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Abstract Background Lotus corniculatus is a widely distributed perennial legume whose great adaptability to different environments and resistance to barrenness make it an excellent forage and ecological restoration plant. However, its molecular genetics and genomic relationships among populations are yet to be uncovered. Result Here we report on a genomic variation map from worldwide 272 L. corniculatus accessions by genome resequencing. Our analysis suggests that L. corniculatus accessions have high genetic diversity and could be further divided into three subgroups, with the genetic diversity centers were located in Transcaucasia. Several candidate genes and SNP site associated with CNglcs content and growth traits were identified by genome-wide associated study (GWAS). A non-synonymous in LjMTR was responsible for the decreased expression of CNglcs synthesis genes and LjZCD was verified to positively regulate CNglcs synthesis gene CYP79D3. The LjZCB and an SNP in LjZCA promoter were confirmed to be involved in plant growth. Conclusion This study provided a large number of genomic resources and described genetic relationship and population structure among different accessions. Moreover, we attempt to provide insights into the molecular studies and breeding of CNglcs and growth traits in L. corniculatus.

In recent years, data has become one of the most important resources in the digital economy. Unlike traditional resources, the digital nature of data makes it difficult to value and contract. Therefore, establishing an efficient and standard data-transaction market system would be beneficial for lowering cost and improving productivity among the parties in this industry. Although numerous studies have been dedicated to the issue of complying with data regulations and other data-transaction issues such as privacy and pricing, little work has been done to provide a comprehensive review of these studies in the fields of machine learning and data science. To provide a complete and up-to-date understanding of this topic, this review covers the three key issues of data transaction: data rights, data pricing, and privacy computing. By connecting these topics, this paper provides a big picture of a data ecosystem in which data is generated by data subjects such as individuals, research agencies, and governments, while data processors acquire data for innovational or operational purposes, and benefits are allocated according to the data’s respective ownership via an appropriate price. With the long-term goal of making artificial intelligence (AI) beneficial to human society, AI algorithms will then be assessed by data protection regulations (i.e., privacy protection regulations) to help build trustworthy AI systems for daily life.

In 2018, Zhang et al. introduced the Persistent Fault Analysis (PFA) for the first time, which uses statistical features of ciphertexts caused by faulty Sbox to recover the key of block ciphers. However, for most of the variants of PFA, the prior knowledge of the fault (location and value) is required, where the corresponding analysis will get more difficult under the scenario of multiple faults. To bypass such perquisite and improve the analysis efficiency for multiple faults, we propose Chosen-Plaintext based Persistent Fault Analysis (CPPFA). CPPFA introduces chosen-plaintext to facilitate PFA and can reduce the key search space of AES-128 to extremely small. Our proposal requires 256 ciphertexts, while previous state-of-the-art work still requires 1509 and 1448 ciphertexts under 8 and 16 faults, respectively, at the only cost of requiring 256 chosen plaintexts. In particular, CPPFA can be applied to the multiple faults scenarios where all fault locations, values and quantity are unknown, and the worst time complexity of CPPFA is O(28+nf ) for AES-128, where nf represents the number of faults. The experimental results show that when nf > 4, 256 pairs of plaintext-ciphertext can recover the master key of AES-128. As for LED-64, only 16 pairs of plaintext-ciphertext reduce the remaining key search space to 210.

Heat stress (HS) is a major problem in poultry business which affects chickens' performance and may trigger large economic losses. This study intends to analyze the impact of HS on broiler chickens' performance compared with those under normal condition. A literature search was performed on PubMed, Web of Science, and Cochrane Library for studies published in English up to January 17, 2020. Outcomes of body weight gain (BWG), feed intake (FI), feed conversion ratio (FCR), and mortality were calculated by weighted difference (WMD) or odds ratio (OR) with 95% confidence interval (CI). A total of 12 studies with 470 broiler chickens were included. HS significantly decreased FI (11 trials: WMD = −97.95, 95% CI: −141.70, −54.20) and BWG (7 trials: WMD = −151.40, 95% CI: −198.59, −104.21) and significantly increased FCR (9 trials: WMD = 0.17, 95% CI: 0.04, 0.29) and mortality (8 trials: OR = 3.74, 95% CI: 1.39, 10.12) compared with the control. In conclusion, HS significantly affected broiler chickens' BWG, FI, FCR, and mortality, indicating the importance to control housing temperature to avoid unnecessary costs.

