In Mobile Ad hoc Networks (MANETs), the mobility of nodes causes frequent changes in the network topology, which directly affects user's Quality of Service (QoS) performance. Therefore, it is crucial for network operator to implement efficient routing optimization (RO) algorithms for diverse traffic flows. Based on the characteristics of centralized control routing in Software-Defined Networking (SDN), in this paper, we propose a routing optimization problem, and formulate the problem as an integer linear programming (ILP) problem. Then to solve the problem efficiently, we propose the QoS-aware routing optimization algorithm (QoS_ROA), which solves the problem in two stages. In the first stage, we use the Wavelet neural network (WNN) to predict the link quality at the next moment. In the second stage, we transform the proposed routing optimization problem into a 0–1 knapsack problem, and use differential search (DS) to solve it. The simulation results verify that, compared with the traditional routing algorithms, our algorithm can achieve high throughput, low packet drop rate, low delay in SDN-based MANETs.

Microservice architecture is applied by an increasing number of systems because of its benefits on delivery, scalability, and autonomy. It is essential but challenging to localize root-cause microservices promptly when a fault occurs. Traces are helpful for root-cause microservice localization, and thus many recent approaches utilize them. However, these approaches are less practical due to relying on supervision or other unrealistic assumptions. To overcome their limitations, we propose a more practical root-cause microservice localization approach named TraceRCA. The key insight of TraceRCA is that a microservice with more abnormal and less normal traces passing through it is more likely to be the root cause. Based on it, TraceRCA is composed of trace anomaly detection, suspicious microservice set mining and microservice ranking. We conducted experiments on hundreds of injected faults in a widely-used open-source microservice benchmark and a production system. The results show that TraceRCA is effective in various situations. The top-1 accuracy of TraceRCA outperforms the state-of-the-art unsupervised approaches by 44.8%. Besides, TraceRCA is applied in a large commercial bank, and it helps operators localize root causes for real-world faults accurately and efficiently. We also share some lessons learned from our real-world deployment.

Compared with traditional cloud computing technology, fog computing provides lower latency services in the next generation mobile networks. However, the imbalance of workloads and computing resources among Fog Nodes (FNs) restricts the further promotion of the network service quality. In this paper, considering the resource sharing of FNs by cooperation where FNs are as agents, we propose a distributed multi-agent cooperative resource sharing (DMCRS) algorithm to minimize the average service latency of the fog network. Firstly, the multi-agent resource sharing problem of the fog network is formulated as a convex optimization problem and the convexity is proved. Then the coalitional graph game framework is applied to achieve the cooperation among agents and the coalition graph Nash equilibrium is proved. To reduce the complexity, the global optimization problem is decomposed into distributed local optimization sub-problems in the DMCRS algorithm and these sub-problems are transformed into root-finding problems of polynomial equations. Simulation results demonstrate that the DMCRS algorithm can balance the workloads of different FNs, reduce the average service latency remarkably and the convergence result of the algorithm is close to the global optimal solution. In addition, the time complexity of the DMCRS algorithm is significantly lower than existing algorithms.

Long-term wellbeing monitoring is an underlying theme for evaluating health status by collecting physiological signs through behavioral traits. In alignment with Internet of Things (IoT), nonintrusive and trustworthy wearable social sensing technology holds a potential way for researchers to find and establish the interrelationships between unobtrusive social cues and physical mental health. This paper implements an IoT structured wearable social sensing platform with the integration of privacy audio feature, behavior monitoring, and environment sensing in a naturalistic environment. Particularly, four privacy protected audio-wellbeing features are embedded into the platform to automatically evaluate speech information without preserving raw audio data. Four weeks of long-term monitoring experimental studies have been conducted. A series of well-being questionnaires in conjunction with a group of students are engaged to objectively investigate the relationships between physical and mental health by utilizing the feature fusion strategy from speech, behavioral activities, and ambient factors.

Motivation: Genome editing using versions of the bacterial CRISPR/Cas9 system can be used to probe the function of selected genes in any organism. Green Listed is a web-based tool that rapidly designs custom CRISPR screens targeting sets of genes defined by the user. It could thus be used to design screens targeting for example all genes differentially expressed during a specific stimuli or all genes related to a specific pathway or function, as well as to generate targeted secondary screens following a large-scale screen. Availability and Implementation: The software, including a demo function as well as explanatory texts and videos, is available through greenlisted.cmm.ki.se . Contact: [email protected].

