With the rapid advancement of cloud computing technologies, microservice architectures have emerged as the de facto standard for modern software systems. However, their highly dynamic and complex nature poses significant challenges for anomaly detection. Although recent diffusion-based models have demonstrated promising capabilities in modeling multi-variate time series, they often overlook the inherent multi-scale spatiotemporal correlations present in microservice systems. This oversight limits their capacity to fully capture system-wide behaviors, thereby constraining detection performance. To address this issue, we propose a novel framework for anomaly detection based on imputed diffusion models, termed Multi-Scale Graph Imputed Diffusion Model. Building upon the imputation–reconstruction paradigm of standard ImDiffusion, our approach introduces a multi-scale spatiotemporal correlation module that is seamlessly integrated into the denoising network. This module employs parallel multi-scale graph convolutions and temporal convolutions to explicitly capture inter-service spatial dependencies from the microservice dependency graph and multi-granular temporal dynamics from service metric time series. By conditioning the diffusion process on richer spatiotemporal contexts, MSG-ImDiffusion enhances the model’s sensitivity to anomalies that deviate from expected temporal and spatial patterns. Extensive experiments conducted on the open-source HipsterShop microservice benchmark demonstrate that our proposed model significantly outperforms existing baselines, including ImDiffusion, in terms of both detection accuracy and F1-score. These results validate the effectiveness of incorporating multi-scale spatiotemporal information into diffusion-based anomaly detection frameworks for microservice systems.

In this work, we propose a novel strategy to ensure infants, who inadvertently displace their quilts during sleep, are promptly and accurately re-covered. Our approach is formulated into two subsequent steps: interference resolution and quilt spreading. By leveraging the DWPose human skeletal detection and the Segment Anything instance segmentation models, the proposed method can accurately recognize the states of the infant and the quilt over her, which involves addressing the interferences resulted from an infant’s limbs laid on part of the quilt. Building upon prior research, the EM*D deep learning model is employed to forecast quilt state transitions before and after quilt spreading actions. To improve the sensitivity of the network in distinguishing state variation of the handled quilt, we introduce an enhanced loss function that translates the voxelized quilt state into a more representative one. Both simulation and real-world experiments validate the efficacy of our method, in spreading and recover a quilt over an infant.

Microservice architecture has been widely adopted in large-scale applications. However, it also brings new challenges to ensuring reliable performance and maintenance due to the huge volume of data and complex dependencies of microservices. Existing approaches still suffer from the over-aggregation of data, interference from anomaly propagation, and ignoration of component differences. To solve these issues, this paper builds a root cause diagnosis framework at the operation granularity, named as MicroNet. Since operations are subfunctions of microservices, recorded as invocation purposes, we propose the operation-centric perspective, to realize fine-grained data aggregation and operation-level anomaly backtracking. We decompose the diagnosis task into four phases: dependency graph construction, anomaly detection, anomaly evaluation, and culprit location. To construct the invocation dependency accurately, we propose the concept of meta call, defined as the triple (caller, operation, callee), the smallest unit that can be aggregated. Based on the dependency graph, we quantify the operation’s abnormality by analyzing the operation execution process, to backtrack the propagated anomalies. Then, we customize a personalized PageRank algorithm to identify the root cause in which invocation latency and different invocation relationships are considered simultaneously. Our experimental evaluation on an open dataset shows that MicroNet can effectively locate root causes with 90% mean average precision, outperforming state-of-the-art methods.

In a medical dialogue diagnosis system, the selection of symptoms for inquiry has a significant impact on diagnostic accuracy and dialogue efficiency. In a typical diagnosis process, the symptoms initially reported by users are often insufficient to support an accurate diagnosis, making it necessary to ask users about other symptoms through dialogue to form a conclusive diagnosis. In this paper, we propose a disease diagnosis algorithm based on Bayesian, which simulates the process of doctor's inquiry and diagnosis by dynamically updating the list of diseases to increase the interpretability of diagnosis results. For the symptom interrogation, we propose a symptom screening algorithm based on the difference of symptom sets to exclude diseases with low probability. Through the intersection and union of disease symptom sets, we can screen out the symptoms that can distinguish diseases in fewer inquiring rounds. The experimental results demonstrate the proposed method performs more efficiently than existing state-of-the-art algorithms.

