The use of artificial intelligence, and the deep-learning subtype in particular, has been enabled by the use of labeled big data, along with markedly enhanced computing power and cloud storage, across all sectors. In medicine, this is beginning to have an impact at three levels: for clinicians, predominantly via rapid, accurate image interpretation; for health systems, by improving workflow and the potential for reducing medical errors; and for patients, by enabling them to process their own data to promote health. The current limitations, including bias, privacy and security, and lack of transparency, along with the future directions of these applications will be discussed in this article. Over time, marked improvements in accuracy, productivity, and workflow will likely be actualized, but whether that will be used to improve the patient-doctor relationship or facilitate its erosion remains to be seen.

High-performance medicine: the convergence of human and artificial intelligence

Matrix Factorization Techniques for Recommender Systems

In this paper, we define and explore proofs of retrievability (PORs). A POR scheme enables an archive or back-up service (prover) to produce a concise proof that a user (verifier) can retrieve a target file F, that is, that the archive retains and reliably transmits file data sufficient for the user to recover F in its entirety.A POR may be viewed as a kind of cryptographic proof of knowledge (POK), but one specially designed to handle a large file (or bitstring) F. We explore POR protocols here in which the communication costs, number of memory accesses for the prover, and storage requirements of the user (verifier) are small parameters essentially independent of the length of F. In addition to proposing new, practical POR constructions, we explore implementation considerations and optimizations that bear on previously explored, related schemes.In a POR, unlike a POK, neither the prover nor the verifier need actually have knowledge of F. PORs give rise to a new and unusual security definition whose formulation is another contribution of our work.We view PORs as an important tool for semi-trusted online archives. Existing cryptographic techniques help users ensure the privacy and integrity of files they retrieve. It is also natural, however, for users to want to verify that archives do not delete or modify files prior to retrieval. The goal of a POR is to accomplish these checks without users having to download the files themselves. A POR can also provide quality-of-service guarantees, i.e., show that a file is retrievable within a certain time bound.

PORs: Proofs of Retrievability for Large Files

Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to leverage useful information contained in multiple related tasks to help improve the generalization performance of all the tasks. In this paper, we give a survey for MTL from the perspective of algorithmic modeling, applications and theoretical analyses. For algorithmic modeling, we give a definition of MTL and then classify different MTL algorithms into five categories, including feature learning approach, low-rank approach, task clustering approach, task relation learning approach and decomposition approach as well as discussing the characteristics of each approach. In order to improve the performance of learning tasks further, MTL can be combined with other learning paradigms including semi-supervised learning, active learning, unsupervised learning, reinforcement learning, multi-view learning and graphical models. When the number of tasks is large or the data dimensionality is high, we review online, parallel and distributed MTL models as well as dimensionality reduction and feature hashing to reveal their computational and storage advantages. Many real-world applications use MTL to boost their performance and we review representative works in this paper. Finally, we present theoretical analyses and discuss several future directions for MTL.

A Survey on Multi-Task Learning

Drug discovery and development pipelines are long, complex and depend on numerous factors. Machine learning (ML) approaches provide a set of tools that can improve discovery and decision making for well-specified questions with abundant, high-quality data. Opportunities to apply ML occur in all stages of drug discovery. Examples include target validation, identification of prognostic biomarkers and analysis of digital pathology data in clinical trials. Applications have ranged in context and methodology, with some approaches yielding accurate predictions and insights. The challenges of applying ML lie primarily with the lack of interpretability and repeatability of ML-generated results, which may limit their application. In all areas, systematic and comprehensive high-dimensional data still need to be generated. With ongoing efforts to tackle these issues, as well as increasing awareness of the factors needed to validate ML approaches, the application of ML can promote data-driven decision making and has the potential to speed up the process and reduce failure rates in drug discovery and development. Machine learning has been applied to numerous stages in the drug discovery pipeline. Here, Vamathevan and colleagues discuss the most useful techniques and how machine learning can promote data-driven decision making in drug discovery and development. They highlight major hurdles in the field, such as the required data characteristics for applying machine learning, which will need to be solved as machine learning matures.

Applications of machine learning in drug discovery and development.