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With the rapid developments of artificial intelligence (AI) and deep learning (DL) techniques, it is critical to ensure the security and robustness of the deployed algorithms. Recently, the security vulnerability of DL algorithms to adversarial samples has been widely recognized. The fabricated samples can lead to various misbehaviors of the DL models while being perceived as benign by humans. Successful implementations of adversarial attacks in real physical-world scenarios further demonstrate their practicality. Hence, adversarial attack and defense techniques have attracted increasing attention from both machine learning and security communities and have become a hot research topic in recent years. In this paper, we first introduce the theoretical foundations, algorithms, and applications of adversarial attack techniques. We then describe a few research efforts on the defense techniques, which cover the broad frontier in the field. Several open problems and challenges are subsequently discussed, which we hope will provoke further research efforts in this critical area. Keywords: Machine learning, Deep neural network, Adversarial example, Adversarial attack, Adversarial defense

Persistence fault analysis (PFA) is a novel fault analysis technique proposed in CHES 2018 and demonstrated with rowhammer-based fault injections. However, whether such analysis can be applied to traditional fault attack scenario, together with its difficulty in practice, has not been carefully investigated. For the first time, a persistent fault attack is conducted on an unprotected AES implemented on ATmega163L microcontroller in this paper. Several critical challenges are solved with our new improvements, including (1) how to decide whether the fault is injected in SBox; (2) how to use the maximum likelihood estimation to pursue the minimum number of ciphertexts; (3) how to utilize the unknown fault in SBox to extract the key. Our experiments show that: to break AES with physical laser injections despite all these challenges, the minimum and average number of required ciphertexts are 926 and 1641, respectively. It is about 38% and 28% reductions of the ciphertexts required in comparison to 1493 and 2273 in previous work where both fault value and location have to be known. Furthermore, our analysis is extended to the PRESENT cipher. By applying the persistent fault analysis to the penultimate round, the full PRESENT key of 80 bits can be recovered. Eventually, an experimental validation is performed to confirm the accuracy of our attack with more insights. This paper solves the challenges in most aspects of practice and also demonstrates the feasibility and universality of PFA on SPN block ciphers.

Recommendation systems are crucially important for the delivery of personalized services to users. With personalized recommendation services, users can enjoy a variety of targeted recommendations such as movies, books, ads, restaurants, and more. In addition, personalized recommendation services have become extremely effective revenue drivers for online business. Despite the great benefits, deploying personalized recommendation services typically requires the collection of users’ personal data for processing and analytics, which undesirably makes users susceptible to serious privacy violation issues. Therefore, it is of paramount importance to develop practical privacy-preserving techniques to maintain the intelligence of personalized recommendation services while respecting user privacy. In this paper, we provide a comprehensive survey of the literature related to personalized recommendation services with privacy protection. We present the general architecture of personalized recommendation systems, the privacy issues therein, and existing works that focus on privacy-preserving personalized recommendation services. We classify the existing works according to their underlying techniques for personalized recommendation and privacy protection, and thoroughly discuss and compare their merits and demerits, especially in terms of privacy and recommendation accuracy. We also identity some future research directions. Keywords: Privacy protection, Personalized recommendation services, Targeted delivery, Collaborative filtering, Machine learning

Visible light communication (VLC) can provide short-range optical wireless communication, together with illumination using light-emitting diode (LED) lightings. Since conventional forward error correction (FEC) codes cannot provide two lighting related features, dimming support, and flicker mitigation, the existing coding schemes for reliable VLC usually rely on auxiliary coding techniques, which cause a complicated structure and a low transmission efficiency. In this paper, based on polar codes, an efficient and flicker-free FEC coding scheme for dimmable VLC is proposed to increase the transmission efficiency and simplify the coding structure. Taking advantage of polar codes' recursive encoding structure, the proposed scheme can guarantee the equal probability and the short runs of 1 s and 0 s for arbitrary code rate without extra coding components. Numerical results show that the proposed scheme can have twice higher transmission efficiency than the existing schemes. Furthermore, at a dimming ratio of 25% or 75%, the coding gain of the proposed scheme is about 4.6 dB and 1.4 dB higher than that of the Reed-Solomon (RS) codes based scheme and the low-density parity check code based scheme, respectively.

