.
WEB 3.0 Applications and Projections

The Web 1.0, Web 2.0, and Web 3.0 concepts have been popular frequently in recent years and they continue to exist as the given name to the evolution of the Web and the whole of the developments in these periods rather than a specific technology, method, or standard. Unlike Web 1.0 and Web 2.0, Web 3.0 has no accepted definition, yet it is considered as the beginning of the end of Web 2.0. The Web 3.0 era focuses on the developments in blockchain and token-based economy. In addition, some opinions characterize the concept of Web 3.0 as a vague, marketing-oriented, and fashionable term. Despite the intense debate about the clarity and maturity of the Web 3.0 definition, the developments associated with Web 3.0 are believed to have led to significant changes. For this reason, in this study, the developments in the field of distributed applications and Blockchain are examined. In this way, it is aimed to raise awareness within the company about current developments in this field and work of HAVELSAN in the area, and to take steps for future studies.