Software Defined Network (SDN) has been considered as one of the most promising next-generation network solutions due to its network programmability. However, the upgrade from legacy IP network to pure SDN network is in general a gradual process. From a global perspective, a dynamic upgrade strategy should not only aim to pursue the local goal at each step, but also strive to optimize the final global solution when the upgrading process terminates. This raises three essential questions: which switches to upgrade, when to upgrade, and how to deploy controllers. Due to the interaction between the local goals at intermediate steps and the global goal at the final step, answering these questions altogether from a global perspective is challenging. In this paper, we study the dynamic SDN upgrade problem from a global perspective and answer these three questions altogether. We formulate the problem as a dynamic optimization problem that optimizes the global and local goals at the same time. We then propose two new formulations to combine the global and local goals, and two heuristic algorithms to solve them, respectively. We evaluate the proposed model and algorithms on realistic network topologies. The results show their feasibility and superiority.

Microservice architecture has been widely adopted for large-scale applications because of its benefits of scalability, flexibility, and reliability. However, due to the heterogeneity of system architecture and variable invocations between services, it is difficult to accurately diagnose the root causes of performance degradation in time. This paper proposes WinG, a system to pinpoint root causes. Firstly, since the characteristic of a system element is difficult to capture due to variable invocations between elements, WinG characterizes an element's status by a feature vector that includes all its invocation relationships. Secondly, WinG adopts a warping procedure to assess an element's anomaly severity based on its status deviation, to mitigate the interference of variable invocations. Thirdly, WinG groups heterogeneous elements with similar invocation characteristics to avoid the interference of diverse elements types. Finally, false alarms are filtered by the anomaly duration and frequency. Experimental evaluation results on the public dataset show that, with the above four methods, WinG can locate root causes with 87% precision, outperforming baseline methods. On average of 78 test cases, it achieves 34% precision improvement over the champion method of the competition.

As an application of the Internet of Things in the automotive field, Vehicular Ad-hoc NETworks (VANETs) are developed to facilitate traffic safety and traffic flow optimization. VANET consists of the communication network and the underlay road network. Due to the characteristics of open access, the communication network is vulnerable to various attacks, especially, the information pollution attack which is highly risky and concealed. Such an attack can lead the vehicles to react to the false messages from attackers, even cause failure cascades. However, the impact of the information pollution attack on the road network has not gained enough attention. To assess such impact, we build a traffic model with polluted information and assess the vulnerability of the road network under different pollution scenarios in terms of attack percentage, attack types, and road network topologies. Experimental results demonstrate that VANETs are vulnerable to the information pollution attack, and transportation performance drops by half when only 15% of edges are attacked in the worst case.

With the development of the smart city, personalized path recommendation has already attracted the attention of researchers. However, it has not been considered that some users need to make personalized recommendations of a route for a given pair of OD (Origin-Destination) and some specified consecutive way-points. To our knowledge, this problem is studied for the first time. Essentially, this problem can be taken as inferring a high sampling rate fine trajectory from a low sampling rate rough trajectory composed of OD and way-points. The biggest challenge is that the user may just have some knowledge about the location rather than the precise location of a waypoint because a place may have many GPS points. This paper proposes a PSR (Personalized Selective Route) model based on trajectory learning for a given OD and a number of way-points. It integrates multi-source information to learn more comprehensive personalized preferences and introduces the Seq2Seq model with Multi-Head self-attention mechanism to automatically adjust the weights to capture more accurate temporal and spatial correlations. The experiments on the real traffic trajectory data show PSR model is robust to low sampling rate and noise. Compared with the best baseline, the accuracy of Top-l under the Euclidean distance of PSR is improved by 32.57%, and the accuracy of Top-3 is improved by 63.87%.