With increasing number of cloud data leakage accidents exposed, outsourced data control becomes a more and more serious concern of their owner. To relieve the concern of these cloud users, reliable logging schemes are widely used to generate proof for data confidentiality auditing. However, high frequency operation and fine operation granularity on cloud data both result in a considerably large volume of operation logs, which burdens communication and computation in log auditing. This paper proposes a multi-grained log auditing scheme to make logs volume smaller and log auditing more efficient. We design a logging mechanism to support multi-grained data access with Merkle Hash Tree structure. Based on multi-grained log, we present a log auditing approach to achieve data confidentiality auditing and leakage investigation by making an Access List. Experiments results indicate that our scheme obtains about 54% log volume and 60% auditing time of fine-grained log auditing scheme in our scenario.

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A Multi-grained Log Auditing Scheme for Cloud Data Confidentiality

Cloud storage is widely used to ease the storage burden of clients. Meanwhile it raises a basic issue in the security of outsourced data: whether the corruption can be detected and recovered? Focusing on this problem, most existing proofs of retrievability (POR) schemes relies on precomputed tokens or tags as well as code redundancy to achieve integrity verification and corruption recovery, which, however, results in extra storage cost for cloud storage and high computational overhead for clients. Fragile watermarking provides a new way to implement the scheme without such drawbacks. In this paper, we propose a novel fragile watermarking based public auditable POR scheme for archival cloud data, which is able to not only improve the efficiency of audit process but also ensure both privacy-preserving and replay attack resistance simultaneously. The simulation results validate both the correctness of our scheme in detecting and recovering data corruption and the large improvement in performance compared to traditional POR schemes.

Fragile Watermarking Based Proofs of Retrievability for Archival Cloud Data

Based on the three-layer model theory for steganography communication system, this paper designs a new steganography transmission protocol, to achieve a more effective frame structure, more reliable transmitting mechanism. Both text message dialogue and file transmission communication based on the proposed method are reliable while the performance is also corresponding improvement. The experimental results has proved theoretical prediction that the proposed protocol is able to achieve reliable and efficient steganography on VoIP network. Key Wordsprotocol, variable length coding, steganography, VoIP, Information hiding

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Protocol of Steganography in Streaming Media on VOIP Network Based on Variable Length Coding

The purpose of the covert communication system is to implement the communication process without causing third party perception. In order to achieve complete covert communication, two aspects of security issues need to be considered. The first one is to cover up the existence of information, that is, to ensure the content security of information; the second one is to cover up the behavior of transmitting information, that is, to ensure the behavioral security of communication. However, most of the existing information hiding models are based on the "Prisoners' Model", which only considers the content security of carriers, while ignoring the behavioral security of the sender and receiver. We think that this is incomplete for the security of covert communication. In this paper, we propose a new covert communication framework, which considers both content security and behavioral security in the process of information transmission. In the experimental part, we analyzed a large amount of collected real Twitter data to illustrate the security risks that may be brought to covert communication if we only consider content security and neglect behavioral security. Finally, we designed a toy experiment, pointing out that in addition to most of the existing content steganography, under the proposed new framework of covert communication, we can also use user's behavior to implement behavioral steganography. We hope this new proposed framework will help researchers to design better covert communication systems.

Behavioral Security in Covert Communication Systems

Transformer is a powerful model for text understanding. However, it is inefficient due to its quadratic complexity to input sequence length. Although there are many methods on Transformer acceleration, they are still either inefficient on long sequences or not effective enough. In this paper, we propose Fastformer, which is an efficient Transformer model based on additive attention. In Fastformer, instead of modeling the pair-wise interactions between tokens, we first use additive attention mechanism to model global contexts, and then further transform each token representation based on its interaction with global context representations. In this way, Fastformer can achieve effective context modeling with linear complexity. Extensive experiments on five datasets show that Fastformer is much more efficient than many existing Transformer models and can meanwhile achieve comparable or even better long text modeling performance.

Yongfeng Huang

Papers

A Multi-grained Log Auditing Scheme for Cloud Data Confidentiality

Fragile Watermarking Based Proofs of Retrievability for Archival Cloud Data

Protocol of Steganography in Streaming Media on VOIP Network Based on Variable Length Coding

Behavioral Security in Covert Communication Systems

Fastformer: Additive Attention Can Be All You Need