MOTIVATION: Synthetic lethality (SL) is a form of genetic interaction that can selectively kill cancer cells without damaging normal cells. Exploiting this mechanism is gaining popularity in the field of targeted cancer therapy and anticancer drug development. Due to the limitations of identifying SL interactions from laboratory experiments, an increasing number of research groups are devising computational prediction methods to guide the discovery of potential SL pairs. Although existing methods have attempted to capture the underlying mechanisms of SL interactions, methods that have a deeper understanding of and attempt to explain SL mechanisms still need to be developed. RESULTS: In this work, we propose a novel SL prediction method, SLGNN. This method is based on the following assumption: SL interactions are caused by different molecular events or biological processes, which we define as SL-related factors that lead to SL interactions. SLGNN, apart from identifying SL interaction pairs, also models the preferences of genes for different SL-related factors, making the results more interpretable for biologists and clinicians. SLGNN consists of three steps: first, we model the combinations of relationships in the gene-related knowledge graph as the SL-related factors. Next, we derive initial embeddings of genes through an explicit message aggregation process of the knowledge graph. Finally, we derive the final gene embeddings through an SL graph, constructed using known SL gene pairs, utilizing factor-based message aggregation. At this stage, a supervised end-to-end training model is used for SL interaction prediction. Based on experimental results, the proposed SLGNN model outperforms all current state-of-the-art SL prediction methods and provides better interpretability. AVAILABILITY AND IMPLEMENTATION: SLGNN is freely available at https://github.com/zy972014452/SLGNN.

Abstract Federated learning (FL), a variant of distributed learning (DL), supports the training of a shared model without accessing private data from different sources. Despite its benefits with regard to privacy preservation, FL’s distributed nature and privacy constraints make it vulnerable to data poisoning attacks. Existing defenses, primarily designed for DL, are typically not well adapted to FL. In this paper, we study such attacks and defenses. In doing so, we start from the perspective of DL and then give consideration to a real-world FL scenario, with the aim being to explore the requisites of a desirable defense in FL. Our study shows that (i) the batch size used in each training round affects the effectiveness of defenses in DL, (ii) the defenses investigated are somewhat effective and moderately influenced by batch size in FL settings and (iii) the non-IID data makes it more difficult to defend against data poisoning attacks in FL. Based on the findings, we discuss the key challenges and possible directions in defending against such attacks in FL. In addition, we propose detect and suppress the potential outliers(DSPO), a defense against data poisoning attacks in FL scenarios. Our results show that DSPO outperforms other defenses in several cases.

Abstract Today, Internet of things (IoT), Cloud and Fog networks have spread out around the world. The more these networks grow, the more their energy consumption comes to attention. Many efforts have been made during recent years to decrease this energy consumption, mainly focused on utilizing low‐power devices. Green algorithms are recently proposed to reduce energy consumption by modifying the structure of many algorithms employed in the network and its protocols. This paper proposes a new green reliability algorithm for Transmission Control Protocol/Internet Protocol (TCP/IP protocol) in Fog computing. The proposed algorithm does not require extensive TCP/IP protocol changes or relevant hardware. It is based on transferring less number of packets in the network by using the advantage of differences between TCP and User Datagram Protocol (UDP). TCP and User Datagram Protocol (UDP) are different in nature as the number of total packets in UDP is half that of TCP. As a result, the number of complete packets in UDP is half that of TCP. The proposed method is built around the loss of some packets in applications, such as voice and online video, does not severely degrade the end results. Therefore, the UDP protocol can substitute TCP in such situations. The criterion to switch between the two is the minimum acceptable Quality of Service (QoS) of the overall network. In other words, the UDP protocol will be used as long as QoS requirements are met. The switching process between UDP and TCP is dynamic, optimized by estimating network noise in the period. Additionally, we evaluated the proposed method based on several QoS functions, including delay, throughput, and energy usage.

Abstract In the era of digital marketing, advertisements have become an indispensable part. One of the central challenges is advertising attribution which explains the amount of contribution every publisher has with the conversions. However, through observation, we have found that current advertising platforms attribution, advertisers attribution, or third‐party platforms attribution all have the problems of trust, data leakage, and data forgery. To fill the gap, our work's main contribution is combining blockchain with advertising attribution to propose an architecture for improving the privacy‐preserving degree and amount. In the proposed architecture, publishers, and advertisers can store real‐time data on a blockchain. The attribution results are credible because blockchain is decentralized, tamper‐proof, and traceable. We combine privacy set intersection and zero‐knowledge proof technology to increase the privacy of flowing data. In addition, we describe a preliminary prototype in which publishers, advertisers, and advertising platforms can get the corresponding attribution details. To show its effectiveness, we analyze it from different perspectives, including communication cost, attribution accuracy, and time cost. The results show that our communication cost has significantly reduced compared to the recent studies.

