While deep face recognition (FR) systems have shown amazing performance in identification and verification, they also arouse privacy concerns for their excessive surveillance on users, especially for public face images widely spread on social networks. Recently, some studies adopt

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Grafting is a green, environmentally friendly, and sustainable way to prevent soil-borne diseases. Although artificial grafting is the main grafting approach used for grafting production, it has some problems which are low productivity, unstable operating quality and labor-intensive. Hence, some countries have been engaged in the development of grafting robots for the past two decades; however, the productivity of these grafting robots has no advantage when compared to artificial grafting. This study aims to develop a high-productivity grafting robot (HPR) for Solanaceae. To improve grafting productivity, this paper adopted plug trays to feed crown-removed rootstocks automatically and carried out multi-plant simultaneous grafting to improve grafting productivity and extensibility. Manipulators were employed to take out rootstocks, increase the distance between them, and transfer them to transfer cups for the simultaneous multi-plant grafting. At the same time, negative pressure mechanisms were designed for speeding up the auxiliary feeding of root-removed scions. Although the HPR was designed in a two-operator mode, a one-operator mode can also be implemented by adjusting the control program. Tests were conducted by varying the artificial feeding speed to analyze the performance of the grafting robot. The results showed that the productivity of the robot in the two-operator mode was 2250 plants/h, and 1542 plants/h in one-operator mode; comparing the artificial feeding productivity with auto grafting productivity, it was found that the capacity of the grafting robot was higher than the feeding speed of the one-operator mode but lower than that of the two-operator mode.

The effects of α-linolenic acid (ALA) on the proliferation and adhesion of probiotics would be investigated in the present study. Effects of ALA on intestinal flora were studied by animal fecal anaerobic fermentation system in vitro, which were analyzed by high-throughput sequencing. Results showed that treatment with ALA could promote the proliferation of probiotics Lactobacillus and Bifidobacterium, and inhibit the growth of harmful bacteria Enterococcus and E. coli. ALA restored the abnormal intestinal flora caused by high-fat diet, which was beneficial to the improvement of intestinal flora structure. In addition, adhesive characteristics of probiotics to epithelial colon cells NCM460 were detected by plate counting and Gram staining, which indicated that ALA promoted adhesion of probiotics to colonic cells. In conclusion, ALA could promote the proliferation and adhesion of intestinal probiotics, which provides a basis for ALA to exert the healthy activities of intestinal probiotics.

The production of hydroponic leafy vegetable plug-seedlings uses coco-peat as culture substrate in South China. Coco-peat has lowered density than peat-moss, and the friction between substrate block and pickup tool is small. So, it is hard to pick up in mechanism transplantation. In order to increase the friction, the existing transplanting manipulator had relatively complex structures. To simplify the structure of transplanting manipulator and improve the stability of picking up substrate block, four stainless steel fingers with rectangular cross-section were used in this research. A vertical driving was used to realize the coupling effect that could insert and shrink at the same time, by applying different combination of constraints to the steel fingers. This could increase friction between the steel fingers and the substrate block, and then enhance the stability of the substrate block. Different combinations of constraints were applied to the rectangular stainless steel fingers (3 mm×0.8 mm). The working videos of steel fingers were taken by high-speed photography. High-speed motioned analysis software was used to acquire and analyze traces of steel fingers movements. When the length which top end of the steel fingers moved outward (M) is equal to 1.5 mm, the length which guiding part widened (N) is equal to 1 mm, the shrinking distance of steel fingers is 4.2 mm. In this research, 16-day hydroponic leafy vegetable plug-seedlings were used for performance, which cultivated with coco-peat substrate with the moisture in the substrate at 81%. The transplanting manipulator was attached to a Denso robotic arm to conduct transplanting performance test. When the shrinking distance of steel fingers increased from 0 mm to 3.2 mm and the inserting angle decreased from 80° to 77°, the lifting force of substrate block increased by 118% from 1.45 N to 3.16 N. However, excessive shrinkage stirred the substrate block, which would reduce the friction between the substrate block and pickup parts and lowered the lifting force of pickup part in the substrate block. The experimental results also demonstrated that when the shrinking distance of the steel fingers reached 3.2 mm and the root distribution rate reached 46%, the success rate of transplantation was 80%. When the leafy vegetable plug-seedlings root distribution rate reached 92%, the success rate of transplantation was 96.67%. The degree of root distribution rate was positively correlated with the transplantation success rate. Therefore, in order to ensure an acceptable success rate of transplantation, the root distribution rate of leafy vegetable plug-seedlings should be at least 90%. This study provides a technical reference for developing simplified transplanting manipulator that can be used to transplant the hydroponic leafy vegetable plug-seedlings with coco-peat as the culture substrate.

The data-centric machine learning aims to find effective ways to build appropriate datasets which can improve the performance of AI models. In this paper, we mainly focus on designing an efficient data-centric scheme to improve robustness for models towards unforeseen malicious inputs in the black-box test settings. Specifically, we introduce a noisedbased data augmentation method which is composed of Gaussian Noise, Salt-and-Pepper noise, and the PGD adversarial perturbations. The proposed method is built on lightweight algorithms and proved highly effective based on comprehensive evaluations, showing good efficiency on computation cost and robustness enhancement. In addition, we share our insights about the data-centric robust machine learning gained from our experiments.

Camera-trapping has become one of the most efficient approach to detect and investigate the large-and medium-sized terrestrial mammals and birds, which can provide reliable data to the biodiversity inventory and wildlife baseline survey of protected areas. Laohegou Protected Area, with an area of 110 km 2 in central Minshan Mountains, locates in Pingwu County, Sichuan Province, and harbors the habitat of a rich fauna such as the endangered giant panda (Ailuropoda melanoleuca), golden snub-nosed monkey (Rhinopithecus roxellana) and chestnut-throated partridge (Tetraophasis obscurus). In this study, we summarized the camera-trapping records in Laohegou between 2011 and 2015, and provided a comprehensive camera-trapping dataset of Laohegou. The dataset includes data collected from 130 camera locations with an elevation range of 1,317-3,265 m and an extensive sample effort of 10,185 camera-days. With a total of 159,694 photographic records, we identified 28 wild mammal species (belonging to 5 orders and 15 families) from the 91,839 records of mammals (No. of independent photograph = 3,017), 60 bird species (belonging to 7 orders and 19 families) from the 37,775 records of mammals (No. of IP = 1,311), 1 amphibian species (belonging to 1 order and 1 family) from the 8 records of amphibians (No. of IP = 2), and 1 domestic animal species from 47 records (No. of IP = 5).

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