This paper addresses the problem of mining named entity translations from comparable corpora, specifically, mining English and Chinese named entity translation. We first observe that existing approaches use one or more of the following named entity similarity metrics: entity, entity context, and relationship. Motivated by this observation, we propose a new holistic approach by 1) combining all similarity types used and 2) additionally considering relationship context similarity between pairs of named entities, a missing quadrant in the taxonomy of similarity metrics. We abstract the named entity translation problem as the matching of two named entity graphs extracted from the comparable corpora. Specifically, named entity graphs are first constructed from comparable corpora to extract relationship between named entities. Entity similarity and entity context similarity are then calculated from every pair of bilingual named entities. A reinforcing method is utilized to reflect relationship similarity and relationship context similarity between named entities. We also discover "latent" features lost in the graph extraction process and integrate this into our framework. According to our experimental results, our holistic graph-based approach and its enhancement using corpus latent features are highly effective and our framework significantly outperforms previous approaches.

This paper describes a statistical approach to generation of Chinese classical poetry and proposes a novel method to automatically evaluate poems. The system accepts a set of keywords representing the writing intents from a writer and generates sentences one by one to form a completed poem. A statistical machine translation (SMT) system is applied to generate new sentences, given the sentences generated previously. For each line of sentence a specific model specially trained for that line is used, as opposed to using a single model for all sentences. To enhance the coherence of sentences on every line, a coherence model using mutual information is applied to select candidates with better consistency with previous sentences. In addition, we demonstrate the effectiveness of the BLEU metric for evaluation with a novel method of generating diverse references.

This article studies the problem of mining entity translation, specifically, mining English and Chinese name pairs. Existing efforts can be categorized into (a) transliteration-based approaches that leverage phonetic similarity and (b) corpus-based approaches that exploit bilingual cooccurrences. These approaches suffer from inaccuracy and scarcity, respectively. In clear contrast, we use under-leveraged resources of monolingual entity cooccurrences crawled from entity search engines, which are represented as two entity-relationship graphs extracted from two language corpora, respectively. Our problem is then abstracted as finding correct mappings across two graphs. To achieve this goal, we propose a holistic approach to exploiting both transliteration similarity and monolingual cooccurrences. This approach, which builds upon monolingual corpora, complements existing corpus-based work requiring scarce resources of parallel or comparable corpus while significantly boosting the accuracy of transliteration-based work. In addition, by parallelizing the mapping process on multicore architectures, we speed up the computation by more than 10 times per unit accuracy. We validated the effectiveness and efficiency of our proposed approach using real-life datasets.

This paper addresses the problem of mining named entity translations from comparable corpora, specifically, mining English and Chinese named entity translation. We first observe that existing approaches use one or more of the following named entity similarity metrics: entity, entity context, and relationship. Inspired by this observation, in this paper, we propose a new holistic approach, by (1) combining all similarity types used and (2) additionally considering relationship context similarity between pairs of named entities, a missing quadrant in the taxonomy of similarity metrics. We abstract the named entity translation problem as the matching of two named entity graphs extracted from the comparable corpora. Specifically, named entity graphs are first constructed from comparable corpora to extract relationship between named entities. Entity similarity and entity context similarity are then calculated from every pair of bilingual named entities. A reinforcing method is utilized to reflect relationship similarity and relationship context similarity between named entities. According to our experimental results, our holistic graph-based approach significantly outperforms previous approaches.

We show that information about social relationships can be used to improve user-level sentiment analysis. The main motivation behind our approach is that users that are somehow "connected" may be more likely to hold similar opinions; therefore, relationship information can complement what we can extract about a user's viewpoints from their utterances. Employing Twitter as a source for our experimental data, and working within a semi-supervised framework, we propose models that are induced either from the Twitter follower/followee network or from the network in Twitter formed by users referring to each other using "@" mentions. Our transductive learning results reveal that incorporating social-network information can indeed lead to statistically significant sentiment classification improvements over the performance of an approach based on Support Vector Machines having access only to textual features.

Tweets have become a comprehensive repository for real-time information. However, it is often hard for users to quickly get information they are interested in from tweets, owing to the sheer volume of tweets as well as their noisy and informal nature. We present QuickView, an NLP-based tweet search platform to tackle this issue. Specifically, it exploits a series of natural language processing technologies, such as tweet normalization, named entity recognition, semantic role labeling, sentiment analysis, tweet classification, to extract useful information, i.e., named entities, events, opinions, etc., from a large volume of tweets. Then, non-noisy tweets, together with the mined information, are indexed, on top of which two brand new scenarios are enabled, i.e., categorized browsing and advanced search, allowing users to effectively access either the tweets or fine-grained information they are interested in.