Nowadays, a lot of data mining applications, such as web traffic analysis and content popularity prediction, leverage users’ web browsing trajectories to improve their performance. However, the disclosure of web browsing trajectory is the most prominent issue. A novel privacy model, named Differential Privacy, is used to rigorously protect user’s privacy. Some works have applied this privacy model to spatial-temporal streams. However, these works either protect the users’ activities in different places separately or protect their activities in all places jointly. The former one cannot protect trajectories that traverse multiple places; while the latter ignores the differences among places and suffers the degradation of data utility (i.e., data accuracy). In this paper, we propose a w , n -differential privacy to protect any spatial-temporal sequence occurring in w successive timestamps and n -range places. To achieve better data utility, we propose two implementation algorithms, named Spatial-Temporal Budget Distribution (STBD) and Spatial-Temporal RescueDP (STR). Theoretical analysis and experimental results show that these two algorithms can achieve a balance between data utility and trajectory privacy guarantee.

With the boosting of application, biometric identification with fingerprint or face-image suffers from forging attacks. The electrocardiogram (ECG) as a kind of biometric identification is of higher resistance against such attacks and receives research attention. The state-of-art method has recognition accuracy of about 95%. However, we find that the accuracy will degrade dramatically to 40% if it is applied in a practical context when a significant interval between training period and applying period. The critical reasons for this failure are as follows: 1) the extracted features are temporal sensitive due to that continuous samples being used in training and testing period in the existing schemes; 2) the features highly relevant to the performance are not utilized sufficiently in CNN classifier; 3) the optimal parameter setting for obtaining enough effective samples for individuals has not been investigated. This paper targets on proposing a practical cross- domain ECG biometric identification method to solve the above problems.Specifically,we:1)determine the best parameters of the non-fiducial random sampling method to obtain enough effective samples for individuals; 2) propose a method to extract deep features across time, frequency and energy domain which are temporal insensitive and individual distinguishable; 3) introduce a channel attention module into the CNN and modify its activation function to optimize the recognition performance. We validate our method on PTBDB and ECG-ID databases. Experiments show that the identification accuracy reaches 56.93% and 85.94% respectively, with an improvement of 41.5% and 20.7% over the existing method.

Continuously sharing user's trajectory data which contain one's location information makes the crowd sensing of the traffic dynamics and mobility trends feasible. This kind of spatial streaming data is beneficial for intelligent transportation but at the risk of disclosing personal privacy, even if it is published in statistical form such as “the number of users in an area at time t”. The user number on a location at time t is similar to that of previous release on the same location, and to that on adjacent locations. Such spatio-temporal correlation makes it a challenge to find solutions to protect user's trajectory privacy. The state-of-the-art privacy protection framework, differential privacy, has been extended to streaming scenario for preventing the privacy leak causing by the temporal correlation. However, such schemes neglect the importance of spatial correlation so that they may suffer the leak of user trajectory privacy or the degradation of data utility. Based on the observation that any piece of trajectory has temporal and spatial locality, we propose a flexible trajectory privacy model of w-event n2-block differential privacy, short as (ω, n)-differential privacy, to ensure any trajectory occurring in an area of n×n blocks during w successive timestamps under the protection of ε-differential privacy. Then we design the Spatial Temporal Budget Distribution (STBD) algorithm for achieving (ω, n)-differential privacy. Validation results of this algorithm on two real-life datasets and one synthetic dataset confirm its practicality.