How to use computational methods to effectively predict the function of proteins remains a challenge. Most prediction methods based on single species or single data source have some limitations: the former need to train different models for different species, the latter only to infer protein function from a single perspective, such as the method only using Protein-Protein Interaction (PPI) network just considers the protein environment but ignore the intrinsic characteristics of protein sequences. We found that in some network-based multi-species methods the networks of each species are isolated, which means there is no communication between networks of different species. To solve these problems, we propose a cross-species heterogeneous network propagation method based on graph attention mechanism, PSPGO, which can propagate feature and label information on sequence similarity (SS) network and PPI network for predicting gene ontology terms. Our model is evaluated on a large multi-species dataset split based on time and is compared with several state-of-the-art methods. The results show that our method has good performance. We also explore the predictive performance of PSPGO for a single species. The results illustrate that PSPGO also performs well in prediction for single species.

Recent research shows that executable and linkable format (ELF) malware detection models based on deep learning are vulnerable to adversarial attacks. The most commonly used method in previous work is adversarial training to defend adversarial examples. Nevertheless, it is inefficient and only effective for specific adversarial attacks. Given that the perturbation byte insertion positions of existing adversarial malware generation methods are relatively fixed, we propose a new method to detect adversarial ELF malware. Using model interpretation techniques, we analyze the decision-making basis of the malware detection model and extract the features of adversarial examples. We further use anomaly detection techniques to identify adversarial examples. As an add-on module of the malware detection model, the proposed method does not require modifying the original model and does not need to retrain the model. Evaluating results show that the method can effectively defend the adversarial attacks against the malware detection model.

Although the vision-and-language pretraining (VLP) equipped cross-modal image-text retrieval (ITR) has achieved remarkable progress in the past two years, it suffers from a major drawback: the ever-increasing size of VLP models restrict its deployment to real-world search scenarios (where the high latency is unacceptable). To alleviate this problem, we present a novel plug-in dynamic contrastive distillation (DCD) framework to compress the large VLP models for the ITR task. Technically, we face the following two challenges: 1) the typical uni-modal metric learning approach is difficult to directly apply to cross-modal task, due to the limited GPU memory to optimize too many negative samples during handling cross-modal fusion features. 2) it is inefficient to static optimize the student network from different hard samples, which have different effects on distillation learning and student network optimization. We try to overcome these challenges from two points. First, to achieve multi-modal contrastive learning, and balance the training costs and effects, we propose to use a teacher network to estimate the difficult samples for students, making the students absorb the powerful knowledge from pre-trained teachers, and master the knowledge from hard samples. Second, to dynamic learn from hard sample pairs, we propose dynamic distillation to dynamically learn samples of different difficulties, from the perspective of better balancing the difficulty of knowledge and students' self-learning ability. We successfully apply our proposed DCD strategy on two state-of-the-art vision-language pretrained models, i.e. ViLT and METER. Extensive experiments on MS-COCO and Flickr 30 K benchmarks show the effectiveness and efficiency of our DCD framework. Encouragingly, we can speed up the inference at least 129 × compared to the existing ITR models. We further provide in-depth analyses and discussions that explain where the performance improvement comes from. We hope our work can shed light on other tasks that require distillation and contrastive learning.

App reviews are crowdsourcing knowledge of user experience with the apps, providing valuable information for app release planning, such as major bugs to fix and important features to add. There exist prior explorations on app review mining for release planning; however, most of the studies strongly rely on predefined classes or manually annotated reviews. Also, the new review characteristic, i.e., the number of users who rated the review as helpful, which can help capture important reviews, has not been considered previously. In the article, we propose a novel framework, named SOLAR, aiming at accurately summarizing helpful user reviews to developers. The framework mainly contains three modules: the review helpfulness prediction module, topic-sentiment modeling module, and multifactor ranking module. The review helpfulness prediction module assesses the helpfulness of reviews, i.e., whether the review is useful for developers. The topic-sentiment modeling module groups the topics of the helpful reviews and also predicts the associated sentiment, and the multifactor ranking module aims at prioritizing semantically representative reviews for each topic as the review summary. Experiments on five popular apps indicate that SOLAR is effective for review summarization and promising for facilitating app release planning.

Abstract With new telecommunications engineering applications, the cognitive radio (CR) network‐based internet of things (IoT) resolves the bandwidth problem and spectrum problem. However, the CR‐IoT routing method sometimes presents issues in terms of road finding, spectrum resource diversity and mobility. This study presents an upgradable cross‐layer routing protocol based on CR‐IoT to improve routing efficiency and optimize data transmission in a reconfigurable network. In this context, the system is developing a distributed controller which is designed with multiple activities, including load balancing, neighbourhood sensing and machine‐learning path construction. The proposed approach is based on network traffic and load and various other network metrics including energy efficiency, network capacity and interference, on an average of 2 bps/Hz/W. The trials are carried out with conventional models, demonstrating the residual energy and resource scalability and robustness of the reconfigurable CR‐IoT.

In real-world applications, each data owner might have only partial information of the complete social networks. They wish to find the community structure within multiple networks but without sharing their data directly. However, the existing works on collaborative community detection rarely consider the edges privacy issue in the networks. In this article, from the view of secure multiparty computation, we present two methods to detect the community structure of the multiple networks without directly exchanging edges’ information. These two methods are developed from the fast modularity algorithm ($fastModular$) and the label propagation algorithm (LPA), and they are called$CofastModular$and$CoLPA$, respectively. Both methods can detect the community structure within multiple networks without the need to directly exchange the edges’ information. Experiments are conducted on several real-world and synthetic networks. Experimental results show that$CofastModular$and$CoLPA$could identify community structure effectively.