A wireless physical layer identification (WPLI) system aims at identifying or classifying authorized devices of different users based on the unique radio frequency fingerprints (RFFs) extracted from their radio frequency signals at physical layer. Most existing works mainly focus on demonstrating feasibility of system by presenting the classification performance of a fixed-user-number network. However, the real-world WPLI systems are expected to work in various scenarios with different scales of user numbers, dynamic changes in user numbers, and various choices of user combinations. Hence, an important question needs to be answered: what's the user number that a WPLI system can support, i.e. the user capacity, under the condition that the minimum system performance is guaranteed. In thispaper, we theoretically characterize the user capacity of WPLI. A theoretical approach is proposed based on ensemble mutual information between RFF and user identity. With this approach and one-time RFF training, the user capacity of WPLI can be characterized under various real-world constraints. Extensive experiments are conducted to validate the accuracy and tightness of the theoretically derived WPLI user capacity.

In this study, the complete mitochondrial genome of Gaojiao chicken (Gallus gallus) was sequenced using samples collected from Puding county of Guizhou province, China. The mitogenome of Gaojiao chicken is 16,786 bp in length, and it contained 22 transfer RNA (tRNA) genes, 2 ribosomal RNA (rRNA) genes, 13 protein-coding genes (PCGs), and a control region (D-loop), the overall base nucleotide compositions encoded are 30.27% A, 32.52% C, 13.49% G, and 23.72% T. The phylogenetic tree was constructed using neighbor-joining (N-J) method, which indicated that the Gaojiao chicken has the close relationship with Nandan chicken, Daweishan chicken, Wuding chicken, and Guangxi three-buff chicken.

Big Data has attracted tremendous attention from all over the world nowadays.Its sheer volume,complex structure and realtime processing requirements often obsolete traditional technologies when coming to provide suffi-cient security protection.To address this challenge,significant research efforts have been carried out by the research community since recent years.Different technical aspects of big data security,including encryption algorithms,data integrity auditing,access control,secure data duplication,assured deletion,and secure search were surveyed,and in-depth discussions on their pros and cons were provided.Various future research directions were also discussed.

Enjoying the rapid growth of image data and cloud computing platforms, various image processing ap-plications have emerged and flourished in recent years. Meanwhile, the privacy concerns over the abuse of sensitive information contained in outsourced data also arise in public. In fact, once uploaded to the cloud, the security of us-ers’ private information purely depends on the reliability of the cloud service providers (CSP). To solve this problem, the security requirements and technical challenges lain in privacy-preserving image processing based on different cloud computing architectures were studied, and several solutions to protect the security of outsourced data while enabling functionality of image processing applications were proposed. Several state-of-the-art techniques for secure image processing were introduced and analyzed, including homomorphic encryption (HE) scheme, secure multiparty computation (SMC) protocol, and differential privacy (DP).

Persistence is an intrinsic nature for many errors yet has not been caught enough attractions for years. In this paper, the feature of persistence is applied to fault attacks, and the persistent fault attack is proposed. Different from traditional fault attacks, adversaries can prepare the fault injection stage before the encryption stage, which relaxes the constraint of the tight-coupled time synchronization. The persistent fault analysis (PFA) is elaborated on different implementations of AES-128, specially fault hardened implementations based on Dual Modular Redundancy (DMR). Our experimental results show that PFA is quite simple and efficient in breaking these typical implementations. To show the feasibility and practicability of our attack, a case study is illustrated on the shared library Libgcrypt with rowhammer technique. Approximately 8200 ciphertexts are enough to extract the master key of AES-128 when PFA is applied to Libgcrypt1.6.3 with redundant encryption based DMR. This work puts forward a new direction of fault attacks and can be extended to attack other implementations under more interesting scenarios.

Neighbor discovery is a crucial operation frequently executed throughout the life cycle of a Wireless Sensor Network (WSN). Various protocols have been proposed to minimize the discovery latency or to prolong the lifetime of sensors. However, none of them have addressed that all the critical concerns stemming from real WSNs, including communication collisions, latency constraints and energy consumption limitations. In this paper, we propose Spear, the first practical neighbor discovery framework to meet all these requirements. Spear offers two new methods to reduce communication collisions, thus boosting the discovery rate of existing neighbor discovery protocols. Spear also takes into consideration latency constraints and facilitates timely adjustments in order to reduce the discovery latency. Spear offers two practical energy management methods that evidently prolong the lifetime of sensor nodes. Most importantly, Spear automatically improves the discovery results of existing discovery protocols, on which no modification is required. Beyond reporting details of different Spear modules, we also present experiment evaluations on several notable neighbor discovery protocols. Results show that Spear greatly improves the discovery rate from 33.0% to 99.2%, and prolongs the sensor nodes lifetime up to 6.47 times.