With the surging demand on high‐quality mobile video services and the unabated development of new network technology, including fog computing, there is a need for a generalized quality of user experience (QoE) model that could provide insight for various network optimization designs. A good QoE, especially when measured as engagement, is an important optimization goal for investors and advertisers. Therefore, many works have focused on understanding how the factors, especially quality of service (QoS) factors, impact user engagement. However, the divergence of user interest is usually ignored or deliberatively decoupled from QoS and/or other objective factors. With an increasing trend towards personalization applications, it is necessary as well as feasible to consider user interest to satisfy aesthetic and personal needs of users when optimizing user engagement. We first propose anExtraction-Inference (E-I)algorithm to estimate the user interest from easily obtained user behaviors. Based on our empirical analysis on a large‐scale dataset, we then build aQoS and user Interest based Engagement (QI-E) regression model. Through experiments on our dataset, we demonstrate that the proposed model reaches an improvement in accuracy by 9.99% over the baseline model which only considers QoS factors. The proposed model has potential for designing QoE‐oriented scheduling strategies in various network scenarios, especially in the fog computing context.

With the prevailing of mobile phones and online video contents, the demand for mobile online video is increasing. The desire, however, is held down by the high mobile traffic cost. To solve this problem, an off-the-shelf solution is WiFi-Direct (WFD), which is widely available on a majority of mobile devices. There is, however, no systematic study of WFD-based group data transfer and cooperative video streaming on real phones. Thus, we designed and implemented a WFD-based LTE cooperative video streaming system, in which the WFD GO (Group Owner) device takes the responsibility of peer information exchange, data relay, and LTE cooperative downloading scheduling. Based on the system, we evaluated the performance of WFD-based group data sharing, including Ping response delay, throughput, and power efficiency. Valuable findings were obtained. For instance, we discovered that when a WFD device connects to a traditional AP (Access Point), even if there is \emph{no} data transmission to/from the AP, the Device-to-Device (D2D) throughput would decrease by at least 72\%. Based on these findings, we provided recommendations for the design and deployment of WFD based D2D systems. We finally demonstrated the feasibility of WFD based LTE cooperative video streaming using our system. We showed that, using multiple realistic LTE networks, a 3-device cooperative system can provide smooth video streaming with bitrate more than 10Mbps.

Some recent work proposed differential private collaborative filtering (DPCF) recommender systems to protect user privacy from indirect access attacks, e.g., KNN attacks. As the cost of such protection, the MAE of recommendation was retained with insignificant increase but the more focused metrics, i.e., the precision and recall of top-k recommendations, degraded unacceptable. To address this problem, we propose a DPCF framework based on privacy-relevance of topics, named DPCFT. Firstly, DPCFT works on topic-preference level which highly aggregates user behaviors and keeps the precision and recall of top-k differential private recommendations acceptable. More importantly, considering the unnoticed fact that the information leakage of some special topics worries users much more than that of other ones in terms of privacy concerns, DPCFT introduces the topic privacy-relevance level in the similarity computation and neighbor selection to impose stronger privacy protection on higher privacy-relevance topics with overall differential privacy and recommendation performance reserved. Finally, to reduce the recommendation performance cost for differential privacy, DPCFT selects the top-k recommendation items at user side further with personal topic-preference without risk of data expose. Experimental results on the MovieLens dataset verify that the proposed framework DPCFT preserves differential privacy and top-k recommendation performance simultaneously.

In online video systems, viewer demographic information (gender, age, etc.) is of huge commercial value for delivering targeted advertising and video recommendations, but generally not available directly. This paper targets inferring viewers' gender based on implicit watching history in the large-scale online video systems. To tackle the sparsity problem without filtering out any cold users or videos, we not only introduce video tags as features, but also use an efficient Chinese word segmentation method to extract hot key-words from video titles as features. Moreover, users' viewing behavior distribute lognormally, hence we apply a logarithmic transformation on the inference matrixes and further find key features via principal components analysis (PCA). We then solve the gender inference as a classification problem and define some modified evaluation metrics adapt to the imbalance classification problem. We compare a set of classifiers including Class prior, EM, SVM, Logistic regression, Partially supervised soft-label and belief-based mixture and find that Logistic regression is the best. The inference results show that our algorithms can obtain high F̃1values for all classes. The highest value of PPTV dataset can reach nearly 0.75. And inference based on key-words results in a 14.63% increase of F̃1contrast to the ratings of MovieLens.