Federated learning (FL) is a framework of distributed machine learning, which aims to protect data privacy by transferring parameters instead of private data from local clients. Compared with the typical cloud-client architecture, applying FL on a cloud-edge-client hierarchical architecture could train the model faster and achieve better communication-computation trade-offs. However, hierarchical federated learning (HFL) still suffers from privacy leakage by analyzing uploaded parameters from clients or edge servers. To address this problem, we propose a privacy-preserving scheme based on the theory of local differential privacy (LDP), where adding the noise to the shared model parameters before uploading them to edge and cloud servers. According to our analysis by the moment accounting, the proposed algorithm can realize the strict differential privacy guarantee for the layers of clients and edge servers with adjustable privacy protection levels. We evaluate its performance based on the image classification tasks, and the result demonstrates that our theoretical analyses are consistent with simulations.

Recent years have witnessed lots of attacks targeted at the widespread Internet of Things (IoT) devices and malicious activities conducted by compromised IoT devices. After some notorious IoT malware released their source code, many new variants emerge, which are usually more powerful and stealthy. Although numerous existing studies have analyzed some exposed families, there is a lack of systematic study to make full use of them, which can be a fundamental step for provenance, triage, labeling, lineage analysis, and authorship attribution. The key challenge of conducting an IoT malware evolutionary study is how to collect sufficient and accurate information about malware and identify the relationships among them. In this article, we take the first step to investigate the IoT malware evolution by leveraging the information from two sources that complement each other. First, we crawl online articles about IoT malware and employ natural language processing techniques to extract the features of malware samples and their relationships with other malware family, which allow us to form the basic lineage graph. Second, we collect real malware samples through our widely deployed honeypots and design a new classifier to group them into families and identify lineage relationships among them. Such results are used to enhance the basic lineage graph. Eventually, we construct the final lineage graph for 72 IoT malware families by correlating the information from the aforementioned sources, which can help the research community better understand and fight IoT malware now and in the future. Our study has been incorporated into the threat awareness system of NSFOCUS company.

Deep neural networks (DNNs) have gained considerable attention in various real-world applications due to the strong performance on representation learning. However, a DNN needs to be trained many epochs for pursuing a higher inference accuracy, which requires storing sequential versions of DNNs and releasing the updated versions to users. As a result, large amounts of storage and network resources are required, significantly hampering DNN utilization on resource-constrained platforms (e.g., IoT, mobile phone).

While the artificial intelligence theory for solving the perfect-information games has been well developed in recent years, great challenges are still posed in dealing with the imperfect-information game due to the huge state space and hidden information involved in it. In this paper, we design an online strategy solving framework for six-player no-limit Texas hold'em poker. Based on hand isomorphism and hand strength evalution, the framework provides an efficient situation estimation method for six-player poker. Such method could greatly reduce the the state space in six-player poker as well as effectively evaluate the current hands. The poker agent based on our method won the third place in the 2018 AAAI-ACPC.

Targeted mobile advertising (TMA) enables organizations to tailor advertisements to specific consumers by analysing the personal information collected from consumers’ mobile devices. Although TMA offers great benefits to advertisers, the privacy concerns associated with it may reduce the advertising effectiveness. It follows that there is a need for an advertisement selection mechanism that can support the existing TMA business model in a manner that takes into account consumers’ privacy concerns. We present such an ad selection mechanism that has the potential to provide benefits to both consumers and advertisers. The mechanism is novel in its combination of a user subscription mechanism, a two-stage ad selection process, and the application of a trustworthy billing system. In particular, (i) the user subscription mechanism helps users to identify their interests and subscribe to desirable categories of ads; (ii) the two-stage ad selection process ensures that ad servers can only obtain coarse-grained user profiles, with fine-grained user profiles stored and used only on the mobile devices and (iii) the trustworthy billing system helps to report ad-clicks without revealing users’ identities and assists in detecting click-fraud attacks. The performance of the mechanism is evaluated in the context of a prototype privacy-preserving TMA framework.

Summary With the continued proliferation of mobile devices, the collection of information associated with such devices and their users—such as location, installed applications and cookies associated with built‐in browsers—has become increasingly straightforward. By analysing such information, organisations are often able to deliver more relevant and better focused advertisements. Of course, such targeted mobile advertising gives rise to a number of concerns, with privacy‐related concerns being prominent. In this paper, we discuss the necessary balance that needs to be struck between privacy and utility in this emerging area and propose privacy‐preserving targeted mobile advertising as a solution that tries to achieve that balance. Our aim is to develop a solution that can be deployed by users but is also palatable to businesses that operate in this space. This paper focuses on the requirements and design of privacy‐preserving targeted mobile advertising and also describes an initial prototype. We also discuss how more detailed technical aspects and a complete evaluation will underpin our future work in this area. Copyright © 2016 John Wiley & Sons, Ltd.