Based on a large-scale dataset extracted from the servers' logs of PPTV, one of the largest online video service providers in China, we study how device types, wireless network connection types and video qualities impact user's watching behaviors and network traffic. We found that 1) the diurnal user viewing patterns on mobile devices are slightly different from that on PC devices; 2) the mobile APP's landing page is the primary source for users to find videos to watch, and the keyword search is the secondary source; 3) with respect to video quality, iOS devices are better than Android devices and tablet are better than smart phones; and 4) comparing to users with PC devices, users using mobile devices watch shorter, but are more concentrated on popular videos so that the video popularity distribution is more skewed. We further provide insights and suggestions in providing mobile video services and improving services' quality.

An essential component of genome function is the syntax of genomic regulatory elements that determine how diverse transcription factors interact to orchestrate a program of regulatory control. A precise characterization of in vivo spacing constraints between key transcription factors would reveal key aspects of this genomic regulatory language. To discover novel transcription factor spatial binding constraints in vivo, we developed a new integrative computational method, genome wide event finding and motif discovery (GEM). GEM resolves ChIP data into explanatory motifs and binding events at high spatial resolution by linking binding event discovery and motif discovery with positional priors in the context of a generative probabilistic model of ChIP data and genome sequence. GEM analysis of 63 transcription factors in 214 ENCODE human ChIP-Seq experiments recovers more known factor motifs than other contemporary methods, and discovers six new motifs for factors with unknown binding specificity. GEM's adaptive learning of binding-event read distributions allows it to further improve upon previous methods for processing ChIP-Seq and ChIP-exo data to yield unsurpassed spatial resolution and discovery of closely spaced binding events of the same factor. In a systematic analysis of in vivo sequence-specific transcription factor binding using GEM, we have found hundreds of spatial binding constraints between factors. GEM found 37 examples of factor binding constraints in mouse ES cells, including strong distance-specific constraints between Klf4 and other key regulatory factors. In human ENCODE data, GEM found 390 examples of spatially constrained pair-wise binding, including such novel pairs as c-Fos:c-Jun/USF1, CTCF/Egr1, and HNF4A/FOXA1. The discovery of new factor-factor spatial constraints in ChIP data is significant because it proposes testable models for regulatory factor interactions that will help elucidate genome function and the implementation of combinatorial control.

MOTIVATION: Clusters of protein-DNA interaction events involving the same transcription factor are known to act as key components of invertebrate and mammalian promoters and enhancers. However, detecting closely spaced homotypic events from ChIP-Seq data is challenging because random variation in the ChIP fragmentation process obscures event locations. RESULTS: The Genome Positioning System (GPS) can predict protein-DNA interaction events at high spatial resolution from ChIP-Seq data, while retaining the ability to resolve closely spaced events that appear as a single cluster of reads. GPS models observed reads using a complexity penalized mixture model and efficiently predicts event locations with a segmented EM algorithm. An optional mode permits GPS to align common events across distinct experiments. GPS detects more joint events in synthetic and actual ChIP-Seq data and has superior spatial resolution when compared with other methods. In addition, the specificity and sensitivity of GPS are superior to or comparable with other methods. AVAILABILITY: http://cgs.csail.mit.edu/gps.

A novel active queue management scheme for Internet DiffServ named class-guided random early discarding (CGRED) is proposed in this paper. The key enhancement of CGRED to standard RED is to introduce per-class states at routers and compute probability of discarding packets of different traffic classes adaptively based on the periodical measurements of actually consumed bandwidth of each classes, CGRED can maintain the assigned priority and pre-allocated bandwidth for each service class, eliminate the starvation of lower-priority classes, and ensure that each individual flows of a higher-priority class to obtain averaged rate not less than that of lower classes. Simulation results are presented to show that CGRED is simple, robust and scalable to be implemented.